Education

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    Extensive research has consistently demonstrated a strong correlation between educational attainment and longevity.[1][2] Higher levels of education are often associated with longer life expectancy. This relationship is believed to be influenced by various factors, including improved health behaviors, better access to healthcare, higher income, and improved social and psychological well-being associated with educational attainment.[3][4][5]

    Statistics

    Relationship between education and adult mortality by age group[6]

    In a comprehensive study conducted in 2024, researchers aimed to understand how education affects the likelihood of adults dying from various causes. They found a consistent pattern: as the years of education increase, the risk of dying decreases. This relationship is known as a "dose-response" connection, meaning the more education (the "dose"), the greater the positive impact (the "response") on reducing death rates.[6]

    Key findings from this study include:

    • Overall Reduction in Mortality with Education: For every additional year of schooling that an adult receives, there is an average decrease of 1.9% in their risk of dying.
    • Greater Impact in Younger Adults: The benefits of education on reducing death risk vary by age. Adults between 18 to 49 years old see a larger decrease in their mortality risk, at 2.9%, for each extra year of schooling. In contrast, for adults over 70 years, the benefit is smaller, with only a 0.8% reduction in death risk per additional year of education.
    • No Gender or Socio-demographic Index Differences: The study also found that the positive effect of education on reducing mortality is similar across different genders and socio-economic backgrounds. The Socio-demographic Index (SDI) combines information on the economy, education, and fertility rate of countries around the world, as a representation of social and economic development. The life expectancy per country closely correlates to SDI.

    In summary, this research highlights the significant role of education in enhancing longevity, particularly noting its stronger impact among younger adults. It underscores the universal benefits of education across different segments of the population.

    Influencing Factors

    Socioeconomic Factors

    Education can lead to better job opportunities, resulting in higher income and improved living conditions. This economic stability often translates into better access to healthcare, healthier diets, and safer living environments, all of which contribute to increased longevity.[7]

    Health Behaviors and Access

    Educated individuals are more likely to engage in health-promoting behaviors such as regular exercise, a balanced diet, and avoidance of harmful habits like smoking and excessive alcohol consumption.[8] Furthermore, education equips individuals with better health literacy, enabling them to navigate the healthcare system more effectively and make informed health decisions.

    Psychological and Social Benefits

    Higher education levels are often linked with better mental health, reduced stress, and stronger social networks.[3][4] These factors are crucial in promoting longevity, as social support and mental well-being are significant determinants of health and lifespan.

    Global Trends

    The global distribution of educational attainment has undergone significant changes over the past decades.[9][10] These shifts have been associated with corresponding effects on mortality rates. For instance, increased parental education has been shown to reduce child mortality rates significantly.[11]

    Further Reading

    • 2024, Effects of education on adult mortality: a global systematic review and meta-analysis [6]

    References

    1. ↑ Foreman KJ et al.: Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet 2018. (PMID 30340847) [PubMed] [DOI] [Full text] BACKGROUND: Understanding potential trajectories in health and drivers of health is crucial to guiding long-term investments and policy implementation. Past work on forecasting has provided an incomplete landscape of future health scenarios, highlighting a need for a more robust modelling platform from which policy options and potential health trajectories can be assessed. This study provides a novel approach to modelling life expectancy, all-cause mortality and cause of death forecasts -and alternative future scenarios-for 250 causes of death from 2016 to 2040 in 195 countries and territories. METHODS: We modelled 250 causes and cause groups organised by the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) hierarchical cause structure, using GBD 2016 estimates from 1990-2016, to generate predictions for 2017-40. Our modelling framework used data from the GBD 2016 study to systematically account for the relationships between risk factors and health outcomes for 79 independent drivers of health. We developed a three-component model of cause-specific mortality: a component due to changes in risk factors and select interventions; the underlying mortality rate for each cause that is a function of income per capita, educational attainment, and total fertility rate under 25 years and time; and an autoregressive integrated moving average model for unexplained changes correlated with time. We assessed the performance by fitting models with data from 1990-2006 and using these to forecast for 2007-16. Our final model used for generating forecasts and alternative scenarios was fitted to data from 1990-2016. We used this model for 195 countries and territories to generate a reference scenario or forecast through 2040 for each measure by location. Additionally, we generated better health and worse health scenarios based on the 85th and 15th percentiles, respectively, of annualised rates of change across location-years for all the GBD risk factors, income per person, educational attainment, select intervention coverage, and total fertility rate under 25 years in the past. We used the model to generate all-cause age-sex specific mortality, life expectancy, and years of life lost (YLLs) for 250 causes. Scenarios for fertility were also generated and used in a cohort component model to generate population scenarios. For each reference forecast, better health, and worse health scenarios, we generated estimates of mortality and YLLs attributable to each risk factor in the future. FINDINGS: Globally, most independent drivers of health were forecast to improve by 2040, but 36 were forecast to worsen. As shown by the better health scenarios, greater progress might be possible, yet for some drivers such as high body-mass index (BMI), their toll will rise in the absence of intervention. We forecasted global life expectancy to increase by 4·4 years (95% UI 2·2 to 6·4) for men and 4·4 years (2·1 to 6·4) for women by 2040, but based on better and worse health scenarios, trajectories could range from a gain of 7·8 years (5·9 to 9·8) to a non-significant loss of 0·4 years (-2·8 to 2·2) for men, and an increase of 7·2 years (5·3 to 9·1) to essentially no change (0·1 years [-2·7 to 2·5]) for women. In 2040, Japan, Singapore, Spain, and Switzerland had a forecasted life expectancy exceeding 85 years for both sexes, and 59 countries including China were projected to surpass a life expectancy of 80 years by 2040. At the same time, Central African Republic, Lesotho, Somalia, and Zimbabwe had projected life expectancies below 65 years in 2040, indicating global disparities in survival are likely to persist if current trends hold. Forecasted YLLs showed a rising toll from several non-communicable diseases (NCDs), partly driven by population growth and ageing. Differences between the reference forecast and alternative scenarios were most striking for HIV/AIDS, for which a potential increase of 120·2% (95% UI 67·2-190·3) in YLLs (nearly 118 million) was projected globally from 2016-40 under the worse health scenario. Compared with 2016, NCDs were forecast to account for a greater proportion of YLLs in all GBD regions by 2040 (67·3% of YLLs [95% UI 61·9-72·3] globally); nonetheless, in many lower-income countries, communicable, maternal, neonatal, and nutritional (CMNN) diseases still accounted for a large share of YLLs in 2040 (eg, 53·5% of YLLs [95% UI 48·3-58·5] in Sub-Saharan Africa). There were large gaps for many health risks between the reference forecast and better health scenario for attributable YLLs. In most countries, metabolic risks amenable to health care (eg, high blood pressure and high plasma fasting glucose) and risks best targeted by population-level or intersectoral interventions (eg, tobacco, high BMI, and ambient particulate matter pollution) had some of the largest differences between reference and better health scenarios. The main exception was sub-Saharan Africa, where many risks associated with poverty and lower levels of development (eg, unsafe water and sanitation, household air pollution, and child malnutrition) were projected to still account for substantive disparities between reference and better health scenarios in 2040. INTERPRETATION: With the present study, we provide a robust, flexible forecasting platform from which reference forecasts and alternative health scenarios can be explored in relation to a wide range of independent drivers of health. Our reference forecast points to overall improvements through 2040 in most countries, yet the range found across better and worse health scenarios renders a precarious vision of the future-a world with accelerating progress from technical innovation but with the potential for worsening health outcomes in the absence of deliberate policy action. For some causes of YLLs, large differences between the reference forecast and alternative scenarios reflect the opportunity to accelerate gains if countries move their trajectories toward better health scenarios-or alarming challenges if countries fall behind their reference forecasts. Generally, decision makers should plan for the likely continued shift toward NCDs and target resources toward the modifiable risks that drive substantial premature mortality. If such modifiable risks are prioritised today, there is opportunity to reduce avoidable mortality in the future. However, CMNN causes and related risks will remain the predominant health priority among lower-income countries. Based on our 2040 worse health scenario, there is a real risk of HIV mortality rebounding if countries lose momentum against the HIV epidemic, jeopardising decades of progress against the disease. Continued technical innovation and increased health spending, including development assistance for health targeted to the world's poorest people, are likely to remain vital components to charting a future where all populations can live full, healthy lives. FUNDING: Bill & Melinda Gates Foundation.
    2. ↑ Vollset SE et al.: Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. Lancet 2020. (PMID 32679112) [PubMed] [DOI] [Full text] BACKGROUND: Understanding potential patterns in future population levels is crucial for anticipating and planning for changing age structures, resource and health-care needs, and environmental and economic landscapes. Future fertility patterns are a key input to estimation of future population size, but they are surrounded by substantial uncertainty and diverging methodologies of estimation and forecasting, leading to important differences in global population projections. Changing population size and age structure might have profound economic, social, and geopolitical impacts in many countries. In this study, we developed novel methods for forecasting mortality, fertility, migration, and population. We also assessed potential economic and geopolitical effects of future demographic shifts. METHODS: We modelled future population in reference and alternative scenarios as a function of fertility, migration, and mortality rates. We developed statistical models for completed cohort fertility at age 50 years (CCF50). Completed cohort fertility is much more stable over time than the period measure of the total fertility rate (TFR). We modelled CCF50 as a time-series random walk function of educational attainment and contraceptive met need. Age-specific fertility rates were modelled as a function of CCF50 and covariates. We modelled age-specific mortality to 2100 using underlying mortality, a risk factor scalar, and an autoregressive integrated moving average (ARIMA) model. Net migration was modelled as a function of the Socio-demographic Index, crude population growth rate, and deaths from war and natural disasters; and use of an ARIMA model. The model framework was used to develop a reference scenario and alternative scenarios based on the pace of change in educational attainment and contraceptive met need. We estimated the size of gross domestic product for each country and territory in the reference scenario. Forecast uncertainty intervals (UIs) incorporated uncertainty propagated from past data inputs, model estimation, and forecast data distributions. FINDINGS: The global TFR in the reference scenario was forecasted to be 1·66 (95% UI 1·33-2·08) in 2100. In the reference scenario, the global population was projected to peak in 2064 at 9·73 billion (8·84-10·9) people and decline to 8·79 billion (6·83-11·8) in 2100. The reference projections for the five largest countries in 2100 were India (1·09 billion [0·72-1·71], Nigeria (791 million [594-1056]), China (732 million [456-1499]), the USA (336 million [248-456]), and Pakistan (248 million [151-427]). Findings also suggest a shifting age structure in many parts of the world, with 2·37 billion (1·91-2·87) individuals older than 65 years and 1·70 billion (1·11-2·81) individuals younger than 20 years, forecasted globally in 2100. By 2050, 151 countries were forecasted to have a TFR lower than the replacement level (TFR <2·1), and 183 were forecasted to have a TFR lower than replacement by 2100. 23 countries in the reference scenario, including Japan, Thailand, and Spain, were forecasted to have population declines greater than 50% from 2017 to 2100; China's population was forecasted to decline by 48·0% (-6·1 to 68·4). China was forecasted to become the largest economy by 2035 but in the reference scenario, the USA was forecasted to once again become the largest economy in 2098. Our alternative scenarios suggest that meeting the Sustainable Development Goals targets for education and contraceptive met need would result in a global population of 6·29 billion (4·82-8·73) in 2100 and a population of 6·88 billion (5·27-9·51) when assuming 99th percentile rates of change in these drivers. INTERPRETATION: Our findings suggest that continued trends in female educational attainment and access to contraception will hasten declines in fertility and slow population growth. A sustained TFR lower than the replacement level in many countries, including China and India, would have economic, social, environmental, and geopolitical consequences. Policy options to adapt to continued low fertility, while sustaining and enhancing female reproductive health, will be crucial in the years to come. FUNDING: Bill & Melinda Gates Foundation.
    3. ↑ 3.0 3.1 Phelan JC et al.: Social conditions as fundamental causes of health inequalities: theory, evidence, and policy implications. J Health Soc Behav 2010. (PMID 20943581) [PubMed] [DOI] Link and Phelan (1995) developed the theory of fundamental causes to explain why the association between socioeconomic status (SES) and mortality has persisted despite radical changes in the diseases and risk factors that are presumed to explain it. They proposed that the enduring association results because SES embodies an array of resources, such as money, knowledge, prestige, power, and beneficial social connections that protect health no matter what mechanisms are relevant at any given time. In this article, we explicate the theory, review key findings, discuss refinements and limits to the theory, and discuss implications for health policies that might reduce health inequalities. We advocate policies that encourage medical and other health-promoting advances while at the same time breaking or weakening the link between these advances and socioeconomic resources. This can be accomplished either by reducing disparities in socioeconomic resources themselves or by developing interventions that, by their nature, are more equally distributed across SES groups.
    4. ↑ 4.0 4.1 Thoits PA: Stress and health: major findings and policy implications. J Health Soc Behav 2010. (PMID 20943582) [PubMed] [DOI] Forty decades of sociological stress research offer five major findings. First, when stressors (negative events, chronic strains, and traumas) are measured comprehensively, their damaging impacts on physical and mental health are substantial. Second, differential exposure to stressful experiences is a primary way that gender, racial-ethnic, marital status, and social class inequalities in physical and mental health are produced. Third, minority group members are additionally harmed by discrimination stress. Fourth, stressors proliferate over the life course and across generations, widening health gaps between advantaged and disadvantaged group members. Fifth, the impacts of stressors on health and well-being are reduced when persons have high levels of mastery, self-esteem, and/or social support. With respect to policy, to help individuals cope with adversity, tried and true coping and support interventions should be more widely disseminated and employed. To address health inequalities, the structural conditions that put people at risk of stressors should be a focus of programs and policies at macro and meso levels of intervention. Programs and policies also should target children who are at lifetime risk of ill health and distress due to exposure to poverty and stressful family circumstances.
    5. ↑ Baker DP et al.: The Population Education Transition Curve: Education Gradients Across Population Exposure to New Health Risks. Demography 2017. (PMID 28875332) [PubMed] [DOI] The salutary effect of formal education on health-risk behaviors and mortality is extensively documented: ceteris paribus, greater educational attainment leads to healthier lives and longevity. Even though the epidemiological evidence has strongly indicated formal education as a leading "social vaccine," there is intermittent reporting of counter-education gradients for health-risk behavior and associated outcomes for certain populations during specific periods. How can education have both beneficial and harmful effects on health, and under which contexts do particular effects emerge? It is useful to conceptualize the influence of education as a process sensitive to the nature, timing of entry, and uniqueness of a new pleasurable and desirable lifestyle and/or product (such as smoking) with initially unclear health risks for populations. Developed herein is a hypothesis that the education gradient comprises multiple potent pathways (material, psychological, cognitive) by which health-risk behaviors are influenced, and that there can be circumstances under which pathways act in opposite directions or are differentially suppressed and enhanced. We propose the population education transition (PET) curve as a unifying functional form to predict shifting education gradients across the onset and course of a population's exposure to new health risks and their associated consequences. Then, we estimate PET curves for cases with prior epidemiological evidence of heterogeneous education gradients with health-risk behaviors related to mass-produced cigarettes in China and the United States; saturated fats, sugar, and processed food diets in Latin America; and HIV infection in sub-Saharan Africa. Each offers speculation on interactions between environmental factors during population exposure and education pathways to health-risk behaviors that could be responsible for the temporal dynamics of PET curves. Past epidemiological studies reporting either negative or positive education gradients may not represent contradictory findings as much as come from analyses unintentionally limited to just one part of the PET process. Last, the PET curve formulation offers richer nuances about educational pathways, macro-historical population dynamics, and the fundamental cause of disease paradigm.
    6. ↑ 6.0 6.1 6.2 IHME-CHAIN Collaborators: Effects of education on adult mortality: a global systematic review and meta-analysis. Lancet Public Health 2024. (PMID 38278172) [PubMed] [DOI] BACKGROUND: The positive effect of education on reducing all-cause adult mortality is known; however, the relative magnitude of this effect has not been systematically quantified. The aim of our study was to estimate the reduction in all-cause adult mortality associated with each year of schooling at a global level. METHODS: In this systematic review and meta-analysis, we assessed the effect of education on all-cause adult mortality. We searched PubMed, Web of Science, Scopus, Embase, Global Health (CAB), EconLit, and Sociology Source Ultimate databases from Jan 1, 1980, to May 31, 2023. Reviewers (LD, TM, HDV, CW, IG, AG, CD, DS, KB, KE, and AA) assessed each record for individual-level data on educational attainment and mortality. Data were extracted by a single reviewer into a standard template from the Global Burden of Diseases, Injuries, and Risk Factors Study. We excluded studies that relied on case-crossover or ecological study designs to reduce the risk of bias from unlinked data and studies that did not report key measures of interest (all-cause adult mortality). Mixed-effects meta-regression models were implemented to address heterogeneity in referent and exposure measures among studies and to adjust for study-level covariates. This study was registered with PROSPERO (CRD42020183923). FINDINGS: 17 094 unique records were identified, 603 of which were eligible for analysis and included data from 70 locations in 59 countries, producing a final dataset of 10 355 observations. Education showed a dose-response relationship with all-cause adult mortality, with an average reduction in mortality risk of 1·9% (95% uncertainty interval 1·8-2·0) per additional year of education. The effect was greater in younger age groups than in older age groups, with an average reduction in mortality risk of 2·9% (2·8-3·0) associated with each additional year of education for adults aged 18-49 years, compared with a 0·8% (0·6-1·0) reduction for adults older than 70 years. We found no differential effect of education on all-cause mortality by sex or Socio-demographic Index level. We identified publication bias (p<0·0001) and identified and reported estimates of between-study heterogeneity. INTERPRETATION: To our knowledge, this is the first systematic review and meta-analysis to quantify the importance of years of schooling in reducing adult mortality, the benefits of which extend into older age and are substantial across sexes and economic contexts. This work provides compelling evidence of the importance of education in improving life expectancy and supports calls for increased investment in education as a crucial pathway for reducing global inequities in mortality. FUNDING: Research Council of Norway and the Bill & Melinda Gates Foundation.
    7. ↑ Heymann J et al.: Improving health with programmatic, legal, and policy approaches to reduce gender inequality and change restrictive gender norms. Lancet 2019. (PMID 31155271) [PubMed] [DOI] Evidence that gender inequalities and restrictive norms adversely affect health is extensive; however, far less research has focused on testing solutions. We first comprehensively reviewed the peer-reviewed and grey literature for rigorously evaluated programmes that aimed to reduce gender inequality and restrictive gender norms and improve health. We identified four mutually reinforcing factors underpinning change: (1) multisectoral action, (2) multilevel, multistakeholder involvement, (3) diversified programming, and (4) social participation and empowerment. Following this review, because little research has investigated the effects of national-level law and policy reforms, we conducted original quasi-experimental studies on laws and policies related to education, work, and income, all social determinants of health in which deep gender inequalities exist. We examined whether the laws and policies significantly affected health outcomes and gender norms, and whether law-induced and policy-induced changes in gender norms mediated the health effects, in areas for which longitudinal data existed. Laws and policies that made primary education tuition-free (13 intervention countries with the law and/or policy and ten control countries without) and that provided paid maternity and parental leave (seven intervention and 15 control countries) significantly improved women's and their children's health (odds ratios [OR] of 1·16-2·10, depending on health outcome) and gender equality in household decision making (OR 1·46 for tuition-free and 1·45 for paid maternity and parental leave) as a proxy indicator of gender norms. Increased equality partially mediated the positive effects on health outcomes. We conclude by discussing examples of how improved governance can support gender-equitable laws, policies, and programmes, immediate next steps, and future research needs.
    8. ↑ The Lancet Public Health: Education: a neglected social determinant of health. Lancet Public Health 2020. (PMID 32619534) [PubMed] [DOI] [Full text]
    9. ↑ Friedman J et al.: Measuring and forecasting progress towards the education-related SDG targets. Nature 2020. (PMID 32350468) [PubMed] [DOI] [Full text] Education is a key dimension of well-being and a crucial indicator of development1-4. The Sustainable Development Goals (SDGs) prioritize progress in education, with a new focus on inequality5-7. Here we model the within-country distribution of years of schooling, and use this model to explore educational inequality since 1970 and to forecast progress towards the education-related 2030 SDG targets. We show that although the world is largely on track to achieve near-universal primary education by 2030, substantial challenges remain in the completion rates for secondary and tertiary education. Globally, the gender gap in schooling had nearly closed by 2018 but gender disparities remained acute in parts of sub-Saharan Africa, and North Africa and the Middle East. It is predicted that, by 2030, females will have achieved significantly higher educational attainment than males in 18 countries. Inequality in education reached a peak globally in 2017 and is projected to decrease steadily up to 2030. The distributions and inequality metrics presented here represent a framework that can be used to track the progress of each country towards the SDG targets and the level of inequality over time. Reducing educational inequality is one way to promote a fairer distribution of human capital and the development of more equitable human societies.
    10. ↑ Graetz N et al.: Mapping local variation in educational attainment across Africa. Nature 2018. (PMID 29493588) [PubMed] [DOI] [Full text] Educational attainment for women of reproductive age is linked to reduced child and maternal mortality, lower fertility and improved reproductive health. Comparable analyses of attainment exist only at the national level, potentially obscuring patterns in subnational inequality. Evidence suggests that wide disparities between urban and rural populations exist, raising questions about where the majority of progress towards the education targets of the Sustainable Development Goals is occurring in African countries. Here we explore within-country inequalities by predicting years of schooling across five by five kilometre grids, generating estimates of average educational attainment by age and sex at subnational levels. Despite marked progress in attainment from 2000 to 2015 across Africa, substantial differences persist between locations and sexes. These differences have widened in many countries, particularly across the Sahel. These high-resolution, comparable estimates improve the ability of decision-makers to plan the precisely targeted interventions that will be necessary to deliver progress during the era of the Sustainable Development Goals.
    11. ↑ Balaj M et al.: Parental education and inequalities in child mortality: a global systematic review and meta-analysis. Lancet 2021. (PMID 34119000) [PubMed] [DOI] [Full text] BACKGROUND: The educational attainment of parents, particularly mothers, has been associated with lower levels of child mortality, yet there is no consensus on the magnitude of this relationship globally. We aimed to estimate the total reductions in under-5 mortality that are associated with increased maternal and paternal education, during distinct age intervals. METHODS: This study is a comprehensive global systematic review and meta-analysis of all existing studies of the effects of parental education on neonatal, infant, and under-5 child mortality, combined with primary analyses of Demographic and Health Survey (DHS) data. The literature search of seven databases (CINAHL, Embase, MEDLINE, PsycINFO, PubMed, Scopus, and Web of Science) was done between Jan 23 and Feb 8, 2019, and updated on Jan 7, 2021, with no language or publication date restrictions. Teams of independent reviewers assessed each record for its inclusion of individual-level data on parental education and child mortality and excluded articles on the basis of study design and availability of relevant statistics. Full-text screening was done in 15 languages. Data extracted from these studies were combined with primary microdata from the DHS for meta-analyses relating maternal or paternal education with mortality at six age intervals: 0-27 days, 1-11 months, 1-4 years, 0-4 years, 0-11 months, and 1 month to 4 years. Novel mixed-effects meta-regression models were implemented to address heterogeneity in referent and exposure measures among the studies and to adjust for study-level covariates (wealth or income, partner's years of schooling, and sex of the child). This study was registered with PROSPERO (CRD42020141731). FINDINGS: The systematic review returned 5339 unique records, yielding 186 included studies after exclusions. DHS data were compiled from 114 unique surveys, capturing 3 112 474 livebirths. Data extracted from the systematic review were synthesized together with primary DHS data, for meta-analysis on a total of 300 studies from 92 countries. Both increased maternal and paternal education showed a dose-response relationship linked to reduced under-5 mortality, with maternal education emerging as a stronger predictor. We observed a reduction in under-5 mortality of 31·0% (95% CI 29·0-32·6) for children born to mothers with 12 years of education (ie, completed secondary education) and 17·3% (15·0-18·8) for children born to fathers with 12 years of education, compared with those born to a parent with no education. We also showed that a single additional year of schooling was, on average, associated with a reduction in under-5 mortality of 3·04% (2·82-3·23) for maternal education and 1·57% (1·35-1·72) for paternal education. The association between higher parental education and lower child mortality was significant for both parents at all ages studied and was largest after the first month of life. The meta-analysis framework incorporated uncertainty associated with each individual effect size into the model fitting process, in an effort to decrease the risk of bias introduced by study design and quality. INTERPRETATION: To our knowledge, this study is the first effort to systematically quantify the transgenerational importance of education for child survival at the global level. The results showed that lower maternal and paternal education are both risk factors for child mortality, even after controlling for other markers of family socioeconomic status. This study provides robust evidence for universal quality education as a mechanism to achieve the Sustainable Development Goal target 3.2 of reducing neonatal and child mortality. FUNDING: Research Council of Norway, Bill & Melinda Gates Foundation, and Rockefeller Foundation-Boston University Commission on Social Determinants, Data, and Decision Making (3-D Commission).
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