Unravelling the determinants of life expectancy during and after the COVID-19 pandemic: a qualitative comparative analysis
Data sources and sample size This study sourced from three global data sets: the Human Development Indicators from the United Nations Development Programme, the World Development Indicators from the World Bank Open Data, and the COVID-19 Containment and Health Index from the Our World in Data, to explore the determinants of life expectancy across countries during the years 2020–2022. This specific three-year time frame was chosen to capture the unique global impact of the COVID-19 pandemic on life expectancy. The data sets were merged using country names to align data points. Initially, the sample size from the World Development Indicators data set was 271 countries per year. After removing five unassigned countries, the sample size was reduced to 266. For the COVID-19 Containment and Health Index data, the initial sample consisted of 202 760 observations. We calculated the average index value for each country by year, resulting in a sample size of 185 countries per year. After merging the World Development Indicators with the averaged COVID-19 Containment and Health Index data, we obtained a merged data set with 183 observations per year. The Human Development Composite Indices data set initially contained 206 countries per year. After merging with the combined data set from the World Development Indicators and the COVID-19 Containment and Health Index, the sample size was reduced to 175 countries per year. Ultimately, after removing the missing data, we obtained a complete data set of 130 countries per year. For detailed information on the data derivation process, please see the . Institutional Review Board approval was waived due to the use of secondary data. Measures Dependent variable In this study, our dependent variable is life expectancy (at birth), which refers to ‘the average number of years that a newborn could expect to live, if he or she were subject to the age-specific mortality rates of a given period’ . Life expectancy reflects mortality rates and serves as an indicator of overall health conditions . The data points are derived from the Human Development Index provided by the United Nations Department of Economic and Social Affairs. Independent variable Our independent variables, or conditions, include indicators related to education attainment, economics prosperity, environment quality, social stability, urban development, and public health capacity. 1. Education attainment: education is represented by the mean years of schooling, which indicates ‘the average number of completed years of education for a country’s population, excluding any years spent repeating grades’ . The data points for education are derived from the Human Development Index, provided by the United Nations Educational, Scientific, and Cultural Organization. These estimates cover the population aged 25 years and older . 2. Economic prosperity: gross national income per capita represents a country’s economic output, expressed in USD using the World Bank Atlas method and divided by the midyear population . This measure includes the value added by all resident producers, product taxes (minus subsidies), and net income from abroad. To reduce price and exchange rate fluctuations, the Atlas method averages exchange rates from the current and two preceding years, adjusted for inflation differences between the country and major economies like the Euro area, Japan, the UK, and USA . The data point sources from LAC Equity Laboratory Tabulations of the World Development Indicators of the World Bank Open Data platform. 3. Environmental quality: environment factors include two indicators: 1) access to electricity (% of population), an indicator of a country’s environmental infrastructure, refers to the percentage of the population with electricity access . The electrification data are sourced from industry reports, national surveys, and international databases , and is compiled by the World Bank’s Global Electrification Database and the World Development Indicators. 2) annual population growth rate. Population, defined using the de facto approach, which includes all residents regardless of legal status or citizenship, is measured by its annual growth, calculated as the exponential growth rate of the midyear population from the previous year to the current year, expressed as a percentage . Population growth serves as an indicator of health because it reflects the broader demographic and social conditions that affect public health. This data are initially gathered and refined by the United Nations Population Division. 4. Social stability: the employment-to-population ratio, representing social stability, refers to the proportion of a country’s working-age population (15 years and older) that is employed . Employment includes individuals engaged in any activity to produce goods or services for pay or profit, even if temporarily absent from work . This ratio reflects how effectively an economy provides jobs . The data are sourced from the International Labor Organization of the World Bank’s World Development Indicators. 5. Urban development: urban population (% of total population), an indicator of urban development, represents the percentage of individuals living in urban areas, defined by national statistical offices, per 100 people in the total population . The data are collected and refined by the United Nations Population Division and featured in the World Bank’s World Development Indicators. 6. Public Health Capacity: we include three health-related indicators: 1) measles immunisation rate – a key indicator of advancing human health, represents the percentage of children aged 12–23 months who received the measles vaccine either before their first birthday or at any time prior to the survey . A child is considered fully immunised after receiving a single dose of the vaccine . This data are collected and refined by the World Health Organization and The United Nations Children's Fund. 2) HIV prevalence – refers to the percentage of individuals aged 15–49 who are infected with HIV in each country’s population. It represents the vulnerability of this population and the potential risk of the virus spreading to the broader community . This data are collected and refined by the Joint United Nations Programme on HIV/AIDS. 3) COVID-19 Containment and Health Index – ranging from 0 to 100 (with 100 being the strictest), is a composite measure based on 13 policy indicators such as school closures, travel bans, testing, and vaccine policies . Sourced from the University of Oxford, this index is crucial for understanding how varying levels of pandemic responses across countries may have influenced life expectancy. The first two indicators are sourced from the World Bank’s World Development Indicators, while the third comes from our Our World in Data. Statistical analyses We employed QCA to identify complex causal patterns among multiple conditions in a case-oriented framework . This method is particularly valuable for identifying configurations of conditions that are necessary or sufficient for life expectancy outcomes, going beyond the additive and linear assumptions of regression models. In addition, the use of QCA was further motivated by the sample size of this study. With 130 observations per year, traditional regression methods might lack the statistical power to identify complex interactions between variables. By contrast, QCA is designed to analyse small- to medium-sized data sets, enabling the identification of configurational effects across diverse cases. Variables were calibrated into either fuzzy sets, which allow for varying degrees of membership between full non-membership and full membership, or crisp sets . This calibration approach enhances the robustness and validity of the QCA findings by accurately capturing the nuanced influence of each condition on the outcome of life expectancy (appendix 1in the ). The analysis further includes both necessary and sufficient condition assessments (configuration analyses), to elucidate the complex interplay among our measures. The necessity analysis assesses whether any of the conditions were required for the outcome to occur , and the degree of necessity was measured using two criteria: consistency and coverage. Consistency measures how often the independent variable is present when the outcome (life expectancy) occurs coverage indicates the extent to which the condition explains the outcome, with higher coverage meaning a larger portion of cases is explained . A consistency threshold of 0.9 and a coverage threshold of 0.5 suggest that the condition may be necessary for the outcome . Based on the results of the necessity analysis, we decided if we perform a sufficiency analysis to identify sets of configurations that were always present when the desired outcome occurred. Additionally, we conducted robustness checks to confirm the main findings, utilising the methods developed by Oana et al. . All analyses were performed with the R Programming software (R Foundation, Boston, MA, USA, Version 4.3.2) with packages ‘QCA 3.22’ and ‘SetMethods 4.0.’ 

This study sourced from three global data sets: the Human Development Indicators from the United Nations Development Programme, the World Development Indicators from the World Bank Open Data, and the COVID-19 Containment and Health Index from the Our World in Data, to explore the determinants of life expectancy across countries during the years 2020–2022. This specific three-year time frame was chosen to capture the unique global impact of the COVID-19 pandemic on life expectancy. The data sets were merged using country names to align data points. Initially, the sample size from the World Development Indicators data set was 271 countries per year. After removing five unassigned countries, the sample size was reduced to 266. For the COVID-19 Containment and Health Index data, the initial sample consisted of 202 760 observations. We calculated the average index value for each country by year, resulting in a sample size of 185 countries per year. After merging the World Development Indicators with the averaged COVID-19 Containment and Health Index data, we obtained a merged data set with 183 observations per year. The Human Development Composite Indices data set initially contained 206 countries per year. After merging with the combined data set from the World Development Indicators and the COVID-19 Containment and Health Index, the sample size was reduced to 175 countries per year. Ultimately, after removing the missing data, we obtained a complete data set of 130 countries per year. For detailed information on the data derivation process, please see the . Institutional Review Board approval was waived due to the use of secondary data. 

Dependent variable In this study, our dependent variable is life expectancy (at birth), which refers to ‘the average number of years that a newborn could expect to live, if he or she were subject to the age-specific mortality rates of a given period’ . Life expectancy reflects mortality rates and serves as an indicator of overall health conditions . The data points are derived from the Human Development Index provided by the United Nations Department of Economic and Social Affairs. Independent variable Our independent variables, or conditions, include indicators related to education attainment, economics prosperity, environment quality, social stability, urban development, and public health capacity. 1. Education attainment: education is represented by the mean years of schooling, which indicates ‘the average number of completed years of education for a country’s population, excluding any years spent repeating grades’ . The data points for education are derived from the Human Development Index, provided by the United Nations Educational, Scientific, and Cultural Organization. These estimates cover the population aged 25 years and older . 2. Economic prosperity: gross national income per capita represents a country’s economic output, expressed in USD using the World Bank Atlas method and divided by the midyear population . This measure includes the value added by all resident producers, product taxes (minus subsidies), and net income from abroad. To reduce price and exchange rate fluctuations, the Atlas method averages exchange rates from the current and two preceding years, adjusted for inflation differences between the country and major economies like the Euro area, Japan, the UK, and USA . The data point sources from LAC Equity Laboratory Tabulations of the World Development Indicators of the World Bank Open Data platform. 3. Environmental quality: environment factors include two indicators: 1) access to electricity (% of population), an indicator of a country’s environmental infrastructure, refers to the percentage of the population with electricity access . The electrification data are sourced from industry reports, national surveys, and international databases , and is compiled by the World Bank’s Global Electrification Database and the World Development Indicators. 2) annual population growth rate. Population, defined using the de facto approach, which includes all residents regardless of legal status or citizenship, is measured by its annual growth, calculated as the exponential growth rate of the midyear population from the previous year to the current year, expressed as a percentage . Population growth serves as an indicator of health because it reflects the broader demographic and social conditions that affect public health. This data are initially gathered and refined by the United Nations Population Division. 4. Social stability: the employment-to-population ratio, representing social stability, refers to the proportion of a country’s working-age population (15 years and older) that is employed . Employment includes individuals engaged in any activity to produce goods or services for pay or profit, even if temporarily absent from work . This ratio reflects how effectively an economy provides jobs . The data are sourced from the International Labor Organization of the World Bank’s World Development Indicators. 5. Urban development: urban population (% of total population), an indicator of urban development, represents the percentage of individuals living in urban areas, defined by national statistical offices, per 100 people in the total population . The data are collected and refined by the United Nations Population Division and featured in the World Bank’s World Development Indicators. 6. Public Health Capacity: we include three health-related indicators: 1) measles immunisation rate – a key indicator of advancing human health, represents the percentage of children aged 12–23 months who received the measles vaccine either before their first birthday or at any time prior to the survey . A child is considered fully immunised after receiving a single dose of the vaccine . This data are collected and refined by the World Health Organization and The United Nations Children's Fund. 2) HIV prevalence – refers to the percentage of individuals aged 15–49 who are infected with HIV in each country’s population. It represents the vulnerability of this population and the potential risk of the virus spreading to the broader community . This data are collected and refined by the Joint United Nations Programme on HIV/AIDS. 3) COVID-19 Containment and Health Index – ranging from 0 to 100 (with 100 being the strictest), is a composite measure based on 13 policy indicators such as school closures, travel bans, testing, and vaccine policies . Sourced from the University of Oxford, this index is crucial for understanding how varying levels of pandemic responses across countries may have influenced life expectancy. The first two indicators are sourced from the World Bank’s World Development Indicators, while the third comes from our Our World in Data. 

In this study, our dependent variable is life expectancy (at birth), which refers to ‘the average number of years that a newborn could expect to live, if he or she were subject to the age-specific mortality rates of a given period’ . Life expectancy reflects mortality rates and serves as an indicator of overall health conditions . The data points are derived from the Human Development Index provided by the United Nations Department of Economic and Social Affairs. 

Our independent variables, or conditions, include indicators related to education attainment, economics prosperity, environment quality, social stability, urban development, and public health capacity. 1. Education attainment: education is represented by the mean years of schooling, which indicates ‘the average number of completed years of education for a country’s population, excluding any years spent repeating grades’ . The data points for education are derived from the Human Development Index, provided by the United Nations Educational, Scientific, and Cultural Organization. These estimates cover the population aged 25 years and older . 2. Economic prosperity: gross national income per capita represents a country’s economic output, expressed in USD using the World Bank Atlas method and divided by the midyear population . This measure includes the value added by all resident producers, product taxes (minus subsidies), and net income from abroad. To reduce price and exchange rate fluctuations, the Atlas method averages exchange rates from the current and two preceding years, adjusted for inflation differences between the country and major economies like the Euro area, Japan, the UK, and USA . The data point sources from LAC Equity Laboratory Tabulations of the World Development Indicators of the World Bank Open Data platform. 3. Environmental quality: environment factors include two indicators: 1) access to electricity (% of population), an indicator of a country’s environmental infrastructure, refers to the percentage of the population with electricity access . The electrification data are sourced from industry reports, national surveys, and international databases , and is compiled by the World Bank’s Global Electrification Database and the World Development Indicators. 2) annual population growth rate. Population, defined using the de facto approach, which includes all residents regardless of legal status or citizenship, is measured by its annual growth, calculated as the exponential growth rate of the midyear population from the previous year to the current year, expressed as a percentage . Population growth serves as an indicator of health because it reflects the broader demographic and social conditions that affect public health. This data are initially gathered and refined by the United Nations Population Division. 4. Social stability: the employment-to-population ratio, representing social stability, refers to the proportion of a country’s working-age population (15 years and older) that is employed . Employment includes individuals engaged in any activity to produce goods or services for pay or profit, even if temporarily absent from work . This ratio reflects how effectively an economy provides jobs . The data are sourced from the International Labor Organization of the World Bank’s World Development Indicators. 5. Urban development: urban population (% of total population), an indicator of urban development, represents the percentage of individuals living in urban areas, defined by national statistical offices, per 100 people in the total population . The data are collected and refined by the United Nations Population Division and featured in the World Bank’s World Development Indicators. 6. Public Health Capacity: we include three health-related indicators: 1) measles immunisation rate – a key indicator of advancing human health, represents the percentage of children aged 12–23 months who received the measles vaccine either before their first birthday or at any time prior to the survey . A child is considered fully immunised after receiving a single dose of the vaccine . This data are collected and refined by the World Health Organization and The United Nations Children's Fund. 2) HIV prevalence – refers to the percentage of individuals aged 15–49 who are infected with HIV in each country’s population. It represents the vulnerability of this population and the potential risk of the virus spreading to the broader community . This data are collected and refined by the Joint United Nations Programme on HIV/AIDS. 3) COVID-19 Containment and Health Index – ranging from 0 to 100 (with 100 being the strictest), is a composite measure based on 13 policy indicators such as school closures, travel bans, testing, and vaccine policies . Sourced from the University of Oxford, this index is crucial for understanding how varying levels of pandemic responses across countries may have influenced life expectancy. The first two indicators are sourced from the World Bank’s World Development Indicators, while the third comes from our Our World in Data. 

We employed QCA to identify complex causal patterns among multiple conditions in a case-oriented framework . This method is particularly valuable for identifying configurations of conditions that are necessary or sufficient for life expectancy outcomes, going beyond the additive and linear assumptions of regression models. In addition, the use of QCA was further motivated by the sample size of this study. With 130 observations per year, traditional regression methods might lack the statistical power to identify complex interactions between variables. By contrast, QCA is designed to analyse small- to medium-sized data sets, enabling the identification of configurational effects across diverse cases. Variables were calibrated into either fuzzy sets, which allow for varying degrees of membership between full non-membership and full membership, or crisp sets . This calibration approach enhances the robustness and validity of the QCA findings by accurately capturing the nuanced influence of each condition on the outcome of life expectancy (appendix 1in the ). The analysis further includes both necessary and sufficient condition assessments (configuration analyses), to elucidate the complex interplay among our measures. The necessity analysis assesses whether any of the conditions were required for the outcome to occur , and the degree of necessity was measured using two criteria: consistency and coverage. Consistency measures how often the independent variable is present when the outcome (life expectancy) occurs coverage indicates the extent to which the condition explains the outcome, with higher coverage meaning a larger portion of cases is explained . A consistency threshold of 0.9 and a coverage threshold of 0.5 suggest that the condition may be necessary for the outcome . Based on the results of the necessity analysis, we decided if we perform a sufficiency analysis to identify sets of configurations that were always present when the desired outcome occurred. Additionally, we conducted robustness checks to confirm the main findings, utilising the methods developed by Oana et al. . All analyses were performed with the R Programming software (R Foundation, Boston, MA, USA, Version 4.3.2) with packages ‘QCA 3.22’ and ‘SetMethods 4.0.’ 

Descriptive statistics The sample characteristics for this study comprisies 130 observations for each year (2020, 2021, and 2022), for a total of 390 observations ( ). Over the three-year period, variables such as GNI per capita , access to electricity, and the COVID-19 Containment and Health Index exhibit high variability, reflecting notable differences across countries. In contrast, life expectancy at birth, mean years of schooling, annual population growth, employment-to-population ratio, urban development, immunisation rates, and HIV prevalence show relatively low variability, indicating stability in these areas during the examined period. QCA results The calibration thresholds for both the dependent and independent variables/conditions used in QCA are presented in Table S1 in the . The calibration was based on three key thresholds: full membership (0.95, representing the 95th percentile), crossover point (0.5, representing the 50th percentile), and full non-membership (0.05, representing the 5th percentile). These thresholds were applied to transform raw data into fuzzy set scores for analysis. As shown in , access to electricity emerged as a highly consistent necessary condition for high life expectancy, with a consistency score of 0.95, surpassing the commonly accepted threshold of 0.9. This suggests that access to electricity is a crucial factor in leading to high life expectancy across the cases examined in our study. We further analysed access to electricity as a necessary condition for life expectancy, as shown in , where the relevance of necessity is 0.749 exceed the threshold of 0.5. Relevance of necessity is another indicator of necessity, which balances consistency and coverage, evaluating whether a condition is both necessary and meaningful, while adjusting for cases where high consistency might be trivial in the data set . These metrics collectively support that access to electricity is a key contributor for achieving high life expectancy across the observed regions: a strong positive correlation between access to electricity and life expectancy. Several other conditions approached this threshold, indicating their importances in the outcome. Specifically, mean years of schooling showed a consistency level of 0.84, while GNI per capita and urban population percentage both had consistencies of 0.82, and the measles immunisation rate scored 0.81. Though these conditions did not meet the strict 0.9 threshold, their proximity underscores their relevance in contributing to high life expectancy. Furthermore, all these conditions demonstrated coverage levels exceeding 0.5, indicating that each one covers a significant portion of the outcome cases. Specifically, GNI per capita had the highest coverage at 0.90, followed by mean years of schooling at 0.80, urban population percentage at 0.78, and immunisation rate at 0.74. The intermediate results and corresponding cases for each solution are shown in . The analysis identified seven distinct pathways leading to the outcome (life expectancy at birth), with each pathway representing a unique combination of explanatory conditions. These pathways reflect different causal mechanisms through which the outcome (high life expectancy at birth) is achieved. The core and peripheral conditions are visually distinguished by the size of the icons in the table. Core conditions, marked with larger solid icons ‘●’, are those that appear in both the intermediate and parsimonious solutions, indicating that they are more directly and consistently associated with the outcome (high life expectancy at birth) across various pathways. In contrast, peripheral conditions, represented with smaller solid icons ‘●’, are present only in the intermediate solution and may play a supporting role but are less critical to explaining the outcome (high life expectancy at birth). The icons of ‘⊗’ indicate the condition is absent in the specific configuration but still contributes to achieving the outcome (high life expectancy at birth). Each of the seven pathways showed consistency scores above 0.8, ranging from 0.957 to 0.983, indicating they are individually sufficient and reliable in predicting high life expectancy at birth. While access to electricity is a central driver, education and economic factors remain critical, and the importance of other factors, such as measles immunisation rates, varies depending on the specific context. Raw coverage measures the proportion of cases in the outcome set that can be explained by each pathway, with values ranging from 0.205 (Configuration 5) to 0.448 (Configuration 2). This indicates that Configuration 2, for example, accounts for 44.8% of the cases where high life expectancy is observed. Unique coverage reflects the proportion of cases that can only be explained by a particular pathway, excluding overlaps with other configurations. For instance, Configuration 2 has a unique coverage of 0.061, meaning that 6.1% of the cases are uniquely explained by this specific pathway, without being covered by any other configuration ( ; ). Additionally, we assessed the reliability and validity of the seven pathways using three key measures: solution consistency, solution coverage, and solution Proportional Reduction in Inconsistency (PRI). Solution consistency reflects how consistently the full set of solutions leads to the desired outcome. A threshold of 0.8 is generally recommended, regardless of sample size. As shown in , the solution consistency of 0.956 indicates a strong and consistent relationship between the pathways and the outcome. Solution coverage, on the other hand, measures the extent to which the identified pathways account for the observed cases. With a solution coverage of 0.632, our results suggest that the seven pathways jointly explain 63.2% of all cases. While there is no standard threshold for solution coverage since it varies based on research design, this value is considered relatively high, especially in large-number QCA studies, where lower coverage is more common. Solution PRI stands for the Proportional Reduction in Inconsistency at the solution level, which evaluates how consistently the identified configurations lead to the presence of the outcome, rather than its absence. In this study, the Solution PRI of 0.909 exceeds the 0.75 threshold, indicating a strong, reliable solution with minimal contradictions. Thus, the high solution consistency, solution coverage, and Solution PRI confirm that the seven pathways are reliable and effectively explain the outcome with minimal contradictions. The robustness tests were conducted for both fit-oriented and case-oriented parameters. Sensitivity ranges were evaluated based on raw consistency and frequency thresholds. The fit-oriented raw consistency (RF cons ) is 0.877, indicating a strong fit between the empirical data and the solution, while the coverage (RF cov ) is 0.901, reflecting the proportion of cases the solution covers. Sensitivity tests showed stability across lower and upper thresholds of raw consistency (0.76–0.88) and frequency (n = 4), with RFSC _minTS and RFSC _maxTS values of 0.790 and 0.892, respectively, suggesting a stable solution across different parameter settings. In terms of cases-oriented measures, the ratio of robust typical cases (RCR typ ) is 0.867, surpassing the ratio of robust deviant cases (RCR dev = 0.487). This indicates that the solution fits better with typical cases compared to deviant cases. These robustness test results affirm the reliability of the findings, demonstrating that the solution is particularly stable when addressing typical cases. Finally, the RCC _Rank value of indicates that the complexity of the case analysis suggests a solution comprising a mix of ‘uncertain’ and ‘plausible’ cases, implying varying levels of robustness across different case types ( ; Table S2 in the ). 

The sample characteristics for this study comprisies 130 observations for each year (2020, 2021, and 2022), for a total of 390 observations ( ). Over the three-year period, variables such as GNI per capita , access to electricity, and the COVID-19 Containment and Health Index exhibit high variability, reflecting notable differences across countries. In contrast, life expectancy at birth, mean years of schooling, annual population growth, employment-to-population ratio, urban development, immunisation rates, and HIV prevalence show relatively low variability, indicating stability in these areas during the examined period. 

The calibration thresholds for both the dependent and independent variables/conditions used in QCA are presented in Table S1 in the . The calibration was based on three key thresholds: full membership (0.95, representing the 95th percentile), crossover point (0.5, representing the 50th percentile), and full non-membership (0.05, representing the 5th percentile). These thresholds were applied to transform raw data into fuzzy set scores for analysis. As shown in , access to electricity emerged as a highly consistent necessary condition for high life expectancy, with a consistency score of 0.95, surpassing the commonly accepted threshold of 0.9. This suggests that access to electricity is a crucial factor in leading to high life expectancy across the cases examined in our study. We further analysed access to electricity as a necessary condition for life expectancy, as shown in , where the relevance of necessity is 0.749 exceed the threshold of 0.5. Relevance of necessity is another indicator of necessity, which balances consistency and coverage, evaluating whether a condition is both necessary and meaningful, while adjusting for cases where high consistency might be trivial in the data set . These metrics collectively support that access to electricity is a key contributor for achieving high life expectancy across the observed regions: a strong positive correlation between access to electricity and life expectancy. Several other conditions approached this threshold, indicating their importances in the outcome. Specifically, mean years of schooling showed a consistency level of 0.84, while GNI per capita and urban population percentage both had consistencies of 0.82, and the measles immunisation rate scored 0.81. Though these conditions did not meet the strict 0.9 threshold, their proximity underscores their relevance in contributing to high life expectancy. Furthermore, all these conditions demonstrated coverage levels exceeding 0.5, indicating that each one covers a significant portion of the outcome cases. Specifically, GNI per capita had the highest coverage at 0.90, followed by mean years of schooling at 0.80, urban population percentage at 0.78, and immunisation rate at 0.74. The intermediate results and corresponding cases for each solution are shown in . The analysis identified seven distinct pathways leading to the outcome (life expectancy at birth), with each pathway representing a unique combination of explanatory conditions. These pathways reflect different causal mechanisms through which the outcome (high life expectancy at birth) is achieved. The core and peripheral conditions are visually distinguished by the size of the icons in the table. Core conditions, marked with larger solid icons ‘●’, are those that appear in both the intermediate and parsimonious solutions, indicating that they are more directly and consistently associated with the outcome (high life expectancy at birth) across various pathways. In contrast, peripheral conditions, represented with smaller solid icons ‘●’, are present only in the intermediate solution and may play a supporting role but are less critical to explaining the outcome (high life expectancy at birth). The icons of ‘⊗’ indicate the condition is absent in the specific configuration but still contributes to achieving the outcome (high life expectancy at birth). Each of the seven pathways showed consistency scores above 0.8, ranging from 0.957 to 0.983, indicating they are individually sufficient and reliable in predicting high life expectancy at birth. While access to electricity is a central driver, education and economic factors remain critical, and the importance of other factors, such as measles immunisation rates, varies depending on the specific context. Raw coverage measures the proportion of cases in the outcome set that can be explained by each pathway, with values ranging from 0.205 (Configuration 5) to 0.448 (Configuration 2). This indicates that Configuration 2, for example, accounts for 44.8% of the cases where high life expectancy is observed. Unique coverage reflects the proportion of cases that can only be explained by a particular pathway, excluding overlaps with other configurations. For instance, Configuration 2 has a unique coverage of 0.061, meaning that 6.1% of the cases are uniquely explained by this specific pathway, without being covered by any other configuration ( ; ). Additionally, we assessed the reliability and validity of the seven pathways using three key measures: solution consistency, solution coverage, and solution Proportional Reduction in Inconsistency (PRI). Solution consistency reflects how consistently the full set of solutions leads to the desired outcome. A threshold of 0.8 is generally recommended, regardless of sample size. As shown in , the solution consistency of 0.956 indicates a strong and consistent relationship between the pathways and the outcome. Solution coverage, on the other hand, measures the extent to which the identified pathways account for the observed cases. With a solution coverage of 0.632, our results suggest that the seven pathways jointly explain 63.2% of all cases. While there is no standard threshold for solution coverage since it varies based on research design, this value is considered relatively high, especially in large-number QCA studies, where lower coverage is more common. Solution PRI stands for the Proportional Reduction in Inconsistency at the solution level, which evaluates how consistently the identified configurations lead to the presence of the outcome, rather than its absence. In this study, the Solution PRI of 0.909 exceeds the 0.75 threshold, indicating a strong, reliable solution with minimal contradictions. Thus, the high solution consistency, solution coverage, and Solution PRI confirm that the seven pathways are reliable and effectively explain the outcome with minimal contradictions. The robustness tests were conducted for both fit-oriented and case-oriented parameters. Sensitivity ranges were evaluated based on raw consistency and frequency thresholds. The fit-oriented raw consistency (RF cons ) is 0.877, indicating a strong fit between the empirical data and the solution, while the coverage (RF cov ) is 0.901, reflecting the proportion of cases the solution covers. Sensitivity tests showed stability across lower and upper thresholds of raw consistency (0.76–0.88) and frequency (n = 4), with RFSC _minTS and RFSC _maxTS values of 0.790 and 0.892, respectively, suggesting a stable solution across different parameter settings. In terms of cases-oriented measures, the ratio of robust typical cases (RCR typ ) is 0.867, surpassing the ratio of robust deviant cases (RCR dev = 0.487). This indicates that the solution fits better with typical cases compared to deviant cases. These robustness test results affirm the reliability of the findings, demonstrating that the solution is particularly stable when addressing typical cases. Finally, the RCC _Rank value of indicates that the complexity of the case analysis suggests a solution comprising a mix of ‘uncertain’ and ‘plausible’ cases, implying varying levels of robustness across different case types ( ; Table S2 in the ). 

Our findings identified the key contributors and their potential complex interactions influencing life expectancy, particularly within the context of the COVID-19 pandemic. Determinants such as access to essential environmental quality, education attainment, economics prosperity, and public health capacity emerged as critical factors. These findings align with existing research emphasising the significant role of socioeconomic and environmental conditions in shaping population life expectancy globally [ – , ]. Our findings suggest the need for a holistic approach to improving global life expectancy, one that integrates multiple policy areas such as enhancing access to essential infrastructure, education, economic growth, and the effectiveness of health systems. In addition, there isn’t a ‘one-size-fits-all’ solution; instead, different countries can follow different pathways to achieve improved life expectancy, depending on their unique socio-economic conditions and policy priorities. Among the contributors to life expectancy, we found that access to electricity, represents environmental quality, emerged as the most influential in affecting life expectancy, reinforcing prior research that highlights the crucial role of basic infrastructure in promoting life expectancy [ , – ]. Despite long-standing global efforts, energy poverty remains a significant issue. As of 2023, an estimated 745 million people worldwide still lacked access to electricity, with the problem being especially severe in low-income regions . This challenge has been further compounded by global crises, including the COVID-19 pandemic and energy disruptions due to geopolitical events . Therefore, continued investment in reliable and sustainable infrastructure, particularly in underserved areas, is critical not only for improving electricity access but also for ensuring clean water, internet, and other essential services. Policies could include subsidising renewable energy sources and developing public-private partnerships to expand grid coverage. Such investments are expected to raise living standards, reduce health disparities, thereby providing a foundational strategy for enhancing life expectancy globally. Educational attainment, measured by mean years of schooling in this study, also is shown as an important factor influencing life expectancy, which is consistent with prior research [ – ]. Education is not only a social good but also a critical public health strategy with long-term effects on life expectancy. Education enhances individuals’ health literacy and awareness, improves access to health care resources, broadens socioeconomic opportunities, and equips individuals to make informed health decisions, thereby contributing to longer, healthier lives [ – ]. As of 2023, the global average years of schooling for adults aged 25 and over was approximately 6.57 years . However, significant disparities exist across regions and socioeconomic groups. For instance, fewer than half of adults have completed primary education in sub-Saharan Africa, whereas in regions like Eastern Asia and Latin America, more than 75% of adults have achieved this milestone . These education attainment disparities contribute directly to global inequalities in life expectancy. Our findings suggest that increasing educational opportunities, especially in underserved areas, have potential to help public health improvements. To address these disparities, targeted interventions should prioritise public investment in such as infrastructure, teacher training, resource allocation, and transportation, especially at the early education stages in underprivileged regions . These efforts would not only help close the education gap but also foster a more educated and health-conscious population, contributing to long-term improvements in life expectancy. Our study also revealed that economics prosperity, as measured by GNI per capita , is a crucial determinant of life expectancy, aligning with the well-established positive correlation between economic prosperity and longevity . The disparity between high-income and low-income countries has widened in recent years. According to the 2023 World Bank classification, high-income countries like the USA, Germany, and Japan report a GNI per capita exceeding 13 205 USD, while low-income nations such as Afghanistan, Chad, and Malawi have a GNI per capita of 1085 USD or less . These economic disparities significantly affect health, education, and living standards. Countries with higher GNI per capita generally provide better access to health care, education, and other essential services, contributing to longer life expectancy and improved quality of life. Additionally, our findings indicate that the percentage of the urban population and the measles immunisation rate are influential drivers of life expectancy, echoing the findings of some prior studies [ – ]. A higher proportion of urban residents often correlates with better access to health care facilities, sanitation, and education , all of which contribute to improved health outcomes and longer life expectancy through more developed health care infrastructure. Measles immunisation rates is a key indicator of public health system effectiveness. Higher vaccination coverage significantly reduces child mortality and prevents outbreaks of infectious diseases, thereby directly contributing to increased life expectancy . To improve life expectancy, efforts should be focused on fostering economic development, expanding access to urban-quality living standards in rural areas, and enhancing health care system effectiveness through stronger immunisation programmes. The key finding of this study is that improving life expectancy requires a holistic, multi-faceted approach due to the complex interplay of factors, including environment quality, education attainment, economics prosperity, urban development, and public health capacity. By addressing these interconnected areas and ongoing challenges, policy interventions can lead to more sustainable and equitable improvements in life expectancy, especially in underserved regions. Policy interventions that simultaneously target these factors are essential to achieving long-term health gains and reducing disparities across populations. Study limitations Our study, while providing valuable insights, has several limitations that must be acknowledged. First, although our data sources represent multiple global data sets, variations in data collection methodologies across these sources may lead to inconsistencies in measurement and reporting. Additionally, we filtered the data to include only countries with complete and reliable data across all variables, resulting in the inclusion of 130 countries. While this stepwise approach minimised the impact of missing data and ensured consistency in the analysis, it may have excluded countries that are underrepresented in global data sets, potentially introducing selection bias and limiting the generalisability of our findings. Next, our analysis spans only three years (2020–2022), a period significantly influenced by the COVID-19 pandemic. While this three-year window may not fully capture long-term trends, it introduces the possibility of bias due to the extraordinary global context during this time. Last but not least, the QCA method, while effective for identifying complex causal pathways, may be sensitive to calibration decisions. Different thresholds or alternative approaches could yield different results. The reliability test results suggest that there is room for deeper exploration of the ‘uncertain’ and ‘plausible’ cases in future studies. Therefore, caution should be exercised in generalising our findings to other contexts. 

Our study, while providing valuable insights, has several limitations that must be acknowledged. First, although our data sources represent multiple global data sets, variations in data collection methodologies across these sources may lead to inconsistencies in measurement and reporting. Additionally, we filtered the data to include only countries with complete and reliable data across all variables, resulting in the inclusion of 130 countries. While this stepwise approach minimised the impact of missing data and ensured consistency in the analysis, it may have excluded countries that are underrepresented in global data sets, potentially introducing selection bias and limiting the generalisability of our findings. Next, our analysis spans only three years (2020–2022), a period significantly influenced by the COVID-19 pandemic. While this three-year window may not fully capture long-term trends, it introduces the possibility of bias due to the extraordinary global context during this time. Last but not least, the QCA method, while effective for identifying complex causal pathways, may be sensitive to calibration decisions. Different thresholds or alternative approaches could yield different results. The reliability test results suggest that there is room for deeper exploration of the ‘uncertain’ and ‘plausible’ cases in future studies. Therefore, caution should be exercised in generalising our findings to other contexts. 

Using the qualitative comparative analysis method, this study contributes to the ongoing global discourse on life expectancy by identifying key determinants that have persisted during and after the COVID-19 pandemic. Access to electricity emerged as a necessary condition, underscoring the need for governments and organisations to prioritise expanding reliable electricity access, particularly in underserved areas, through investments in renewable energy and infrastructure development. Other factors, including mean years of schooling, immunisation rates, GNI per capita , and urban population, played critical roles in different pathways. Expanding access to education can be achieved through such as conditional cash transfer programmes, investments in teacher training, and the development of schools in rural areas. Strengthening health care systems requires improving vaccine delivery infrastructure, providing public education to enhance vaccine acceptance, and increasing incentives for immunisation efforts. Additionally, fostering job creation and promoting inclusive economic growth are essential to ensuring equitable access to health care, housing, and sanitation. Furthermore, our study suggests the need for integrated policies that are tailored to each country’s unique socio-economic contexts to promote sustainable and equitable health outcomes. 

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