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For decades, researchers have noted systematic shifts in cause‐of‐death patterns as mortality levels change. The notion of the "epidemiologic transition" has influenced thinking about the evolution of health in different societies and the response of the health system to these changes. This article re‐examines the epidemiologic transition in terms of empirical regularities in the cause composition of mortality by age and sex since 1950, and considers whether the theory of epidemiologic transition presents a durable framework for understanding more recent patterns. Age‐sex‐specific mortality rates from three broad cause groups are analyzed: Group 1 (communicable diseases, maternal and perinatal causes, and nutritional deficiencies); Group 2 (noncommunicable diseases); and Group 3 (injuries), using the most extensive international database on mortality by cause, including 1,576 country‐years of observation, and new statistical models for compositional data. The analyses relate changes in cause‐of‐death patterns to changing levels of all‐cause mortality and income per capita. The results confirm that declines in overall mortality are accompanied by systematic changes in the composition of causes in many age groups. These changes are most pronounced among children, for whom Group 1 causes decline as overall mortality falls, and in younger adults, where strikingly different patterns are found for men (shift from Group 3 to Group 2) compared to women (shift toward Group 2 then Group 3). The underlying patterns that emerge from this analysis offer insights into the epidemiologic transition from high‐mortality to low‐mortality settings.

Background Depression is currently the second largest contributor to non-fatal disease burden globally. For that reason, economic evaluations are increasingly being conducted using data from depression prevalence estimates to analyze return on investments for services that target mental health. Psychiatric epidemiology studies have reported large cross-national differences in the prevalence of depression. These differences may impact the cost-effectiveness assessments of mental health interventions, thereby affecting decisions regarding government and multi-lateral investment in mental health services. Some portion of the differences in prevalence estimates across countries may be due to true discrepancies in depression prevalence, resulting from differential levels of risk in environmental and demographic factors. However, some portion of those differences may reflect non-invariance in the way standard tools measure depression across countries. This paper attempts to discern the extent to which measurement differences are responsible for reported differences in the prevalence of depression across countries. Methods and findings This analysis uses data from the World Mental Health Surveys, a coordinated series of psychiatric epidemiology studies in 27 countries using multistage household probability samples to assess prevalence and correlates of mental disorders. Data in the current study include responses to the depression module of the World Mental Health Composite International Diagnostic Interview (CIDI) in four countries: Two high-income, western countries—the United States (n = 20, 015) and New Zealand (n = 12,992)—an upper-middle income sub-Saharan African country, South Africa (n = 4,351), and a lower-middle income sub-Saharan African country, Nigeria (n = 6,752). Latent class analysis, a type of finite mixture modeling, was used to categorize respondents into underlying categories based on the variation in their responses to questions in each of three sequential parts of the CIDI depression module: 1) The initial screening items, 2) Additional duration and severity exclusion criteria, and 3) The core symptom questions. After each of these parts, exclusion criteria expel respondents from the remainder of the diagnostic interview, rendering a diagnosis of "not depressed". Latent class models were fit to each of the three parts in each of the four countries, and model fit was assessed using overall chi-square values and Pearson standardized residuals. Latent transition analysis was then applied in order to model participants' progression through the CIDI depression module. Proportion of individuals falling into each latent class and probabilities of transitioning into subsequent classes were used to estimate the percentage in each country that ultimately fell into the more symptomatic class, i.e. classified as "depressed". This latent variable design allows for a non-zero probability that individuals were incorrectly excluded from or retained in the diagnostic interview at any of the three exclusion points and therefore incorrectly diagnosed. Prevalence estimates based on the latent transition model reversed the order of depression prevalence across countries. Based on the latent transition model in this analysis, Nigeria has the highest prevalence (21.6%), followed by New Zealand (17.4%), then South Africa (15.0%), and finally the US (12.5%). That is compared to the estimates in the World Mental Health Surveys that do not allow for measurement differences, in which Nigeria had by far the lowest prevalence (3.1%), followed by South Africa (9.8%), then the United States (13.5%) and finally New Zealand (17.8%). Individuals endorsing the screening questions in Nigeria and South Africa were more likely to endorse more severe depression symptomology later in the module (i.e. they had higher transition probabilities), suggesting that individuals in the two Western countries may be more likely to endorse screening questions even when they don't have as severe symptoms. These differences narrow the range of depression prevalence between countries 14 percentage points in the original estimates to 6 percentage points in the estimate taking account of measurement differences. Conclusions These data suggest fewer differences in cross-national prevalence of depression than previous estimates. Given that prevalence data are used to support key decisions regarding resource-allocation for mental health services, more critical attention should be paid to differences in the functioning of measurement across contexts and the impact these differences have on prevalence estimates. Future research should include qualitative methods as well as external measures of disease severity, such as impairment, to assess how the latent classes predict these external variables, to better understand the way that standard tools estimate depression prevalence across contexts. Adjustments could then be made to prevalence estimates used in cost-effectiveness analyses.

Tuberculosis transmission and progression are largely driven by social factors such as poor living conditions and poor nutrition. Increased standards of living and social approaches helped to decrease the burden of tuberculosis before the introduction of chemotherapy in the 1940s. Since then, management of tuberculosis has been largely biomedical. More funding for tuberculosis since 2000, coinciding with the Millennium Development Goals, has yielded progress in tuberculosis mortality but smaller reductions in incidence, which continues to pose a risk to sustainable development, especially in poor and susceptible populations. These at-risk populations need accelerated progress to end tuberculosis as resolved by the World Health Assembly in 2015. Effectively addressing the worldwide tuberculosis burden will need not only enhancement of biomedical approaches but also rebuilding of the social approaches of the past. To combine a biosocial approach, underpinned by social, economic, and environmental actions, with new treatments, new diagnostics, and universal health coverage, will need multisectoral coordination and action involving the health and other governmental sectors, as well as participation of the civil society, and especially the poor and susceptible populations. A biosocial approach to stopping tuberculosis will not only target morbidity and mortality from disease but would also contribute substantially to poverty alleviation and sustainable development that promises to meet the needs of the present, especially the poor, and provide them and subsequent generations an opportunity for a better future.

We describe a risk‐based analytical framework for estimating traffic fatalities that combines the probability of a crash and the probability of fatality in the event of a crash. As an illustrative application, we use the methodology to explore the role of vehicle mix and vehicle prevalence on long‐run fatality trends for a range of transportation growth scenarios that may be relevant to developing societies. We assume crash rates between different road users are proportional to their roadway use and estimate case fatality ratios (CFRs) for the different vehicle‐vehicle and vehicle‐pedestrian combinations. We find that in the absence of road safety interventions, the historical trend of initially rising and then falling fatalities observed in industrialized nations occurred only if motorization was through car ownership. In all other cases studied (scenarios dominated by scooter use, bus use, and mixed use), traffic fatalities rose monotonically. Fatalities per vehicle had a falling trend similar to that observed in historical data from industrialized nations. Regional adaptations of the model validated with local data can be used to evaluate the impacts of transportation planning and safety interventions, such as helmets, seat belts, and enforcement of traffic laws, on traffic fatalities.

We address two long-standing survey research problems: measuring complicated concepts, such as political freedom and efficacy, that researchers define best with reference to examples; and what to do when respondents interpret identical questions in different ways. Scholars have long addressed these problems with approaches to reduce incomparability, such as writing more concrete questions—with uneven success. Our alternative is to measure directly response category incomparability and to correct for it. We measure incomparability via respondents' assessments, on the same scale as the self-assessments to be corrected, of hypothetical individuals described in short vignettes. Because the actual (but not necessarily reported) levels of the vignettes are invariant over respondents, variability in vignette answers reveals incomparability. Our corrections require either simple recodes or a statistical model designed to save survey administration costs. With analysis, simulations, and cross-national surveys, we show how response incomparability can drastically mislead survey researchers and how our approach can alleviate this problem.

We address two long-standing survey research problems: measuring complicated concepts, such as political freedom & efficacy, that researchers define best with reference to examples; & what to do when respondents interpret identical questions in different ways. Scholars have long addressed these problems with approaches to reduce incomparability, such as writing more concrete questions -- with uneven success. Our alternative is to measure directly response category incomparability & to correct for it. We measure incomparability via respondents' assessments, on the same scale as the self-assessments to be corrected, of hypothetical individuals described in short vignettes. Because the actual (but not necessarily reported) levels of the vignettes are invariant over respondents, variability in vignette answers reveals incomparability. Our corrections require either simple recodes or a statistical model designed to save survey administration costs. With analysis, simulations, & cross-national surveys, we show how response incomparability can drastically mislead survey researchers & how our approach can alleviate this problem. 3 Tables, 6 Figures, 2 Appendixes, 42 References. Adapted from the source document.

We address two long-standing survey research problems: measuring complicated concepts, such as political freedom and efficacy, that researchers define best with reference to examples; and what to do when respondents interpret identical questions in different ways. Scholars have long addressed these problems with approaches to reduce incomparability, such as writing more concrete questions—with uneven success. Our alternative is to measure directly response category incomparability and to correct for it. We measure incomparability via respondents' assessments, on the same scale as the self-assessments to be corrected, of hypothetical individuals described in short vignettes. Because the actual (but not necessarily reported) levels of the vignettes are invariant over respondents, variability in vignette answers reveals incomparability. Our corrections require either simple recodes or a statistical model designed to save survey administration costs. With analysis, simulations, and cross-national surveys, we show how response incomparability can drastically mislead survey researchers and how our approach can alleviate this problem.