Person:

Vollmer, Sebastian

We examine the joint distribution of levels of income per capita, life expectancy, and years of schooling across countries in 1960 and in 2000. In 1960 countries were clustered in two groups; a rich, highly educated, high longevity “developed” group and a poor, less educated, high mortality, “underdeveloped” group. By 2000 however we see the emergence of three groups; one underdeveloped group remaining near 1960 levels, a developed group with higher levels of education, income, and health than in 1960, and an intermediate group lying between these two. This finding is consistent with both the ideas of a new “middle income trap” that countries face even if they escape the “low income trap”, as well as the notion that countries which escaped the poverty trap form a temporary “transition regime” along their path to the “developed” group.

We use the method of deficit accumulation to describe prevalent and incident levels of frailty in community-dwelling older persons and compare prevalence rates in higher income countries in Europe, to prevalence rates in six lower income countries. Two multi-country data collection efforts, SHARE and SAGE, provide nationally representative samples of adults aged 50 years and older. Forty items were used to construct the frailty index in each data set. Our study shows that the level of frailty was distributed along the socioeconomic gradient in both higher and lower income countries such that those individuals with less education and income were more likely to be frail. Frailty increased with age and women were more likely to be frail in most countries. Across samples we find that the level of frailty was higher in the higher income countries than in the lower income countries.

Anna Bershteyn and colleagues provide a useful comparison of estimates of the association between economic growth and early childhood undernutrition in our study1 with those of previous studies. Their comparative exercise supports our key conclusion that the contribution of economic growth to the reduction in early childhood undernutrition in low-income and middle-income countries is very small. We had already reported both absolute and relative changes in our study (tables 2 and 3) underlining that absolute changes are much smaller than relative ones.

BACKGROUND: Economic growth is widely regarded as a necessary, and often sufficient, condition for the improvement of population health. We aimed to assess whether macroeconomic growth was associated with reductions in early childhood undernutrition in low-income and middle-income countries. METHODS: We analysed data from 121 Demographic and Health Surveys from 36 countries done between Jan 1, 1990, and Dec 31, 2011. The sample consisted of nationally representative cross-sectional surveys of children aged 0-35 months, and the outcome variables were stunting, underweight, and wasting. The main independent variable was per-head gross domestic product (GDP) in constant prices and adjusted for purchasing power parity. We used logistic regression models to estimate the association between changes in per-head GDP and changes in child undernutrition outcomes. Models were adjusted for country fixed effects, survey-year fixed effects, clustering, and demographic and socioeconomic covariates for the child, mother, and household. FINDINGS: Sample sizes were 462,854 for stunting, 485,152 for underweight, and 459,538 for wasting. Overall, 35·6% (95% CI 35·4-35·9) of young children were stunted (ranging from 8·7% [7·6-9·7] in Jordan to 51·1% [49·1-53·1] in Niger), 22·7% (22·5-22·9) were underweight (ranging from 1·8% [1·3-2·3] in Jordan to 41·7% [41·1-42·3] in India), and 12·8% (12·6-12·9) were wasted (ranging from 1·2% [0·6-1·8] in Peru to 28·8% [27·5-30·0] in Burkina Faso). At the country level, no association was seen between average changes in the prevalence of child undernutrition outcomes and average growth of per-head GDP. In models adjusted only for country and survey-year fixed effects, a 5% increase in per-head GDP was associated with an odds ratio (OR) of 0·993 (95% CI 0·989-0·995) for stunting, 0·986 (0·982-0·990) for underweight, and 0·984 (0·981-0·986) for wasting. ORs after adjustment for the full set of covariates were 0·996 (0·993-1·000) for stunting, 0·989 (0·985-0·992) for underweight, and 0·983 (0·979-0·986) for wasting. These findings were consistent across various subsamples and for alternative variable specifications. Notably, no association was seen between per-head GDP and undernutrition in young children from the poorest household wealth quintile. ORs for the poorest wealth quintile were 0·997 (0·990-1·004) for stunting, 0·999 (0·991-1·008) for underweight, and 0·991 (0·978-1·004) for wasting. INTERPRETATION: A quantitatively very small to null association was seen between increases in per-head GDP and reductions in early childhood undernutrition, emphasising the need for direct health investments to improve the nutritional status of children in low-income and middle-income countries.

Composite indices have been prominently used in poverty research. However, validity of these indices remains subject to debate. This paper examines the validity of a common type of composite poverty indices using data from a cross-sectional survey of 2477 households in urban and rural KwaZulu-Natal, South Africa. Multiple-group comparisons in structural equation modelling were employed for testing differences in the measurement model across urban and rural groups. The analysis revealed substantial variations between urban and rural respondents both in the conceptualisation of poverty as well as in the weights and importance assigned to individual poverty indicators. The validity of a ‘one size fits all’ measurement model can therefore not be confirmed. In consequence, it becomes virtually impossible to determine a household’s poverty level relative to the full sample. Findings from our analysis have important practical implications in nuancing how we can sensitively use composite poverty indices to identify poor people.

Background Cardiovascular disease (CVD) is the leading cause of mortality in India. Yet, evidence on the CVD risk of India’s population is limited. To inform health system planning and effective targeting of interventions, this study aimed to determine how CVD risk—and the factors that determine risk—varies among states in India, by rural–urban location, and by individual-level sociodemographic characteristics.

Methods and findings We used 2 large household surveys carried out between 2012 and 2014, which included a sample of 797,540 adults aged 30 to 74 years across India. The main outcome variable was the predicted 10-year risk of a CVD event as calculated with the Framingham risk score. The Harvard–NHANES, Globorisk, and WHO–ISH scores were used in secondary analyses. CVD risk and the prevalence of CVD risk factors were examined by state, rural–urban residence, age, sex, household wealth, and education. Mean CVD risk varied from 13.2% (95% CI: 12.7%–13.6%) in Jharkhand to 19.5% (95% CI: 19.1%–19.9%) in Kerala. CVD risk tended to be highest in North, Northeast, and South India. District-level wealth quintile (based on median household wealth in a district) and urbanization were both positively associated with CVD risk. Similarly, household wealth quintile and living in an urban area were positively associated with CVD risk among both sexes, but the associations were stronger among women than men. Smoking was more prevalent in poorer household wealth quintiles and in rural areas, whereas body mass index, high blood glucose, and systolic blood pressure were positively associated with household wealth and urban location. Men had a substantially higher (age-standardized) smoking prevalence (26.2% [95% CI: 25.7%–26.7%] versus 1.8% [95% CI: 1.7%–1.9%]) and mean systolic blood pressure (126.9 mm Hg [95% CI: 126.7–127.1] versus 124.3 mm Hg [95% CI: 124.1–124.5]) than women. Important limitations of this analysis are the high proportion of missing values (27.1%) in the main outcome variable, assessment of diabetes through a 1-time capillary blood glucose measurement, and the inability to exclude participants with a current or previous CVD event.

Conclusions This study identified substantial variation in CVD risk among states and sociodemographic groups in India—findings that can facilitate effective targeting of CVD programs to those most at risk and most in need. While the CVD risk scores used have not been validated in South Asian populations, the patterns of variation in CVD risk among the Indian population were similar across all 4 risk scoring systems.