Background In sub-Saharan Africa, adolescent girls, and young women (AGYW) are among those at the highest risk of acquiring HIV. Risk factors for HIV in AGYM are well studied and known in the literature. However, there is need to combine these factors into a single summary measure that could be used in the identification of the AGYW who are more likely to acquire HIV. This study aimed at developing and validating an HIV risk prediction tool for AGYW. Methods We analyzed existing HIV-related data on 4,399 AGYW from South Africa. The HIV risk scores were computed from summing predictor coefficients of the resulting logistic regression model. The performance of the final model at discriminating between HIV infected and non-HIV infected AGYM was assessed using area under the receiver-operating curve (AUC) and measures of discriminative abilities such as predictive values, sensitivity, and specificity. The optimal cut-point of the risk score was determined using youden index. Results The weighted HIV prevalence was estimated at 12.4% (11.7–14.0). Our risk scores ranged from − 1.26 to 3.80 with a mean score of 1.38 and a standard deviation of 0.86. The optimal cut-point was estimated at 1.80 with sensitivity of 62% and specificity of 70%. The prediction model’s sensitivity was 15.19% and specificity of 98.92%. The model’s positive predictive value was 67.42% while the negative predictive value was 88.79%. Our model performed well at predicting HIV positivity with training AUC of 0.770 and a testing AUC of 0.751. Conclusion Our risk score tool has shown good discrimination and calibration at predicting undiagnosed HIV. This tool could provide a simple and low-cost strategy for screening AGYW in primary health care clinics or community-based settings. The risk assessment tool will help service providers identify and link AGYW to PreP services.

Background: Family planning (FP) is known to bring multiple benefits to people both individually and collectively. Individually, FP has been associated with reduction in risk of unintended pregnancy which also correlates well with low child mortality rates. Child mortality rates in women with child spacing of less than two years are reported to be 45% higher than their counterparts. Several factors that predict FP utilisation have been investigated but there is limited literature on the effect of migration status on FP utilisation in Malawi. Our study aimed at quantifying the effect of migration status on modern contraceptive use. Methods: Data for this study came from a nationally representative 2019/20 Malawi multiple cluster indicator survey (MICS). At total of 22,730 women aged 15 to 45 participated in the survey. We applied sampling weights to facilitate survey data analysis to correct unequal representation of participants at cluster, district, and regional level. We used multivariable binary logistic regression to assess the effect of migration status on any modern contraceptive use. The final model had participants age, age at first sex, age at marriage, region, marital status, levels of education, children ever born, residence and wealth index as confounders. Results: The overall CPR among women aged 15 to 45 was 53.16%. The contraceptive prevalence rates of participants by migration status were 48.38% for migrants and 53.40% for non-migrants. The fully adjusted effect of migration on modern contraceptive use was 0.77 (95% CI 0.67 – 0.91, p=0.001). Conclusions: Our study concludes that women migrants and adolescent girls have low CPR and are less likely to access modern contraceptives compared to non-migrant women. Deliberate efforts are required to increase CPR for migrants as well as for adolescent girls.

Background Adolescent girls and young women (AGYW) constitute the highest proportion of all new HIV infections in sub-Saharan Africa. Age at sexual debut is one of the sexual behavior factors that predict HIV among AGYW. We aim to assess the effect of age at sexual debut on HIV acquisition among AGYW in Malawi using 2016 Malawi population-based HIV impact assessment (MPHIA). Methods We analyzed HIV data on 1,921 AGYW from the 2016 MPHIA. Associations between HIV infection and predictor variables were assessed using both univariate and multivariate logistic regression. The effect of age at sexual debut on HIV acquisition was assessed using binary logistic regression model with random adjustment of standard errors. Results The weighted HIV prevalence among AGYW was estimated at 4.7%. The prevalence was high for AGYW from southern region (7.8%) compared to the central (2.3%) and the northern region (2.1%). AGYW from urban areas had twice as much the prevalence of HIV compared to those from rural areas (9.1% versus 3.7% for urban and rural respectively). This study has identified 9 behavior and contextual factors that are associated with HIV infection among AGYW, and these are: region (p < 0.001), residence (p < 0.001), age (p = 0.008), age at first sex (p = 0.001), age at marriage (p0.0131), marital status (p < 0.001), education (p = 0.002), wealth quintile (p = 0.05) and partner at last sex (p < 0.001). AGYW who started sex before the age of 15 were more than two times (OR 2.47, 95% CI 1.52–4.05) likely to be test HIV positive compared to those who started after the age of 15. Conclusion This study concludes that early sexual debut occurring before the age of 15 significantly predict HIV among AGYW in Malawi. To reduce their risk to acquiring HIV, such AGYW should be linked to HIV PreP services to minimize risk of HIV transmission.

Background: Adolescent girls and young women (AGYW) constitute the highest proportion of all new HIV infections in sub-Saharan Africa (SSA). Age at sexual debut is one of the sexual behavior factors that predict HIV in AGYW. This study aimed at assessing the effect of age at sexual debut on HIV acquisition among AGYW in Malawi using the 2016 Malawi Population-based HIV Impact Assessment (MPHIA). Methods: We analyzed HIV related data on 1,921 AGYW from the 2015/16 MPHIA. Associations between HIV infection and predictor variables were assessed using both univariate and multivariate logistic regression. The effect of age at sexual debut on HIV acquisition was assessed using binary logistic regression model. Results: The weighted HIV prevalence among AGYW was 4.7%. The prevalence was high for AGYW from southern region (7.8%) compared to the central (2.3%) and the northern region (2.1%). AGYW from urban areas had more than twice the prevalence compared to those from rural areas (9.1% versus 3.7% for urban and rural areas respectively). This study has identified 9 behavioral and contextual factors that are associated with HIV infection among AGYW, and these are: Region (p<0.001), Residence (p<0.001), Age (p=0.008), Age at first sex (p=0.001), Age at marriage (p0.0131), Marital status (p<0.001), Education (p=0.002), Wealth quintile (p=0.05) and Partner at last sex (p<0.001). AGYW who started sex before the age of 15 were more than two times more likely (OR 2.47, 95% CI 1.52 – 4.05) to test HIV positive compared to those who started after the age of 15. Conclusion: We conclude that early sexual debut occurring before the age of 15, significantly predict HIV among AGYW in Malawi. To reduce their risk to acquiring HIV, such AGYW should be linked to HIV preventions services such to HIV pre-exposure prophylaxis services in order to minimize their risk of HIV transmission.

Background: In sub-Saharan Africa (SSA), adolescent girls and young women (AGYW) have the highest risk of acquiring HIV. This has led to several studies aimed at identifying risk factors for HIV in AGYM. However, a combination of the purported risk variables in a multivariate risk model could be more useful in determining HIV risk in AGYW than one at a time. The purpose of this study was to develop and validate an HIV risk prediction model for AGYW. Methods: We analysed HIV-related HERStory survey data on 4,399 AGYW from South Africa. We identified 16 purported risk variables from the data set. The HIV acquisition risk scores were computed by combining coefficients of a multivariate logistic regression model of HIV positivity. The performance of the final model at discriminating between HIV positive and HIV negative was assessed using the area under the receiver-operating characteristic curve (AUROC). The optimal cut-point of the prediction model was determined using the Youden index. We also used other measures of discriminative abilities such as predictive values, sensitivity, and specificity. Results: The estimated HIV prevalence was 12.4% (11.7% – 14.0) %. The score of the derived risk prediction model had a mean and standard deviation of 2.36 and 0.64 respectively and ranged from 0.37 to 4.59. The prediction model’s sensitivity was 16. 7% and a specificity of 98.5%. The model’s positive predictive value was 68.2% and a negative predictive value of 85.8%. The prediction model’s optimal cut-point was 2.43 with sensitivity of 71% and specificity of 60%. Our model performed well at predicting HIV positivity with training AUC of 0.78 and a testing AUC of 0.76. Conclusion: A combination of the identified risk factors provided good discrimination and calibration at predicting HIV positivity in AGYW. This model could provide a simple and low-cost strategy for screening AGYW in primary healthcare clinics and community-based settings. In this way, health service providers could easily identify and link AGYW to HIV PrEP services.