Violence by male partners against women during the childbearing year: a contextual analysis

Objectives. Many contextual analyses that bridge the micro-level--macro-level gap in identifying risk factors for adverse outcomes have not used methods appropriate for multilevel data. The purpose of this paper is to illustrate the application of appropriate multi-level analytic methods and discuss their implications for public health. Methods. A previously published individual-level model of physical violence perpetrated by male partners during the childbearing year was reanalyzed to include variables describing the neighborhoods where the women resided. Logistic regression with estimation methods of the generalized estimating equation was used for the contextual analysis. To assess the advantages of the generalized estimating equation over conventional logistic regression, both were used for the two-level model. Results. The regression coefficients from the contextual model differed from the betas obtained in the individual-level model. Not only were neighborhood-level variables related to the risk of partner-perpetrated violence, but the presence of these macro-level variables in the models modified the relationships of the individual-level variables to the risk of violence. Conclusions. Two-level models that include individual- and community-level factors may be beneficial for purposes of explanation in public health research. (Am J Public Health. 1995; 85:1092-1097)
Community factors may affect study outcomes as well as individual factors. This has implications for planning appropriate interventions. To illustrate the effects of community factors, a study of violence against pregnant women by their male partners was reanalyzed. Previously only individual factors such as the woman's support system were considered. Now, information about income, employment level, and crime level in the woman's neighborhood were factored in. This resulted in substantial changes in many of the previously calculated risk predictions. Most prominently, adding neighborhood factors increased the risk associated with being white from nil to a nine-fold risk. This suggests that high-risk neighborhoods could be targeted for programs or that changing social factors such as reducing unemployment could reduce the risk of battering.