Dataset Information

Quantifying clinic transfers among people living with HIV in the Western Cape, South Africa: a retrospective spatial analysis.

ABSTRACT:

Objectives

For 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.

Design

Retrospective spatial analysis.

Setting

PLWH 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.

Participants

4176 ART initiators in South Africa (68% women).

Methods

We 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.

Results

Two-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.

Conclusion

This 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.

SUBMITTER: Espinosa Dice AL

PROVIDER: S-EPMC8640660 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCOVID-19 outcomes and risk factors, including comorbidities and medication regimens, in people living with diabetes (PLWD) are poorly defined for low- and middle-income countries.MethodsThe Provincial Health Data Centre (Western Cape, South Africa) is a health information exchange collating patient-level routine health data for approximately 4 million public sector health care seekers. Data from COVID-19 patients diagnosed between March and July 2020, including PLWD, were analysed to describe risk factors, including dispensed diabetes medications and comorbidities, and their association with COVID-19 outcomes in this population.FindingsThere were 64,476 COVID-19 patients diagnosed. Of 9305 PLWD, 44.9% were hospitalised, 4.0% admitted to ICU, 0.6% received ventilation and 15.4% died. In contrast, proportions of COVID-19 patients without diabetes were: 12.2% hospitalised, 1.0% admitted, 0.1% ventilated and 4.6% died. PLWD were significantly more likely to be admitted (OR:3.73, 95 %CI: 3.53, 3.94) and to die (OR:3.01, 95 %CI: 2.76,3.28). Significant hospitalised risk factors included HIV infection, chronic kidney disease, current TB, male sex and increasing age. Significant risk factors for mortality were CKD, male sex, HIV infection, previous TB and increasing age. Pre-infection use of insulin was associated with a significant increased risk for hospitalisation (OR:1·39, 95 %CI:1·24,1·57) and mortality (OR1·49, 95 %CI:1·27; 1·74) and metformin was associated with a reduced risk for hospitalisation (OR:0·62,95 %CI:0·55, 0·71) and mortality (OR 0·77, 95 %CI:0·64; 0·92).InterpretationUsing routine health data from this large virtual cohort, we have described the association of infectious and noncommunicable comorbidities as well as pre-infection diabetes medications with COVID-19 outcomes in PLWD in the Western Cape, South Africa.FundingThis research was funded in part, by the Wellcome Trust 203135/Z/16/Z, through support of NT. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The Wellcome Centre for Infectious Diseases Research in Africa is supported by core funding from the Wellcome Trust [203135/Z/16/Z]. NT receives funding from the CIDRI-Africa Wellcome Trust grant (203135/Z/16/Z), and NT and TT receive funding from the NIH H3ABioNET award (U24HG006941). NT receives funding from the UKRI/MRC (MC_PC_MR/T037733/1).

Project description:In 2023, passive laboratory-based surveillance showed an increase in hepatitis A virus (HAV) infection. We investigated hepatitis A incidence using the notifiable medical condition surveillance system (NMCSS) data and molecularly characterised positive blood samples from the Western Cape province for 2023. All HAV IgM seropositive cases from the NMCSS from 1 January to 31 October 2023 in South Africa were investigated. HAV RNA from blood samples that had tested positive for HAV IgM from Western Cape was amplified in the VP1/P2B junction and sequenced (3500Xl Genetic Analyser). Sequences were assembled, aligned (Sequencher) and analysed (Aliview 1.27 and MEGA11). Statistical analysis was performed using Excel and the CuSum2 Threshold to determine suspected outbreaks. In 2023, the incidence of HAV IgM was 6.28/100,000 in South Africa, with the highest incidence in Western Cape province (15.86/100,000). Children aged 5 to 14 years were affected the most in the Western Cape. The positive cases in the Western Cape were above the CuSum2 threshold from January to May 2023, with the highest incidence observed in the City of Cape Town Metropolitan (14.8/100,000). Genotyping was successfully performed on 92.7% (139/150) of serum samples, for which the IB sub-genotype was detected. Three primary mutations R63K, R71S and M104I were observed in more than 49% of the samples. Most of the samples sequenced belonged to patients residing in areas close to each other within the City of Cape Town Southern, Western, and Mitchells Plain sub-districts. The CuSum2 threshold method allowed the identification of suspected HAV outbreaks in the districts within the Western Cape in 2023 while genotyping identified clusters of sub-genotype IB. Genotyping could assist with determining the common source of infection during an outbreak, especially if coupled with epidemiological and geographical data. Further active surveillance can assist in investigating the HAV risk factors for targeted public health responses.