Sharon Stein Merkin1, Hadar Arditi-Babchuk, Tamy Shohat. 1. Division of Geriatrics, Department of Medicine, UCLA Geffen School of Medicine, 10945 Le Conte Avenue, Suite 2339, Los Angeles, CA, 90095-1687, USA, smerkin@mednet.ucla.edu.
Abstract
OBJECTIVES: To explore neighborhood socioeconomic status (NSES) differentials in self-rated health (SRH) in Israel. METHODS: Study sample included 6296 Jewish participants in the Israeli National Health Interview Survey of 2007-2008. Neighborhoods were assigned socioeconomic scores by the Central Bureau of Statistics, incorporating 16 demographic and socioeconomic measures using a 20-point scale. Generalized estimating equation models with a multinomial distribution assessed the relative cumulative odds for decreasing SRH by quartiles of NSES, while accounting for neighborhood clustering. Base models were adjusted for age, religiosity, immigration from the former Soviet Union, education, income, and then additionally for employment, living in the periphery and co-morbidity. RESULTS: We found a strong association between poor SRH and living in disadvantaged neighborhoods, after adjusting for individual-level SES. The combination of living in deprived areas with below average income was associated with over twice the risk of poor SRH. CONCLUSIONS: The association between low NSES and worsening SRH, exacerbated by lower income, highlights the importance of considering socioeconomic environmental and individual conditions in targeting high-risk populations.
OBJECTIVES: To explore neighborhood socioeconomic status (NSES) differentials in self-rated health (SRH) in Israel. METHODS: Study sample included 6296 Jewish participants in the Israeli National Health Interview Survey of 2007-2008. Neighborhoods were assigned socioeconomic scores by the Central Bureau of Statistics, incorporating 16 demographic and socioeconomic measures using a 20-point scale. Generalized estimating equation models with a multinomial distribution assessed the relative cumulative odds for decreasing SRH by quartiles of NSES, while accounting for neighborhood clustering. Base models were adjusted for age, religiosity, immigration from the former Soviet Union, education, income, and then additionally for employment, living in the periphery and co-morbidity. RESULTS: We found a strong association between poor SRH and living in disadvantaged neighborhoods, after adjusting for individual-level SES. The combination of living in deprived areas with below average income was associated with over twice the risk of poor SRH. CONCLUSIONS: The association between low NSES and worsening SRH, exacerbated by lower income, highlights the importance of considering socioeconomic environmental and individual conditions in targeting high-risk populations.