Small area variation in severe, moderate, and mild anemia among women and children: A multilevel analysis of 707 districts in India

To provide current global and regional estimates of anaemia prevalence and number of persons affected in the total population and by population subgroup. We used anaemia prevalence data from the WHO Vitamin and Mineral Nutrition Information System for 1993-2005 to generate anaemia prevalence estimates for countries with data representative at the national level or at the first administrative level that is below the national level. For countries without eligible data, we employed regression-based estimates, which used the UN Human Development Index (HDI) and other health indicators. We combined country estimates, weighted by their population, to estimate anaemia prevalence at the global level, by UN Regions and by category of human development. Survey data covered 48.8 % of the global population, 76.1 % of preschool-aged children, 69.0 % of pregnant women and 73.5 % of non-pregnant women. The estimated global anaemia prevalence is 24.8 % (95 % CI 22.9, 26.7 %), affecting 1.62 billion people (95 % CI 1.50, 1.74 billion). Estimated anaemia prevalence is 47.4 % (95 % CI 45.7, 49.1 %) in preschool-aged children, 41.8 % (95 % CI 39.9, 43.8 %) in pregnant women and 30.2 % (95 % CI 28.7, 31.6 %) in non-pregnant women. In numbers, 293 million (95 % CI 282, 303 million) preschool-aged children, 56 million (95 % CI 54, 59 million) pregnant women and 468 million (95 % CI 446, 491 million) non-pregnant women are affected. Anaemia affects one-quarter of the world's population and is concentrated in preschool-aged children and women, making it a global public health problem. Data on relative contributions of causal factors are lacking, however, which makes it difficult to effectively address the problem.

Background and Aim Many multilevel logistic regression analyses of “neighbourhood and health” focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between “specific” (measures of association) and “general” (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health. Methods We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics. Results For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%. Conclusion Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible influence on individual use of psychotropic drugs, but appears to strongly condition individual choice of a private GP. However, the latter was only modestly explained by the socioeconomic circumstances of the neighbourhoods. Our analyses are based on real data and provide useful information for understanding neighbourhood level influences in general and on individual use of psychotropic drugs and choice of GP in particular. However, our primary aim is to illustrate how to perform and interpret a multilevel analysis of individual heterogeneity in social epidemiology and public health. Our study shows that neighbourhood “effects” are not properly quantified by reporting differences between neighbourhood averages but rather by measuring the share of the individual heterogeneity that exists at the neighbourhood level.