Prospective Clinical Surveillance for Severe Acute Respiratory Illness in Kenyan Hospitals during the COVID-19 pandemic (preprint)

There are limited data from sub-Saharan Africa describing the pattern of admissions to public hospitals with severe acute respiratory infections during the COVID-19 pandemic. We conducted a prospective longitudinal hospital-based sentinel surveillance between May 2020 and December 2022 at 16 public hospitals in Kenya. All patients aged above 18 years admitted to adult medical wards in the participating hospitals were included. Demographic and clinical characteristics, COVID-19 infection and vaccination status and outcome data were collected. Of the 52,714 patients included in the study, 18,001 (35%) were admitted with severe acute respiratory illness (SARI). The mean age was 51 years. Patients were equally distributed across sexes. Pneumonia was the most common diagnosis at discharge. Hypertension, HIV and diabetes mellitus were the most common comorbidities. COVID-19 test results were positive in 2,370 (28%) of the 8,517 (47%) patients that underwent testing. Overall inpatient case fatality for SARI was 21% (n=3,828). After adjusting for age, sex and presence of a comorbidity, SARI patients had higher inpatient mortality compared to non-SARI patients regardless of their COVID-19 status (aHR 1.31, 95% CI 1.19 - 1.46). COVID-19 positive SARI patients had a higher inpatient mortality rate compared to their negative counterparts (aHR 1.31, 95% CI 1.12 - 1.54, p value < 0.0001). COVID-19 vaccine effectiveness against mortality due to SARI after adjusting for age, sex and presence of a comorbidity was 34% (95% CI 11% - 51%). We have provided a comprehensive description of the pattern of admissions with respiratory illnesses in Kenyan hospitals during the COVID-19 pandemic period. We have demonstrated the utility of routine surveillance activities within public hospitals in low-income settings which if strengthened can enhance the response to emerging health threats.

Effects of the COVID-19 pandemic on hospital admissions and inpatient mortality in Kenya (preprint)

Background: The impact of COVID-19 in Africa remains poorly defined. We sought to describe trends in hospitalisation due to all medical causes, pneumonia-specific admissions, and inpatient mortality in Kenya before and during the first five waves of the COVID-19 pandemic in Kenya. Methods: We conducted a hospital-based observational study of patients admitted to 13 public referral facilities in Kenya from January 2018 to December 2021. The pre-COVID population included patients admitted before 1 March 2020. We fitted time series models to compare observed and predicted trends for each outcome. To estimate the impact of the COVID-19 pandemic we calculated incidence rate ratios (IRR) and corresponding 95% confidence intervals (CI) from negative binomial mixed-effects models. Results: Out of 302,703 patients (range 7453 to 27168) hospitalised across the 13 surveillance sites 84,337 (55.2%) were aged 15 years and older. Compared with the pre-COVID period, hospitalisations declined markedly among adult (IRR 0.68, 95% CI 0.63 to 0.73) and paediatric (IRR 0.67, 95% CI 0.62 to 0.73) patients. Adjusted in-hospital mortality also declined among both adult (IRR 0.83, 95% CI 0.77 to 0.89) and paediatric (IRR 0.85, 95% CI 0.77 to 0.94) admissions. Pneumonia-specific admissions among adults were higher during the pandemic (IRR 1.75, 95% CI 1.18 to 2.59), while the paediatric pneumonia cases were lower than pre-pandemic levels in the first year of the pandemic and elevated in late 2021 (IRR 0.78, 95% CI 0.51 to 1.20). Conclusions: Contrary to initial predictions, the COVID-19 pandemic was associated with lower rates of hospitalisation and in-hospital mortality, despite increased pneumonia admissions among adults. These trends were sustained after the withdrawal of containment measures that resulted in the disruption of essential health services, suggesting a role for additional factors that warrant further investigation.

Improving the quality of in-patient neonatal routine data as a pre-requisite for monitoring and improving quality of care at scale: A multi-site retrospective cohort study in Kenyan hospitals (preprint)

Objectives The objectives of this study were to (1) determine if membership of a clinical information network (CIN) was associated with an improvement in the quality of documentation of in-patient neonatal care provided over time, and (2) characterise accuracy of prescribing for basic treatments provided to neonatal in-patients if data are adequate. Design and Settings This was a retrospective cohort study involving all children aged ≤28 days admitted to New-Born Units (NBUs) between January 2018 and December 2021 in 20 government hospitals with an interquartile range of annual NBU inpatient admissions between 550 and 1640 in Kenya. These hospitals participated in routine audit and feedback processes on quality of documentation and care over the study period. Outcomes The study’s outcomes were the number of patients as a proportion of all eligible patients with (1) complete domain-specific documentation scores, and (2) accurate domain-specific treatment prescription scores at admission. Findings 80060 NBU admissions were eligible for inclusion. Upon joining the CIN, documentation scores in the monitoring (vital signs) , other physical examination and bedside testing , discharge information , and maternal history domains demonstrated a statistically significant month-to-month relative improvement in number of patients with complete documentation of 7.6%, 2.9%, 2.4%, and 2.0% respectively. There was also statistically significant month-to-month improvement in prescribing accuracy after joining the CIN of 2.8% and 1.4% for feeds and fluids but not for Antibiotic prescriptions. Findings suggest that much of the variation observed is due to hospital-level factors. Conclusions It is possible to introduce tools that capture important clinical data at least 80% of the time in routine African hospital settings but analyses of such data will need to account for missingness using appropriate statistical techniques. These data allow trends in performance to be explored and could support better impact evaluation, performance benchmarking, exploration of links between health system inputs and outcomes and scrutiny of variation in quality and outcomes of hospital care.