Logistic Regression Models have been widely used in many areas of research, namely in health sciences, to study risk factors associated to diseases. Many population based surveys, such as Demographic and Health Survey (DHS), are constructed assuming complex sampling, i.e., probabilistic, stratified and multistage sampling, with unequal weights in the observations; this complex design must be taken into account in order to have reliable results. However, this very relevant issue usually is not well analyzed in the literature. The aim of the study is to specify the logistic regression model with complex sample design, and to demonstrate how to estimate it using the R software survey package. More specifically, we used Mozambique Demographic Health and Survey data 2011 (MDHS 2011) to illustrate how to correct for the effect of sample design in the particular case of estimating the risk factors associated to the probability of using mosquito bed nets. Our results show that in the presence of complex sampling, appropriate methods must be used both in descriptive and inferential statistics.
The use of population-based survey data together with sound statistical methods can enhance better estimation of HIV risk factors and explain variations across subgroups of the population. The distribution and determinants of HIV infection in populations must be taken into consideration. We analysed data from the HIV Prevalence and Behaviour Survey in Mozambique aiming to find risk factors associated with HIV infection among Mozambican women. The paper provides a complex survey logistic regression model to explain the variation in HIV seropositivity using demographic, socio-economic and behavioural factors. Results show that women aged 25-29 years, living in female-headed households, living in richer households and those widowed, divorced or not living with a partner have higher odds of being HIV-positive. Findings from our study provide a unique and integrated perspective on risk factors for being HIV-positive among Mozambican women and could support the implementation of programmes aiming to reduce HIV infection in Mozambique.
Most analyses of spatial patterns of disease risk using health survey data fail to adequately account for the complex survey designs. Particularly, the survey sampling weights are often ignored in the analyses. Thus, the estimated spatial distribution of disease risk could be biased and may lead to erroneous policy decisions. This paper aimed to present recent statistical advances in disease-mapping methods that incorporate survey sampling in the estimation of the spatial distribution of disease risk. The methods were then applied to the estimation of the geographical distribution of child malnutrition in Malawi, and child fever and diarrhoea in Mozambique. The estimation of the spatial distributions of the child disease risk was done by Bayesian methods. Accounting for sampling weights resulted in smaller standard errors for the estimated spatial disease risk, which increased the confidence in the conclusions from the findings. The estimated geographical distributions of the child disease risk were similar between the methods. However, the fits of the models to the data, as measured by the deviance information criteria (DIC), were different.
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