Statistical analysis
REGRESSION, CERVICAL CANCER, SECONDARY
Counts/proportions and median (interquartile range) were calculated for baseline characteristics of SAIA clinics and control clinics. Counts/proportions were also used to describe micro-interventions that were fully, partially, or not implemented. Prevalence ratios were calculated using Poisson regression with robust standard errors to compare the effect of SAIA versus control conditions on the proportion of visits where eligible women were screened for cervical cancer during the last 3 months of the trial. The primary analysis followed the intent-to-treat principle. It was hypothesized that multiple cycles of SAIA might be needed to increase cervical cancer screening when some micro-interventions would not work, and this informed the decision to focus on the last 3 months of the trial. The unexpected COVID-19 pandemic initially restricted research activities as research teams were not permitted to visit the clinics. Also, routine procedures in healthcare clinics were disrupted when COVID-19 cases were high in Mombasa County. For these reasons, the study was extended by an additional 6 months beyond the initial 12 months planned for the trial.Based on a previous analysis examining cervical cancer screening practices in FP clinics in Mombasa County, having at least one provider trained to perform cervical cancer screening was associated with this procedure being performed [The national healthcare worker go-slow and strike that occurred in quarters three through five affected clinic operations and likely also impacted the primary results of the trial in quarter 6. To get a better sense of the effect of SAIA during the entire period of the trial, a post hoc secondary analysis was conducted using aggregated results from all 18 months of implementation.