Modeling inequality in access to agricultural productive resources and socioeconomic determinants of household food security in Ghana: a cross-sectional study

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Abstract

Women in rural communities remain the most vulnerable population in accessing agricultural productive resources with dire implications for food security, malnutrition, and poverty. Effective agricultural and food-related policies should be based on a better understanding of the complex inter-relationship of how socioeconomic, demographic, gender, women empowerment, and geographical location indicators simultaneously affect access to agricultural productive resources and food security. The study quantified the level of inequality in access to agricultural productive resources and explored the mechanism through which socioeconomic status mediates the effect of geographic location on food security. This is a community-based cross-sectional study using a multi-stage stratified cluster random sampling design to generate a representative sample of the target population who live in coastal and non-coastal communities. The Gini inequality index, generalized structural equation models, multivariable modified Poisson and Negative binomial regression models were used. The inequality in access to agricultural productive resources was marginally higher among women than in men, higher in the coastal areas than in the non-coastal areas, and higher among women with low empowerment in agricultural production decision-making. The empowerment of women in agricultural decision-making was found to increase with age, as older women were more empowered to make decisions in agriculture. Approximately 17% [95% CI 15.6–18.6] of the population were food-secured (coastal = 13.9%, non-coastal communities = 20.7%). Socioeconomic status mediates the effect of living in coastal versus non-coastal rural communities on food security. To improve food security, the government should prioritize interventions geared toward improving women's access to productive agricultural resources. These interventions must consider gender-specific constraints, poverty alleviation schemes, legal framework, sociocultural factors, and decision-making power.

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Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio

Aluisio Barros, Vânia Hirakata (2003)

Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.