ABSTRACT
Pathology, clinical care teams, and public health experts often operate in silos. We hypothesized that large data sets from laboratories when integrated with other healthcare data can provide evidence that can be used to optimize planning for healthcare needs, often driven by health-seeking or delivery behavior. From the hospital information system, we extracted raw data from tests performed from 2019 to 2021, prescription drug usage, and admission patterns from pharmacy and nursing departments during the COVID-19 pandemic in Kenya (March 2020 to December 2021). Proportions and rates were calculated. Regression models were created, and a t-test for differences between means was applied for monthly or yearly clustered data compared to pre-COVID-19 data. Tests for malaria parasite, Mycobacterium tuberculosis, rifampicin resistance, blood group, blood count, and histology showed a statistically significant decrease in 2020, followed by a partial recovery in 2021. This pattern was attributed to restrictions implemented to control the spread of COVID-19. On the contrary, D-dimer, fibrinogen, CRP, and HbA1c showed a statistically significant increase (p-value <0.001). This pattern was attributed to increased utilization related to the clinical management of COVID-19. Prescription drug utilization revealed a non-linear relationship to the COVID-19 positivity rate. The results from this study reveal the expected scenario in the event of similar outbreaks. They also reveal the need for increased efforts at diabetes and cancer screening, follow-up of HIV, and tuberculosis patients. To realize a broader healthcare impact, pathology departments in Africa should invest in integrated data analytics, for non-communicable diseases as well.
ABSTRACT
Pathology, clinical care teams, and public health experts often operate in silos. We hypothesized that large data sets from laboratories when integrated with other healthcare data can provide evidence that can be used to optimize planning for healthcare needs, often driven by health-seeking or delivery behavior. From the hospital information system, we extracted raw data from tests performed from 2019 to 2021, prescription drug usage, and admission patterns from pharmacy and nursing departments during the COVID-19 pandemic in Kenya (March 2020 to December 2021). Proportions and rates were calculated. Regression models were created, and a t-test for differences between means was applied for monthly or yearly clustered data compared to pre-COVID-19 data. Tests for malaria parasite, Mycobacterium tuberculosis, rifampicin resistance, blood group, blood count, and histology showed a statistically significant decrease in 2020, followed by a partial recovery in 2021. This pattern was attributed to restrictions implemented to control the spread of COVID-19. On the contrary, D-dimer, fibrinogen, CRP, and HbA1c showed a statistically significant increase (p-value <0.001). This pattern was attributed to increased utilization related to the clinical management of COVID-19. Prescription drug utilization revealed a non-linear relationship to the COVID-19 positivity rate. The results from this study reveal the expected scenario in the event of similar outbreaks. They also reveal the need for increased efforts at diabetes and cancer screening, follow-up of HIV, and tuberculosis patients. To realize a broader healthcare impact, pathology departments in Africa should invest in integrated data analytics, for non-communicable diseases as well.
ABSTRACT
Introduction From the first case of SARS-Co-2 in Wuhan, China, to the virus being declared as a pandemic in March 2020, the world has witnessed morbidity and mortality on a global scale. Scientists have worked at a record pace to deliver a vaccine for the prevention of this deadly disease. Tocilizumab, an interleukin-6 (IL-6) blocker, received an emergency use authorization (EUA) by the Federal Drug Agency (FDA) in June 2021. Methods This retrospective observational cohort study was conducted at the Aga Khan University Hospital, Nairobi, from March 8, 2020, to December 31, 2020. All patients with PCR confirmed COVID-19 pneumonia were included. Data were obtained from the medical records, and the admission registry was used to identify the patients, and both their electronic and paper-based files were retrieved from the medical records. Patient demographic data, medical history, baseline comorbidities, clinical characteristics, and outcome data were collected to study the infectious complications of Tocilizumab in patients affected by COVID-19 pneumonia. Results A total of 913 patients who were diagnosed with COVID-19 were included. The overall superinfection infection rate among the COVID-19 patients was 6%. Superinfection in patients who received the Tocilizumab was 17.2% and in the non-Tocilizumab group was 4.8%. The superinfection rate among severe and critically ill patients was even higher at 41.8% and 69.9% (Tocilizumab group) and 2.1% and 11.8% (non-Tocilizumab group), respectively (p < 0.001). There was no difference in mortality observed between the groups (p = 0.846). Infection among HIV co-infection was very low at 2.3%. Conclusion Contrary to some studies, a higher rate of infection was observed among the Tocilizumab group, and no difference in mortality was observed between Tocilizumab and the non-Tocilizumab group. Infection among patients with HIV remains low in this susceptible population.