The Consequences of Intimate Partner Violence on Health: A Further Disaggregation of Psychological Violence—Evidence From Spain

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Abstract

Using a nationally representative sample of 10,171 women, this study examines the association between current and previous intimate partner violence and current health status. Current physical or sexual violence was associated with the highest likelihood of reporting health outcomes, followed by current emotional or economic abuse. Current controlling behavior increased the odds of reporting poor health whereas previous controlling behavior has less effect on current health. Controlling behavior alone often is the first expression of mistreatment. If health professionals could identify violence at this early stage, long-term effects on women's health could be minimized. The implications for health of the cumulative effects of violence and of its timing are discussed.

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Health consequences of intimate partner violence.

Jacquelyn Campbell (2002)

Intimate partner violence, which describes physical or sexual assault, or both, of a spouse or sexual intimate, is a common health-care issue. In this article, I have reviewed research on the mental and physical health sequelae of such violence. Increased health problems such as injury, chronic pain, gastrointestinal, and gynaecological signs including sexually-transmitted diseases, depression, and post-traumatic stress disorder are well documented by controlled research in abused women in various settings. Intimate partner violence has been noted in 3-13% of pregnancies in many studies from around the world, and is associated with detrimental outcomes to mothers and infants. I recommend increased assessment and interventions for intimate partner violence in health-care settings.

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Physical and mental health effects of intimate partner violence for men and women

Ann L Coker, Keith E Davis, Ileana Arias … (2002)

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Logistic regression: a brief primer.

C Stoltzfus (2011)

Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model's overall fit to the sample data is assessed using various goodness-of-fit measures, with better fit characterized by a smaller difference between observed and model-predicted values. Use of diagnostic statistics is also recommended to further assess the adequacy of the model. Finally, results for independent variables are typically reported as odds ratios (ORs) with 95% confidence intervals (CIs). © 2011 by the Society for Academic Emergency Medicine.