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. 2024 May 31;19(5):e0303574.
doi: 10.1371/journal.pone.0303574. eCollection 2024.

Hotspot areas of risky sexual behaviour and associated factors in Ethiopia: Further spatial and mixed effect analysis of Ethiopian demographic health survey

Denekew Tenaw Anley  1 , Melkamu Aderajew Zemene  1 , Asaye Alamneh Gebeyehu  1 , Natnael Atnafu Gebeyehu  2 , Getachew Asmare Adella  3 , Gizachew Ambaw Kassie  4 , Misganaw Asmamaw Mengstie  5 , Mohammed Abdu Seid  6 , Endeshaw Chekol Abebe  5 , Molalegn Mesele Gesese  2 , Yenealem Solomon  7 , Natnael Moges  8 , Berihun Bantie  9 , Sefineh Fenta Feleke  10 , Tadesse Asmamaw Dejenie  11 , Ermias Sisay Chanie  8 , Wubet Alebachew Bayih  12   13 , Natnael Amare Tesfa  14 , Wubet Taklual  1 , Dessalegn Tesfa  1 , Rahel Mulatie Anteneh  1 , Anteneh Mengist Dessie  1
Affiliations

Hotspot areas of risky sexual behaviour and associated factors in Ethiopia: Further spatial and mixed effect analysis of Ethiopian demographic health survey

Denekew Tenaw Anley et al. PLoS One. .

Abstract

Introduction: Sexual behaviour needs to take a central position in the heart of public health policy makers and researchers. This is important in view of its association with Sexually Transmitted Infections (STIs), including HIV. Though the prevalence of HIV/AIDS is declining in Ethiopia, the country is still one of the hardest hit in the continent of Africa. Hence, this study was aimed at identifying hot spot areas and associated factors of risky sexual behavior (RSB). This would be vital for more targeted interventions which can produce a sexually healthy community in Ethiopia.

Methods: In this study, a cross-sectional survey study design was employed. A further analysis of the 2016 Ethiopia Demographic and Health Survey data was done on a total weighted sample of 10,518 women and men age 15-49 years. ArcGIS version 10.7 and Kuldorff's SaTScan version 9.6 software were used for spatial analysis. Global Moran's I statistic was employed to test the spatial autocorrelation, and Getis-Ord Gi* as well as Bernoulli-based purely spatial scan statistics were used to detect significant spatial clusters of RSB. Mixed effect multivariable logistic regression model was fitted to identify predictors and variables with a p-value ≤0.05 were considered as statistically significant.

Result: The study subjects who had RSB were found to account about 10.2% (95% CI: 9.64%, 10.81%) of the population, and spatial clustering of RSB was observed (Moran's I = 0.82, p-value = 0.001). Significant hot spot areas of RSB were observed in Gambela, Addis Ababa and Dire Dawa. The primary and secondary SaTScan clusters were detected in Addis Ababa (RR = 3.26, LLR = 111.59, P<0.01), and almost the entire Gambela (RR = 2.95, LLR = 56.45, P<0.01) respectively. Age, literacy level, smoking status, ever heard of HIV/AIDS, residence and region were found to be significant predictors of RSB.

Conclusion: In this study, spatial clustering of risky sexual behaviour was observed in Ethiopia, and hot spot clusters were detected in Addis Ababa, Dire Dawa and Gambela regions. Therefore, interventions which can mitigate RSB should be designed and implemented in the identified hot spot areas of Ethiopia. Interventions targeting the identified factors could be helpful in controlling the problem.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Over all prevalence of risky sexual behaviour in Ethiopia, 2016.
Fig 2
Fig 2. Spatial clustering of risky sexual behaviour in Ethiopia, 2016.
Fig 3
Fig 3. Spatial distribution of risky sexual behviour in Ethiopia, 2016.
Fig 4
Fig 4. The hot spot analysis of risky sexual behaviour in Ethiopia, 2016.
Fig 5
Fig 5. The SaTScan analysis of significant most likely clusters of risky sexual behaviour in Ethiopia, 2016.
Fig 6
Fig 6. The Kriging interpolation of risky sexual behaviour in Ethiopia, 2016.
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