2008
DOI: 10.1016/j.annepidem.2008.02.007
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Neighborhood Poverty and American Indian Infant Death: Are The Effects Identifiable?

Abstract: Purpose-Poor living conditions are posited as an underlying cause of American Indian (AI) infant mortality, which is unusually high in the post-neonatal period. We explore whether the effects of neighborhood poverty on AI infant death are identifiable using observational data.Methods-Vital records for infants born between 1990 and 1999 to AI women in a metropolitan area (n = 4,751) are linked with tract-level poverty data. A counterfactual framework, an explicit causal contrast study design, and propensity sco… Show more

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Cited by 14 publications
(10 citation statements)
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“…However in our study, the large sample size allowed for 100% matching between deprived and reference groups. PSM is not a new approach [21], but it has only started to be used in social epidemiology and reproductive health research in recent years [23,27,28]. However, to our knowledge, no studies have used PSM method to examine the association between NDI and adverse birth outcomes.…”
Section: Discussionmentioning
confidence: 97%
“…However in our study, the large sample size allowed for 100% matching between deprived and reference groups. PSM is not a new approach [21], but it has only started to be used in social epidemiology and reproductive health research in recent years [23,27,28]. However, to our knowledge, no studies have used PSM method to examine the association between NDI and adverse birth outcomes.…”
Section: Discussionmentioning
confidence: 97%
“…In comparison, 44% of women were employed during pregnancy in the 1960s (T. D. Johnson, 2008). Postpartum employment shows similar historical trends: in 2010, 55% of all mothers of infants were employed, up from 38% in 1980 (Bureau., 2010).…”
Section: Introductionmentioning
confidence: 99%
“…Income category was not included in the matching model because it is endogenous to the outcome (i.e., a result of employment, rather than a predictor of employment). Although race-ethnicity, parity, and age were also included in the propensity score matching model, we measures for these factors in regression models using the matched groups because they are known to be strong predictors of the study outcomes, as are obesity, perceived pressure from a clinician, and belief that intervention in childbirth should be avoided unless medically necessary (Bell, Zimmerman, & Diehr, 2008; Johnson, Oakes, & Anderton, 2008). …”
Section: Methodsmentioning
confidence: 99%