, Volume 32, Issue 2, pp 183-198

Estimates of Survival of Diabetics from Repeated, Independent Sample Surveys

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Abstract

Little is known about death rates among diabetic populations. The few prior estimates have used two data systems, usually a registry or a survey to identify diabetics and death certificates to identify deaths. In this research, the diabetic population aged 18–94 in 1996–1998 and those surviving in 2001–2003 were estimated from repeated cross-sectional surveys, the Behavioral Risk Factor Surveillance System of the Centers for Disease Control and Prevention. Forward survival ratios were computed using a method developed for successive censuses and these were used to compute death rates. Nonlinear regression models for age-sex specific survival ratios were used to estimate parametric rates and thereby increase the accuracy of estimates. About 81.4 % (SE = 1.3 %) of diabetics survived 5 years, for an annual death rate of 41.1 per thousand (SE = 3.2). Among men survival was 84.7 % (SE = 2.1 %) with an annual death rate of 33.8 (SE = 4.9) per thousand; among women survival was 78.5 % (SE = 2.2 %) with an annual death rate of 48.1 (SE = 4.1) per thousand. Model estimates of mortality rates showed an odds ratio of 3.17 (95 % CI 2.64, 3.82) for each 10 year age interval and of 1.35 (95 % CI 1.02, 1.79) for women compared with men. Pooled annual samples, longer time intervals for survival, and parametric estimates of rates all help overcome the small numbers and large sampling variation of survey estimates of survival and mortality. Useful estimates of survival rates can be made from a single data system, a sample survey of the general population. This can be done for any condition where a respondent’s status at the earlier survey time is obtained at the later survey time. It could also be used to make estimates from periodic surveys for nations with limited information systems.