case study of six different countries

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• Development Co-operation Report

Development Co-operation Report 2018

Case studies from developing countries: what works and why, joining forces to leave no one behind.

When Member States of the United Nations approved the 2030 Agenda for Sustainable Development in 2015, they agreed that the Sustainable Development Goals and Targets should be met for all nations and peoples and for all segments of society. Governments and stakeholders negotiating the 2030 Agenda backed the ambition of leaving no one behind, an ambition increasingly referred to in development policies, international agendas and civil society advocacy.

How can we transform this ambition into reality? Policy makers, civil society and business are asking for more clarity on how to ensure that no one is left behind in practice. What does it mean for the design and delivery of economic, social and environmental policies? How should development co-operation policies, programming and accountability adapt? What should governments, development partners and the international community do differently to ensure that sustainable development goals benefit everyone and the furthest behind first?

The 2018 Development Co-operation Report: Joining Forces to Leave No One Behind addresses all of these questions and many more. Informed by the latest evidence on what it means to be left behind, it adopts a wide range of perspectives and draws lessons from policies, practices and partnerships that work. The report proposes a holistic and innovative framework to shape and guide development co-operation policies and tools that are fit for the purpose of leaving no one behind.

English Also available in: French

• Case Studies on Leaving No One Behind
• https://doi.org/10.1787/dcr-2018-en
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These five case studies show initiatives already in place to deliver on the Sustainable Development Goals for all people in specific countries and regions. In Indonesia, an electronic food voucher programme supports the most vulnerable of households. In Benin, the government is applying a new approach that focuses on the needs of the poorest 20% of the country’s people. Around Latin America, financial inclusion is integrated within social protection programmes to help the region’s poorest accumulate savings. In Muthithi, Kenya, a multidimensional study on welfare has informed local government interventions to help those furthest behind. And in West Africa, neighbouring countries are working together to improve economies and lives in remote border regions.

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Author(s) OECD

11 Dec 2018

Pages: 181 - 207

06 Apr 2022

International Organization for Migration (IOM), 2021. How Countries Manage Migration Data: Evidence from six countries . Geneva.

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How Countries Manage Migration Data: Evidence from six countries

With migration data in the global spotlight thanks to processes such as the Global Compact for Safe, Orderly and Regular Migration and others, this report aims to provide a comprehensive picture of how migration data systems work in practice at the national level. IOM’s Global Migration Data Analysis Centre carried out an international comparative study to understand better how countries are meeting a range of different migration data challenges, focusing on six case study countries. The objective of the study is to explore how far countries have been making progress in improving migration data and to understand the practical challenges facing national statistical offices and other stakeholders, the impact of frameworks such as the 2030 Agenda for Sustainable Development and the Global Compact for Migration, and more. 

This first-of-its-kind report on migration data at the global level will help authorities better identify data capacity-building needs and opportunities related to migration data, and it presents a key opportunity to showcase good practices among countries and practitioners. The report is based on interviews with stakeholders in six countries (Canada, Djibouti, Ireland, Jamaica, the Republic of Moldova and Nigeria), including representatives from national statistical offices, line ministries and academia. The report contains six short migration data country profiles and national-level findings, along with a global-level section synthesizing these and discussing the overall implications for the migration data landscape.

• ACKNOWLEDGEMENTS
• LIST OF FIGURES AND TABLES
• A. INTRODUCTION
• 1. Introduction
• 2.1. Demand for international migration data
• 2.2. Key developments in international migration data
• 2.3. Methodology
• 3.1. Key institutions
• 3.2. Coordination and cooperation
• 4.1. Statistical data sources
• 4.2. Administrative data sources
• 4.3. Data integration
• 5. Data dissemination and use for policy
• The 2030 Agenda for Sustainable Development
• The Global Compact for Safe, Orderly and Regular Migration
• 7. Capacity development and international cooperation
• 8. Impact of COVID-19 on migration data
• A. Increase awareness of stakeholders on migration governance 
• B. Improve data collection and diversify data sources
• C. Improve migration data governance
• 2. Migration data governance 
• 3. Key migration data sources
• 4. Data dissemination and use for global processes 
• 5. Building data capacity and international cooperation 
• 6. Migration data and COVID-19 
• 7. Concluding remarks
• 2. Migration data governance
• 3. Key migration data sources 
• 4. Data dissemination and use for global processes
• 5. Building data capacity and international cooperation
• 6. Migration data and COVID-19
• 7. Concluding remarks 

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Comparability of COVID-19 Epidemiological Data: A case study of six countries

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Executive Summary

Cross-country comparisons of COVID-19 data are important to understand differences in the burden of COVID-19, determine countries’ relative success containing the virus and guide policies. The comparison of COVID-19 epidemiological data across countries is, however, challenging due to differences in terms of how data are collected and reported.

Research questions The objective of this research was to assess issues associated with comparing national-level COVID-19 epidemiological data in six countries: Bangladesh Burkina Faso, Colombia, DRC, Nigeria and Syria. Specifically, this report sought to address the following questions:

What is the state of the COVID-19 pandemic in Burkina Faso, Colombia, DRC, Nigeria, Bangladesh, and Syria? How do indicators used to measure COVID-19 testing, cases and mortality differ across the six countries? What factors may have an impact on the accuracy of COVID-19 indicators and observed differences across countries?

What COVID-19 indicators and information should be reported to increase comparability across countries? Methodology This research consisted of two data collection methods: secondary data review and semistructured key informant interviews. A review of the grey and peer-reviewed literature was conducted, and key informants were interviewed in Burkina Faso and Nigeria Key findings: This analysis revealed that data collection, measurement and reporting practices for COVID-19 testing, case identification, and mortality vary greatly across the six countries. As a result of data availability and quality issues, COVID-19 measures often either underestimate or overestimate the number of people tested, cases identified and people dying from COVID-19 to varying degrees across countries. Factors that lead to differences in COVID-19 data comparability include: variability in testing strategies including testing availability, eligibility criteria, cost of testing and contact tracing efforts; differing case definitions and use of COVID-19 tests; and overall lack of documentation regarding how indicators are measured. Ambiguous information is particularly prevalent for mortality calculations. Cross-country comparisons are also subject to key differences in reporting practices and contextual factors that may not be documented. When they are not addressed, these country-specific biases and cross-country differences lead to biased comparisons.

Recommendations: Based on the findings, the following set of recommendations are proposed to improve the comparability of data across countries:

To countries: Publish online and in-country bulletins the definitions of the COVID-19 measures used. Changes in definitions and measurements should also be documented and communicated. Publish information on processes for data collection and reporting, including reporting sample and processing times. This information should be updated regularly. Share COVID-19 data updates online using the same time interval and location (e.g., government website). Ideally, updates should be posted daily and any delay in reporting should be documented and explained. Make COVID-19 datasets readily available online. The dataset should be updated using a defined time interval (e.g., twice a week). Document and communicate contextual factors that may affect the interpretation of the reported data (strike by laboratory personnel or medical doctors). Publish multiple COVID-19 data measures. For instance, both the number of tests conducted, and the number of people tested should be reported. Countries with stronger registration systems should aim to publish excess mortality in addition to other mortality measures such as case fatality ratios. Report the number of tests and number of positive cases separately for travelers versus suspected cases.

Researchers/policy makers

When comparing data across countries, use the same data sources to minimize differences in reporting. Any differences that could affect comparisons should be documented. International websites should specify the reason they omit data for certain countries and provide information on their data collection practices Local, regional, and international health organizations should stress the importance of COVID-19 data quality, comprehensiveness, reliability, and timeliness and provide support and guidance to strengthen data quality. This is particularly important as these data may be used in the future to inform resource allocation such as vaccine distribution.

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Volume 2: Developing Country Trends and Insights from Four Country Case Studies

Photo: Yawar Nazir/Getty Images

Table of Contents

Report by Romina Bandura and MacKenzie Hammond

Published October 19, 2018

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The future of work has recently attracted much attention from a variety of institutions, from governments to universities to private companies and news outlets: a simple Google search of the term future of work produces more than two billion results. Our world of work—both in rich and poor countries—is changing fast. Technology, globalization, environmental changes, and shifting demographics are impacting workplace environments and the types of jobs that will be available in the future. Everyone can relate to these issues, since people depend on work for their livelihoods. This volume presents a summary of the future of work discussion in developing countries and provides insights from four country case studies: Brazil, India, Kazakhstan and Nigeria.

Much of the current discussion on the future of work centers on fast-paced technological changes and the perceived job losses and transformations in Western economies. The focus is on the pace of impact of the Fourth Industrial Revolution (4IR), that is, how the interaction of automation, robotics, artificial intelligence, and other technological drivers will have an unprecedented and distinctive disruption in the labor market in terms of its “velocity, scope and systems impact”. In the developing world, other forces beyond technology stand poised to impact labor markets. First, these countries are rapidly urbanizing, creating challenges for cities in terms of infrastructure, job creation, and basic social services. Second, different regions are following varied demographic transition paths that will affect the number of potential workers, the composition of the workforce, and the types of jobs created. Third, global trends like increased trade, environmental challenges, and migration will also continue to create challenges and opportunities in labor markets around the world. At the same time, many economies are facing “jobless growth” and grappling to create meaningful work opportunities for their citizens.

Managing the future of work challenges will require responses from individuals, governments, educational institutions, non-government and civil society organizations (CSOs), and companies on several fronts. Better education systems and reskilling to adapt to changing technological disruptions will without doubt be important, but economies also need to create more and better jobs, and safety nets and social protection systems will need strengthening to help workers transition through the different stages of their working lives. Economies will need to create more and better work opportunities, even with the disruptions taking place. We cannot simply give up on work—we need to shape its future and defend it.  

Romina Bandura is a senior fellow with the Project on Prosperity and Development and the Project on U.S. Leadership in Development at CSIS. MacKenzie Hammond is a program coordinator for the CSIS Project on Prosperity and Development (PPD) and Project on U.S. Leadership in Development (USLD)

This report would not have been possible without the generous support of Chevron and the Royal Danish Embassy in Washington, D.C.

Romina Bandura

Mackenzie hammond.

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

• Research article
• Open access
• Published: 03 December 2020

Comparative analysis of COVID-19 guidelines from six countries: a qualitative study on the US, China, South Korea, the UK, Brazil, and Haiti

• Ji Youn Yoo 1   na1 ,
• Samia Valeria Ozorio Dutra 2   na1 ,
• Dany Fanfan 3 ,
• Sarah Sniffen 4 ,
• Hao Wang 5 ,
• Jamile Siddiqui 6 ,
• Hyo-Suk Song 7 ,
• Sung Hwan Bang 8 ,
• Dong Eun Kim 9 ,
• Shihoon Kim 10 &
• Maureen Groer 1  

BMC Public Health volume  20 , Article number:  1853 ( 2020 ) Cite this article

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In late January, a worldwide crisis known as COVID-19 was declared a Public Health Emergency of International Concern by the WHO. Within only a few weeks, the outbreak took on pandemic proportions, affecting over 100 countries. It was a significant issue to prevent and control COVID-19 on both national and global scales due to the dramatic increase in confirmed cases worldwide. Government guidelines provide a fundamental resource for communities, as they guide citizens on how to protect themselves against COVID-19, however, they also provide critical guidance for policy makers and healthcare professionals on how to take action to decrease the spread of COVID-19. We aimed to identify the differences and similarities between six different countries’ (US, China, South Korea, UK, Brazil and Haiti) government-provided community and healthcare system guidelines, and to explore the relationship between guideline issue dates and the prevalence/incidence of COVID-19 cases.

To make these comparisons, this exploratory qualitative study used document analysis of government guidelines issued to the general public and to healthcare professionals. Documents were purposively sampled ( N  = 55) and analyzed using content analysis.

The major differences in the evaluation and testing criteria in the guidelines across the six countries centered around the priority of testing for COVID-19 in the general population, which was strongly dependent on each country’s healthcare capacity. However, the most similar guidelines pertained to the clinical signs and symptoms of COVID-19, and methods to prevent its contraction.

In the initial stages of the outbreak, certain strategies were universally employed to control the deadly virus’s spread, including quarantining the sick, contact tracing, and social distancing. However, each country dealt with differing healthcare capacities, risks, threats, political and socioeconomic challenges, and distinct healthcare systems and infrastructure. Acknowledging these differences highlights the importance of examining the various countries’ response to the COVID-19 pandemic with a nuanced view, as each of these factors shaped the government guidelines distributed to each country’s communities and healthcare systems.

Peer Review reports

Introduction

The recent outbreak of COVID-19 has led to a major concern of increased mortality in the world. The first outbreak of COVID-19 was reported in Wuhan city, the capital of China’s Hubei Province, in late December, 2019 [ 1 ]. Only a few months later, on March 11, 2020, the World Health Organization (WHO) designated the COVID-19 outbreak a pandemic and provided guidelines for COVID-19 case management in the health facility and community. Globally, approximately 3,506,577 confirmed cases of COVID-19 have been reported, including over 247,467 deaths (Johns Hopkins University, May 03, 2020) [ 2 , 3 ]. The fast spread of the virus reflects how health is connected globally and the necessity of investing in global research efforts to explore, clarify, and address global health emergencies.

When the first COVID-19 outbreak was reported in China, the Chinese government established guidelines that recommended keeping social distance in public places, staying at home, and isolating infected populations to contain the epidemic. After a month, South Korea was assailed by a COVID-19 outbreak. Both governments’ early actions were aggressive in an attempt to stop the virus from spreading, involving both widespread testing for the virus and contact tracing. The COVID-19 response in China and South Korea provided the model for other countries where COVID-19 was just beginning to expand. While it was uncertain whether other countries could implement or adapt the stringent measures endorsed by China and South Korea, the heterogeneous nature of the virus worldwide warranted further investigation into the healthcare and community responses from governments across many nations.

Considering how each country had different capacities, risks, threats, political and socioeconomic challenges, as well as different health care systems, it was unsurprising that each country responded to this threat with different measures and different timings. However, it is also clearly critically important to look at how different countries addressed the first pandemic of coronavirus. Thus, we compared six different countries’ guidelines to investigate their management, incidence, and prevalence of COVID-19 cases. Additionally, we explored the relationship between the guidelines’ issue dates and the prevalence-incidence curves of the different countries.

The objective was to compare government guidelines on COVID-19 by six different countries (The United States (US), China, South Korea, The United Kingdom (UK), Brazil and Haiti). This included general public guidelines and healthcare professionals’ (medical institutions) guidelines. We aimed to identify differences and similarities between the countries’ community and healthcare professional guidelines and additionally to explore the relationship between guidelines issue dates and the prevalence/incidence of the COVID-19 cases. This is significant because we can examine how various countries responded to COVID-19 and identify best practices. This approach also allows us to understand how healthcare system and policy capacities shape COVID-19 responses and to share this information to improve responses to COVID-19.

Research design

Our study used document analysis, a standard qualitative research method for evaluating communication and policy research, to explore the differences and similarities between government COVID-19 guidelines from six countries [ 4 , 5 ]. The following steps were included in the analysis: (i) establishing the document inclusion criteria, (ii) gathering documents, (iii) analyzing key areas, (iv) coding the document, (v) verification, and (vi) analysis [ 6 ]. In this approach, the investigators are the primary means of data selection and analysis. This study used purposive sampling to recruit investigators internationally by email and/or phone.

Country selection

COVID-19 is a global pandemic warranting a cross-national perspective from countries differing on several levels (e.g., geographic region, health and economic resources, stage of COVID-19 spread and response) to maximize range and diversity when exploring the scope of COVID-19-related community and healthcare system guidelines. When selecting the six countries, the following were considered: 1) COVID-19 starting and spiking period, 2) geographic proximity to China, where the COVID-19 spread began, 3) population size, 4) gross domestic product (GDP) status, and 5) eligibility of a bilingual expert in the public health field. For instance, the first COVID-19 outbreak occurred in China in December 2019. Outbreaks in other countries like South Korea, the US, and the UK soon followed in January and February 2020. Finally, in March 2020, Brazil and Haiti noted increased incidence and deaths from the virus. By April 2020, China and South Korea were in the recovery stage of the pandemic while the spread of COVID-19 intensified in countries such as the US, UK, Haiti and Brazil. Geographical differences in the selected countries reflected geographic proximity to China (Korea), largest population in North America (the US), largest population in South America (Brazil), and island countries with significant geographic distance from China (Haiti and the UK). Furthermore, the countries’ population sizes were strongly related to the effectiveness and widespread dissemination of information regarding COVID-19 guidelines [ 6 ]. Additionally, the countries’ GDP represented their resources, ability, and strategies employed in response to COVID-19. For instance, at the start of the COVID-19 spread in the UK and Haiti, both countries had very little available resources for COVID testing. With Haiti’s fragile health care system, Haiti did not have the infrastructure to fight the spread of the virus, warranting economic support from other countries. Finally, it was necessary to collaborate with authors who met the following inclusion criteria in order to facilitate document analysis of different countries’ government guidelines: hold a graduate degree, have experience with healthcare material, are fluent in English and have native language fluency (Chinese, Korean, Portuguese, and French/ Haitian Creole) in at least one of the six countries selected.

Document inclusion/exclusion criteria

Six members of the research team, which consisted of multidisciplinary, cross-cultural researchers, collected the data. The research team only reviewed documents from publicly available government websites for each country (see Additional file  1 ). Guidelines from government websites were available in different formats, including action and response plans, healthcare and general population guidelines, prevention measures/recommendation flyers, videos, government memos and webpages. Documents or information from nongovernment websites, social media, online newspapers/editorials, peer-reviewed articles, and heath institution guidelines were excluded from the study.

Data collection

To guide document selection among investigators, a codebook was developed which highlighted the information necessary for each theme (see Additional files  2 and 3 ). Each document for each country was reviewed to determine the extent to which the document provided answers to at least one of the pre-identified themes (i.e. areas of analysis). The team reviewed government websites weekly for approximately 6 weeks from March 2020–May 2020 to obtain information from government guidelines, and to ascertain whether any new documents were published, or whether old guidelines had been updated. A total of 55 documents (e.g., government guidelines, flyers, memo, webpages) were reviewed to extract data (10 for the US, 3 for China, 10 for South Korea, 8 for the UK, 13 for Brazil, 11 for Haiti) (see Additional file  1 ). Texts from COVID-19 general public guidelines and government health promotion materials relevant to each pre-identified theme were copied verbatim in their original language and added to an excel spreadsheet. Extracted verbatim texts written in a language other than English (Portuguese, Chinese, Korean, and Haitian Creole/French) were translated to English by bilingual members of the research team and added to the excel spreadsheet to allow for review and analysis of the information as a group (see Additional file  3 ). Text translation focused primarily on maintaining meaning consistent with health care language rather than cultural nuances, thus, formal translation procedures (e.g., forward and back translation) were not completed. The authors evaluated the English translations, providing constructive feedback about the translation, and confirmed their validity through governmental and professional guidelines and articles.

Data analysis

Key areas of analysis were articulated in the codebook, which provided a list of codes (e.g., themes and sub-themes) and included six basic components: the code, a brief definition, a full definition, guidelines for when to use or not use the code, and examples of the code. Explicitly, the codebook helped the research team determine the meanings of themes and provided clarity about what to look for within the text of the guidelines. The codes’ sensitivity and specificity were used as a tool for measuring the adequacy of answers to research questions. Data analysis entailed appraising and synthesizing texts from guidelines, which were then organized into major themes and sub-categories through content analysis [ 7 ]. Content analysis within the research team was facilitated through online meetings, which were convenient and removed geographic barriers. Researchers assessed the data for coding patterns (e.g., similarity, differences, frequency) across different countries regarding their government guidelines for the general public and healthcare professionals. All texts from guidelines were allocated deductively to the a priori themes (deductive codes). During the iterative content analysis process, new themes also emerged (inductive codes). When disagreement ensued during data analysis, the research team recoded, or the primary coder sought advice from another team member for verification and clarification [ 7 ]. The original data of the confirmed and deaths cases were downloaded from the Johns Hopkins University Center for Systems Science and Engineering [ 3 ]. Tableau, a well-known website for analyzing big data, was used to clean and reshape the data for sharing with the public. The data was then converted to excel files and used to create figures (see Additional file  4 ) [ 8 ].

Trustworthiness of the data

We established trustworthiness of the data by: 1) focusing on government guidelines, as they are a credible source of information (credibility), 2) using information from guidelines, which maintain dependable and consistent patterns over time and are periodically updated to reflect the evolving understanding of the coronavirus (dependability), 3) using government guidelines, which limited the research team’s bias at the data collection and interpretation level, and improved accuracy with the use of a well-developed codebook ( confirmability), and 4) analyzing the guidelines of six countries experiencing the coronavirus pandemic at the same time but within different contextual realities (transferability) [ 9 , 10 ].

Theme: Evaluation and testing

Sub-them: screening criteria.

When comparing the different government guidelines on screening for signs and symptoms in suspected COVID-19 cases, all countries listed respiratory symptoms as a criterion and the majority – Brazil being the exception – emphasized fever, as well. Interestingly, the US and UK did not list travel history as a criterion. We also noticed that the US and Brazil did not categorize pneumonia as a screening criterion, whereas South Korea, the UK and Haiti emphasized unknown case of pneumonia, clinical or radiological evidence of pneumonia, and bronchopneumonia. A major distinction the authors noted was that only China and the UK specified the detection of suspected COVID-19 cases within the hospital through either radiological evidence via chest X-ray and thoracic Computed Tomography (CT) or lymphocyte counts. Haiti, as of April 20, 2020, expanded the screening criterion from ‘have a fever greater than 38°C within the last 10 days’ to ‘anyone with fever greater than or equal to 38 °C (see Additional file  3 ).’ Haiti’s screening criteria also included body aches, sudden changes in taste (ageusia) or smell (anosmia), possibility of coming in contact with a healthcare professional diagnosed with COVID-19, or being an occupant of a high risk area while experiencing symptoms compatible with COVID-19 (see Table  1 ).

Although information about contact with suspected and/or confirmed cases was vital for screening criterion in most countries, the US and UK did not include this. Furthermore, specifically examining symptomatic patients aged over 65 or who had underlying conditions was only found in the US guidelines (see Table  1 ). Of note, South Korea created a new category in addition to the suspected cases called the Patient Under Investigation (PUI) on April 03, 2020. A PUI is a person who has an epidemiologic link to a collective outbreak of COVID-19 in an area, or a possible contact to a COVID-19 positive person. The US used the term PUI to describe people who exhibit symptoms, or were otherwise suspected of having COVID-19, but had not yet been confirmed via laboratory testing.

Sub-theme: Screening center types

Across different countries, we identified three different types of screening centers: healthcare facilities, drive-through screening clinics, and walk-through screening clinics. While walk-through screening clinics were available in South Korea, healthcare facilities were the only screening centers available in Brazil and Haiti. Interestingly, Brazil and China did create other satellite facilities for treatment, even though they did not create separate facilities for screening. The US, UK and South Korea conducted drive-through screening clinics.

In particular, South Korea took a distinctly different approach to managing suspected COVID-19 cases. Patients with respiratory symptoms that fit the COVID-19 suspected case criteria were blocked from entering the designated healthcare facilities (called the Public Relief Hospital System) and were redirected to other COVID-19 screening/test centers, or if the hospital was a screening center itself, the patient suspected of having COVID-19 was directed to use a specific entrance for COVID-19 screening before entering the main hospital building. The purpose of establishing the Public Relief Hospital System was to provide safe hospital environments protected against COVID-19 spread. In other words, this was an attempt to block patients with COVID-19 from spreading the virus to general patients who did not have COVID-19.

Theme: Infection control

Sub-theme: general outpatient guidance.

Outpatients are patients outside of the hospital who need periodical medical attention due to other morbidities (hypertension, cancer, HIV/AIDS, etc.). In South Korea, outpatients who require healthcare service due to non-COVID-19 diseases were directed to the Public Relief Hospital for follow-up or to see a doctor. These outpatients were strictly separated from patients with any respiratory symptoms.

In Brazil and the US, outpatients were advised to call ahead of their appointment time and were asked whether they had experienced symptoms of respiratory infection. The UK and Haiti avoided treating outpatients in their healthcare facilities. Still, the UK continued with outpatient appointments either through video or phone clinics. Chinese patients, on the other hand, could make an appointment via the phone or online and could then complete their appointment in the hospital as long as the patient made the appointment with a specialist and avoided using the emergency room (ER) or fever clinics where COVID-19 patients had been treated. Haiti did not provide recommendations regarding whether outpatients should make appointments with clinics, and outpatient services were unavailable to the general population. However, Haiti did provide some guidance for those with HIV/AIDS, as Haiti has a high number of individuals suffering from HIV/AIDS. The US updated their guidelines in April 13, 2020, advising healthcare facilities to implement alternatives to face-to-face triage and visits, and instructing patients to utilize cloth face coverings regardless of symptoms upon entry to a healthcare facility. However, the guideline did not specify what alternatives were implemented.

Theme: Cost support

Sub-theme: cost support.

Financial support for testing and treatment was provided mainly or totally by the government in South Korea, the UK, and Brazil. In Haiti, the Haitian government, the US, and the World Bank’s Board of Executive Directors, in conjunction with several international and private organizations, donated money to cover the cost of the country’s COVID-19 response. In China, an individual’s medical cost was subsidized based on the subsidy policy of the local area if the patient was suspected of having COVID-19. However, once the patient received confirmation of COVID-19 infection, the medical cost was subsidized by the authorities. The cost of the clinic visit and testing was made free for all US citizens regardless of insurance status, per The Families First Coronavirus Response Act, which required private and federal insurance to pay for Food and Drug Administration (FDA)-approved testing, and for testing to be free to those who were uninsured. The extent to which the COVID-19 treatment was covered differed between insurance companies.

Sub-theme: Confirmation of COVID-19

All six countries performed real time PCR to confirm COVID-19 cases. Uniquely, the UK did not provide testing for COVID-19 to the community (at the time of writing), and instead reserved testing for National Health Service (NHS) staff, their relatives and – later in the pandemic – select essential workers. Some unique types of confirmatory lab tests were via virus isolation in South Korea, virus gene sequencing in China, and serological examination in Brazil, Haiti, and the US.

Brazil made the decision to include epidemiological criteria, meaning a confirmed case could be included if the individual met clinical criteria and epidemiological evidence, despite a lack of confirmatory laboratory testing for COVID-19. The US, however, made the distinction that an individual who met those guidelines was considered a probable case. The US also described probable cases as a person meeting the presumptive laboratory evidence and either the clinical criteria or the epidemiological evidence. Finally, an individual could be considered a probable case by the US if their vital records, as in their death certificate, indicated the person died of causes related to COVID-19, despite not having a confirmed laboratory test result.

Theme: Triage protocols

Sub-theme: hospital admission criteria.

All countries’ hospitalization decisions were made on a case-by-case basis. While Haiti’s hospitalization criteria were not specified by the government, Brazil relied on post-collection medical evaluation for hospitalization decisions. The Chinese guideline did not indicate hospital admission criteria. The US recommended hospitalization of people with severe symptoms: septic shock, sepsis, pneumonia, hypoxemic respiratory failure, acute respiratory distress syndrome (ARDS), and cardiomyopathy, etc. The UK required either clinical evidence of pneumonia or radiological evidence with a high suspicion for COVID-19, with ARDS-like or influenza-like symptoms for hospitalization.

Uniquely, South Korea created three different categories, ranging from moderate, severe, to extremely severe for hospitalization. Asymptomatic COVID-19 positive individuals or those with mild symptoms were sent to the Living Treatment Center, a facility that monitored symptoms twice a day and transferred support to the hospital in the event of a worsening of symptom severity.

Sub-theme: Healthcare triage isolation

All six countries developed an isolated area for screening and follow up for symptomatic patients in order to isolate suspected cases. Brazil and the US advised healthcare facilities to place suspected cases in well ventilated spaces that allowed sufficient space between patients. South Korea, Haiti, and China organized their healthcare facilities into different levels of care according to the absence or presence of respiratory symptoms. More specifically, China categorized triage isolation areas into those for confirmed, suspected, or non-COVID-19 patients.

Sub-theme: Visitor access to healthcare facilities

In China, visitors were prohibited from accessing healthcare facilities, whereas the UK and South Korea made exemptions for seriously ill patients receiving end-of-life care, who were allowed one visitor per ward patient. Brazil and Haiti limited the number of visitors to the minimum amount possible, but only Haiti required that all visitors entering the hospital wear a face mask. Although earlier in the pandemic the US made no recommendations regarding visitor access, by April the US Center for Disease Control and Prevention (CDC) advised hospitals to limit the number of visitors allowed.

Except for China’s guidelines, all countries took extra precautions towards visitors, establishing protocols for visitors regarding proper Personal Protective Equipment (PPE) and hygiene. Although the US CDC guidelines were not as restrictive as other countries regarding visitor limitations, the US guidelines suggested actively screening visitors for fever and COVID-19 symptoms upon entry to healthcare facilities. If COVID-19 symptoms were present, the guidelines advised that the visitor not be allowed entry to the facility. Similarly, Brazil suggested avoiding entry of visitors with respiratory symptoms. The US CDC and the Brazilian government also recommended posting visual alerts advising visitors to wash their hands frequently, limiting visitors to the most vulnerable patients (i.e. oncology and transplant awards), encouraging the use of videocall applications in place of in-person visits, and recommending visitors leave the patient during aerosol generating procedures or other specimen collection procedures. Lastly, Brazil and the US instructed visitors to only visit the patient’s room, not any other locations in the facility.

Community guidelines

Theme: prevent getting sick, sub themes: prevent getting sick.

Most recommendations to the community on preventing getting sick were similar between the six different countries. In order to explore the major differences, the sub-themes were organized according to singular actions (i.e. total time washing hands, covering cough and sneezes, face-cover recommendations, etc.).

Generally, face-cover recommendations changed throughout the pandemic, however South Korea and China recommended the use of face masks in public places from the beginning of the pandemic, even if the individual was not sick. The UK did not indicate clear guidance on this matter. The US, Brazil, and Haiti did not initially recommend wearing a face covering, however, the guidelines were updated by the US CDC on April 4th, 2020, by the Brazilian Health Ministry on April 5th, 2020, and by Haiti in the middle of April 2020 to indicate that all people, regardless of whether they were sick, should wear a cloth face covering in public. However, medical grade face masks were still not recommended for the community, as they were to be reserved for health care workers due to shortages.

South Korea, Brazil, Haiti, the US and the UK did not provide guidance on the sharing of personal items in the general community guidelines regarding the prevention of getting sick. China was the only country who mentioned not sharing any personal items to the community as a method for preventing contraction of COVID-19.

Even though most community guidelines on preventing illness recommended maintaining 1.8–2.0 m of physical distance between people to avoid viral transmission, Haiti’s guidance on physical distancing initially recommended staying two steps away from other individuals. Haiti updated their recommendation to staying three steps away from others on April 20th.

As of April 8th, 2020, as a unique measure to prevent viral spread, the South Korean government made it mandatory for all Koreans and long-term stay foreigners who entered South Korea to (1) be tested for COVID-19, (2) install an application on their cell phones: the Self-quarantine Safety Protection App, and (3) abide by the guidelines for self-quarantined persons, including conducting self-diagnosis for a period of 14 days (see Table  2 ).

Theme: If you are sick

Sub theme: what to do if you are sick.

Based on the guidelines, we were able to extract 8 important terms, including avoid using public transport and crowded places, isolation days and next steps, face mask or cloth face covering, use a separate room or bathroom, sharing household items, sick room ventilation, cleaning instructions, call center for COVID-19 (see Table  3 ) . These terms were ascertained from at least two countries’ guidelines.

Five of the countries recommended people with respiratory symptoms stay at home for certain periods, whereas the Chinese guidelines advised sick people to immediately go to a designated medical care institution for testing, and to then follow the quarantine protocols requested. Each country designated different isolation periods and procedures. As reported by the US and the UK governments, people with respiratory symptoms were to isolate at home and only stop home isolation under the following conditions; no fever for at least 72 h without the use of medications that reduced fever, improvement of other symptoms, and the passage of at least 7 days since symptom onset. Brazil and China advised that, in addition to the person with respiratory symptoms, all family members or fellow residents were to be quarantined for 14 days. In South Korea, any person who had COVID-19 symptoms was mandated to stay at home for at least 3 to 4 days and was then called and given advice by the Korea Centers for Disease Control and Prevention (KCDC) call center.

To prevent the spread of the virus between family/household members, the US, South Korea, and Brazil recommended the ill person be confined to a separate room and bathroom and avoid sharing personal household items. Haiti, China, and the UK did not provide guidance on providing a separate room/bathroom or on sharing personal items.

Isolation room ventilation, such as keeping the window open for air circulation or closing the door, were mentioned in the South Korean and Brazilian guidelines. Cleaning instructions for containing the virus were indicated in different ways in each country, except for in the UK and Haiti. Call centers for COVID-19 were conducted in South Korea, Haiti, and Brazil in the very early stages of the pandemic.

Sub theme: Threshold to contact a healthcare provider

Across the countries examined, the threshold symptoms for when to contact healthcare providers varied. South Korea advised sick people to contact a healthcare provider if the person had a fever (37.5 °C) or if symptoms worsened. Brazil recommended seeking help if the ill person experienced shortness of breath. The US advised individuals to get medical attention if they experienced persistent pain, chest pressure, cyanosis on lips or face, new confusion, or if unable to be awakened. Haiti mentioned contacting a healthcare provider if the individual had respiratory symptoms. Besides the usual respiratory acute signs (fever, shortness of breath), China also mentioned acute digestive tract symptoms as a reason to reach out to a healthcare facility. In the UK, if a person’s symptoms worsened to the point where they were having difficulty breathing, they were advised to go to the hospital by ambulance facilitated by the online NHS service.

Sub-theme: Transport to healthcare facilities

The US, South Korea, and China recommended using personal vehicles and to avoid using public transportation to reach healthcare facilities, however South Korea and China specified that individuals should cover their face with a face mask before reaching healthcare facilities. Five of the countries allowed ambulance transport, with the exclusion of China.

WHO acknowledged and announced the impact of COVID-19 on both public health and economic sectors via two interim guidelines published in late March 2020. WHO also emphasized the importance of preparedness for the COVID-19 pandemic, accounting for the countries’ health care capacities [ 11 , 12 ]. The six countries tend to align with the WHO interim guideline, however there are some differences in the response to the pandemic in each country.

The major differences in evaluation and testing criteria in the guidelines across the six countries centered around the priority of testing for COVID-19 in the population, which strongly depended upon each country’s healthcare capacity including accessibility to healthcare providers, having enough testing kits and reagents, availability of hospital beds, and so on. The most similar recommendations in the evaluation and testing criteria from each government were those pertaining to the clinical signs and symptoms, such as fever and respiratory symptoms, as the priority criteria to initiate COVID-19 testing.

During the writing of this paper, there were no known vaccine or antiviral therapies for COVID-19. Therefore, early detection and diagnostic testing for SARS-CoV-2 were vital to reducing transmission, managing active cases, contact tracing, and understanding epidemiology [ 13 ]. The government guidelines concerning screening criteria and capacity for screening – including screening centers, and laboratory testing for COVID-19 in suspected or confirmed cases –were crucial factors in protecting the public from the virus. The WHO criticized countries that had not prioritized testing for COVID-19. Tedros Ghebreyesus, the chief executive of WHO, emphasized the importance of testing by stating, “The most effective way to prevent infections and save lives is breaking the chains of transmission. You cannot fight a fire blindfolded, and we cannot stop this pandemic if we don’t know who is infected. We have a simple message for all countries: test, test, test, test” [ 14 ]. However, lack of reagents and/or testing capacity for the SARS-CoV-2 virus challenged all nations included in the study, at least at the beginning of the pandemic. The US, UK, Haiti, and Brazil, in particular, experienced problems with shortages of testing kits for SARS-COV2 due to rapidly increasing demand compounded by national supply chains under stress and national laboratories with limited experience in COVID-19 virus testing [ 15 , 16 ]. This had a negative impact by potentially obstructing the expansion of COVID-19 testing criteria, resulting in a narrowed range of people undergoing COVID-19 testing, which may have led to increases in the actual number of cases and overall risk of death by COVID-19, but falsely decreased the number of confirmed cases and deaths reported in the nations’ statistics.

According to the UK’s NHS, testing priority was given to 1) intensive care unit patients with suspected coronavirus, 2) patients with severe respiratory illness including pneumonia, 3) isolated cluster outbreaks, and 4) random testing for surveillance purposes [ 17 , 18 ]. The first 2 confirmed cases occurred in the UK on January 31, 2020 and the first COVID-19 victims died on March 7, 2020 (see Fig.  1 ). After 20 days, although the UK only tested people who were admitted to hospitals, the number of confirmed cases and disease-related deaths dramatically increased (confirmed cases: 14,745, deaths: 1163) [ 19 ]. By April 7, 2020, 1 month after the first COVID-19 deaths, more than 1000 people were dying every day due to viral infection (see Fig.  1 ). In April 9, 2020, despite the thousands of citizens dying daily due to COVID-19 related causes, the UK government launched large COVID-19 testing centers which prioritized processing samples from health-care workers in self-isolation, allowing them to go back to work [ 18 ]. Therefore, people who were not considered a priority, such as non-health care providers or community members with mild respiratory symptoms, were not given access to testing. The limited scope of the UK’s testing approach for COVID-19 was due to a capacity problem, resultant to the consolidation in the number of pathology laboratories nationwide [ 18 ]. Many laboratories were centralized, which led to the possibility that each hospital would not necessarily be equipped with a fully functioning lab. This systemic capacity problem may have increased the risk of spread by free movement of people who were suspected of having the disease, since testing was unavailable to those individuals to enforce a stay-at-home order. In the UK, 90,000 people were tested as of the 24th of March - around 1300 COVID-19 tests per million people. Although it was a higher portion than some nations, including the US (around 74 per million as of the 16th of March), it was far behind South Korea (5200 per million as of the 17th of March) [ 20 , 21 ].

COVID-19 Cases in six countries

Initially, the US’s CDC included fewer testing criteria than the WHO guidelines. The CDC guidelines recommended testing individuals with a body temperature above 38 °C (fever) and lower respiratory symptoms, those who had a fever and a travel history to China, or those who had a fever and were possibly exposed to a suspected or confirmed COVID-19 case. However, once a patient who did not have any travel history or exposure to any confirmed COVID-19 cases was reported COVID-19 positive, the CDC expanded their testing criterion to include any individuals admitted to a hospital due to lower respiratory symptoms and fever. This addition broadened the spectrum of patients being tested, but also led to rapid increase in the demand for testing.

In February 2020, the CDC acquired, developed, and distributed COVID-19 testing kits to laboratories nationwide, almost one hundred of which reported experiencing several issues with the testing kits. These issues included the failure of negative controls and presentation of inconclusive results. After an internal investigation on February 12, 2020, the CDC reported a faulty reagent as the issue. The CDC immediately recalled all unreliable testing kits and promised to re-manufacture the faulty component and distribute the newly developed reagent to the public health labs as soon as possible. Ultimately, the shortage of COVID-19 test kits at this critical time point possibly interfered with the prevention of increasing confirmed cases early in the outbreak. Furthermore, although the number of confirmed cases and death rate significantly increased each day after March, 20, 2020 in the US (confirmed cases per day around 15,000, deaths per day around 1000), only 97 public health laboratories finished verification and were offering testing on May 6, 2020 [ 22 ]. As further evidence of inadequate testing capability, the CDC announced that “although supplies of tests are increasing, it may still be difficult to find a place to get tested” [ 22 ].

Together, the capacity for widespread testing and presence of prepared health facilities were key to controlling the dissemination of coronavirus, as evidenced by South Korea. The first COVID-19 incident in South Korea was announced on January 31, 2020, with 7 confirmed cases. The daily confirmed cases remained low for the following month (confirmed cases: 100, deaths: 1) until a super spreader event was initiated on February 29, 2020. Each day for 9 days afterward, the country’s epidemic curve resembled a steep staircase as infections climbed, resulting in dramatically increased confirmed cases and deaths (see Fig.  1 ). However, by implementing large-scale governmental COVID-19 testing, health officials were able to effectively contact trace and send potentially infected people into quarantine as a preventative measure. By March 25, 2020, more than 357,000 Koreans had been tested. The country reported 10,804 total coronavirus cases and 254 deaths as of May 1, 2020. This was the lowest death rate among the countries examined [ 3 , 23 ]. Having previously dealt with the Middle East respiratory syndrome (MERS) in 2015, South Korea had already prepared for potential outbreaks of large-scale epidemics, for example by installing negative pressure rooms in hospitals in 2018. Additionally, the country rapidly developed large-scale availability of COVID-19 testing locations, such as K-Walk-Thru and Drive-thru testing stations. These were the first testing centers of their kind in the world and facilitated the quick and safe collection of samples for COVID-19 testing. These unique centers helped not only reduce the risk of cross infections at the in-hospital testing centers, but also increased daily testing capacity amid rapidly rising rates of new cases [ 24 ].

WHO emphasized the prioritization of isolated care for patients with higher risk of infection, such as severe and critical illness patients aged over 60 years, and those with underlying medical conditions [ 25 ]. Still, exponential escalation in the number of daily confirmed cases placed enormous strain on national medical systems, resulting in limited or total lack of beds for COVID-19 treatment. Therefore, the US, UK, South Korea, Brazil, and Haiti decided patients with mild to moderate coronavirus symptoms should be observed in “Home Isolation”. This approach was a crucial option that only required modification in individual behavior without supplementary expenditure.

Interestingly, China opposed observing mild to moderate coronavirus cases at home, instead directing all potentially infected persons to designated medical care institutions. This policy was initiated in Wuhan, the city where COVID-19 emerged in early February 2020 [ 26 ]. On March 27, 2020, more than 60% of coronavirus cases in the country were at Wuhan (see Fig.  1 ). The city converted exhibition centers and stadiums into shelter hospitals within mere weeks. Epidemiological evidence at the beginning of the pandemic revealed high intrafamily transmission, with 75–80% of all clustered infections diagnosed within families [ 26 , 27 ]. Quickly emerging alternative hospitals, such as the Fangcang Shelter Hospitals, dedicated to testing and admitting only COVID-19 patients may have led to a reduction in the spread of the virus in the community, thereby decreasing the number of new cases during the pandemic.

On January 22, 2020, the WHO announced the presence of travel-related cases linked to Wuhan City, human-to-human transmission, and reported COVID-19 had been observed outside of China. The WHO strongly advised individuals to report their travel history to their health care providers [ 28 ]. However, the UK did not track travel history as it was not considered valuable information in their testing criteria. This was problematic since people who traveled to COVID-19-occurring areas could have potentially acted as carriers of the virus to their respective communities and families, which might have strongly influenced the increasingly steep confirmed case curves. Neither the American, Brazilian nor Haitian governments considered a history of travel to a region of high COVID-19 incidence to be a high priority for testing, or to be an important criterion for suspected cases. Those with a travel history to high spread areas were only encouraged to seek testing if they developed a fever or respiratory symptoms. In direct contrast, the Chinese guidelines suggested that any travelers who traveled to a region or country with occurrence of COVID-19 must be tested, regardless of whether they had developed symptoms.

Although WHO provided a definition of symptoms observed in suspected cases that warranted further surveillance [ 11 ], it was a challenge to define the full clinical characteristics of COVID-19. Fever (> 38 °C), breathing problems, and chest radiographs showing bilateral lung infiltrates were the main clinical signs and symptoms reported during the outbreak [ 13 , 29 ]. For this reason, most countries considered fever, respiratory symptoms, and pneumonia as clinical justification for initiating diagnostic testing. Although by March/April 2020, the UK and US countries were defined as ‘ countries experiencing larger outbreaks ’ (as referred to in Group 4 of the WHO guidelines), they did appear to be largely acting in accordance with WHO advice at that point in time, despite not acting on the previous advice regarding the screening of travelers [ 11 ].

Although there was ample evidence of human-to-human transmission, the US and UK did not include contact with confirmed or suspected cases as screening criteria very early in the pandemic. The absence of this criteria early in the pandemic may have led to increased risk of viral spread. In contrast, South Korea undertook an intense contact-tracing program: upon confirmation of a COVID-19 case through laboratory testing, the South Korean government conducted interviews with the infected person, traced their travel history, used GPS phone tracking, and checked their credit-card history. The anonymized data detailing the travel history before diagnosis was published on a public website by the South Korean government. This allowed government officials to quickly release information about potential COVID-19 exposed locations and help people who may have been near those locations make quick decisions on whether they needed to be tested. Though effective, there were and continue to be concerns regarding individual privacy.

With the global spike of COVID-19 and consequent surge in suspected cases and geographic areas affected, the need for implementing screening criteria to better cope with each country’s capacity for screening and laboratory testing became increasingly evident. However, beyond supply chain issues with provision of testing kits, there were significant limitations of the government guidelines for COVID-19 testing in several domains. National health systems and coverage of COVID-19 medical expenses were vital to fostering a sense of financial certainty and a safe environment for those who were infected. Testing and treatment support came mainly or totally from the government in South Korea, the UK, China, and Brazil. All US citizens were covered for FDA-approved COVID-19 testing, regardless of private or federal insurance status, however, treatment coverage was subject to the insurer’s policy. Despite the larger role the governments took in most of the countries examined, Haiti’s COVID-19 health care response was primarily financially supported by the private sector (60%). Hospitals and newly established screening clinics from the private sector worked together with the Haitian Ministry of Health to screen Haitians, however health care facilities from the private sector were not regulated by government officials (hence the paucity of government screening guidelines) [ 30 ]. Given these limitations in testing capacity, WHO launched the ‘ COVID-Solidarity Response Fund for WHO’ to support COVID-19 rapid tests for low and middle-income countries [ 31 ].

In March 19, 2020, WHO recommended that “when symptomatic, patients are required to wait, ensure they have a separate waiting area” [ 32 ]. As an example of the increased preparedness the WHO called for, the South Korean government created temporary ‘ Public Relief Hospitals ’ which provided isolated treatment rooms for patients with respiratory and non-respiratory symptoms to ensure safe medical services to general patients and to prevent viral spread. Public Relief Hospitals were divided into two types: Type A and Type B. Both had separate outpatient treatment zones for patients without respiratory symptoms and for patients with respiratory symptoms but differed in whether their testing centers were contained within the hospital. The Korean government also permitted patients who have a chronic disease, but did not have any respiratory symptoms, to receive counseling and prescriptions by telephone or by proxy, therefore decreasing the risk of internal cross-infection within health care facilities for higher-risk patients. This approach was also utilized in the US and UK. In South Korea, non-respiratory patients, such as cancer patients or patients with heart problems, were directed to the general outpatient area at a Public Relief Hospital . Patients with mild respiratory symptoms were directed to see a doctor nearby, or to go to a respiratory outpatient area at a Public Relief Hospital . Suspected patients or PUI who developed COVID-19 symptoms were referred to a COVID-19 testing center after receiving guidance from a competent clinic or the 1339 call center. Using this triage workflow, Korean hospital systems were better able to prevent internal spreading of the COVID-19 virus in the hospitals and potentially reduced a higher infection-related risk of mortality across the population. The South Korean death rate provided evidence to support this hypothesis, showing that although they had a high rate of confirmed cases (10,780), the total number of deaths was only 250.

WHO recommended healthcare facilities limit the number of visits to suspected or confirmed COVID-19 patients by health care providers, family members, and visitors while being treated in health care facilities. WHO also suggested maintaining a record of all staff and visitors who entered suspected or confirmed COVID-19 patients’ rooms [ 32 ]. Even though the US’s federal guidance on hospital visitation seemed more liberal than other countries, especially when contrasted with South Korea and the UK, more restrictions were adopted depending on the local circumstances. For example, although limiting visitors was not advised by the US CDC until April, several hospitals in New York city restricted visitor access as early as March. Brazil’s government strongly recommended individuals with flu or respiratory symptoms are not allowed entry to the hospitals. The government also recommend the hospitals reduce visitor numbers, which, while not mandatory, was heavily implied to be. Although limitations of visitors were not mandatory, wearing a face mask was mandatory for all visitors in Haiti.

The figure illustrates the incidence of confirmed cases and deaths in six countries from January to April.

Despite being consistently recommended for use by symptomatic individuals and those in health-care settings, discrepancies were observed in the recommendations on wearing face masks in the general public and community settings. The WHO consistently maintained that the benefits of healthy people using masks in the community setting was not supported by the current evidence, and additionally could contribute to uncertainties or create critical risks [ 29 ]. This advice to decision makers remained in place up until the time of this paper submission in May 2020.

Several nations, such as the US and Brazil, changed their face cover recommendations as new studies were conducted that supported the use of face masks as an effective means to limit viral spread. Some studies may under-estimate their protective effects, while observational studies exaggerate them [ 33 ]. However, with the emerging evidence of asymptomatic or presymptomatic COVID-19 transmission, the authors note that the community guidance regarding utilizing a face mask and not sharing personal items could significantly prevent potential asymptomatic or presymptomatic transmission, which corroborates other publications [ 16 ]. Mask shortages were prevalent across countries in their early stage of use. For example, at the beginning of the pandemic, there was a mask shortage in South Korea due to mass panic-induced purchases by citizens. The South Korean government requested manufacturers increase mask production, and then ensured the newly manufactured masks were directly allocated to pharmacies where only a limited number of masks could be provided to individuals. The number of available masks was displayed in government- and private sector-created apps to prevent citizens from lining up outside pharmacies, which could have resulted in violating physical distancing measures. Additionally, the National Health Insurance Service database showed how many masks were sold to individuals per week.

Generally, the guidance provided across the six nations regarding avoiding infection by washing hands or using alcohol-based hand sanitizer frequently, performing respiratory etiquette when coughing or sneezing, and avoiding touching the face corroborated the WHO guidelines [ 34 ].

Despite physical distancing being vital to mitigating the spread of the novel coronavirus, political beliefs affected compliance with COVID-19 social distancing guidelines. This was especially evident in the US, where, in general, people who held contrasting political beliefs to the resident state governing body were less responsive to stay-at-home orders. For example, Republicans did not fully respect and react to stay-at-home orders when Democratic counties announced the order. In a similar fashion, Democrats were less likely to respond to stay-at-home orders when a Republican governor issued the decree [ 35 ].

On that point, it is worth noting that although the countries examined all referred to the government issued COVID-19 notices as ‘guidelines,’ these notices were not enforceable equally across countries. As an example, in the US, the CDC’s guidance acted as a framework that could be adapted for use by individual hospitals or by local/state governments for legislative purposes. However, in South Korea the guidelines essentially acted as enforceable legislation with serious financial repercussions.

Another important political development to note occurred in Brazil, when the Ministry of Health included a video on their website focused on clarifying “fake news” about the coronavirus. The video requested users confirm whether information presented in various medias was true before sharing that information with others. It also suggested individuals consult with an official number via WhatsApp for information clarification and communication.

An additional concern was raised regarding the use of health-tracking apps. Various countries used voluntary health-tracking apps to manage the COVID-19 pandemic either for informational, health vigilance, or contact tracing purposes. However, a unique aspect of the South Korean response was the mandate for all Koreans and long-term expatriates to install a health tracking app for contact tracing purposes. Privacy concerns were raised by several publications, some of whom referenced the possibility of preserving data protection [ 36 ], while others reflected on the legal implications and the need to refine the data into an aggregate, rather than individual-level data, to better deter the misuse of the data [ 37 ].

The countries’ guidelines on how to care for people infected with COVID-19 experiencing mild symptoms at home aligned with the WHO guidance [ 38 ]. According to the WHO, ensuring the sick person used a separate room and bathroom in the home would be essential to containing the virus, however, only the US, South Korea, and Brazil made this recommendation to their respective communities. Haiti, the UK, and China did not mention this recommendation in their guidelines. Although those suspected of contracting the coronavirus were requested to stay at home in the UK, limited information was provided to guide the home care process, such as how to disinfect the ill person’s room or how to handle sharing household items in the home. In China, all people suspected of having the coronavirus were instructed to seek testing at a testing center and were admitted to ‘Fangcang Shelter Hospitals.’ Therefore, it could be argued it was not necessary to provide information to the community on how to deal with sick people at home. The decision to advise all people suspected of having the coronavirus to go directly to the hospital is at odds with at least one study, which proposed that instead of guiding the COVID-19 patient to seek healthcare facilities, it would be preferable to provide at-home testing and monitoring [ 39 ]. However, while staying at home it is critical to carefully monitor worsening symptoms since medical care is not necessarily immediately available.

The symptom thresholds to contact healthcare providers varied between countries, with a wider array of symptoms (beyond the respiratory types) being included by countries that had dealt with the epidemic for longer periods of time. Clearly a great deal of clinical judgement was necessary for monitoring disease progression, since acting in a timely manner to differentiate a more serious case of COVID-19 was crucial to limiting fatality.

Finally, WHO provided information regarding the transportation of patients with confirmed and suspected COVID-19 to referral health care facilities, however, WHO did not give any information regarding transport mode to individuals with suspected COVID-19 [ 40 ]. The guidelines on transportation to healthcare facilities varied in emphasis between governments. A publication from China showed key involvement of public transportation in the dissemination of coronavirus. According to the study, the daily frequency of public transportation entrance and exit from Wuhan was significantly related to the number of COVID-19 cases in other cities [ 41 ]. When traveling to a hospital due to the presence of potential COVID-19 symptoms, wearing a face mask, using a personal vehicle, avoiding public transports and/or calling an ambulance were recommended by the Korean, US, and Chinese government’s guidelines. The UK and Haiti advised such patients utilize ambulance transport when heading to the hospital. The Brazilian government did not provided advice regarding transport mode.

Limitations

These findings are related to the guidelines for healthcare facilities and communities, as updated until April 20, 2020, however some guidelines may have been continuously updated beyond this date. In Haiti, because of the low prevalence of COVID-19 (total confirmed case: 100, deaths: 8 as of May 1, 2020), some information was unable to be obtained from the government guidelines, even though it was provided by news outlets or other medias, which were not included here. This study only used government guidelines accessible by the public, which may have limited the scope of the study’s usable information.

In summary, all six countries updated their guidelines, especially screening criteria, as the incidence of COVID-19 increased to take more aggressive actions against the progression of COVID-19 spread and to help “flatten the curve,” thus easing some of the burden on the respective healthcare systems. In the initial stages of the outbreak, certain strategies were universally employed to control the deadly virus’s spread, including quarantining the sick, contact tracing, and social distancing. However, these measures would have limited value if the people suspected of contracting the disease were not tested. It is difficult, if not impossible, to identify any one factor as the greatest cause for coronavirus dissemination, but by comparing these countries’ approaches it is possible to identify multiple factors that contribute to an overall effective strategy for reducing its spread. Additionally, there are multiple characteristics that influence the prevalence and incidence of COVID-19, including population density, differences in healthcare infrastructure, and primary means of transportation. Future studies should focus in more detail on these factors and their influence on the prevalence and incidence of COVID-19.

Abbreviations

Coronavirus disease

Computed Tomography

Center for Disease Control and Prevention

Emergency room

Food and Drug Administration

Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome

Intensive care unit

Korea Centers for Disease Control and Prevention

Middle East respiratory syndrome

National Health Service

Patient Under Investigation

Personal Protective Equipment

United States

United Kingdom

World Health Organization

W-j G, Z-y N, Hu Y, Liang W-h, Ou C-q, He J-x, Liu L, Shan H, Lei C-l, Hui DS. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708–20.

Article   Google Scholar  

Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R. World health organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg. 2020.

COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html . Accessed: 03 May 2020.

Karppinen K, Moe H. What we talk about when we talk about document analysis. Trends Commun Policy Res. 2012:177–93.

Bowen GA. Document analysis as a qualitative research method. Qual Res J. 2009;9(2):27.

Cantini F, Niccoli L, Matarrese D, Nicastri E, Stobbione P, Goletti D. Baricitinib therapy in COVID-19: a pilot study on safety and clinical impact. J Infect. 2020.

Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–88.

Coronavirus Data Hub FAQ: What you should know about our COVID-19 dataset. https://www.tableau.com/about/blog/2020/3/coronavirus-data-hub-faq . Accessed: 06 May 2020.

Krefting L. Rigor in qualitative research: the assessment of trustworthiness. Am J Occup Ther. 1991;45(3):214–22.

Article   CAS   Google Scholar  

Holloway I, Galvin K. Qualitative research in nursing and healthcare: Wiley; 2016.

World Health Organization. Global surveillance for COVID-19 caused by human infection with COVID-19 virus: interim guidance, 20 March 2020. Geneva: World Health Organization; 2020.

Google Scholar  

World Health Organization. Critical preparedness, readiness and response actions for COVID-19; Interim guidance, 24 June 2020. Geneva: World Health Organization; 2020.

Chan JF-W, Yuan S, Kok K-H, To KK-W, Chu H, Yang J, Xing F, Liu J, Yip CC-Y, Poon RW-S. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395(10223):514–23.

World Health Organization WHO Director-General’s opening remarks at the media briefing on COVID-19 - 16 March 2020. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19%2D%2D-16-march-2020 . Accessed: 30 Apr 2020.

Zhao W, Zhong Z, Xie X, Yu Q, Liu J. Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study. Am J Roentgenol. 2020;214(5):1072–7.

Ranney ML, Griffeth V, Jha AK. Critical supply shortages—the need for ventilators and personal protective equipment during the Covid-19 pandemic. N Engl J Med. 2020.

Al-Muharraqi M. Testing recommendation for COVID-19 (SARS-CoV-2) in patients planned for surgery-continuing the service and ‘suppressing’the pandemic. Br J Oral Maxillofac Surg. 2020.

Lacobucci G. Covid-19: What is the UK’s testing strategy? British Medical Journal Publishing Group; 2020.

JHU CCSE JHUCfSSaE Novel Coronavirus (COVID-19) Cases Data: This dataset is part of COVID-19 Pandemic. https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases . Accessed: 06 May 2020.

CDC CfDCaP Shipping of CDC 2019 Novel Coronavirus Diagnostic Test Kits Begins. https://www.cdc.gov/media/releases/2020/p0206-coronavirus-diagnostic-test-kits.html . Accessed: 25 Mar 2020.

Cohen J, Kupferschmidt K. Countries test tactics in ‘war’against COVID-19. In: American Association for the Advancement of Science; 2020.

CDC CfDCaP Testing in the U.S. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/testing-in-us.html . Accessed: 7 May 2020.

KCDC 질 코로나바이러스감염증-19 국내 발생 현황(3월 25일, 0시 기준). https://www.cdc.go.kr/board/board.es?mid=a20501000000&bid=0015&act=view&list_no=366635&tag=&nPage=1 . Accessed: 15 Apr 2020.

partnerships USPCfKetS Flattening the Curve on COVID-19. http://www.undp.org/content/seoul_policy_center/en/home/presscenter/articles/2019/flattening-the-curve-on-covid-19.html . Accessed: 9 May 2020.

World Health Organization. Considerations in the investigation of cases and clusters of COVID-19: interim guidance, 22 October 2020. Geneva: World Health Organization; 2020.

Chen S, Zhang Z, Yang J, Wang J, Zhai X, Bärnighausen T, Wang C. Fangcang shelter hospitals: a novel concept for responding to public health emergencies. Lancet. 2020.

World Health Organization. Report of the who-china joint mission on coronavirus disease 2019 (covid-19). Geneva; 2020.

WHO WHO WHO Timeline - COVID-19. https://www.who.int/news-room/detail/27-04-2020-who-timeline%2D%2D-covid-19 . Accessed: 30 Apr 2020.

World Health Organization Updated WHO recommendations for international traffic in relation to COVID-19 outbreak. https://www.who.int/news-room/articles-detail/updated-who-recommendations-for-international-traffic-in-relation-to-covid-19-outbreak . Accessed: 8 May 2020.

Durham J, Michael M, Hill P, Paviignani E. Haïti and the health marketplace: the role of the private, informal market in filling the gaps left by the state. BMC Health Serv Res. 2015;15(1):424.

World Health Organization COVID-19 Solidarity Response Fund: Help WHO fight COVID-19. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/donate . Accessed: 15 Apr 2020.

World Health Organization. Infection prevention and control during health care when novel coronavirus (nCoV) infection is suspected: Interim guidance, 19 March 2020. In. Edited by WASH C-Ipac. Geneva: World Health Organization; 2020.

Brainard JS, Jones N, Lake I, Hooper L, Hunter P. Facemasks and similar barriers to prevent respiratory illness such as COVID-19: A rapid systematic review. medRxiv. 2020.

World Health Organization Coronavirus disease (COVID-19) advice for the public. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public . Accessed: May 2020.

Painter M, Qiu T. Political beliefs affect compliance with covid-19 social distancing orders. 2020. Available at SSRN 3569098.

Book   Google Scholar  

Abeler J, Bäcker M, Buermeyer U, Zillessen H. COVID-19 contact tracing and data protection can go together. JMIR mHealth uHealth. 2020;8(4):e19359.

Park S, Choi GJ, Ko H. Information technology–based tracing strategy in response to COVID-19 in South Korea—privacy controversies. JAMA. 2020.

World Health Organization. Home care for patients with COVID-19 presenting with mild symptoms and management of their contacts: interim guidance, 17 March 2020: World Health Organization; 2020.

Glauser W. Proposed protocol to keep COVID-19 out of hospitals. ICan Med Assoc. 2020.

World Health Organization. Rational use of personal protective equipment for coronavirus disease (COVID-19) and considerations during severe shortages: Interim guidance, 6 April 2020. In. Edited by WASH C-Ipac. Geneva: World Health Organization; 2020.

Zheng R, Xu Y, Wang W, Ning G, Bi Y. Spatial transmission of COVID-19 via public and private transportation in China. Travel Med Infect Dis. 2020.

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Acknowledgements

Samia Valeria Ozorio Dutra acknowledges support from the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) for graduate education.

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Yoo, J., Dutra, S.V.O., Fanfan, D. et al. Comparative analysis of COVID-19 guidelines from six countries: a qualitative study on the US, China, South Korea, the UK, Brazil, and Haiti. BMC Public Health 20 , 1853 (2020). https://doi.org/10.1186/s12889-020-09924-7

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Multilevel Research Designs: Case Selection, Levels of Analysis, and Scope Conditions

• Published: 15 August 2020
• Volume 55 , pages 460–480, ( 2020 )

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Subnational research in comparative politics has been growing steadily in the last two decades. However, methodological advances have been rather limited. This article builds upon Snyder’s (Stud Comp Int Dev 36(1):93–110, 2001 ) subnational comparative method and extends its logic to the comparison of subnational units from different countries. It proposes a novel typology of multilevel research designs that focuses particularly on cross-national small-N analysis (CSNA). This research design offers three different logics of qualitative case selection to achieve a sound trade-off between internal and external validity. This article analyzes the advantages and limitations of the underlying logics of CSNA and illustrates their use with recent empirical research from Latin American countries. It concludes by highlighting its versatility and offers a series of best practices in order to produce more generalizable findings than the majority of single-country subnational comparisons.

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Internationally Comparative Research Designs in the Social Sciences: Fundamental Issues, Case Selection Logics, and Research Limitations

Meta-Analysis and Meta-Regression: An Alternative to Multilevel Analysis When the Number of Countries Is Small

Theory Development in Comparative Social Research

Although there is no consensus in the literature on whether SCM is a distinctive “method,” most recent works seem to assume so, more or less explicitly (Fox 2007 ; Pepinsky 2017 ; Giraudy et al. 2019 ). In any case, what makes it distinctive is not so much the logic of case selection as the levels where the cases are selected. Selecting both at the subnational and national level introduces further complexity and need for justification and also requires the analysis of (possible) vertical interactions between levels. In this sense, it goes beyond the Millean methods (see also Sellers 2019 ).

See Giraudy et al. ( 2019 ).

See Pepinsky ( 2019 ) for a recent discussion on “the return of the single country study” and its tradeoffs.

For the same point, see Lankina ( 2012 ) and Sinha ( 2015 ) who further argues for cross-regional comparisons.

Many authors use the term in the passing (Pepinsky 2017 ; Post 2018 ; Giraudy et al. 2019 ) but none really develop the concept. It seems to broadly refer to increased interest in the study of politics at the subnational level.

Indeed, rather few of the works reviewed by Snyder ( 2001 : footnote 1) featured comparison of subnational units from different countries (see Lipset 1950 ; Linz and de Miguel 1966 ; O’Donnell 1973 ; Tarrow 1976 ; Montero 2001 ); the vast majority were single-country studies.

These three logics partly concur with those proposed recently by Sellers ( 2019 ). However, see below for differences in conceptualization and logic of case selection.

The term “multilevel research design” is certainly not new. It has been used before by other authors, see, e.g., Murillo ( 2001 ), Luna ( 2014 ), Niedzwiecki ( 2018 ), or Giraudy et al. ( 2019 ). However, Giraudy et al. ( 2019 ) use it more in terms of theoretical explanations than research design (case selection).

Normally, the higher level refers to countries. However, as will be made clear below, this research design can also be applied at two different subnational levels within a given country.

Some important outlets for comparative research like Democratization, Studies in Comparative International Development, Politics and Society or Local Government Studies, to name a few, are normally not included.

They most probably include the vast majority of the articles found by Sellers ( 2019 ), assuming they cite Snyder ( 2001 ).

See Sellers ( 2019 ) for a quantitative analysis of leading comparative politics journals and monographs.

Indeed, much of what has been somewhat ambiguously dubbed “subnational turn” owes a big debt to Snyder’s seminal article and goes beyond the sphere of publications.

As of October 2018, according to Google Scholar.

This analysis does not mean to imply that there were no empirical works using multilevel research design or the subnational method before Snyder’s article was published. Of course, as his review shows, there were many of them (see particularly footnote 1, Snyder 2001 : 104). However, only one (Montero 2001 ) among the more recent works compares subnational units from different countries. Some of the classic studies are discussed below.

In fact, in Fig.  1 the third group consists of works that I was unable to check. These include studies that are unavailable (Google Scholar does not provide any link) or had restricted access (mainly dissertations). Also, there are books and book chapters I do not have access to and works in languages I do not speak (mainly, German, Russian, or Chinese).

On the one hand, many works include his article in the bibliography but do not cite it in the text of the article and thus make it impossible to determine how it was used. On the other hand, Google Scholar erroneously includes a number of studies that do not cite the article.

Indeed, this proportion is even larger if one considers only original empirical works.

Some authors nevertheless state, and Snyder ( 2001 ) himself recognizes, that in some countries the within-country variation of these variables can be huge (Pepinsky 2017 ).

Pepinsky ( 2017 : 1034) puts forward a solution to this problem in what he calls a “problem-driven approach” to examine how “such cases are defined by their relationship to the causal questions under consideration.”

Nonetheless, authors like Hiskey and Canache ( 2005 ) propose an empirical statistical model designed specifically to test the diffusion argument in subnational settings.

See Zuo ( 2015 : 324) for similar findings.

This typology differs from the one proposed by Giraudy et al. ( 2019 ) concerning “strategies of subnational research.” These authors combine the criteria of “type of causal relationship” and “number of levels of analysis.”

In general terms, all these designs are observational in nature. It is nevertheless true that they can be combined with experiments as a method in some of the lower level

cases or that the case selection at this level can take the form of a natural experiment.

In this paper, the international level is excluded for the sake of clarity.

See Moncada and Snyder ( 2012 ) and, particularly, Rodrigues-Silveira ( 2013 ) for the notion of “institutionally unbounded processes”.

See Giraudy et al. ( 2019 ) for a thorough discussion.

This is in line with Snyder’s article, where the author does not discuss single case studies and all the works that use the qualitative version of SCM analyze at least two subnational units. However, case and small-N studies are often grouped together for the purposes of research design (Munck and Verkuilen 2005 ; Gerring 2007 ).

Indeed, the same logic of case selection can be applied at different subnational levels, e.g., comparing municipalities within one province.

Here, all subnational units refer to all states, provinces, departments, or municipalities in a given country. The criterion for selecting (almost) all units might depend on data availability and may be more flexible, as long as the analysis is large-N and no additional qualitative case selection is employed.

For example, in her subnational analysis of clientelism in Argentina, Weitz-Shapiro ( 2016 : 18) justifies her national case selection basically by referring to the “relative pervasiveness of the practice” in this country. However, it could be argued that in other Latin American countries clientelism is equally or even more pervasive (Gonzalez-Ocantos and Oliveros 2019 ), though it has certainly received less attention in the comparative literature. Again, as with SSNA, the justification of the national-level case selection tends to be rather loose.

This refers to situations where two or more theoretically justifiable cases can be selected and choosing between them entails practical motives (e.g., time or logistical constraints, previous contacts and networks, funding access to information or language skills).

See Raudenbush and Bryk ( 2002 ) or Gelman and Hill ( 2007 ), among many others. Sellers ( 2019 ) includes this research design in his “subnational comparison across borders” classification, as an “encompassing subnational comparison.”

Indeed, as the above meta-analysis shows, Snyder’s article is also least cited in this type of studies.

Lijphart ( 1971 ) calls them “intranation comparisons.”

This refers to the use of national averages in cross-national comparison that conceal territorial variation in a given phenomenon within a country (Snyder 2001 : 98).

See below for a discussion on using borders for research designs based on natural experiments.

This challenge, in fact, advocated for a shift of focus to the transnational sphere.

Nested analysis or nested inference follows a similar logic and both refer to a mixed-method research strategy, whereby a scholar combines large-N statistical analysis with a subsequent intensive small-N analysis. The results of the former guide the case selection for the latter (Lieberman 2005 ). Lieberman was thinking of national-level cases; for application on at the subnational level, see Ingram ( 2015 ) and Niedzwiecki ( 2018 ).

Moreover, Denk ( 2010 ) sparked a discussion on using a new type of Qualitative Comparative Analysis (QCA) for a “comparative multilevel analysis” (Rohlfing 2012 ; Denk and Lethinen 2014 ; Thiem 2016 ). Harbers and Ingram’s ( 2017 ) article contributes to the discussion. Of course, there is now much more debate on mixed- and multi-method methodology. For two recent contributions, see Seawright ( 2016 ) and Goertz ( 2017 ). For a review of the current literature and a new research agenda, see Brookes ( 2017 ).

These four ways are (i) multilevel analysis: two level interaction; (ii) multilevel analysis: hierarchical model; (iii) testing the uniformity of national traits; and (iv) borders as quasi-experiments, see Riedl ( 2017 : 933-943) for details on each of them.

On the contrary, if, for example, the researcher tries to explain the variation of turnout in subnational elections using municipal data, it is considered SLNA.

Natural experiments are considered strictly speaking, observational studies (Dunning 2012 ), see Keele and Titiunik ( 2016 ) for natural experiments based on geography.

As Montambeault ( 2016 : 13) points out, “the weaknesses of small-N comparison are overcome by the fact that the analysis is based on multilevel comparisons. In fact, comparing both similar and different cities within two countries generates findings that increase the potential for midrange generalizations”.

However, they differ from the second logic of CSNA (see below), in that they select more than one case at the subnational level and use paired comparisons.

In fact, both Giraudy and McMann choose bordering subnational units. Both authors study the reproduction of subnational non-democratic regimes; Giraudy ( 2015 ) compares Mexican states and Argentine provinces and McMann ( 2006 ) Russian and Kyrgyzstani provinces ( oblast ).

Montambeault ( 2016 ) compares local participatory governance mechanisms and their effect on the quality of democracy in Brazilian and Mexican cities.

These pathways permit subnational undemocratic regimes’ continuity, although the predominant instrument of presidential power and the principal attribute of subnational governments to neutralize presidential power vary by country (Giraudy 2015 ).

These reasons relate to the interaction between the institutional design and local actors’ attitudes and strategies (Montambeault 2016 ).

In more theoretical terms, Giraudy et al. ( 2019 ) argue that some national-level events can have heterogeneous effects at the subnational level (top-down theories). They mention, among others, economic reforms, violence, or state capacities.

Luna ( 2014 ) studies differences in political parties’ linkage strategies in Chile and Uruguay. Ingram ( 2015 ) examines the variation in subnational judicial capacity and the institutional reforms in Brazilian and Mexican states. Niedzwiecki ( 2018 ) seeks to explain territorial differences in the implementation of social policies in Brazilian states and Argentine provinces and municipalities in both countries.

Both Ingram ( 2015 ) and Niedzwiecki ( 2018 ) introduce time dimension to their analysis and run time-series cross-section models with intermediate subnational units. Luna captures the temporal dimension by comparing data from fieldwork in two different moments of time.

As Luna ( 2014 : 14) underlines, this case selection confers “greater levels of internal validity to my causal inferences, even where there are limited degrees of freedom.”

This is less so in the case of Ingram ( 2015 ). The variation is based only on two variables.

Even before natural experiments became popular (and the terminology coined), Lancaster ( 1987 ) offered a study with a similar logic (see also Lipset 1950 ). In his article, the author compared Basques in Spain and France and identified significant differences in their national self-identification. Lancaster ( 1987 ) argued that this phenomenon was due to differing state policies toward them in both countries, see also Linz ( 1986 ) for a similar comparison.

This logic roughly corresponds to what Sellers ( 2019 ) calls “Matched Subnational Cases in ‘Most Different’ National Systems.”

See for example Pasotti ( 2017 ).

Simmons ( 2016 : 6) maintains that in these cases “a micro-level analysis reveals important commonalities.”

In this sense, they resemble the “explaining-outcome process tracing” proposed by Beach and Pedersen ( 2016 ).

Although Eaton does make it clear that these conservative movements differ from their progressive counterparts, and that consequently so do the explanations of their possibilities of success.

However, Eaton ( 2011 ) briefly mentions Guatemala, Peru, and Venezuela.

Beach D, Pedersen RB. Process-tracing methods. Foundations and guidelines. Ann Arbor: The University of Michigan Press; 2016.

Google Scholar  

Brady HE, Collier D. Editors. Rethinking social inquiry. Diverse tool, shared standards. Lanham: Rowman & Littlefield Publishers, Inc; 2010.

Brookes M. The road less traveled: an agenda for mixed-methods research. PS: Pol Sci & Pol. 2017;50(4):1015–8.

Coppedge M. Explaining democratic deterioration in Venezuela through nested inference. In: Mainwaring SP, Hagopian F, editors. The Third Wave of Democratization in Latin America. Advances and Setbacks. New York: Cambridge University Press; 2005. p. 289–316.

Denk T. Comparative multilevel analysis: proposal for a methodology. Int J Soc Res Methodol. 2010;13(1):29–39.

Denk T, Lethinen S. Contextual analyses with QCA-methods. Qual Quant. 2014;48(6):3475–87.

Diez J. Explaining policy outcomes: the adoption of same-sex unions in Buenos Aires and Mexico City. Comp Polit Stud. 2013;46(2):212–35.

Došek T. La persistencia de caudillos locales en América Latina [Ph.D.]. Santiago: Pontificia Universidad Católica de Chile; 2019.

Dunning T. Natural experiments in the social sciences: a design-based approach. New York: Cambridge University Press; 2012.

Eaton K. Conservative Autonomy Movements. Territorial dimensions of ideological conflict in Bolivia and Ecuador. Comp Polit. 2011;43(3):291–310.

Elder JW. Comparative cross-national methodology. Annu Rev Soc. 1976;2:209–30.

Article   Google Scholar  

Eulau H. Comparative political analysis: a methodological note. Midwest J Pol Sci. 1962;6(4):397–407.

Fox J. Accountability politics: power and voice in rural Mexico. New York: Oxford University Press; 2007.

Book   Google Scholar  

Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press; 2007.

Gerring J. Case study research: principles and practices. Cambridge: Cambridge University Press; 2007.

Gibson EL. Boundary control. Subnational authoritarianism in democratic countries. World Pol. 2005;58(1):101–32.

Giraudy A. Democrats and autocrats. Pathways of subnational undemocratic regime continuity within democratic countries. New York: Oxford University Press; 2015.

Giraudy A, Moncada E, Snyder R. Subnational research in comparative politics: achievements and future directions. In: Giraudy A, Moncada E, Snyder R, editors. Inside countries: subnational research in comparative politics. New York: Cambridge University Press; 2019. p. 3–54.

Goertz G. Multimethod research, causal mechanisms, and case studies. An integrated approach. Princeton: Princeton University Press; 2017.

Goertz G, Mahoney J. A tale of two cultures. Qualitative and quantitative research in the social sciences. Princeton: Princeton University Press; 2012.

Goldfrank B. The politics of deepening local democracy: decentralization, party institutionalization, and participation. Comp Polit. 2007;39(2):147–68.

Gonzalez-Ocantos E, Oliveros E. Clientelism in Latin American politics. In: Gary Prevost G, Vanden H, editors. The Oxford Encyclopedia of Latin American Politics. Oxford: Oxford University Press; 2019. https://doi.org/10.1093/acrefore/9780190228637.013.1677 .

Harbers I, Ingram MC. Incorporating space in multimethod research: combining spatial analysis with case-study research. PS: Pol Sci & Pol. 2017;50(4):1032–7.

Hiskey J, Canache D. The demise of one-party politics in Mexican municipal elections. Br J Pol Sci. 2005;35(2):257–84.

Holland AC. Forbearance as Redistribution. The politics of informal welfare in Latin America. New York: Cambridge University Press; 2017.

Ingram MC. Crafting courts in new democracies: the politics of subnational judicial reform in Brazil and Mexico. New York: Cambridge University Press; 2015.

Jeffery C, Schakel AH. Editorial: towards a regional political Science. Reg Stud. 2013;47(3):299–302.

Jeffery C, Wincott D. The challenge of territorial politics: beyond methodological nationalism. In: Hay C, editor. New directions in political science. London: Palgrave; 2010. p. 167–88.

Keele L, Titiunik R. Natural experiments based on geography. Pol Sci Res Met. 2016;4(1):65–95.

Koivu KL, Hinze AM. 2017. Cases of convenience? The divergence of theory from practice in case selection in qualitative and mixed-methods research. PS: Polit Sci Pol. 2017;50(4):1023–7.

Lancaster TD. Comparative nationalism: the Basques in Spain and France. Eur J Polit Res. 1987;15(5):561–90.

Lankina T. Sisyphean endeavor or worthwhile undertaking? Transcending within-nation, within region sub-national democracy analysis. American Political Science Association, APSA Comparative Democratization Newsletter. 2012;2:14–8.

Lieberman ES. Nested analysis as a mixed-method strategy for comparative research. Am Pol Sci Rev. 2005;99(3):435–52.

Lijphart A. Comparative politics and the comparative method. Am Pol Sci Rev. 1971;65(3):682–93.

Linz JJ. Conflicto en Euskadi. Madrid: Espasa Calpe; 1986.

Linz JJ, de Miguel A. Within-nation differences and comparisons: the eight Spains. In: Merritt RL, Rokkan S, editors. Comparing nations: the use of quantitative data in cross-National Research. New Haven: Yale University Press; 1966. p. 267–319.

Lipset SM. Agrarian socialism: the cooperative commonwealth Federation in Saskatchewan. Berkeley: University of California Press; 1950.

Luna JP. Segmented representation. Political party strategies in unequal democracies. New York: Oxford University Press; 2014.

McMann KM. Economic autonomy and democracy. Hybrid regimes in Russia and Kyrgyzstan. New York: Cambridge University Press; 2006.

Moncada E, Snyder R. Subnational comparative research on democracy: taking stock and looking forward. American Political Science Association, APSA Comparative Democratization Newsletter 2012;1:4–9.

Montambeault F. The politics of local participatory democracy in Latin America. Institutions, actors, and interactions. Stanford: Stanford University Press; 2016.

Montero AP. Delegative dilemmas and horizontal logics: Subnational industrial policy in Spain and Brazil. Stud in Comp Int Dev. 2001;36(3):58–87.

Munck G; Verkuilen J. Research Designs. In: Kempf-Leonard K., editor. Encyclopedia of Social Measurement (Vol. 3). San Diego: Academic Press; 2005. p. 385–395.

Munck GL, Snyder, R. Debating the direction of comparative politics. An analysis of leading journals. Comp Polit Stud. 2007;(40)1:5–31.

Murillo MV. Labor unions, partisan coalitions, and market reforms in Latin America. New York: Cambridge University Press; 2001.

Murillo MV, Ronconi L. Teachers’ strikes in Argentina: partisan alignments and public-sector labor relations. Stud Comp Int Dev. 2004;39(l):77–98.

Niedzwiecki S. Uneven social policies. The politics of subnational variation in Latin America. New York: Cambridge University Press; 2018.

O’Donnell G. Modernization and bureaucratic-authoritarianism: studies in south American politics. Berkeley: Institute of International Studies, University of California, Berkeley; 1973.

Ostrom E. Governing the commons: the evolution of institutions for collective action. New York: Cambridge University Press; 1990.

Paredes M, Došek T. The subnational indigenous quota in Peru: the paradoxes of political representation. Lat Am Pol Soc. 2020;62(3):123–48.

Pasotti E. Political branding in cities. The decline of machine politics in Bogotá, Naples, and Chicago. New York: Cambridge University Press; 2017.

Pepinsky T. Regions of exception. Perspectives on Pol. 2017;15(4):1034–52.

Pepinsky T. The return of the single country study. Annu Rev of Pol Sci. 2019;22:187–203.

Pino Uribe JF. Entre democracias y autoritarismos: una mirada crítica al estudio de la democracia subnacional en Colombia y Latinoamérica. Col Int. 2017;91:215–42.

Posner DN. The political salience of cultural difference: why Chewas and Tumbukas are allies in Zambia and adversaries in Malawi. Am Pol Sci Rev. 2004;98(4):529–45.

Post AF. Cities and politics in the developing world. Annu Rev of Pol Sci. 2018;21:115–33.

Przeworski A, Teune H. The logic of comparative social inquiry. New York: John Wiley and Sons; 1970.

Raudenbush SW, Bryk AS. Hierarchical linear models. Applications and data analysis methods. Thousand Oaks: Sage Publications; 2002.

Riedl RB. Sub-National–Cross-National Variation: Method and analysis in sub-Saharan Africa. Am Behav Sci. 2017;61(8):932–59.

Rodrigues-Silveira R. The Subnational Method and Social Policy Provision: Socioeconomic Context, Political Institutions and Spatial Inequality. Working Paper No. 36, desiguALdades.net , Berlin, Germany; 2013.

Rogers MZ. Taxing with dictators and democrats: regime effects, transfers and revenue in Argentina’s provinces, in: journal of politics in Latin America. J Pol Latin Am. 2014;6(1):3–44.

Rohlfing I. Analyzing multilevel data with QCA: a straightforward procedure. Int J Soc Res Methodol. 2012;15(6):497–506.

Rokkan S. Citizens, elections, parties: approaches to the comparative study of the processes of development. New York: David McKay Company; 1970.

Rueschemeyer D. Can one of a few cases yield theoretical gains. In: Mahoney J, Rueschemeyer D, editors. New York: Cambridge University Press; 2003. p. 305–36.

Seawright J. Multi-Method Social Science. Combining qualitative and quantitative tools. New York: Cambridge University Press; 2016.

Sellers JM. From within to between nations: subnational comparison across borders. Perspectives on Pol. 2019;17(1):85–105.

Simmons ES. Meaningful resistance: market reforms and the roots of social protest in Latin America. New York: Cambridge University Press; 2016.

Sinha A. Scaling up: beyond the subnational comparative method for India. Stud Ind Pol. 2015;3(1):128–33.

Slater D, Ziblatt D. The enduring indispensability of the controlled comparison. Comp Polit Stud. 2013;46(10):1301–27.

Smelser NJ. Notes on the methodology of comparative analysis of economic activity. Paris: UNESCO; 1965.

Snyder R. Scaling down: the subnational comparative method. Stud Comp Int Dev. 2001;36(1):93–110.

Szwarcberg M. Mobilizing Poor Voters. Machine politics, clientelism, and social networks in Argentina. New York: Cambridge University Press; 2017.

Tarrow S. From Center to Periphery: Alternative models of national-local policy impact and an application to France and Italy. Ithaca: Western Societies Program, Cornell University; 1976.

Thiem A. Analyzing multilevel data with QCA: yet another straightforward procedure. Qual & Quant. 2016;50(1):121–8.

Weitz-Shapiro R. Curbing clientelism in Argentina. Politics, poverty, and social policy. New York: Cambridge University Press; 2016.

Wimmer A, Schiller NG. Methodological nationalism, the social sciences, and the study of migration: an essay in historical epistemology. Int Migr Rev. 2002;37(3):576–610.

Zuo C. Scaling down: Subnational comparative case studies in comparative politics and Chinese politics. Eur Pol Sci. 2015;14(3):318–39.

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Acknowledgments

I thank two anonymous reviewers and members of the SCID Editorial Collective for their helpful comments and suggestions on earlier drafts. I am also grateful to the participants of Fourth Southwest Workshop on Mixed Methods Research, University of California, Santa Cruz, November 8-9, 2018, for their insights.

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Dosek, T. Multilevel Research Designs: Case Selection, Levels of Analysis, and Scope Conditions. St Comp Int Dev 55 , 460–480 (2020). https://doi.org/10.1007/s12116-020-09313-6

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Educational system adequacy varies globally, with some countries struggling due to internal conflicts, economic challenges, or underfunded programs.

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According to the Global Partnership for Education , education is considered to be a human right and plays a crucial role in human, social, and economic development . Education promotes gender equality, fosters peace, and increases a person's chances of having more and better life and career opportunities.

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The annual Best Countries Report , conducted by US News and World Report, BAV Group, and the Wharton School of the University of Pennsylvania , reserves an entire section for education. The report surveys thousands of people across 78 countries, then ranks those countries based upon the survey's responses. The education portion of the survey compiles scores from three equally-weighted attributes: a well-developed public education system, would consider attending university there, and provides top-quality education. As of 2023, the top ten countries based on education rankings are:

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It's also worthwhile to note that while the Best Countries study is certainly respectable, other studies use different methodologies or emphasize different criteria, which often leads to different results. For example, the Global Citizens for Human Rights' annual study measures ten levels of education from early childhood enrollment rates to adult literacy. Its final 2020 rankings look a bit different:

Education Rates of Children Around the World

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Worldwide delineation of multi-tier city–regions

• Andrea Cattaneo   ORCID: orcid.org/0000-0002-8845-5020 1 ,
• Serkan Girgin   ORCID: orcid.org/0000-0002-0156-185X 2 ,
• Rolf de By 2 ,
• Theresa McMenomy   ORCID: orcid.org/0000-0002-6100-7850 1 ,
• Andrew Nelson   ORCID: orcid.org/0000-0002-7249-3778 2 &
• Sara Vaz   ORCID: orcid.org/0000-0003-1263-8098 1  

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Urban centers are pivotal in shaping societies, yet a systematic global analysis of how countries are organized around multiple urban centers is lacking. We enhance understanding by delineating city–regions worldwide, classifying over 30,000 urban centers into four tiers—town, small, intermediate and large city—based on population size and mapping their catchment areas based on travel time, differentiating between primary and secondary city–regions. Here we identify 1,403 primary city–regions employing a 3 h travel time cutoff and increasing to 4,210 with a 1 h cutoff, which is more indicative of commuting times. Our findings reveal substantial interconnectedness among urban centers and with their surrounding areas, with 3.2 billion people having physical access to multiple tiers within an hour and 4.7 billion within 3 h. Notably, among people living in or closest to towns or small cities, twice as many have easier access to intermediate than to large cities, underscoring intermediate cities’ crucial role in connecting surrounding populations. This systematic identification of city–regions globally uncovers diverse organizational patterns across urban tiers, influenced by geography, level of development and infrastructure, offering a valuable spatial dataset for regional planning, economic development and resource management.

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Sustainable development requires understanding how societies are organized spatially, especially around urban centers, given that 92% of the global population either lives in an urban center or within 1 h travel time 1 . City–regions, typically intended as combinations of an urban center or centers linked to periurban areas and a rural hinterland by functional ties, are increasingly attracting the attention of academics and policymakers for the implementation of development policies 2 , 3 . However, research on city–regions frequently relies on case studies of major cities 4 , lacking a comprehensive systems perspective on the relationships between cities of varying sizes and their surrounding areas. This knowledge gap is especially pronounced between North and South and between small and large cities 4 , 5 . This study helps fill this critical knowledge gap by providing a comprehensive framework to characterize city–regions worldwide.

An urban hierarchy exists in terms of services provided by cities of different sizes (as recently conveyed by the universal visitation law of human mobility) 6 or the travel time to access such services 1 , 7 . For example, one may rely on the closest town for groceries but may commute to work in a small city and travel to a hospital in a large city for a specialized medical opinion. Acknowledging such hierarchy is particularly relevant to populations living in rural or periurban areas or smaller urban centers, whereby the size of nearby cities and their distance will affect the breadth of services and opportunities (for example, employment) available and their accessibility 8 . Recent work on the degree of urbanization has introduced vital nuance to help overcome the rural/urban dichotomy 9 . However, the possible interconnections among urban centers and their relationship with surrounding areas have not been analyzed in a systematic manner at a global level.

Against this background, this paper builds upon recent work mapping functional urban areas (FUAs) 10 and urban‒rural catchment areas (URCAs) 1 , which express the interconnection between urban centers and their surrounding rural areas. While valuable, these approaches link locations to a single urban center of reference, ignoring that a location may have multiple centers of reference for varying activities 11 . Our study introduces the concept of locations accessing multiple urban tiers; the original URCA and FUA approaches with only one urban center of reference are considered a special case.

The aim of this study is to create a comprehensive spatial representation of city–regions globally and to gain a better understanding of their diversity. In this Article, we provide a detailed worldwide representation of how people are organized around urban centers in city–regions, covering 213 countries/territories and their populations for the year 2020. Acknowledging that urban issues in the Global South are distinct from those in the Global North and the dearth of evidence on the former 12 , we further bridge this knowledge gap and illustrate the diversity of city–regions around the world by comparing Ethiopia, France, Nigeria and Pakistan (and for 86 countries in Supplementary Information ). We investigate two areas of particular importance for sustainable regional development: the relationship between urban centers and their surrounding areas and intercity relationships within city–regions. These can inform strategies to enhance connectivity to improve food systems, access to health and education services and environmental and natural resource management.

Defining city–regions

Despite a long tradition, there is no agreed-upon definition of city–regions 13 . Rodríguez-Pose summarizes existing definitions, highlighting how most have one or more core cities linked to a hinterland by functional ties such as economic, housing-market, commuting, marketing or retail catchment factors 2 .

The starting point for our perspective on city–regions is that people access different types of activities with different frequency, where ‘activities’ refers to services and employment opportunities located in urban centers. Buying groceries and commuting to work are usually done on a more frequent basis than visiting a hospital. It follows that the level of specialization of activity depends on the size of an urban center, whereby larger centers are expected to provide more specialized activities that require a lower visitation frequency (for example, airports) than smaller ones. This notion of a nested hierarchy of locations dates back to central place theory developed in the 1930s 14 and is still relevant in regional science 15 . The recent universal visitation law of human mobility is a formal expression of this intuition 6 .

In view of this, we identify city–regions based on catchment areas, in turn, based on travel time. In other words, we identify the areas delineated by the minimum travel time (up to 1, 2 or 3 h away) to access each urban tier expressing urban centers of different sizes, assumed to have different levels of specialization ( Methods ). We assume that towns of between 20,000 and 50,000 people provide basic activities, such as grocery shopping, primary healthcare services and primary schools, while incrementally more specialized activities are provided by small cities of between 50,000 and 250,000 people and intermediate cities of between 250,000 and 1 million people, while large cities of more than 1 million people provide higher-end activities such as sizeable airports and specialized hospitals. A higher tier is assumed to provide all activities provided by lower tiers.

The activities provided by urban centers of the same population size may vary greatly with context, particularly across country average income levels 16 , 17 , 18 , 19 , 20 . Nonetheless, this tiered classification can capture how larger urban centers are likely to provide more specialized activities in a given context in addition to those already provided by lower tiers. It also captures how people may rely on more than one urban center for different needs, reflecting a stratification of access to urban centers that characterizes city–regions. For instance, an individual might use a nearby town for groceries, commute to an intermediate city for work and travel to a large city for international flights. Their location will thus be part of catchment areas of three distinct urban centers. Someone living very close to a large city, in contrast, will be able to access all the above activities and belong to only the catchment area having the large city as reference.

The proposed approach allows us to delineate city–regions in an exhaustive manner by using accessibility within specified travel times to encompass urban centers within a larger center’s catchment area. Catchment areas are determined by a given travel time, whereby the longer the travel time, the broader the catchment area. A catchment area based on 1 h travel represents daily commuting potential, whereas a 3 h travel time suggests access to essential, albeit less frequent services.

This paper adopts John Parr’s conceptual framing of city–regions to provide a comprehensive, worldwide spatial representation of central place theory 13 . We identify, for all land-based locations at 30 arc-seconds resolution (approximately 1 km at the equator), the center of reference by urban tier category and the associated travel time. This is achieved using an established method that estimates the time required to reach each grid cell of the world’s surface 21 , 22 , 23 . Drawing on this dataset and providing a new approach to determine access to multiple urban centers of reference, we then combine it with the Global Human Settlement Layer—Population (GHS-POP) to allocate population to individual pixels ( Methods ). The approach is tested using three alternative population datasets ( Supplementary Information ).

Mapping access to different urban tiers

A total of 30,079 urban centers are covered: 18,619 towns, 9,440 small cities, 1,538 intermediate cities and 482 large cities. The catchment areas provide the boundaries for accessing the closest urban center in a given tier within a given travel time cutoff and, thus, determine how many city–regions of different types there are globally for the given travel time cutoff. We distinguish between primary and secondary city–regions; unlike primary city–regions, in secondary city–regions catchment areas of urban centers overlap with that of larger centers (see Table 1 for the terminology and definitions used in this paper).

We differentiate primary city–regions into four categories (from one tier to four tiers) based on how many urban tiers these span within the catchment area of the largest urban center of reference. For example, two-tier city–regions may contain any two sizes of urban centers, such as a large city surrounded by towns. The urban centers whose catchment area does not overlap with those of urban centers in other tiers are the special case of ‘single-tier city–regions’. Following Duranton, we further provide alternative delineations for parameter thresholds 24 by distinguishing between different travel-time cutoffs (1, 2 and 3 h) for the overlapping catchment areas of different urban centers.

Analyzing 30,079 urban centers, spanning 213 countries and territories, we identified 4,210 primary and 25,869 secondary city–regions with an urban center within a 1 h travel time for all locations within their catchment area, relevant for commuting (Extended Data Table 1 ). Of these, 1,524 are single tier and 2,686 are multi-tier, with 1,751 being two-tier systems. More complex city–regions are fewer, with 627 and 308 city–regions with three and four urban tiers, respectively. We note that standalone single-tier city–regions are relatively few (1,524 out of 30,079 urban centers). Most of these are towns and their surrounding areas, which are more numerous and, being the smallest urban centers considered, cannot form a multi-tier city–region with a smaller urban center.

The most common city–regions are two-tier systems centered around small cities that are also a reference center for one or more towns (1,560 within 1 h travel time). In general, the smaller the population of an urban center, the more probable that urban center is to be part of a higher-tier city–region: 95% of towns, 78% of small cities, and 56% of intermediate cities are part of a higher-tier city–region. The results for 2 and 3 h travel time cutoff—relevant for activities that may be critical but are accessed less frequently—can be viewed in Extended Data Table 1 . Using a 3 h travel time cutoff reduces fragmentation of catchment areas, making larger urban centers accessible to a broader population for activities that necessitate longer travel times between 1 and 3 h. This reduces the number of urban centers that are at the apex of a city–region for these less-frequent activities to 1,403.

Having outlined how many city–regions of different types exist worldwide, the next step is to present the global population distribution across these. Figure 1 shows the share of population living in, or within a certain travel time (1, 2 or 3 h) to the closest urban center, whether a town, small, intermediate or large city. Each column sums up to 100%, and the share above the gray boxes is the summation of all the subshares in the latter. Within the gray boxes, petal diagrams are provided to indicate the share of population that, within a given travel time, can also access urban centers in higher tiers, beyond the one they are closest to. For example, individuals for which the closest urban center is a town but where a small city may also exist within 1 h. The percentages in blue refer to the share of population with access to just one urban center within the given travel time. To illustrate, we find that 5% of the world’s population lives within 1 h from a town with no other urban center in the same vicinity (see row B). Conversely, 7% live within 1 h from a town and a small city, with the town being the closest, while 4% live closest to a town but can also access a small and an intermediate city within 1 h travel time, the small city being the closer of the two.

The figure presents the share of population living in or within a certain travel time of the closest urban center, broken down by size of the urban center. It is divided into four columns, each of which sums to 100% and determines the share of the population living in the core or within a certain time range—1, 2 or 3 h—of an urban center, whether a town, small, intermediate or large city. Row A refers to the share of population living in a rural area with no surrounding urban center, given travel time. Rows B–E apply to locations whereby the closest urban center is a town, small city, intermediate city or large city, respectively. Moving from left to right, the values increase with travel time as they incorporate the population in preceding columns. To illustrate, the population considered within 1 h travel time also includes those living in the core. The petal diagrams in the gray boxes differentiate the share of population with access to different urban tiers. The percentages in blue refer to the share of population with access to just one urban center within the given travel time.

Looking at row A, almost half of the world’s population lives outside an urban center (of 20,000 people or more), while only 8% have to travel more than 1 h to access an urban center. This implies that 92% of people live within a commuting distance of an urban center, a considerable contrast to Moreno-Monroy et al., who find that 53% of the population lived in FUAs 10 . This can be attributed to differences in delineation methods and to the FUA population accounting only for cells with more than 300 people km −2 , which excludes a priori 23.5% of the global population 25 . Another difference is that we consider city–regions of urban centers between 20,000 and 50,000 people, accounting for 5% of the global population (see row B of Fig. 1 , blue font, for 1 h travel).

Our findings reveal substantial interconnectedness among urban centers and with their surrounding areas: of the 7.8 billion people worldwide in 2020, 41% had physical access to multiple tiers within 1 h travel (3.2 billion) and 57% and 64% within 2 and 3 h, respectively (4.5 and 5 billion). Furthermore, out of the 92% who are within 1 h travel time from an urban center, 55% live in or closest to a town or small city and 37% to an intermediate or large city. However, many locations closest to a town or a small city also have access to a higher tier within 1 h travel time. Indeed, considering both single-tier and multi-tier access, by summing all combinations of ‘petals’ that include either an intermediate and/or large city, we find that two-thirds of the world’s population (over 5 billion people) live within 1 h travel time from one or both of these urban tiers providing more specialized activities.

The population distribution in Fig. 1 also aligns with central place theory, whereby smaller urban centers tend to gravitate around larger centers with greater economies of scale and more specialized activities. In other words, they are more likely to belong to city–regions with a larger center as reference. Indeed, more than one-third of the global population (34%) is part of city–regions with at least one intermediate or large city within 1 h travel (obtained summing all ‘petals’ in Fig. 1 with at least one intermediate or large city linked to a smaller urban center). When differentiated by level, we find that 28% of the world’s population is part of two-tier city–regions, and 13% of three tier or four tier. These shares increase to 32% and 33%, respectively, when considering 3 h travel time—encompassing 5.0 billion people. Additional results for 35 countries (representing 5.8 billion people) are illustrated in Extended Data Fig. 1 .

Among people living in or within 1 h travel time to towns or small cities, twice as many have easier access to intermediate cities (20%) than to large cities (10%). This share increases to 35% and 17%, respectively, for 3 h travel time. This occurs despite urban cores of large cities hosting more individuals (23%) than intermediate ones (9%), as can be observed in the very first column of Fig. 1 . This suggests that large cities are less relevant than smaller ones in regard to engaging populations outside their urban core for activities that can also be provided by the latter. Indeed, most people do not need frequent access to the very specialized activities provided by large cities only and, thus, may choose to live further away. Figure 1 shows that, within a 1 h travel time, two-thirds of the global population has easier access to small or intermediate cities than to large ones. Still, for the most specialized activities, accessed with lower frequency by many, large cities can be accessed by a sizeable share of the population—up to 50% for 3 h travel time, in addition to another 24% living in their core).

Global averages, however, can be misleading. To illustrate, while a relatively small share (8%) of the global population lives in rural locations that are more than 1 h away from any urban center, at the country level, this number can be far higher—well beyond 50% in countries such as Madagascar, South Sudan and Zimbabwe (Supplementary Table A1.1 ). For these populations, access to basic activities is probably more challenging. Even more worrisome are the 50% of individuals in these countries for whom access to a small city or larger requires more than 2 h (against the global average of 5%) (Supplementary Table A1.1 , adding the second and fifth column entries). For these countries, special attention may be required for regional planning and development to overcome infrastructural challenges. This focus may also apply to countries in less extreme situations, though still problematic, such as Brazil and the Russian Federation, which have populations distributed over a very large territory and for which 8–9% of the population are more than 2 h away from a small city or larger.

A closer look at city–regions in four countries

This section explores how city–regions are structured within countries, using the examples of Ethiopia, France, Nigeria and Pakistan (Fig. 2 ), chosen because they have comparable national surface area but different population sizes, physical geography and level of development and infrastructure. Figure 2 represents the spatial distribution of catchment areas composing city–regions for the four countries within 3 h travel time. The maps in the first row refer to catchment areas for accessing the closest urban center and are thus conceptually equivalent to the URCAs 1 and most relevant for activities that are typically accessed with higher frequency 1 . Spanning the four urban tiers, each tier is color-coded differently, with higher level tiers providing more specialized activities, as well as the more basic ones. Moving down in Fig. 2 , lower urban tiers are incrementally excluded, for example, in the second row, catchment areas are delineated for the closest small city or larger, in the third row for the closest intermediate city or larger and in the fourth row for the closest large city. A global version of these maps is provided in Supplementary Fig. A2.1 ).

Higher-tier urban centers are assumed to provide all activities provided by lower-tier urban centers; therefore, their catchment areas are included in the lower tiers. Light green indicates locations that are more than 3 h away from an urban center.

Predictably, as the urban tier level increases so does the hinterland (light green), where people do not have access to the activities of the higher tier. What is interesting, however, is how this area changes by country. France and Ethiopia are a case in point. In France—a high-income country with adequate infrastructure—the catchment areas of three-tier city–regions cover almost the entire country and, consequently, probably almost all of its population. Conversely, in Ethiopia—a low-income country with weaker infrastructure—most areas are more than 3 h travel time from an intermediate or large city. It would, thus, appear that more specialized activities are less accessible in the latter country. The weaker infrastructure in Ethiopia is also responsible for the high number of primary city–regions (20) as a share of all urban centers in the country (358). Most of these primary city–regions (16) have a small city as the largest center of reference, indicating limited connectivity across the country. In comparison, France has fewer primary city–regions (4) relative to its total number of urban centers (223), and these are either intermediate or large cities (Extended Data Table 2 ).

Following Fig. 1 , Extended Data Fig. 2 indicates the population distribution—for the same four countries—across city–regions. It emerges that, while the distribution profile changes quite considerably, a sizeable proportion of the population belongs to city–regions—even for the limited 1 h travel time—with an intermediate or large city as reference to access specialized activities: 36% in France, 45% in Nigeria and up to 48% in Pakistan. This share increases to 47–55% if city–regions composed of a small city and towns are included, if slightly less specialized activities are needed (Supplementary Figs. A3.1 and A3.2 for 2 h and 3 h travel, respectively).

Ethiopia is the exception: 76% of the population lives more than 1 h away from any intermediate or large city. Multi-tier city–regions are also relatively limited and an option for only 36% of the population in Ethiopia, with the majority involving towns and small cities (21%). This is unsurprising, given the results in Fig. 2 and the fact that 21% cannot access any urban center within 1 h travel, and only 5% have access to a large city. However, despite low accessibility to intermediate and large cities, towns and small cities can be reached within 1 h travel by 70% of the population. Even in France, Nigeria and Pakistan—where large cities play a notable role and are better connected—we find city–regions with small or intermediate cities as the highest urban tier can engage twice or more of the population compared to city–regions with a large city as the highest tier. This supports the aggregate findings in Fig. 1 , whereby large cities were found to be less relevant than smaller ones in engaging populations. In sum, these national differences are, in part, not only due to how urban centers are spatially distributed but also due to the quality of the transport infrastructure connecting urban centers, which can considerably reduce travel time.

This research pioneers a comprehensive framework for representing city–regions worldwide (Figs. 3 and 4 ), providing insights into how societies organize around urban centers across multiple tiers. The analysis of over 30,000 urban centers reveals a predominance of multi-tier city–regions within a 1 h travel time—relevant for commuting—with single-tier city–regions being relatively rare. This interconnection is extensive, enabling over 5 billion people to access urban centers of 250,000 people or more within 1 h travel time. Expanding the travel time to three hours reduces fragmentation of catchment areas, leading to a decrease of both single-tier and multi-tier primary city–regions, making larger urban centers accessible to an even broader population for activities that necessitate travel times between 1 and 3 h.

To categorize city–regions, we first classify reference urban centers by population size as a proxy for the range of activities they provide. An urban center in a higher tier is assumed to provide all activities provided by lower tiers. Next, we determine urban centers’ catchment area for each activity tier, within a specified travel time cutoff, and overlay these catchment areas to identify patches. Unique patch IDs are created for describing locations served by the same set of urban centers.

A patch is made up of locations served by the same set of urban centers within a specified travel time cutoff. As catchment areas (semi-transparent circles) of the different urban centers increasingly overlap, different types of patches are created. Each box shows a location indicated by a star and the type of patch it belongs to. The four-digit classification in each panel is to identify the type of patch based on the size of urban centers providing different levels of activity. The allowed values for each digit are 0–4. Thus, the four -digit value ‘0000’ expresses no access to urban activities, while ‘4444’ expresses that all four level of activities are provided by a large city (see Methods for details).

This research represents the first systematic worldwide delineation of city–regions across urban tiers, helping identify potential needs for infrastructure investment at country level. National results point to striking diversity in city–regions worldwide (Supplementary Annexes 1 and 3 ). Accessibility patterns vary greatly depending on geography, infrastructure and income level. The diversity of accessibility patterns worldwide calls for national urban policies tailored to unique geographic and income contexts rather than one-size-fits-all solutions often focusing on primate cities. We find intermediate and small cities and, in some cases, towns play an essential role in engaging populations outside urban cores, especially in low income countries. By overcoming infrastructure deficits that hinder regional integration, investments in these urban nodes and in tertiary roads and transport services radiating from them would promote inclusive economic development.

We acknowledge noteworthy caveats that would need to be addressed in future work. First, we assume that larger cities have more specialized activities; however, these may vary appreciably even within the same urban tier across different countries and regions. We also acknowledge that urban population alone does not fully capture economic composition, infrastructure, and political and cultural roles that differentiate urban centers. However, our approach can be a first step in considering these important dimensions. We also do not delve into how historical development patterns, resource distribution and governance have shaped present urban hierarchies and rural–urban linkages, warranting further exploration. On a positive note, we test the sensitivity of populations within different city–regions for different population datasets and find results to be robust (Supplementary Annex 5 ). In sum, the dataset is a valuable tool for regional planning in countries around the world, especially those where spatial analyses are currently not available. Future research can build upon this analysis and dataset by incorporating place-specific data and narratives to provide more grounded geographic insights into city–regions and sustainability.

Our hope is that this research—grounded in where people live and their physical access to urban centers of different sizes—will stimulate further debate on developing sustainable city–regions worldwide. The dataset can enhance socioeconomic research, regional planning and natural resource management by integrating with spatial socioeconomic data. For sustainable resource management, a coordinated regional approach can be important to plan land use, for provision of amenities, to manage watersheds and to mitigate pollution and ecological impacts transcending urban boundaries. Analyzing city–regions, including their primary and secondary urban centers, can offer insights into urban form, environmental sustainability and environmental amenities provision. The study also invites further examination into the resilience and strategic advantages of polycentric urban regions, where cities share influence without one overshadowing the others. Polycentric regions can be understood as a network of city–regions, each centered around a major hub, impacting governance and development policy considerably.

The rationale

The aim is to represent city–regions in a systematic manner that can help analyze the interaction between an urban core or cores and the surrounding periurban and rural areas. The historical reference for this approach is central place theory developed by Christaller in the 1930s 14 , which defines regions as areas of a certain market size distributed around a central place, representing a common catchment area for goods and services.

Our proposed city–regions, based on multiple catchment areas to access different urban tiers, follow more closely the conceptual characterization of city–region proposed by Parr 13 , whereby the city (C zone) consists of a continuous urbanized area with a population above a predetermined lower bound, surrounded by a territory (S zone). The characteristic of the S zone is that it is linked more with the C zone in question than with the C zone of some adjacent city–region due to its physical proximity to the former. The S zone can contain a rural population as well as an urban population, with the urban population of the S zone located in centers of varying size. As noted by Parr (p. 562) 13 ,

[…] within the S zone of a given CR [city–region] there might exist one or more urban centers of different size. For the territory surrounding such a secondary center, the overall level of interaction with this secondary center may well be greater than (though different from) that with the functionally more complex C zone of the CR. […] This suggests the existence of a primitive hierarchy of CRs in which one or more secondary CRs (each comprising a secondary C zone and a secondary S zone) are contained within the primary CR.

The approach taken is to detect systems of city–regions at the global level by identifying what locations are part of primary or secondary city–regions and mapping their S zones, which, to the best of our knowledge, has never been done systematically. This overcomes the critique that city–regions are politically constructed by including some cities and not others 26 . In our approach, any urban center with a population of 20,000 or more may be identified endogenously as part of a city–region (as primary or secondary C zone).

For the purposes of this paper, a city–region describes accessibility for a given travel time to urban activities and is identified by an ensemble of patches sharing the same urban center of reference; we distinguish between primary and secondary city–regions. We consider urban centers to be agglomerations with a population of 20,000 people or more, and the travel time used is from a location to the closest edge of an urban center. Here the term ‘activities’ encompasses both services and employment opportunities. This is because an individual might visit an urban center to access a service (for example, health center) or to commute to work.

In Table 1 , we summarize the terminology introduced in this article to facilitate the description of the workflow and algorithm. On a technical front, all spatial analysis required to delineate city–region patches was done by using code developed in Go, PHP and Python. QGIS was used for prototyping and visualization purposes. A script in PHP was used to perform the analysis identifying city–regions and providing summary information. All research code is available in the code repository of the paper.

Identifying catchment areas is an intermediate step in delineating city–regions

Urban centers.

We assume four urban tiers based on the population size of the urban centers and assign a unique identifier for each urban center and its catchment area. This was the starting point to create a global representation of city–regions based on the data for accessing each of the four tiers.

Basic activities are provided by all urban centers, of which the locations and populations have been derived starting from the Global Human Settlement Layer 27 , 28 . We process data so that each urban center has a unique number to identify it and its classification as a town, small, intermediate or large city (see Table 1 for a definition of each). To prevent bias when classifying urban centers into different levels, four different gridded global population datasets are utilized in this study, and decision rules are applied that aim at an unbiased classification considering information provided by all datasets (Supplementary Annexes 4 and 5 ).

Urban centers of four tiers are grouped into four different urban grids (locating urban centers on a map) based on activity levels. These urban grids are crucial because the city–regions we want to identify describe spatial distribution of availability of multiple levels of specialization of activities. Because higher-tier urban centers can provide all activities that can be provided by lower-tiers (recall Table 1 ), urban centers have to be grouped into four activity tiers representing the level of accessibility of activities: the first tier urban grid including all urban centers above 20,000 people with access to basic activities (for example, grocery shopping and primary education); the second tier urban grid including small, intermediate and large cities providing higher-level activities (for example, large supermarkets, secondary and higher education); the third tier including intermediate and large cities with more sophisticated and diversified activities (for example, broader employment opportunities and multiple specialized health care options); and the fourth tier including only large cities providing most specialized activities (for example, an airport).

Travel time

After classifying urban centers into our four urban tiers and creating the urban grids based on activity tiers, the next step in representing city–regions in a systematic manner is delineating the catchment areas around the urban centers based on representative metrics of accessibility. Physical accessibility based on travel time is one of the most common metrics used for this purpose. The calculations of travel time from an arbitrary location and its most proximate urban center apply a least-cost path algorithm that determines the fastest route over a travel cost surface, while keeping track of the destination urban center (see ref. 22 for details). This allows efficient delineation of the least-travel-time catchments around urban centers.

The travel cost grid derives from spatial datasets that represent the surface transport network (that is, roads, railroads, navigable rivers and other surfaces traversed by foot) and land-cover data, elevation and slope and international borders. The characteristics of these datasets allow the estimation of plausible travel speeds across different parts of the transport network, foot-based speeds over different types of terrain, speed adjustment factors associated with slope and extreme elevation and delays at international border crossings 22 . The resulting cost surface estimates the time required to cross each grid cell of the world’s surface. We use the most up-to-date global estimates of travel times available at a spatial resolution of about 1 km and build on earlier work that applied the method across a spectrum of settlement sizes 21 , 23 .

Catchment areas

For each urban grid, the catchment area around each urban center is determined based on minimum travel time that is calculated by using a grid-based minimum cumulative weighted distance method developed for the study that considers eight-connectivity on a spherical coordinate system with rolling at the International Date Line and the poles; hence, it enables a global travel time analysis. By using the urban centers as starting points and utilizing the travel cost grid for the unit travel cost at each grid cell (min km −1 ), the method generates global minimum travel time and minimum travel time sheds concurrently.

The catchment area of an urban center for a given activity tier is thus defined by the set of locations for which that urban center is closest for that tier. Should two or more urban centers be equally distant from a location, we assume that people will prefer to travel to a smaller city if it is sufficient for the level of activity that is required. This is based on intuition but also serves the purpose of keeping track of the smaller urban center for providing less specialized activities. As a result, each location has up to four urban center of reference, one for each activity tier level. Figure 2 presents sequentially the catchment areas for the different activity levels for four countries. It highlights how the catchment areas of urban centers expand as the urban tier level increases; hence, the activity level being sought increases, which follows from our definition that lower-tier urban centers cannot provide the more specialized activities of higher urban tiers.

City–region delineation and stratified access to activities

City–region patches.

To enable categorization of city–regions based on the number and types of their reference urban centers we first identify patches. The patches describe the spatial distribution of the availability of multiple levels of activities and by which urban centers they are provided. For this purpose, urban center identifications (IDs) of each overlapping catchment grid cell for four different activity levels are merged into a patch ID by using a variable-bit encoding method, which results in unique IDs for each unique set of urban centers providing different levels of activities (Supplementary Annex 4 ). Land-based locations worldwide are divided among over 100,000 unique patches. Figure 3 presents the workflow, starting from the identification of urban centers, delineation of catchments and aggregation into patches, which form the basis of city–regions.

We use a four-digit classification system to identify the types of patches based on the urban tiers of the related reference urban centers. The first digit is used to indicate the tier of the urban center for tier 1 activities (that is, basic activities), the second one for tier 2, the third for tier 3 and finally the fourth for tier 4 (that is, most specialized activities). The allowed values for each digit are 0–4, with 0 representing no access to activities (that is, no urban center is within reach of a patch to provide the indicated level of activity) and 1–4 representing the urban tier of the urban center providing the indicated level of activity. For the tiers, 1 indicates a town, 2 indicates a small city, 3 indicates an intermediate city and 4 indicates a large city. There are 16 possible type codes that range between 0000 (no access to activities) and 4444 (all activities provided by a large city), which are illustrated in Fig. 4 .

In Fig. 4 , each box shows a location indicated by a star and the patch it belongs to, which can be viewed as part of a city–region. Urban centers are shown with solid circles with different colors and increasing diameters from town to large city. The exception being the top box where no urban center is accessible within a given travel time. The catchment areas are then shown with semitransparent circles of the same color, which assumes for visual simplicity a uniform unit travel-time grid. Hence, the distance between the outer boundaries of the urban centers and the edge of their corresponding catchment areas are identical for a specific travel time cutoff ( t *). As catchment areas of the different urban centers increasingly overlap, different types of patches are created. The code at the bottom-right of each box indicates the type of the patch where the star is located. As illustrated in Fig. 4 (and recalling Table 1 ), patches can be of four types: single-tier (second row; codes 1000, 2200, 3330 and 4444), two-tier (third row; codes 1200, 1330, 1444, 2230, 2244 and 3334), three-tier (fourth row; codes 1230, 1244, 1334 and 2234) and four-tier (fifth row; code 1234). As shown in Fig. 4 , three-tier and four-tier patches encompass two-tier patches in their vicinity, reflecting a more complex urbanization pattern.

For illustration purposes, a four-digit code of 2230 shows that for all grid cells in the patch the closest urban center is a small city (2), which provides both tier 1 and tier 2 activities, but there is also an intermediate city (3) within a given travel time cutoff providing more specialized activities. The code also indicates that there are no large cities within travel time cutoff. If there were a large city, then the code would be 2234, or possibly 2244 if the large city were closer than the intermediate city or 4444 if it were the closest urban center. If there were a town closer than the small city, then the code would be 1230.

City–regions

By identifying patches associated with the same highest-tier urban center, we delineate the boundaries of that center’s city–region. The resulting city–regions are mutually exclusive from each other for each activity level and have a global coverage for different travel time cutoffs. The travel time cutoffs used in our analysis are 1 (to reflect a commuting cutoff), 2 and 3 h (to reflect accessing activities that require less frequent trips), but it is straightforward to select different cutoffs. The patch IDs are persistent between different travel time cutoffs, that is, the same patch ID is assigned to the same set of urban centers of various tiers, which allows tracking of the change of patch, hence, city–region, extents depending on the travel time cutoffs.

In the final step, we determine whether an urban center is the apex of a primary city–region or whether it belongs to a secondary city–region (first branching in Extended Data Fig. 3 ). When an urban center serves as a reference to locations in just one single patch it is classified as a single-tier city–region; it is not part of the catchment area of a larger urban center, and no lower-tier urban centers are accessible within the travel time cutoff. For secondary city–regions we distinguish between satellite ones, which basically just gravitate around a larger urban center, and nested ones, whose catchment areas overlap with those of lower-tier centers. An example of primary and secondary city–regions for the area surrounding Birmingham is given in Supplementary Fig. A4.2 .

For each city–region, the country where the city–region is located is identified by using the Global Administrative Areas Database 29 . For city–regions that spread to multiple countries, the country with the largest proportion by surface area is considered as country of the city–region. To enable city–region population statistics at country level, separate population values are also calculated for each country segment of multicountry city–regions.

Similar to the urban centers, the population of each city–region is calculated by using the GHS-POP, Gridded Population of the World, LandScan and WorldPop population datasets for all three travel time cutoffs. The results are presented here only for GHS-POP (for a comparison across population datasets, see Supplementary Annex 5 ).

By using this data, we compute the following variables: (1) area and population of each urban center, (2) area and population of the ‘proximate catchments’ that are less than 1 h from the edge of an urban center (not including the area of the urban core), (3) area and population that are up to 2 h away from an urban center (not including the area of the urban core) and (4) area and population of the ‘full catchment’, which include areas that are up to 3 h away from an urban center (not including the area of the urban core).

When computing the number of city–regions of different types, we calculated different city–region types separately for each tier to avoid double counting. Some high-tier city–regions may contain subareas (patches) associated with lower tiers that could be wrongly counted as separate systems; we adjusted for this by accounting for secondary city–regions embedded within higher ones. This provided the number of primary city–regions associated with different travel time cutoffs to reach the urban centers of reference (Table 1 ).

Our approach is an advancement in several respects relative to the URCA 1 methodology that most closely aligns with ours: it allows locations to reference multiple urban centers across four tiers, introduces precise travel time measurements instead of broad ranges, tracks the specific urban center and its city–region for each location and uses four population datasets to minimize bias in population estimates for city–regions (Supplementary Annex 4 ). Our algorithm for identifying city–regions also eliminates the prerequisite of predefining a hierarchy based on urban center size within each travel time range, a constraint in the URCA approach since it limited each location to a single urban center of reference. Furthermore, our approach is also complementary and relevant for a polycentric perspective focusing on an area containing a cluster of urban centers, none of which has a pronounced dominance over the others 30 , 31 . Polycentric regions can be viewed as a series of city–regions—each based on a major center of the supposed polycentric urban region—providing access to different urban tiers within each 31 .

In spite of the innovations introduced by our approach, there are key assumptions that need to be highlighted. The number and types of city–regions identified will be sensitive to the travel time cutoff adopted and to the population range prespecified for the four urban tiers. The first issue can be easily adjusted by specifying different travel time cutoffs. So, for example, it is straightforward to calculate a set of results for city–regions delineated by a 90 min travel time instead of the ones used in the paper. On the other hand, changing the population ranges for the urban tiers is possible but would essentially entail adjusting the code to construct a new dataset ex novo.

Our study establishes a global city–region classification using uniform criteria to ensure broad applicability and comparability, albeit at the expense of contextual factors. By defining urban tiers through population thresholds and physical accessibility, we offer a first-step approximation to global patterns, acknowledging the inevitable loss of local geographic nuances shaped by history, culture, economics and environment. This framework aims to inspire further research into urban–rural connections and regional connectivity, offering our dataset as a foundation to progress from broad patterns to detailed, localized analyses.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The global spatial dataset of city–regions for different travel time cutoffs is available in a public repository (check README.md). In the repository we also provide, for each population dataset, a tool in Excel that can generate the distribution of populations across types of city–region (shown in Fig. 1 and Extended Data Fig. 2 of this paper using the GHS-POP version) for any country in the world. All files are available on the City–Region System Toolbox from Zenodo via https://doi.org/10.5281/zenodo.11187634 (ref. 32 ). The input data used for the analysis can be found in Supplementary refs. 1 and 3–6 .

Code availability

Scripts in Python and PHP were used to preprocess input data. They are available via GitHub at http://github.com/ITC-CRIB/city-regions . Minimum cumulative weighted distance and catchment delineation code is available via GitHub at https://github.com/ITC-CRIB/globetrotter . Final data cleaning and analysis was done in Microsoft Excel (commercial). All maps were made in QGIS 3.22 and R.

Cattaneo, A., Nelson, A. & McMenomy, T. Global mapping of urban–rural catchment areas reveals unequal access to services. Proc. Natl Acad. Sci. USA 118 , e2011990118 (2021).

Article   Google Scholar  

Rodríguez-Pose, A. The rise of the ‘city–region’ concept and its development policy implications. Eur. Plan. 16 , 1025–1046 (2008).

Moisio, S. & Jonas, A. E. in Handbook on the Geographies of Regions and Territories (eds Paasi, A. et al.) 285–297 (Edward Elgar Publishing, 2018).

Acuto, M., Parnell, S. & Seto, K. C. Building a global urban science. Nat. Sustain. 1 , 2–4 (2018).

Keith, M. et al. A new urban narrative for sustainable development. Nat. Sustain. 6 , 115–117 (2022).

Schläpfer, M. et al. The universal visitation law of human mobility. Nature 593 , 522–527 (2021).

Hutchings, P. et al. Understanding rural–urban transitions in the Global South through peri-urban turbulence. Nat. Sustain. 5 , 924–930 (2022).

Liotta, C., Viguié, V. & Creutzig, F. Environmental and welfare gains via urban transport policy portfolios across 120 cities. Nat. Sustain. 6 , 1067–1076 (2023).

Dijkstra, L. et al. Applying the degree of urbanisation to the globe: a new harmonised definition reveals a different picture of global urbanisation. J. Urban Econ. 125 , 103312 (2021).

Moreno-Monroy, A. I., Schiavina, M. & Veneri, P. Metropolitan areas in the world. Delineation and population trends. J. Urban Econ. 125 , 103242 (2020).

Cattaneo, A. et al. Economic and social development along the urban–rural continuum: new opportunities to inform policy. World Dev. 157 , 105941 (2022).

Nagendra, H., Bai, X., Brondizio, E. S. & Lwasa, S. The urban south and the predicament of global sustainability. Nat. Sustain. 1 , 341–349 (2018).

Parr, J. Perspectives on the city–region. Reg. Stud. 39 , 555–566 (2005).

Christaller, W. Die zentralen Orte in Süddeutschland (the Central Places in Southern Germany) (Gustav Fischer, 1933).

Mulligan, G. F., Partridge, M. & Carruthers, J. I. Central place theory and its reemergence in regional science. Ann. Reg. Sci. 48 , 405–431 (2012).

Kompil, M., Jacobs-Crisioni, C., Dijkstra, L. & Lavalle, C. Mapping accessibility to generic services in Europe: a market-potential based approach. Sustain. Cities Soc. 47 , 101372 (2019).

Milbert, A., Breuer, I., Rosik, P., Stępniak, M. & Velasco, X. Accessibility of services of general interest in Europe. Romanian J. Reg. Sci. 7 , 37–65 (2019).

Google Scholar  

Jacobs-Crisioni, C., Kompil, M. & Dijkstra, L. Big in the neighbourhood: identifying local and regional centres through their network position. Pap. Reg. Sci. 102 , 421–457 (2023).

Post, A. E. & Kuipers, N. City size and public service access: evidence from Brazil and Indonesia. Perspect. Politics 21 , 811–830 (2023).

Austin, S. et al. Access to urban acute care services in high- vs. middle-income countries: an analysis of seven cities. Intensive Care Med. 40 , 342–352 (2014).

Weiss, D. J. et al. Global maps of travel time to healthcare facilities. Nat. Med. 26 , 1835–1838 (2020).

Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553 , 333–336 (2018).

Nelson, A. et al. A suite of global accessibility indicators. Sci. Data 6 , 266 (2019).

Duranton, G. Classifying locations and delineating space: an introduction. J. Urban Econ. 125 , 103353 (2021).

OECD and European Commission. Cities in the World: A New Perspective on Urbanisation (OECD, 2020).

Harrison, J. & Heley, J. Governing beyond the metropolis: placing the rural in city–region development. Urban Stud. 52 , 1113–1133 (2015).

Schiavina, M. et al. GHSL Data Package 2022—Public Release GHS P2022 (Publications Office of the European Union, 2022).

Schiavina, M., Freire, S. & MacManus, K. GHS-POP R2022A—GHS population grid multitemporal (1975–2030). Joint Research Centre Data Catalogue https://doi.org/10.2905/D6D86A90-4351-4508-99C1-CB074B022C4A (2022).

Hijmans, R. Global Administrative Areas Database (GADM) Version 4.1 (GADM, 2022); https://gadm.org/

Parr, J. The polycentric urban region: a closer inspection. Reg. Stud. 38 , 231–240 (2004).

Parr, J. B. The regional economy, spatial structure and regional urban systems. Reg. Stud. 48 , 1926–1938 (2014).

Cattaneo, A. et al. Worldwide delineation of multi-tier city–regions [data set]. Zenodo https://doi.org/10.5281/zenodo .11187634 (2024).

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Acknowledgements

We would like to thank P. D’odorico, I. Soloaga, K. Stamoulis, K. Svobodova and C. Tuholske for feedback provided in earlier drafts of this paper. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the institutions they are affiliated with.

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A.C. and S.G. jointly conceived and coordinated the study. S.G. developed the algorithm needed to implement the approach and test its robustness, supported by A.N. and R.d.B. A.C. developed the petal diagrams reporting population distribution. All authors contributed equally to analyzing and interpreting the results and drafting the paper.

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Extended data

Extended data fig. 1 percentage of population that has access to more than one urban tier based on travel time..

Note: This Figure is derived from information contained in the petal-diagram of the type presented in Fig. 1 of the manuscript, when computed for the respective countries (see Fig. 1 and Figs. A3.1 and A3.2 for Ethiopia, France, Nigeria, and Pakistan). They reflect the sum of the bold values in rows B, C and D (the share of the population in locations whereby the closest urban centre is a town, small city, intermediate city, respectively) and subtract the blue values in those rows (the share of population with access to just one urban centre across the given travel time).

Extended Data Fig. 2 Population distribution across different types of city–regions for Ethiopia, France, Nigeria, and Pakistan within 1-hour travel time, 2020.

Legend: The figure presents the share of population living in, or within a certain travel time of, the closest urban centre, broken down by size of the urban centre. It is divided into four columns, each of which sums to 100% and determines the share of the population living in the core or within a certain time range – 1, 2 or 3 hours – of an urban centre, whether a town, small, intermediate, or large city. Row A refers to the share of population living in a rural area with no surrounding urban centre, given travel time. Rows B–E apply to locations whereby the closest urban centre is a town, small city, intermediate city, or large city, respectively. Moving from left to right, values increase with travel time as they incorporate the population in preceding columns. To illustrate, the population considered within 1-hour travel time also includes those living in the core. The petal diagrams in the grey boxes differentiate the share of population with access to different urban tiers. Percentages in blue refer to the share of population with access to just one urban centre within the given travel time.

Extended Data Fig. 3 Determination of primary and secondary city–regions.

Note: In order to classify city-regions as primary, secondary, and satellite city-regions, the following analysis is performed. For each city-region, the highest-tier urban centre of all patches of the city-region are enumerated. For example, for a city-region that is composed of five patches, five highest-tier urban centres are listed. If all highest-tier urban centres of the city-region is equal to the urban centre of the city-region, then the city-region is classified as a primary city-region. This indicates that the city-region does not overlap with a city-region of a higher-tier urban centre. If at least one highest-tier urban centre is equal to the urban centre of the city-region, but there are also other highest-tier urban centres, then the city-region is classified as a secondary city-region. This indicates that the city-region overlaps with one or more city-regions of higher-tier urban centres. Finally, among secondary city-regions, we distinguish between satellite city–regions, which have no lower tiers embedded below it, and nested city–regions that instead do overlap with lower tiers.

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Supplementary Annexes 1–5, Discussion, Tables A1.1, A4.1, A5.1 and A5.2, and Figs. A2.1, A3.1, A3.2., A4.1, A4.2, A5.1 and A5.2.

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Cattaneo, A., Girgin, S., de By, R. et al. Worldwide delineation of multi-tier city–regions. Nat Cities (2024). https://doi.org/10.1038/s44284-024-00083-z

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Please note you do not have access to teaching notes, lean six sigma implementation: multiple case studies in a developing country.

International Journal of Lean Six Sigma

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Article publication date: 24 October 2018

Issue publication date: 14 March 2019

This paper aims to report the results of a study on the implementation of Lean Six Sigma (LSS) in a developing country. The purpose of this paper is to determine the barriers, critical success factors (CSFs) and implementation strategy of LSS.

Design/methodology/approach

A qualitative approach was taken, in which a multiple-case study designed to gather data on the LSS implementation process was used.

The literature and interviews show that any organization can customize these methodologies according to their needs. This also indicates that there are no stringent rules to follow, and that the process of adoption and implementation is quite flexible. The findings from the multiple-case study identify that the CSFs for implementing LSS are management support and commitment, communication, culture change, education and training and a recognition and reward system. The salient features which serve as barriers are lack of top management commitment, lack of knowledge, lack of training, and internal resistance.

Practical implications

The findings have implications for consultants and practitioners with regard to the implementation of LSS within organizations and to focus on the selection LSS tools for implementation.

Originality/value

This paper reports on the implementation of LSS in Malaysia can be valuable to consultants, practitioners and researchers of LSS in developing countries.

• Lean Six Sigma
• Business improvement
• Quality initiatives

Mustapha, M.R. , Abu Hasan, F. and Muda, M.S. (2019), "Lean Six Sigma implementation: multiple case studies in a developing country", International Journal of Lean Six Sigma , Vol. 10 No. 1, pp. 523-539. https://doi.org/10.1108/IJLSS-08-2017-0096

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Bird Flu (H5N1) Explained: Finland Will Start Vaccinating Humans In A Global First

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Here’s the latest news about a global outbreak of H5N1 bird flu that started in 2020, and recently spread among cattle in U.S. states and marine mammals across the world, which has health officials closely monitoring it and experts concerned the virus could mutate and eventually spread to humans, where it has proven rare but deadly.

A sign warns of a outbreak of bird flu.

June 25 Finland said it plans to begin vaccinating vulnerable populations like farm workers against bird flu as early as next week using 10,000 vaccine series—each with two doses—acquired as part of a European Union deal with vaccine maker CSL Seqirus to provide up to 40 million vaccines to 15 countries.

June 11 The World Health Organization announced a four-year-old child in India was infected with H9N2 bird flu—a different flu strain from H5N1—but recovered after suffering from seizures, respiratory distress, fever and abdominal cramps; H9N2 has infected around 100 people globally since 1998, and this is the second human case in India.

June 6 Dozens of cows infected with bird flu have either died or been slaughtered in Colorado, Ohio, Michigan, South Carolina and Texas, which is unusual since—unlike poultry—cows cost more to slaughter and around 90% usually make a full recovery, Reuters reported .

June 5 A new study examining the 2023 bird flu outbreak in South America that killed around 17,400 elephant seal pups and 24,000 sea lions found the disease spread between the animals in several countries, the first known case of transnational virus mammal-to-mammal bird flu transmission.

May 30 Another human case of bird flu has been detected in a dairy farm worker in Michigan—though the cases aren’t connected—and this is the first person in the U.S. to report respiratory symptoms connected to bird flu, though their symptoms are “resolving,” according to the Centers for Disease Control and Prevention.

May 23 A new study with mice suggests that drinking infected milk can spread the disease—and that a certain type of pasteurization may not always be effective in killing the virus.

May 22 Michigan reported bird flu in a farmworker—the second U.S. human case tied to transmission from dairy cows—though the worker had a mild infection and has since recovered.

May 21 Australia reported its first human case of bird flu after a child became infected in March after traveling to India, though the child has since recovered after suffering from a “severe infection,” according to the Victorian Department of Health.

May 16 The USDA conducted a study, and discovered that after high levels of the virus was injected into beef, no trace was left after the meat was cooked medium to well done, though the virus was found in meat cooked to lower temperatures.

May 14 The Centers for Disease Control and Prevention released influenza A waste water data for the weeks ending in April 27 and May 4, and found several states like Alaska, California, Florida, Illinois and Kansas had unusually high levels, though the agency isn’t sure if the virus came from humans or animals, and isn’t able to differentiate between influenza A subtypes, meaning the H5N1 virus or other subtypes may have been detected.

May 10 The Food and Drug Administration announced it will commit an additional $8 million to ensure the commercial milk supply is safe, while the Department of Agriculture said it will pay up to $28,000 per farm to help mitigate the spread of the disease, totaling around $98 million in funds.

May 9 Some 70 people in Colorado are being monitored for bird flu due to potential exposure, and will be tested for the virus if they show any symptoms, the Colorado Department of Public Health told Forbes—it was not immediately clear how or when the people were potentially exposed.

May 1 The Department of Agriculture said it tested 30 grocery store ground beef products for bird flu and they all came back negative, reaffirming the meat supply is safe.

May 1 The Food and Drug Administration confirmed dairy products are still safe to consume, announcing it tested grocery store samples of products like infant formula, toddler milk, sour cream and cottage cheese, and no live traces of the bird flu virus were found, although some dead remnants were found in some of the food—though none in the baby products.

April 30 Wenqing Zhang, head of WHO's Global Influenza Programme, said during a news briefing "there is a risk for cows in other countries to be getting infected," with the bird flu virus, since it’s commonly spread through the movement of migratory birds.

April 29 The Department of Agriculture told Forbes it will begin testing ground beef samples from grocery stores in states with cow outbreaks, and test ground beef cooked at different temperatures and infected with the virus to determine if it's safe to eat.

April 24 The USDA said cow-to-cow transmission may be occurring due to the cows coming into contact with raw milk—and warned against humans and other animals, including pets, consuming unpasteurized milk to prevent potential infection.

April 18 Jeremy Farrar, chief scientist for the World Health Organization, said during a press conference the threat of bird flu spreading between humans was a “great concern,” since it’s evolved and has increasingly been infecting mammals (on land and sea), which means it could possibly spread to humans.

April 1 The CDC reported the second U.S. human case of bird flu in a Texas dairy farmer who became infected after contracting the virus from infected dairy cows, but said the person was already recovering.

Get Forbes Breaking News Text Alerts: We’re launching text message alerts so you'll always know the biggest stories shaping the day’s headlines. Sign up here .

Can Bird Flu Spread Between Humans?

Bird flu doesn’t “transmit easily from person-to-person,” according to the World Health Organization. Bird flu rarely affects humans, and most previous cases came from close contact with infected poultry, according to the CDC. Because human-to-human spread of bird flu poses “pandemic potential,” each human case is investigated to rule out this type of infection. Though none have been confirmed, there are a few global cases—none in the U.S.—where human-to-human transmission of bird flu was thought to be “probable,” including in China , Thailand , Indonesia and Pakistan .

Is Bird Flu Fatal To Humans?

It is very deadly. Between January 2003 and March 28, 2024 there have been 888 human cases of bird flu infection in humans, according to a report by the World Health Organization. Of those 888 cases, 463 (52%) died. To date, only two people in the U.S. have contracted H5N1 bird flu, and they both were infected after coming into contact with sick animals. The most recent case was a dairy worker in Texas who became ill in March after interacting with sick dairy cows, though he only experienced pink eye. The first incident happened in 2022 when a person in Colorado contracted the disease from infected poultry, and fully recovered.

Is It Safe To Drink Milk Infected With Bird Flu?

Raw, unpasteurized milk is unsafe to drink, but pasteurized milk is fine, according to the FDA. Bird flu has been detected in both unpasteurized and pasteurized milk, but the FDA recommends manufacturers against making and selling unpasteurized milk since there’s a possibility consuming it may cause bird flu infection. However, the virus remnants in pasteurized milk have been deactivated by the heat during the pasteurization process , so this type of milk is still believed safe to consume.

Is It Safe To Consume Meat Infected With Bird Flu?

The CDC warns against eating raw meat or eggs from animals “confirmed or suspected” of having bird flu because of the possibility of transmission. However, no human has ever been infected with bird flu from eating properly prepared and cooked meat, according to the agency. The possibility of infected meat entering the food supply is “extremely low” due to rigorous inspection, so properly handled and cooked meat is safe to eat, according to the USDA. To know when meat is properly cooked, whole beef cuts must be cooked to an internal temperature of 145 degrees Fahrenheit, ground meat must be 160 degrees and poultry must be cooked to 165 degrees. Rare and medium rare steaks fall below this temperature. Properly cooked eggs with an internal temperature of 165 degrees Fahrenheit kills bacteria and viruses including bird flu, according to the CDC. “It doesn’t matter if they may or may not have [avian] influenza… runny eggs and rare pieces of meat” are never recommended, Francisco Diez-Gonzalez, director and professor for the Center for Food Safety at the University of Georgia, told Forbes. To “play it safe,” consumers should only eat fully cooked eggs and make sure “the yolks are firm with no runny parts,” Daisy May, veterinary surgeon with U.K.-based company Medivet, said .

What Are Bird Flu Symptoms In Humans?

Symptoms of bird flu include a fever, cough, headache, chills, shortness of breath or difficulty breathing, runny nose, congestion, sore throat, nausea or vomiting, diarrhea, pink eye, muscle aches and headache. However, the CDC advises it can’t be diagnosed based on symptoms alone, and laboratory testing is needed. This typically includes swabbing the nose or throat (the upper respiratory tract), or the lower respiratory tract for critically ill patients.

How Is Bird Flu Affecting Egg Prices?

This year’s egg prices have increased as production decreased due to bird flu outbreaks among poultry, according to the USDA. A dozen large, grade A eggs in the U.S. costed around $2.99 in March, up almost a dollar from the fall. However, this price is down from a record $4.82 in January 2023, which was also spiked by bird flu outbreaks . Earlier this month, Cal-Maine Foods—the country’s largest egg producer—temporarily halted egg production after over one million egg-laying hens and chickens were killed after being infected with bird flu.

Why Do Poultry Farmers Kill Chickens With Bird Flu?

Once chickens have been infected with bird flu, farmers quickly kill them to help control the spread of the virus, since bird flu is highly contagious and fatal in poultry. The USDA pays farmers for all birds and eggs that have to be killed because of bird flu, as an incentive to responsibly try and curb the spread of the disease. The USDA has spent over $1 billion in bird flu compensation for farmers since 2022, according to the nonprofit Food & Environment Reporting Network.

Is There A Vaccine For The Bird Flu (h5n1)?

The FDA has approved a few bird flu vaccines for humans. The U.S. has a stockpile of vaccines for H5N1 bird flu, but it wouldn’t be enough to vaccinate all Americans if an outbreak were to happen among humans. If a human outbreak does occur, the government plans to mass produce vaccines, which can take at least six months to make enough for the entire population. CSL Seqirus, the maker of one of the approved vaccines, expects to have 150 million vaccines ready within six months of an announcement of a human bird flu pandemic. Although there are approved vaccines for other variants designed for birds, there are none for the H5N1 variant circulating. However, the USDA began trials on H5N1 animal-specific vaccines in 2023.

Key Background

As of May 30, more than 92 million poultry (primarily chickens) in 48 states have been euthanized because of bird flu since 2022, and 57 dairy cow herds across nine states have tested positive, according to data from the CDC (unlike chickens, cows appear to recover from the virus). The USDA believes wild migratory birds are the original source of the cow outbreaks that recently has experts concerned it may mutate and spread more easily in humans, though the CDC said its risk to the public remains low . Farrar called the cattle infections in the U.S. a “huge concern,” urging public health officials to continue closely monitoring the situation “because it may evolve into transmitting in different ways.” The increased number of mammal bird flu infections since 2022 “could indicate that the virus is looking for new hosts, and of course, moving closer to people,” Andrea Garcia, vice president of science, medicine and public health for the American Medical Association, said . The first report of a walrus dying from bird flu was detected in April on one of Norway’s Arctic Islands, and the first U.S. dolphin infected with bird flu died back in 2022, according to a report published April 18. More than 10 human bird flu cases were reported to the World Health Organization in 2023, and all but one survived. Bird flu has devastated bird populations, and 67 countries reported the deaths of 131 million poultry in 2022 alone. Although bird flu typically infects wild birds and poultry, it’s spread to other animals during the outbreak, and at least 10 countries have reported outbreaks in mammals since 2022. Around 17,400 elephant seal pups died from bird flu in Argentina in 2023, and at least 24,000 sea lions died in South America the same year. Besides cattle, bird flu has been detected in over 200 other mammals—like seals, raccoons and bears—in the U.S. since 2022. Although rare, even domestic pets like dogs and cats are susceptible to the virus, and the FDA warns against giving unpasteurized milk to cats to avoid possible transmission.

On June 5, WHO confirmed the first human death of a strain of bird flu that’s never before been seen in humans and is separate from H5N1. A 59-year-old man in Mexico contracted H5N2, and died on April 24 after being hospitalized and developing a fever, diarrhea, nausea, shortness of breath and general discomfort. Cases of H5N2 have been reported in poultry in Mexico, but the man had no history with poultry or animals, WHO said. It’s unclear how he became infected. He was bedridden for weeks prior to the infection, and suffered from several other health conditions.

Further Reading

Another Bird Flu Variant Reaches Humans: What To Know About H5N2—After First-Ever Confirmed Death

WHO Warns Threat Of Bird Flu Spreading To Humans Is ‘Great Concern’ (Forbes)

One In Five Milk Samples From Across US Had Traces Of Bird Flu Virus, FDA Says (Forbes)

Can Pets Get Bird Flu? Here’s What To Know (Forbes)

Avian H5N1 (Bird) Flu: Why Experts Are Worried—And What You Should Know (Forbes)

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