Harmful Alcohol Use Among Healthcare Workers at the Beginning of the COVID-19 Pandemic in Kenya.

Background: Healthcare workers play a key role in responding to pandemics like the on-going COVID-19 one. Harmful alcohol use among them could result in inefficiencies in health service delivery. This is particularly concerning in sub-Saharan Africa where the health workforce is already constrained. The aim of this study is to document the burden and correlates of harmful alcohol use among healthcare workers at the beginning of the COVID-19 pandemic in Kenya with the aim of informing policy and practice. Methods: This study was a cross-sectional analysis of data obtained from a parent online survey that investigated the burden and factors associated with mental disorders among healthcare workers during the COVID-19 pandemic in Kenya. We analyzed data obtained from a sub-population of 887 participants who completed the Alcohol Use Disorder Identification Test questionnaire. We used descriptive statistics to summarize the socio-demographic characteristics of the participants and multivariate analysis to determine the factors associated with harmful alcohol use. Results: Three hundred and eighty nine (43.9%) participants reported harmful alcohol use. The factors significantly associated with increased odds of endorsing harmful alcohol use were: being male (AOR = 1.56; 95% CI = 1.14, 2.14; p = 0.006), being unmarried (AOR = 2.06; 95% CI = 1.48, 2.89; p < 0.001), having 11-20 years of experience as compared to having 20+ years of experience (AOR = 1.91; 95% CI = 1.18, 3.12; p = 0.009), and being a specialist (AOR = 2.78; CI = 1.64, 4.78; p = < 0.001) or doctor (AOR = 2.82; 95% CI = 1.74, 4.63; p < 0.001) as compared to being a nurse. Conclusions: A high proportion of healthcare workers reported harmful alcohol use at the beginning of the COVID-19 pandemic in Kenya. Males, the unmarried, those with 11-20 years of experience in the health field, doctors and specialists, were more likely to report harmful alcohol use. These findings highlight the need to institute interventions for harmful alcohol use targeting these groups of healthcare workers in Kenya during the COVID-19 pandemic in order to optimize functioning of the available workforce.

Mental Disorders Among Health Care Workers at the Early Phase of COVID-19 Pandemic in Kenya; Findings of an Online Descriptive Survey.

Background: Healthcare workers responding to the Corona Virus Pandemic (COVID-19) are at risk of mental illness. Data is scanty on the burden of mental disorders among Kenyan healthcare workers responding to the pandemic that can inform mental health and psychosocial support. The purpose of this study was to establish the frequency and associated factors of worry, generalized anxiety disorder, depression, posttraumatic stress disorder and poor quality of sleep among Kenyan health care workers at the beginning of COVID-19 pandemic. Methods: We conducted an online survey among 1,259 health care workers in Kenya. A researcher developed social demographic questionnaire and several standardized tools were used for data collection. Standardized tools were programmed into Redcap, (Research Electronic Data Capture) and data analysis was performed using R Core Team. In all analysis a p-value < 0.05 was considered significant. Results: 66% of the participants reported experiencing worry related to COVID-19. 32.1% had depression, 36% had generalized anxiety, 24.2% had insomnia and 64.7% scored positively for probable Post Traumatic Stress Disorder (PTSD). Depression was higher among females compared to men (36.5 vs. 26.9%, p = 0.003), workers <35 years old compared to older ones (38.1 vs. 26.4%, p < 0.001), and those who were not married compared to those who were married (40.6 vs. 27.6%, p < 0.001). Generalized anxiety was commoner among workers aged <35 years (43.5 vs. 29.3%, p < 0.001), females (41.7 vs. 29.2%, p < 0.001), those who mere not married compared to the married (45.2 vs. 31.2%, p < 0.001) and those with <10 years working experience (41.6 to 20.5%, p < 0.001). Younger health care professional had a higher proportion of insomnia compared to the older ones (30.3 vs. 18.6%, p < 0.001). Insomnia was higher among those with <10 years' experience compared to those with more than 20 years' experience(27.3 vs. 17.6%, p = 0.043) Conclusion: Many Kenyan healthcare workers in the early phase of COVID-19 pandemic suffered from various common mental disorders with young, female professionals who are not married bearing the bigger burden. This data is useful in informing interventions to promote mental and psychosocial wellbeing among Kenyan healthcare workers responding to the pandemic.

Therapeutic turnaround times for common laboratory tests in a tertiary hospital in Kenya.

METHODS: We evaluated therapeutic TAT for a tertiary hospital in Western Kenya, using a time-motion study focusing specifically on common hematology and biochemistry orders. The aim was to determine significant bottlenecks in diagnostic testing processes at the institution. RESULTS: A total of 356 (155 hematology and 201 biochemistry) laboratory tests were fully tracked from the time of ordering to availability of results to care providers. The total therapeutic TAT for all tests was 21.5 ± 0.249 hours (95% CI). The therapeutic TAT for hematology was 20.3 ± 0.331 hours (95% CI) while that for biochemistry tests was 22.2 ± 0.346 hours (95% CI). Printing, sorting and dispatch of the printed results emerged as the most significant bottlenecks, accounting for up to 8 hours of delay (Hematology-8.3 ± 1.29 hours (95% CI), Biochemistry-8.5 ± 1.18 hours (95% CI)). Time of test orders affected TAT, with orders made early in the morning and those in the afternoon experiencing the most delays in TAT. CONCLUSION: Significant inefficiencies exist at multiple steps in the turnaround times for routine laboratory tests at a large referral hospital within an LMIC setting. Multiple opportunities exist to improve TAT and streamline processes around diagnostic testing in this and other similar settings.

Development and Usability of Mobile-based Healthcare Protocols in Kenya.

Healthcare protocols have been shown to improve the quality of health service delivery by offering explicit guidelines and recommendations for clinicians who are uncertain about how to proceed in a given clinical situation. While various modalities are used to implement protocols, few rigorous evaluations of protocol use exist in low-resource clinical settings. This study aimed to develop mobile-based protocols (MBPs) and test their usability against currently used paper-based protocol (PBPs). Satisfaction, efficiency and effectiveness of the protocols were evaluated through a think-aloud usability exercise, in-depth interviews, and through a questionnaire. Compared to PBPs, satisfaction scores were higher with MBPs (83.8 versus 66.8, p=0.0498), number of errors lower with MBPs (2/25 versus 5/25, p=0.1089), with average time for task completion higher with MBPs (23.3s versus21.6s, p=0.7394). MBPs offer more satisfaction and trend towards being more effective as a dissemination modality for healthcare protocols in low-resource settings.

Intensive Care Outcomes and Mortality Prediction at a National Referral Hospital in Western Kenya.

RATIONALE: The burden of critical care is greatest in resource-limited settings. Intensive care unit (ICU) outcomes at public hospitals in Kenya are unknown. The present study is timely, given the Kenyan Ministry of Health initiative to expand ICU capacity. OBJECTIVES: To identify factors associated with mortality at Moi Teaching and Referral Hospital and validate the Mortality Probability Admission Model II (MPM0-II). METHODS: A retrospective cohort of 450 patients from January 1, 2013, to April 5, 2015, was evaluated using demographics, presenting diagnoses, interventions, mortality, and cost data. RESULTS: ICU mortality was 53.6%, and 30-day mortality was 57.3%. Most patients were male (61%) and at least 18 years old (70%); the median age was 29 years. Factors associated with high adjusted odds of mortality were as follows: age younger than 10 years (adjusted odds ratio [aOR], 3.59; P ≤ 0.001), ages 35-49 years (aOR, 3.13; P = 0.002), and age above 50 years (aOR, 2.86; P = 0.004), with reference age range 10-24 years; sepsis (aOR, 3.39; P = 0.01); acute stroke (aOR, 8.14; P = 0.011); acute respiratory failure or mechanical ventilation (aOR, 6.37; P < 0.001); and vasopressor support (aOR, 7.98; P < 0.001). Drug/alcohol poisoning (aOR, 0.33; P = 0.005) was associated with lower adjusted odds of mortality. MPM0-II discrimination showed an area under the receiver operating characteristic curve of 0.78 (95% confidence interval, 0.72-0.82). The result of the Hosmer-Lemeshow test for calibration was significant (P < 0.001). CONCLUSIONS: In a Kenyan public ICU, high mortality was noted despite the use of advanced therapies. MPM0-II has acceptable discrimination but poor calibration. Modification of MPM0-II or development of a new model using a prospective multicenter global collaboration is needed. Standardized triage and treatment protocols for high-risk diagnoses are needed to improve ICU outcomes.

Setting the Agenda for Personal Health Records in Low- and Middle-Income Countries.

In the developed world, Personal Health Records (PHRs) have been demonstrated to improve patient adherence, reduce medical errors, improve patient-provider communication, improve chronic disease management, and promote behavior change. PHRs have not been widely adopted in low- and middle-income countries. There is rising use of smartphones, adoption of national-level electronic health systems, and change in historical perceptions on acceptance of technology. It is thus an opportune time to critically examine the potential role and approaches to PHRs in LMICs.

An Evaluation of Two Methods for Generating Synthetic HL7 Segments Reflecting Real-World Health Information Exchange Transactions.

Motivated by the need for readily available data for testing an open-source health information exchange platform, we developed and evaluated two methods for generating synthetic messages. The methods used HL7 version 2 messages obtained from the Indiana Network for Patient Care. Data from both methods were analyzed to assess how effectively the output reflected original 'real-world' data. The Markov Chain method (MCM) used an algorithm based on transitional probability matrix while the Music Box model (MBM) randomly selected messages of particular trigger type from the original data to generate new messages. The MBM was faster, generated shorter messages and exhibited less variation in message length. The MCM required more computational power, generated longer messages with more message length variability. Both methods exhibited adequate coverage, producing a high proportion of messages consistent with original messages. Both methods yielded similar rates of valid messages.