ObjectivesFor persons living with HIV (PLWH) in long-term care, clinic transfers are common and influence sustained engagement in HIV care, as they are associated with significant time out-of-care, low CD4 count, and unsuppressed viral load on re-entry. Despite the geospatial nature of clinic transfers, there exist limited data on the geospatial trends of clinic transfers to guide intervention development. In this study, we investigate the geospatial characteristics and trends of clinic transfers among PLWH on antiretroviral therapy (ART) in the Western Cape Province of South Africa.DesignRetrospective spatial analysis.SettingPLWH who initiated ART treatment between 2012 and 2016 in South Africa’s Western Cape Province were followed from ART initiation to their last visit prior to 2017. Deidentified electronic medical records from all public clinical, pharmacy, and laboratory visits in the Western Cape were linked across space and time using a unique patient identifier number.Participants4176 ART initiators in South Africa (68% women).MethodsWe defined a clinic transfer as any switch between health facilities that occurred on different days and measured the distance between facilities using geodesic distance. We constructed network flow maps to evaluate geospatial trends in clinic transfers over time, both for individuals’ first transfer and overall.ResultsTwo-thirds of ART initiators transferred health facilities at least once during follow-up. Median distance between all clinic transfer origins and destinations among participants was 8.6 km. Participant transfers were heavily clustered around Cape Town. There was a positive association between time on ART and clinic transfer distance, both among participants’ first transfers and overall.ConclusionThis study is among the first to examine geospatial trends in clinic transfers over time among PLWH. Our results make clear that clinic transfers are common and can cluster in urban areas, necessitating better integrated health information systems and HIV care.
Objective: To develop a predictive model to identify persons with recent gestational diabetes (GDM) most likely to progress to impaired glucose tolerance postpartum.
Study Design: We conducted an observational study among persons with GDM in their most recent pregnancy, defined by Carpenter-Coustan criteria. Participants were followed from delivery through 1-year postpartum. We used lasso regression with k-fold cross validation to develop a multivariable model to predict progression to impaired glucose tolerance, defined as HbA1c ≥ 5.7%, at 1 year postpartum. Predictive ability was assessed by the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values.
Results: Of 203 participants, 71(35%) had impaired glucose tolerance at 1 year postpartum. The final model had an AUC of 0.79 (95% CI 0.72, 0.85) and included eight indicators of weight, body mass index, family history of type 2 diabetes, GDM in a prior pregnancy, GDM diagnosis < 24 weeks’ gestation, and fasting and 2-hour plasma glucose at 2 days postpartum. A cut-point of ≥ 0.25 predicted probability had sensitivity 80% (95% CI 69, 89), specificity 58% (95% CI 49, 67), PPV 51% (95% CI 41, 61) and NPV 85% (95% CI 76, 91) to identify women with impaired glucose tolerance at 1 year postpartum.
Conclusion: Our predictive model had reasonable ability to predict impaired glucose tolerance around delivery for persons with recent GDM.
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