The COVID-19 pandemic and disruptions to essential health services in Kenya:A retrospective time-series analysis

Background: Public health emergencies can disrupt the provision of and access to essential health-care services, exacerbating health crises. We aimed to assess the effect of the COVID-19 pandemic on essential health-care services in Kenya. Methods: Using county-level data routinely collected from the health information system from health facilities across the country, we used a robust mixed-effect model to examine changes in 17 indicators of essential health services across four periods: the pre-pandemic period (from January, 2018 to February, 2020), two pandemic periods (from March to November 2020, and February to October, 2021), and the period during the COVID-19-associated health-care workers' strike (from December, 2020 to January, 2021). Findings: In the pre-pandemic period, we observed a positive trend for multiple indicators. The onset of the pandemic was associated with statistically significant decreases in multiple indicators, including outpatient visits (28·7%; 95% CI 16·0-43·5%), cervical cancer screening (49·8%; 20·6-57·9%), number of HIV tests conducted (45·3%; 23·9-63·0%), patients tested for malaria (31·9%; 16·7-46·7%), number of notified tuberculosis cases (26·6%; 14·7-45·1%), hypertension cases (10·4%; 6·0-39·4%), vitamin A supplements (8·7%; 7·9-10·5%), and three doses of the diphtheria, tetanus toxoid, and pertussis vaccine administered (0·9%; 0·5-1·3%). Pneumonia cases reduced by 50·6% (31·3-67·3%), diarrhoea by 39·7% (24·8-62·7%), and children attending welfare clinics by 39·6% (23·5-47·1%). Cases of sexual violence increased by 8·0% (4·3-25·0%). Skilled deliveries, antenatal care, people with HIV infection newly started on antiretroviral therapy, confirmed cases of malaria, and diabetes cases detected were not significantly affected negatively. Although most of the health indicators began to recover during the pandemic, the health-care workers' strike resulted in nearly all indicators falling to numbers lower than those observed at the onset or during the pre-strike pandemic period. Interpretation: The COVID-19 pandemic and the associated health-care workers' strike in Kenya have been associated with a substantial disruption of essential health services, with the use of outpatient visits, screening and diagnostic services, and child immunisation adversely affected. Efforts to maintain the provision of these essential health services during a health-care crisis should target the susceptible services to prevent the exacerbation of associated disease burdens during such health crises. Funding: Bill & Melinda Gates Foundation

The COVID-19 pandemic and disruptions to essential health services in Kenya: a retrospective time-series analysis

Background: Public health emergencies can disrupt the provision of and access to essential health-care services, exacerbating health crises. We aimed to assess the effect of the COVID-19 pandemic on essential health-care services in Kenya. Methods: Using county-level data routinely collected from the health information system from health facilities across the country, we used a robust mixed-effect model to examine changes in 17 indicators of essential health services across four periods: the pre-pandemic period (from January, 2018 to February, 2020), two pandemic periods (from March to November 2020, and February to October, 2021), and the period during the COVID-19-associated health-care workers’ strike (from December, 2020 to January, 2021). Findings: In the pre-pandemic period, we observed a positive trend for multiple indicators. The onset of the pandemic was associated with statistically significant decreases in multiple indicators, including outpatient visits (28·7%; 95% CI 16·0–43·5%), cervical cancer screening (49·8%; 20·6–57·9%), number of HIV tests conducted (45·3%; 23·9–63·0%), patients tested for malaria (31·9%; 16·7–46·7%), number of notified tuberculosis cases (26·6%; 14·7–45·1%), hypertension cases (10·4%; 6·0–39·4%), vitamin A supplements (8·7%; 7·9–10·5%), and three doses of the diphtheria, tetanus toxoid, and pertussis vaccine administered (0·9%; 0·5–1·3%). Pneumonia cases reduced by 50·6% (31·3–67·3%), diarrhoea by 39·7% (24·8–62·7%), and children attending welfare clinics by 39·6% (23·5–47·1%). Cases of sexual violence increased by 8·0% (4·3–25·0%). Skilled deliveries, antenatal care, people with HIV infection newly started on antiretroviral therapy, confirmed cases of malaria, and diabetes cases detected were not significantly affected negatively. Although most of the health indicators began to recover during the pandemic, the health-care workers’ strike resulted in nearly all indicators falling to numbers lower than those observed at the onset or during the pre-strike pandemic period. Interpretation: The COVID-19 pandemic and the associated health-care workers’ strike in Kenya have been associated with a substantial disruption of essential health services, with the use of outpatient visits, screening and diagnostic services, and child immunisation adversely affected. Efforts to maintain the provision of these essential health services during a health-care crisis should target the susceptible services to prevent the exacerbation of associated disease burdens during such health crises

The impact of routine data quality assessments on electronic medical record data quality in Kenya

<div><p>Background</p><p>Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya.</p><p>Methods</p><p>RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs.</p><p>Results</p><p>27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33).</p><p>Conclusions</p><p>This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings.</p></div

The association between facility characteristics and the risk that records contained missing values during baseline RDQAs.

<p>The association between facility characteristics and the risk that records contained missing values during baseline RDQAs.</p

The association between facility characteristics and the frequency of concordant data elements in paper records and KenyaEMR during baseline RDQAs.

<p>The association between facility characteristics and the frequency of concordant data elements in paper records and KenyaEMR during baseline RDQAs.</p

The relative risk of missing data and mean change in concordance score from the baseline to the follow-up RDQAs.

<p>The relative risk of missing data and mean change in concordance score from the baseline to the follow-up RDQAs.</p

Flow diagram of facility selection for RDQA.

<p>RDQAs analysis included 27 facilities out 341 facilities implementing KenyaEMR.</p

Characteristics of the 27 facilities that participated in both baseline and follow-up RDQAs.

<p>Characteristics of the 27 facilities that participated in both baseline and follow-up RDQAs.</p

Near-Complete SARS-CoV-2 Seroprevalence among Rural and Urban Kenyans despite Significant Vaccine Hesitancy and Refusal

Considering the early inequity in global COVID-19 vaccine distribution, we compared the level of population immunity to SARS-CoV-2 with vaccine uptake and refusal between rural and urban Kenya two years after the pandemic onset. A population-based seroprevalence study was conducted in the city of Nairobi (n = 781) and a rural western county (n = 810) between January and February 2022. The overall SARS-CoV-2 seroprevalence was 90.2% (95% CI, 88.6–91.2%), including 96.7% (95% CI, 95.2–97.9%) among urban and 83.6% (95% CI, 80.6–86.0%) among rural populations. A comparison of immunity profiles showed that >50% of the rural population were strongly immunoreactive compared to 75%), inadequate information (26%), and concern about vaccine effectiveness (9%). Less than 2% of vaccine refusers cited religious or cultural beliefs. These findings indicate that despite vaccine inequity, hesitancy, and refusal, herd immunity had been achieved in Kenya and likely other African countries by early 2022, with natural infections likely contributing to most of this immunity. However, vaccine campaigns should be sustained due to the need for repeat boosters associated with waning of SARS-CoV-2 immunity and emergence of immune-evading virus variants