Quantifying the indirect impact of COVID-19 pandemic on utilisation of outpatient and immunisation services in Kenya: a longitudinal study using interrupted time series analysis

Abstract

ObjectiveIn this study, we assess the indirect impact of COVID-19 on utilisation of immunisation and outpatient services in Kenya.DesignLongitudinal study.SettingData were analysed from all healthcare facilities reporting to Kenya’s health information system from January 2018 to March 2021. Multiple imputation was used to address missing data, interrupted time series analysis was used to quantify the changes in utilisation of services and sensitivity analysis was carried out to assess robustness of estimates.Exposure of interestCOVID-19 outbreak and associated interventions.Outcome measuresMonthly attendance to health facilities. We assessed changes in immunisation and various outpatient services nationally.ResultsBefore the first case of COVID-19 and pursuant intervention measures in March 2020, uptake of health services was consistent with historical levels. There was significant drops in attendance (level changes) in April 2020 for overall outpatient visits for under-fives (rate ratio, RR 0.50, 95% CI 0.44 to 0.57), under-fives with pneumonia (RR 0.43, 95% CI 0.38 to 0.47), overall over-five visits (RR 0.65, 95% CI 0.57 to 0.75), over-fives with pneumonia (RR 0.62, 95% CI 0.55 to 0.70), fourth antenatal care visit (RR 0.86, 95% CI 0.80 to 0.93), total hypertension (RR 0.89, 95% CI 0.82 to 0.96), diabetes cases (RR 0.95 95% CI, 0.93 to 0.97) and HIV testing (RR 0.97, 95% CI 0.94 to 0.99). Immunisation services, first antenatal care visits, new cases of hypertension and diabetes were not affected. The post-COVID-19 trend was increasing, with more recent data suggesting reversal of effects and health services reverting to expected levels as of March 2021.ConclusionCOVID-19 pandemic has had varied indirect effects on utilisation of health services in Kenya. There is need for proactive and targeted interventions to reverse these effects as part of the pandemic’s response to avert non-COVID-19 indirect mortality.

Transmission networks of SARS-CoV-2 in Coastal Kenya during the first two waves: A retrospective genomic study

“…The nine response indicators used to form the SI are (1) school closures, (2) workplace closures, (3) cancellation of public events, (4) restrictions on public gatherings, (5) closures of public transport, (6) stay-at-home requirements, (7) public information campaigns, (8) restrictions on internal movements, and (9) international travel controls. The various government COVID-19 measures and the dates they took effect or when they were lifted are provided in Supplementary file 1 and are also reviewed in detail in Brand et al, 2021 ; Wambua et al, 2022 .…”

“…In response, the government outlined a series of countermeasures to minimize the effects of a pandemic locally ( Brand et al, 2021 ). For instance, international travel was restricted, international borders closed, public gatherings prohibited, meetings with over 15 participants forbidden, travel from hotspot counties restricted, places of worship, bars, schools, and other learning institutions closed, and a nationwide dusk-to-dawn curfew enforced ( Wambua et al, 2022 ). Despite these measures, the COVID-19 case numbers consistently grew and serological surveys in June 2020 indicated the local epidemic had progressed more than it could be discerned from the limited laboratory testing ( Etyang et al, 2021 ; Uyoga et al, 2021a ).…”

Reporting of rapid diagnostic testing in Kenya's health system, 2018-2020: triangulation of routine and survey data

“…We excluded DHIS2 data submitted after December 2020 from our analysis because KHFA was conducted in 2018, thus would result in a substantial temporal mismatch. We included data from 2020 to understand reporting biases attributable to the COVID-19 pandemic [15]. Data sourced from DHIS2 was merged to the health facility database using the unique DHIS2 identi er.…”

“…Figure 6 Discussion Routine health data is essential for quantifying health care utilisation, estimating the reach of interventions in the community and monitoring progress toward national and global targets such as SDGs [28]. However, data quality concerns, primarily due to the non-reporting of health facilities, have continued to persist, impacting the accurate assessment of the performance of a country's health system [15,[19][20][21]23]. These concerns become even more signi cant when attempting to estimate the current supply and demand for point-of-care testing to guarantee an adequate diagnostics supply.…”

“…Since 2011, the platform has provided routine data on tests done at a facility level every month in Kenya. Despite the availability of routine data across the country for an entire year, data quality has been a concern, primarily due to nonreporting and incomplete entries across health facilities [13,15,[19][20][21][22][23]. Dealing with these challenges requires an understanding of the causes driving non-reporting.…”