Two modeling approaches are commonly used to estimate the associations between neighborhood characteristics and individual-level health outcomes in multilevel studies (subjects within neighborhoods). Random effects models (or mixed models) use maximum likelihood estimation. Population average models typically use a generalized estimating equation (GEE) approach. These methods are used in place of basic regression approaches because the health of residents in the same neighborhood may be correlated, thus violating independence assumptions made by traditional regression procedures. This violation is particularly relevant to estimates of the variability of estimates. Though the literature appears to favor the mixed-model approach, little theoretical guidance has been offered to justify this choice. In this paper, we review the assumptions behind the estimates and inference provided by these 2 approaches. We propose a perspective that treats regression models for what they are in most circumstances: reasonable approximations of some true underlying relationship. We argue in general that mixed models involve unverifiable assumptions on the data-generating distribution, which lead to potentially misleading estimates and biased inference. We conclude that the estimation-equation approach of population average models provides a more useful approximation of the truth.
BackgroundIn low- and middle-income countries, scaling essential health interventions to achieve health development targets is constrained by the lack of skilled health professionals to deliver services.MethodsWe take a labor market approach to project future health workforce demand based on an economic model based on projected economic growth, demographics, and health coverage, and using health workforce data (1990–2013) for 165 countries from the WHO Global Health Observatory. The demand projections are compared with the projected growth in health worker supply and the health worker “needs” as estimated by WHO to achieve essential health coverage.ResultsThe model predicts that, by 2030, global demand for health workers will rise to 80 million workers, double the current (2013) stock of health workers, while the supply of health workers is expected to reach 65 million over the same period, resulting in a worldwide net shortage of 15 million health workers. Growth in the demand for health workers will be highest among upper middle-income countries, driven by economic and population growth and aging. This results in the largest predicted shortages which may fuel global competition for skilled health workers. Middle-income countries will face workforce shortages because their demand will exceed supply. By contrast, low-income countries will face low growth in both demand and supply, which are estimated to be far below what will be needed to achieve adequate coverage of essential health services.ConclusionsIn many low-income countries, demand may stay below projected supply, leading to the paradoxical phenomenon of unemployed (“surplus”) health workers in those countries facing acute “needs-based” shortages. Opportunities exist to bend the trajectory of the number and types of health workers that are available to meet public health goals and the growing demand for health workers.Electronic supplementary materialThe online version of this article (doi:10.1186/s12960-017-0187-2) contains supplementary material, which is available to authorized users.
The events of September 11, 2001 in NYC were associated with immediate increases in births <2,000 g, slightly delayed decreased preterm delivery, and delayed increases in LBW among infants exposed periconception or in the first two trimesters. Stress may contribute to observed associations.
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