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State-level women’s status and psychiatric disorders among US women

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Social Psychiatry and Psychiatric Epidemiology Aims and scope Submit manuscript

Abstract

Purpose

Although greater gender equality at the state-level is associated with fewer depressive symptoms in women after controlling for individual-level confounders, the extent to which state-level women’s status is related to psychiatric disorders in women and gender differences in psychopathology has never been examined. We examined these associations in the current report.

Methods

We used data from the National Epidemiologic Survey on Alcohol and Related Conditions (n = 34,653), a national probability sample of US adults. Respondents completed structured diagnostic assessments of DSM-IV psychiatric disorders. We used generalized estimating equations to examine associations between four state-level indicators of women’s status (political participation, employment/earnings, social/economic autonomy, and reproductive rights) and odds of 12-month mood and anxiety disorders among women. We also tested whether women’s status predicted the magnitude of gender differences in psychiatric disorders.

Results

State-level political participation, employment/earnings, and social/economic autonomy were unrelated to odds of 12-month mood and anxiety disorders among women. However, the prevalence of major depression and post-traumatic stress disorder was lower in states where women have greater reproductive rights (OR 0.93–0.95), controlling for individual-level risk factors. None of the women’s status indicators predicted gender differences in mood and anxiety disorder prevalence.

Conclusions

State-level women’s status was largely unrelated to mood and anxiety disorders in women or to gender differences in these disorders. Investigation of social factors that play a role in shaping the distribution of individual-level risk factors that are associated with gender disparities in psychiatric disorders represents an important avenue for future research.

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Notes

  1. We conducted a multi-level analysis in order to replicate the results of our GEE analysis regarding the association between state-level women’s status and mood and anxiety disorders among women. We estimated three-level models with individuals (level 1) nested within primary sampling units (level 2), nested within states (level 3). Because we were unable to scale the weights in order to apply them to each level of the multi-level model, we estimated un-weighted models. In situations where scaling the weights is not possible, an un-weighted analysis is preferable to using the raw weights without scaling [47]. We first estimated a series of “empty” models with no covariates. These models revealed significant variation in the prevalence of mood and anxiety disorders across states. We then estimated a series of models including the same individual- and state-level covariates as the GEE analysis. The parameter estimates and confidence intervals from the multi-level models were consistent with the results of our GEE analysis. In only one case did the results of the multi-level analysis differ from the corresponding GEE analysis. The association between state-level reproductive rights and female mood disorders was not significant in the multi-level model, in contrast to the GEE model where the association was statistically significant. Caution is therefore warranted in interpreting this association.

    We reported the GEE approach in the text because of several complexities associated with estimating multi-level models in complex survey data. Most notably, difficulties arise in determining how to utilize survey weights, which must be applied to adjust for unequal selection probabilities of units within each level of the model. Failure to account for differential selection probabilities generates biased variance and parameter estimates [48, 49]. To account for these biases, numerous investigators have explored options for including weights in multi-level models. These approaches involve scaling the weights and applying the weights separately at each level of the model [47, 49]. We were unable to use this approach in the NESARC, because sufficient information is not provided in the publicly available data to allow the weights to be disaggregated and applied to each level of the model.

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Acknowledgments

This work was supported by the Robert Wood Johnson Foundation (Grant Number 053572) and by the National Institute of Health (Grant Numbers MH078928 and MH070627). Disclosure: the authors have no competing interests to report.

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Authors and Affiliations

  1. Department of Society, Human Development, and Health, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA

    Katie A. McLaughlin, Ziming Xuan, S. V. Subramanian & Karestan C. Koenen

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  1. Katie A. McLaughlin
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  2. Ziming Xuan
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  3. S. V. Subramanian
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  4. Karestan C. Koenen
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Correspondence to Katie A. McLaughlin.

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McLaughlin, K.A., Xuan, Z., Subramanian, S.V. et al. State-level women’s status and psychiatric disorders among US women. Soc Psychiatry Psychiatr Epidemiol 46, 1161–1171 (2011). https://doi.org/10.1007/s00127-010-0286-z

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