The impact of PEPFAR transition on HIV service delivery at health facilities in Uganda

Abstract

BackgroundSince 2004, the President’s Emergency Plan for AIDS Relief (PEPFAR) has played a large role in Uganda’s HIV/AIDS response. To better target resources to high burden regions and facilities, PEPFAR planned to withdraw from 29% of previously-supported health facilities in Uganda between 2015 and 2017.MethodsWe conducted a cross-sectional survey of 226 PEPFAR-supported health facilities in Uganda in mid-2017. The survey gathered information on availability, perceived quality, and access to HIV services before and after transition. We compare responses for facilities transitioned to those maintained on PEPFAR, accounting for survey design. We also extracted data from DHIS2 for the period October 2013–December 2017 on the number of HIV tests and counseling (HTC), number of patients on antiretroviral therapy (Current on ART), and retention on first-line ART (Retention) at 12 months. Using mixed effect models, we compare trends in service volume around the transition period.ResultsThere were 206 facilities that reported transition and 20 that reported maintenance on PEPFAR. Some facilities reporting transition may have been in a gap between implementing partners. The median transition date was September 2016, nine months prior to the survey. Transition facilities were more likely to discontinue HIV outreach following transition (51.6% vs. 1.4%, p<0.001) and to report declines in HIV care access (43.5% vs. 3.1%, p<0.001) and quality (35.6% vs. 0%, p<0.001). However, transition facilities did not differ in their trends in HIV service volume relative to maintenance facilities.ConclusionsTransition from PEPFAR resulted in facilities reporting worsening patient access and service quality for HIV care, but there is insufficient evidence to suggest negative impacts on volume of HIV services. Facility respondents’ perceptions about access and quality may be overly pessimistic, or they may signal forthcoming impacts. Unrelated to transition, declining retention on ART in Uganda is a cause for concern.

Data Cleaning Process for HIV- Indicator Data Extracted from DHIS2 National Reporting System: A Case study of Kenya

“…These include identifying and dealing with non-reporting facilities (situation A), and non-service providing facilities (situation B and C) in a data set [19], [48]. This comes about as some of the reports extracted contain blanks, as DHIS2 is unable to record zeros as identi ed in other studies [16][17][18][19], [49]. As such, DHIS2 is unable to distinguish between missing values and true zero values.…”

“…Further still, another nding consistent with similar studies is that of identifying and dealing with incomplete reporting, which can be viewed from various perspectives. This can include a situation where a report for a service provided has been successfully submitted but is incomplete [17], [19], [48]; or missing reports (expected reports have not been submitted consistently for all 12 months), hence making it di cult to identify whether services were provided or not, in months were reports were missing [48].…”

“…With this in mind and based on our knowledge, no standard consensus-based approach exists to ensure that replicable and rigorous data cleaning approaches and documentation are applied on extracted DHIS2 data to be used in secondary analyses. As such, ad hoc data cleaning approaches have been employed for the extracted data prior to analyses [16][17][18]. Moreover, whereas some studies provide brief documentation of data cleaning procedures used [19], others lack documentation, leaving the data cleaning approaches used undisclosed and behind-the-scenes [20].…”

“…Failure to detect data quality issues and to clean these data can lead to inaccurate analyses outcomes [13]. Various studies have extracted data from DHIS2 for analyses [16][17][18][19][20]. Nonetheless, few studies attempt to explicitly disclose the data cleaning strategies used, resulting errors identi ed and the action taken [16][17][18].…”

“…Various studies have extracted data from DHIS2 for analyses [16][17][18][19][20]. Nonetheless, few studies attempt to explicitly disclose the data cleaning strategies used, resulting errors identi ed and the action taken [16][17][18]. In addition, some of these studies largely fail to exhaustively and systematically describe the steps used in data cleaning of the DHIS2 data before analyses are done [19,20].…”