James Hargreaves

Growing consensus indicates that progress in tuberculosis control in the low- and middle-income world will require not only investment in strengthening tuberculosis control programs, diagnostics, and treatment but also action on the social determinants of tuberculosis. However, practical ideas for action are scarcer than is notional support for this idea. We developed a framework based on the recent World Health Organization Commission on Social Determinants of Health and on current understanding of the social determinants of tuberculosis. Interventions from outside the health sector-specifically, in social protection and urban planning-have the potential to strengthen tuberculosis control.

OBJECTIVES: We sought to obtain evidence about the scope of women's empowerment and the mechanisms underlying the significant reduction in intimate partner violence documented by the Intervention With Microfinance for AIDS and Gender Equity (IMAGE) cluster-randomized trial in rural South Africa. METHODS: The IMAGE intervention combined a microfinance program with participatory training on understanding HIV infection, gender norms, domestic violence, and sexuality. Outcome measures included past year's experience of intimate partner violence and 9 indicators of women's empowerment. Qualitative data about changes occurring within intimate relationships, loan groups, and the community were also collected. RESULTS: After 2 years, the risk of past-year physical or sexual violence by an intimate partner was reduced by more than half (adjusted risk ratio=0.45; 95% confidence interval=0.23, 0.91). Improvements in all 9 indicators of empowerment were observed. Reductions in violence resulted from a range of responses enabling women to challenge the acceptability of violence, expect and receive better treatment from partners, leave abusive relationships, and raise public awareness about intimate partner violence. CONCLUSIONS: Our findings, both qualitative and quantitative, indicate that economic and social empowerment of women can contribute to reductions in intimate partner violence.

Much has been written about the measurement of socio-economic position (SEP) in high-income countries (HIC). Less has been written for an epidemiology, health systems and public health audience about the measurement of SEP in low- and middle-income countries (LMIC). The social stratification processes in many LMIC-and therefore the appropriate measurement tools-differ considerably from those in HIC. Many measures of SEP have been utilized in epidemiological studies; the aspects of SEP captured by these measures and the pathways through which they may affect health are likely to be slightly different but overlapping. No single measure of SEP will be ideal for all studies and contexts; the strengths and limitations of a given indicator are likely to vary according to the specific research question. Understanding the general properties of different indicators, however, is essential for all those involved in the design or interpretation of epidemiological studies. In this article, we describe the measures of SEP used in LMIC. We concentrate on measures of individual or household-level SEP rather than area-based or ecological measures such as gross domestic product. We describe each indicator in terms of its theoretical basis, interpretation, measurement, strengths and limitations. We also provide brief comparisons between LMIC and HIC for each measure.

BACKGROUND: Epidemiological studies often require measures of socio-economic position (SEP). The application of principal components analysis (PCA) to data on asset-ownership is one popular approach to household SEP measurement. Proponents suggest that the approach provides a rational method for weighting asset data in a single indicator, captures the most important aspect of SEP for health studies, and is based on data that are readily available and/or simple to collect. However, the use of PCA on asset data may not be the best approach to SEP measurement. There remains concern that this approach can obscure the meaning of the final index and is statistically inappropriate for use with discrete data. In addition, the choice of assets to include and the level of agreement between wealth indices and more conventional measures of SEP such as consumption expenditure remain unclear. We discuss these issues, illustrating our examples with data from the Malawi Integrated Household Survey 2004-5. METHODS: Wealth indices were constructed using the assets on which data are collected within Demographic and Health Surveys. Indices were constructed using five weighting methods: PCA, PCA using dichotomised versions of categorical variables, equal weights, weights equal to the inverse of the proportion of households owning the item, and Multiple Correspondence Analysis. Agreement between indices was assessed. Indices were compared with per capita consumption expenditure, and the difference in agreement assessed when different methods were used to adjust consumption expenditure for household size and composition. RESULTS: All indices demonstrated similarly modest agreement with consumption expenditure. The indices constructed using dichotomised data showed strong agreement with each other, as did the indices constructed using categorical data. Agreement was lower between indices using data coded in different ways. The level of agreement between wealth indices and consumption expenditure did not differ when different consumption equivalence scales were applied. CONCLUSION: This study questions the appropriateness of wealth indices as proxies for consumption expenditure. The choice of data included had a greater influence on the wealth index than the method used to weight the data. Despite the limitations of PCA, alternative methods also all had disadvantages.