BackgroundDespite the availability and knowledge of various contraceptive methods, consistent utilisation in women living with HIV/AIDS (WLWHA) within the reproductive age group remains below the Sustainable Development Goals (SDGs) and Family Planning 2020 goals. This study examines the association between sociodemographic factors and contraceptive use including the effect of clustering tendencies of these factors on contraceptive usage among WLWHA in Kenya.MethodsWeighted multivariate logistic regression models were conducted to determine the association of sociodemographic factors on contraception use among WLWHA using the 2008–2009 Kenya Demographic Health Survey. Spatial autocorrelation techniques were used to explore clustering tendencies of these factors on contraception utilisation. Our study population included 304 HIV positive women, aged 15–49 years.ResultsAmong 304 HIV-positive women in our study population, 92 (30.3%) reported using one method of contraception. Contraceptive use was significantly associated with wealth and education after adjustment for other sociodemographic variables. Women classified as having low and middle wealth index were less likely to use contraceptives (OR=0.17, 95% CI 0.07 to 0.43; OR=0.33, 95% CI 0.11 to 0.98, respectively) compared with women classified as having high wealth index. Similarly, women with primary education only were less likely to use contraceptives compared with women with secondary or higher education (OR=0.42, 95% CI 0.18 to 0.98). Spatial autocorrelation revealed significant positive clusters with weak clustering tendencies of non-contraceptive use among different levels of wealth index and education within different regions of Kenya.ConclusionThese findings underscores the need for intervention programmes to further target socially disadvantaged WLWHA, which is necessary for achieving the SDGs.
This research utilizes a similar method, using what may prove to be more precise spatial statistics to determine clustering tendencies and economic data to assess their location in relation to low income areas. It is also important to note that race/ethnicity is not included in this research; rather it focuses on designation of population density and persons living at or below poverty level. This research provides a way to spatially determine if fast food restaurants are more prominent in low socioeconomic areas.
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