Academic journal article Demographic Research

Factors Responsible for Mortality Variation in the United States: A Latent Variable Analysis

Academic journal article Demographic Research

Factors Responsible for Mortality Variation in the United States: A Latent Variable Analysis

Article excerpt

1. Introduction

Understanding the underlying causes of variation in mortality is useful for health policy and intervention design. However, risk factors can be difficult to measure directly, as observed measures are often products of traits or circumstances that are unobserved, partially observed, or complex and multidimensional. An alternative is to infer the effects of risk factors indirectly using a latent variable approach. Because factors that influence mortality typically manifest themselves in several causes of death, associations among causes of death over time or space may provide important information about underlying causal factors. Without explicitly introducing a latent variable model, such reasoning has been used to infer the role of cigarette smoking (Peto et al. 1992; Preston et al. 2011) and the quality of the health care system in explaining variation in US mortality (Nolte and Mckee 2004).

In this paper we take advantage of variation in mortality by cause of death across US states to identify the underlying factors that are creating such variation. The emphasis is on behavioral factors that affect the risk of death. Previous studies have suggested that behavioral factors play a leading role in explaining US mortality (Mokdad et al. 2004; Mokdad et al. 2005; Murray et al. 2006; Danaei et al. 2009; 2010; Mehta and Preston 2012; Murray et al. 2013).

The primary approach to identifying the role of behavioral factors in interstate mortality variation is to apply relative risks derived from epidemiologic studies to the risk factor distribution of populations using the population attributable fraction (PAF) (Danaei et al. 2009; Danaei et al. 2010). In order to provide reliable results, such an approach requires accurate data on both relative risks and on risk factor distributions. Neither is measured with a high degree of accuracy or certainty and in some instances the data are altogether unavailable. For example, the fraction of deaths attributable to obesity in the US varies by a factor of 3-4 depending on which set of national estimates of relative risks is employed (Mehta and Chang 2009). Relative risks from smoking depend on the number of cigarettes consumed per day, inhalation, filtration, tar content, and especially the duration and past intensity of the habit. These elements are not readily captured in a single variable. Data on other behaviors, such as use of illicit drugs and unsafe sex, are often unreliable because of their sensitive nature. Finally, the source of data for most regional analyses of health patterns in the US, the Behavioral Risk Factor Surveillance Survey (BRFSS), is subject to several important limitations related to validity and comparability of data, including reliance on self-reported data, exclusion of households without telephones, and high rates of non-response. The national response rates for BRFSS in 2011 were 53.0% for landlines and 27.9% for cell phones (Centers for Disease Control and Prevention 2013a).

The present study takes an entirely different approach. It treats behavioral factors as latent variables that are identifiable through covariation of causes of death across populations. The operation of a particular risk factor is expected to appear in the form of high correlations across states within the cluster of causes of death for which its relative risks are greatest. Unlike prior studies, the present study is not limited to the subset of risk factors that can be reliably measured; thus it has the potential to uncover previously overlooked patterns of risk in populations. Also, because this approach is independent of the attributable-risk approach, it provides a valuable independent assessment of the contribution of behavioral risk factors to mortality variation.

2. Background and approach

Through straightforward decompositional methods, inter-population differences in death rates or life expectancy can be readily assigned to various causes of death (Preston et al. …

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