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Multilevel survival analysis of health inequalities in life expectancy.

ABSTRACT: BACKGROUND: The health status of individuals is determined by multiple factors operating at both micro and macro levels and the interactive effects of them. Measures of health inequalities should reflect such determinants explicitly through sources of levels and combining mean differences at group levels and the variation of individuals, for the benefits of decision making and intervention planning. Measures derived recently from marginal models such as beta-binomial and frailty survival, address this issue to some extent, but are limited in handling data with complex structures. Beta-binomial models were also limited in relation to measuring inequalities of life expectancy (LE) directly. METHODS: We propose a multilevel survival model analysis that estimates life expectancy based on survival time with censored data. The model explicitly disentangles total health inequalities in terms of variance components of life expectancy compared to the source of variation at the level of individuals in households and parishes and so on, and estimates group differences of inequalities at the same time. Adjusted distributions of life expectancy by gender and by household socioeconomic level are calculated. Relative and absolute health inequality indices are derived based on model estimates. The model based analysis is illustrated on a large Swedish cohort of 22,680 men and 26,474 women aged 6569 in 1970 and followed up for 30 years. Model based inequality measures are compared to the conventional calculations. RESULTS: Much variation of life expectancy is observed at individual and household levels. Contextual effects at Parish and Municipality level are negligible. Women have longer life expectancy than men and lower inequality. There is marked inequality by the level of household socioeconomic status measured by the median life expectancy in each socio-economic group and the variation in life expectancy within each group. CONCLUSION: Multilevel survival models are flexible and efficient tools in studying health inequalities of life expectancy or survival time data with a geographic structure of more than 2 levels. They are complementary to conventional methods and override some limitations of marginal models. Future research on determinants of health inequalities in the LE of the specific cohort on the household and individual factors could reveal some important causes over the marked household level inequalities.

SUBMITTER: Yang M

PROVIDER: S-EPMC2740845 | biostudies-other | 2009

REPOSITORIES: biostudies-other

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Multilevel survival analysis of health inequalities in life expectancy.

Yang Min M Eldridge Sandra S Merlo Juan J

International journal for equity in health 20090823

<h4>Background</h4>The health status of individuals is determined by multiple factors operating at both micro and macro levels and the interactive effects of them. Measures of health inequalities should reflect such determinants explicitly through sources of levels and combining mean differences at group levels and the variation of individuals, for the benefits of decision making and intervention planning. Measures derived recently from marginal models such as beta-binomial and frailty survival, ...[more]

PMID: 19698159

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Project description:BackgroundLatin America is one of the most unequal regions in the world, but evidence is lacking on the magnitude of health inequalities in urban areas of the region. Our objective was to examine inequalities in life expectancy in six large Latin American cities and its association with a measure of area-level socioeconomic status.MethodsIn this ecological analysis, we used data from the Salud Urbana en America Latina (SALURBAL) study on six large cities in Latin America (Buenos Aires, Argentina; Belo Horizonte, Brazil; Santiago, Chile; San José, Costa Rica; Mexico City, Mexico; and Panama City, Panama), comprising 266 subcity units, for the period 2011-15 (expect for Panama city, which was for 2012-16). We calculated average life expectancy at birth by sex and subcity unit with life tables using age-specific mortality rates estimated from a Bayesian model, and calculated the difference between the ninth and first decile of life expectancy at birth (P90-P10 gap) across subcity units in cities. We also analysed the association between life expectancy at birth and socioeconomic status at the subcity-unit level, using education as a proxy for socioeconomic status, and whether any geographical patterns existed in cities between subcity units.FindingsWe found large spatial differences in average life expectancy at birth in Latin American cities, with the largest P90-P10 gaps observed in Panama City (15·0 years for men and 14·7 years for women), Santiago (8·9 years for men and 17·7 years for women), and Mexico City (10·9 years for men and 9·4 years for women), and the narrowest in Buenos Aires (4·4 years for men and 5·8 years for women), Belo Horizonte (4·0 years for men and 6·5 years for women), and San José (3·9 years for men and 3·0 years for women). Higher area-level socioeconomic status was associated with higher life expectancy, especially in Santiago (change in life expectancy per P90-P10 change unit-level of educational attainment 8·0 years [95% CI 5·8-10·3] for men and 11·8 years [7·1-16·4] for women) and Panama City (7·3 years [2·6-12·1] for men and 9·0 years [2·4-15·5] for women). We saw an increase in life expectancy at birth from east to west in Panama City and from north to south in core Mexico City, and a core-periphery divide in Buenos Aires and Santiago. Whereas for San José the central part of the city had the lowest life expectancy and in Belo Horizonte the central part of the city had the highest life expectancy.InterpretationLarge spatial differences in life expectancy in Latin American cities and their association with social factors highlight the importance of area-based approaches and policies that address social inequalities in improving health in cities of the region.FundingWellcome Trust.

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Multilevel survival analysis of health inequalities in life expectancy.

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Multilevel survival analysis of health inequalities in life expectancy.

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