Resilience, Health Disparities, and Ethnic Identity: A Source Guide

Frame the question

This guide examines how societal resilience intersects with global health challenges and ethnic identity. The anchor source [1] provides foundational definitions of resilience across disciplines, while [2] and [3] offer quantitative data on mortality patterns and cancer burdens. Source [4] critiques traditional ethnic categorization frameworks. Together, these sources reveal how resilience manifests differently across populations and how health outcomes are shaped by both biological and sociocultural factors. The analysis highlights the need for nuanced approaches to understanding resilience in the context of global health inequities and ethnic identity dynamics.

What the evidence shows

The resilience framework in [1] emphasizes adaptive functioning after adversity, noting that definitions vary across individual, familial, and societal contexts. This aligns with [2]'s findings on global mortality trends, where sub-Saharan Africa experienced the highest COVID-19 death rates (271 per 100,000) despite overall life expectancy improvements. Cancer burden data from [3] further illustrates health disparities: high SDI countries had 1 in 2 lifetime cancer risks, while low SDI regions had 1 in 7, reflecting access to screening and treatment. Source [4] challenges reductive ethnic categorizations, arguing that 'ethnicity without groups' requires analyzing practical categories and discursive frames rather than bounded identities. These sources collectively demonstrate how resilience, health outcomes, and identity are interconnected through complex social determinants.

Follow the source trail

The resilience framework in [1] provides conceptual foundations for understanding adaptive functioning across contexts. [2]'s global mortality data reveals how health disparities persist despite overall progress, with pandemic-related mortality disrupting life expectancy gains. [3]'s cancer burden analysis complements this by showing how socioeconomic factors shape disease risk and outcomes. Source [4] offers a critical lens for interpreting these patterns, arguing that traditional ethnic categorizations obscure the fluidity of identity. Together, these sources form a tripartite analysis: [1] defines resilience, [2] and [3] quantify health inequities, while [4] critiques the frameworks used to interpret these patterns. This interplay reveals how resilience is both a personal capacity and a socially constructed phenomenon shaped by systemic inequities.

Use these sources well

Students can structure an essay by first defining resilience using [1]'s multidisciplinary framework, then contextualizing it within global health data from [2] and [3]. For example, [1]'s discussion of cultural determinants of resilience could be paired with [2]'s findings on regional mortality disparities. When addressing ethnic identity, [4]'s critique of group-based analyses provides a counterpoint to traditional approaches. Avoid overstating causal relationships: while [2] shows correlation between SDI and health outcomes, [1] cautions against assuming direct causation. Use [4]'s conceptual strategies to analyze how health disparities might be framed through practical categories rather than ethnic groups. For follow-up research, explore specific regions mentioned in [2] or compare cancer incidence data across SDI quintiles in [3].

What to search next

How might the concept of resilience in [1] be applied to understand pandemic recovery in sub-Saharan Africa, where [2] notes the highest COVID-19 mortality rates? Could [4]'s critique of ethnic categorization offer new frameworks for analyzing health disparities in [3]'s cancer burden data? What role do cultural narratives play in shaping resilience, as suggested by [1], when intersecting with global health inequities documented in [2] and [3]? How might the 'practical categories' approach in [4] reshape our understanding of health disparities as both biological and sociocultural phenomena?

Verbatim source abstracts

[1] Resilience definitions, theory, and challenges: interdisciplinary perspectives — European Journal of Psychotraumatology, 2014-10-01, doi:10.3402/ejpt.v5.25338

In this paper, inspired by the plenary panel at the 2013 meeting of the International Society for Traumatic Stress Studies, Dr. Steven Southwick (chair) and multidisciplinary panelists Drs. George Bonanno, Ann Masten, Catherine Panter-Brick, and Rachel Yehuda tackle some of the most pressing current questions in the field of resilience research including: (1) how do we define resilience, (2) what are the most important determinants of resilience, (3) how are new technologies informing the science of resilience, and (4) what are the most effective ways to enhance resilience? These multidisciplinary experts provide insight into these difficult questions, and although each of the panelists had a slightly different definition of resilience, most of the proposed definitions included a concept of healthy, adaptive, or integrated positive functioning over the passage of time in the aftermath of adversity. The panelists agreed that resilience is a complex construct and it may be defined differently in the context of individuals, families, organizations, societies, and cultures. With regard to the determinants of resilience, there was a consensus that the empirical study of this construct needs to be approached from a multiple level of analysis perspective that includes genetic, epigenetic, developmental, demographic, cultural, economic, and social variables. The empirical study of determinates of resilience will inform efforts made at fostering resilience, with the recognition that resilience may be enhanced on numerous levels (e.g., individual, family, community, culture). [1]

[2] Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021 — The Lancet, 2024-04-03, doi:10.1016/s0140-6736(24)00367-2

BACKGROUND: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING: Bill & Melinda Gates Foundation. [2]

[3] Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017 — JAMA Oncology, 2019-09-27, doi:10.1001/jamaoncol.2019.2996

Importance Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs). Conclusions and Relevance The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care. [3]

[4] Ethnicity without groups — European Journal of Sociology, 2002-08-01, doi:10.1017/s0003975602001066

This paper offers a critical analysis of ‘groupism’ and suggests alternative ways of conceptualizing ethnicity without invoking the imagery of bounded groups. Alternative conceptual strategies focus on practical categories, cultural idioms, cognitive schemas, discursive frames, organizational routines, institutional forms, political projects, and contingent events. The conceptual critique has implications for the ways in which researchers, journalists, policymakers and NGOs address ‘ethnic conflict’ and ‘ethnic violence’. The paper concludes with an analysis of an empirical case from Eastern Europe. [4]