PubMed · 14235700
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L P LEFKOVITCH. 1964-11-21. ESTIMATING THE MALTHUSIAN PARAMETER FROM CENSUS DATA.. https://doi.org/10.1038/204810a0
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As the latest statistics surfaced showing the uninsured population has hit 45 million, politicians added spin and healthcare providers were still hoping for some solutions. Census official Daniel Weinberg, left, said the data don't reveal what caused a drop in employer-sponsored coverage.
OBJECTIVE: To determine the shape of the income-mortality association, before and after adjusting for confounding by other socioeconomic variables. METHODS: Poisson regression analyses were conducted on 11.7 million years of follow-up of 25-59 year old New Zealand census respondents spanning four separate cohort studies (1981-1984, 1986-1989, 1991-1994, and 1996-1999). RESULTS: Mortality among low-income people was approximately two times that among high-income people. Adjustment for potential socioeconomic confounders (marital status, education, car access, and neighbourhood socioeconomic deprivation) halved the strength of the income-mortality association, but did not appreciably change the shape of the association. Further adjustment for labour force status largely removed the income-mortality association. The association of non-transformed income with mortality was non-linear, with a flattening out of the slope at higher incomes. Both the logarithm and rank of income appeared to have a better linear fit with the mortality rate, although the association of mortality with the logarithm of income flattened out notably at low incomes. CONCLUSIONS: Much, but not all, of the crude association of income with mortality could be due to confounding. Adjusting income-mortality associations for labour force status (also a proxy for health status) is problematic: on the one hand, it over-adjusts the association as poor health will be on the pathway from income to mortality; on the other hand, it appropriately adjusts for both confounding by labour force status and reverse causation whereby income changes as a result of poor health. Both logarithmic and rank transformations of income have a reasonable linear fit with income.
BACKGROUND: Shigellosis incidence rates in Israel have declined continuously over the past 50 years, but they remain 20 times greater than those in the United States. Socioeconomic factors may influence shigellosis morbidity, but this may be difficult to demonstrate in the absence of data for individual patients and when using composite rates for large geographic areas. Use of census tract data for small, relatively homogeneous geographic areas may lessen the effects of the "ecological fallacy." The present study analyzes the effect of socioeconomic status (SES) on shigellosis morbidity in the Haifa metropolitan region. METHODS: The study population consisted of the 7 cities in the Haifa subdistrict that constitute the greater metropolitan region. Cases of shigellosis reported during the years 2000 and 2001 were mapped, and age-standardized rates were calculated for the census tract areas. The incidence rates were then compared with the SES category of the census tract using the Kruskal-Wallis test. RESULTS: No association was found between incidence rates of shigellosis and SES category of the census tract areas in the Haifa metropolitan area for the years 2000 and 2001 (Kruskal-Wallis chi(2)=0.440; P=.803). CONCLUSION: We found no association between shigellosis morbidity and socioeconomic status. This finding is probably real and not the result of reporting bias. Analysis of morbidity using small geographical units such as census tracts is more accurate than analysis using large geographical areas such as cities.