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Local distributions of wealth to describe health inequalities in India: a new approach for analyzing nationally representative household survey data, 1992-2008.

Bassani DG, Corsi DJ, Gaffey MF, Barros AJ - PLoS ONE (2014)

Bottom Line: Urban and rural decile cut-off values for India, for the six regions and for the 24 major states revealed large variability in wealth by geographical area and level, and rural wealth score gaps exceeded those observed in urban areas.The large variability in sub-national distributions of national wealth index scores indicates the importance of accounting for such variation when constructing wealth indices and deriving score distribution cut-off points.Such an approach allows for proper within-sample economic classification, resulting in scores that are valid indicators of wealth and correlate well with health outcomes, and enables wealth-related analyses at whichever geographical area and level may be most informative for policy-making processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada.

ABSTRACT

Background: Worse health outcomes including higher morbidity and mortality are most often observed among the poorest fractions of a population. In this paper we present and validate national, regional and state-level distributions of national wealth index scores, for urban and rural populations, derived from household asset data collected in six survey rounds in India between 1992-3 and 2007-8. These new indices and their sub-national distributions allow for comparative analyses of a standardized measure of wealth across time and at various levels of population aggregation in India.

Methods: Indices were derived through principal components analysis (PCA) performed using standardized variables from a correlation matrix to minimize differences in variance. Valid and simple indices were constructed with the minimum number of assets needed to produce scores with enough variability to allow definition of unique decile cut-off points in each urban and rural area of all states.

Results: For all indices, the first PCA components explained between 36% and 43% of the variance in household assets. Using sub-national distributions of national wealth index scores, mean height-for-age z-scores increased from the poorest to the richest wealth quintiles for all surveys, and stunting prevalence was higher among the poorest and lower among the wealthiest. Urban and rural decile cut-off values for India, for the six regions and for the 24 major states revealed large variability in wealth by geographical area and level, and rural wealth score gaps exceeded those observed in urban areas.

Conclusions: The large variability in sub-national distributions of national wealth index scores indicates the importance of accounting for such variation when constructing wealth indices and deriving score distribution cut-off points. Such an approach allows for proper within-sample economic classification, resulting in scores that are valid indicators of wealth and correlate well with health outcomes, and enables wealth-related analyses at whichever geographical area and level may be most informative for policy-making processes.

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Distribution of wealth scores from a subsample of the rural Himachal Pradesh population by reference wealth quintiles for the rural state (top) and for rural India (bottom) in 2005–06 (NFHS-3).
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pone-0110694-g003: Distribution of wealth scores from a subsample of the rural Himachal Pradesh population by reference wealth quintiles for the rural state (top) and for rural India (bottom) in 2005–06 (NFHS-3).

Mentions: For analyzing health inequalities, the importance of using reference distributions from the most appropriate geographical level is further illustrated in Figure 3. We compare the wealth score distributions of a sub-sample of eight rural villages in Himachal Pradesh with the full rural distribution for Himachal Pradesh (top panel) and with the rural distribution for all of India (bottom panel). If the sub-sampled villages had a similar wealth distribution to that of the state, all bars in the upper histogram (representing each quintile) would include approximately 20% of the sub-sampled village households. However, the sub-sample distribution is in fact largely skewed towards the lowest state-specific wealth quintile. Alternately, when compared to the rural wealth distribution of the whole country the sub-sample distribution is skewed to the higher national quintiles.


Local distributions of wealth to describe health inequalities in India: a new approach for analyzing nationally representative household survey data, 1992-2008.

Bassani DG, Corsi DJ, Gaffey MF, Barros AJ - PLoS ONE (2014)

Distribution of wealth scores from a subsample of the rural Himachal Pradesh population by reference wealth quintiles for the rural state (top) and for rural India (bottom) in 2005–06 (NFHS-3).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214688&req=5

pone-0110694-g003: Distribution of wealth scores from a subsample of the rural Himachal Pradesh population by reference wealth quintiles for the rural state (top) and for rural India (bottom) in 2005–06 (NFHS-3).
Mentions: For analyzing health inequalities, the importance of using reference distributions from the most appropriate geographical level is further illustrated in Figure 3. We compare the wealth score distributions of a sub-sample of eight rural villages in Himachal Pradesh with the full rural distribution for Himachal Pradesh (top panel) and with the rural distribution for all of India (bottom panel). If the sub-sampled villages had a similar wealth distribution to that of the state, all bars in the upper histogram (representing each quintile) would include approximately 20% of the sub-sampled village households. However, the sub-sample distribution is in fact largely skewed towards the lowest state-specific wealth quintile. Alternately, when compared to the rural wealth distribution of the whole country the sub-sample distribution is skewed to the higher national quintiles.

Bottom Line: Urban and rural decile cut-off values for India, for the six regions and for the 24 major states revealed large variability in wealth by geographical area and level, and rural wealth score gaps exceeded those observed in urban areas.The large variability in sub-national distributions of national wealth index scores indicates the importance of accounting for such variation when constructing wealth indices and deriving score distribution cut-off points.Such an approach allows for proper within-sample economic classification, resulting in scores that are valid indicators of wealth and correlate well with health outcomes, and enables wealth-related analyses at whichever geographical area and level may be most informative for policy-making processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada.

ABSTRACT

Background: Worse health outcomes including higher morbidity and mortality are most often observed among the poorest fractions of a population. In this paper we present and validate national, regional and state-level distributions of national wealth index scores, for urban and rural populations, derived from household asset data collected in six survey rounds in India between 1992-3 and 2007-8. These new indices and their sub-national distributions allow for comparative analyses of a standardized measure of wealth across time and at various levels of population aggregation in India.

Methods: Indices were derived through principal components analysis (PCA) performed using standardized variables from a correlation matrix to minimize differences in variance. Valid and simple indices were constructed with the minimum number of assets needed to produce scores with enough variability to allow definition of unique decile cut-off points in each urban and rural area of all states.

Results: For all indices, the first PCA components explained between 36% and 43% of the variance in household assets. Using sub-national distributions of national wealth index scores, mean height-for-age z-scores increased from the poorest to the richest wealth quintiles for all surveys, and stunting prevalence was higher among the poorest and lower among the wealthiest. Urban and rural decile cut-off values for India, for the six regions and for the 24 major states revealed large variability in wealth by geographical area and level, and rural wealth score gaps exceeded those observed in urban areas.

Conclusions: The large variability in sub-national distributions of national wealth index scores indicates the importance of accounting for such variation when constructing wealth indices and deriving score distribution cut-off points. Such an approach allows for proper within-sample economic classification, resulting in scores that are valid indicators of wealth and correlate well with health outcomes, and enables wealth-related analyses at whichever geographical area and level may be most informative for policy-making processes.

Show MeSH