Statistical methods
REGRESSION
Data were collected by a team comprising a study clinician or study nurse and 3 field workers, mainly via hand-held tablets using REDCap® version 7.6.3. Data from REDCap® were checked in the field by the clinician or the nurse and were then actively synced or uploaded on to study servers at the end of each day. Facility inspection data were collected on paper checklists and double entered in REDCap®. All data entry from paper sources was overseen by a senior data manager and REDCap® programmer. If necessary, queries were raised and communicated to the field team for verification.All analyses were conducted in Stata 17.0 (College Station, Texas, USA). Continuous variables were summarised as means and standard deviations (SD), or medians and interquartile ranges (IQR). Categorical variables were summarised with frequency counts and percentages. Characteristics of health workers and patients surveyed in 2020 and 2016 were compared using the Pearson chi-squared statistic with the second-order correction of Rao and Scott to account for the clustered design.The aggregate FPS was the primary outcome used to assess the impact of the lack of research-driven support on service availability and readiness assessment in 2020. The analysis used methods designed for pair-matched cluster randomised trials, with each HF in 2020 being treated as paired with its observation from 2016. First, the mean aggregate FPS was calculated for each HF in 2016 and in 2020, and log transformed. Then, the difference in log mean scores between 2020 and 2016 was calculated for each HF, and a paired t-test was performed on the pair-wise differences. A similar analysis was done for each component of the FPS: the facility inspection score and the health worker knowledge score.The association between the aggregate FPS in 2020 and factors that may have influenced service quality was explored using linear regression. These factors included the functionality of patient clubs, whether the HFs had external support such as extra drug supplies from a non-governmental organisation (NGO) or community-based organisation (CBO), HF level, area (rural, peri-urban, or urban), district (Mpigi or Wakiso), and pairing at randomisation (before the start of the original trial). Initial models evaluated each factor individually and adjusted for the FPS in 2016. All variables that were associated with the 2020 score at The QoCE score, and proportion of adequately managed patients (QoCE ≥ 7), was used to assess the impact of the lack of research-driven support on the quality of patient care in 2020, using the same analysis approach as for the FPS. First, the mean QoCE score and the prevalence of adequately managed patients in each HF in 2016, and in 2020, was calculated and log transformed. Then, the difference in log mean scores, and in log prevalence, between 2020 and 2016 was calculated for each HF, and a paired t-test was performed on the pair-wise differences.The association between QoCE scores in 2020 and factors that may have influenced quality of care was explored in an individual-level analysis using a Tobit regression with random effects to account for the correlation of multiple observations within HF. Since the QoCE score is bounded by a maximum of 10, ordinary linear regression can lead to biased standard errors. Therefore, Tobit regression is used to reduce the bias in the estimation of standard errors when the outcome is censored (bounded) [