Health Workers' Perceptions on Data-informed Decision-Makingpracticesin Primary Health Care Units at Awi Zone, Northwest Ethiopia

Background: Data-informed decision making is influenced by organizational, technical, and behavioral factors. Behavioral factors are the major contributing factors for data-informed decision-making practices. This study aimed to explore health workers' perceptions of data-informed decision making at primary health care units in Awi zone. Method: A cross-sectional qualitative study was undertaken to explore health workers' perceptions on the barriers of health data-informed decision-making practices. Eleven healthcare workers were purposively selected from primary hospitals, health centers and health posts. Medical doctors, nurses, midwifes and health extension workers were selected as key informants for the in-depth interview. The selected healthcare workers were asked about their perceptions that affect health data use practices. The data obtained was analyzed through thematic analysis using Open Code software. Analysis was performed using three themes namely, organizational, behavioral, and technical barriers of data-informed decision making. Results: All the health care workers including health extension workers utilized a data-informed decision-making practice at least once during their point of care. Five of the eleven key informants reported their data-informed decision-making practice as reviewing quality of facility data, while none of them reported data-informed decision-making practices for their monthly performance monitoring. Behavioral factors included negligence, workarounds, and skill gaps. Organizational factors included staff turnover and shortage of recording tools. Technical factors included high workloads which lead to data error and paper-based systems were considered major barriers to data-informed decision-making practices.

Health workers' perceptions on data-informed decision-making practices in primary health care units at Awi zone, northwest Ethiopia

Data-informed decision making is influenced by organizational, technical,and behavioral factors. Behavioral factors are the major contributing factorsfor data-informed decision-makingpractices. This study aimed to explorehealth workers'perceptionsofdata-informed decision making at primary health care units inAwi zone.Method:A cross-sectional qualitative study was undertaken to explore health workers'perceptionson the barriers of health data-informed decision-makingpractices. Eleven healthcare workers were purposively selected from primary hospitals, health centers and health posts. Medical doctors, nurses, midwifesand health extension workers were selected askey informantsfor the in-depth interview.The selected healthcare workers were asked about theirperceptionsthat affect health data usepractices.The data obtained was analyzedthroughthematic analysis using Open Codesoftware. Analysis was performed usingthree themes namely,organizational, behavioral,and technicalbarriers of data-informed decision making. Results:All the health care workers including health extension workers utilizedadata-informed decision-makingpractice atleast once duringtheirpoint of care. Five ofthe eleven key informants reported their data-informed decision-makingpractice asreviewingquality of facility data, while none of them reported data-informed decision-makingpracticesfor their monthly performancemonitoring. Behavioral factors includednegligence, workarounds,and skill gaps.Organizational factors includedstaff turnover and shortage of recording tools.Technical factors includedhigh workloads whichlead to data error and paper-based systemswere considered major barriers todata-informed decision-makingpractices.Conclusion:Data-informed decision-makingpracticeswerelow at primaryhealthcare units.Behavioral, organizational,and technical factors contributed to the decreased use of data.[Ethiop. J. Health Dev. 2022; 36(2):000-000]Keywords:Data-informed decision making, Perception, Organizational, Behavioral, Technical, Ethiopia

COVID-19 Pandemic Planning: Simulation Models to Predict Biochemistry Test Capacity for Patient Surges.

BACKGROUND: Patient surges beyond hospital capacity during the initial phase of the COVID-19 pandemic emphasized a need for clinical laboratories to prepare test processes to support future patient care. The objective of this study was to determine if current instrumentation in local hospital laboratories can accommodate the anticipated workload from COVID-19 infected patients in hospitals and a proposed field hospital in addition to testing for non-infected patients. METHODS: Simulation models predicted instrument throughput and turn-around-time for chemistry, ion-selective-electrode, and immunoassay tests using vendor-developed software with different workload scenarios. The expanded workload included tests from anticipated COVID patients in 2 local hospitals and a proposed field hospital with a COVID-specific test menu in addition to the pre-pandemic workload. RESULTS: Instrumentation throughput and turn-around time at each site was predicted. With additional COVID-patient beds in each hospital, the maximum throughput was approached with no impact on turnaround time. Addition of the field hospital workload led to significantly increased test turnaround times at each site. CONCLUSIONS: Simulation models depicted the analytic capacity and turn-around times for laboratory tests at each site and identified the laboratory best suited for field hospital laboratory support during the pandemic.

COVID-19: resuming International Travel in the Caribbean

This webinar, hosted by the PAHO/WHO Office for Barbados and the Eastern Caribbean Countries was for the health, tourism, immigration, and other relevant sectors to share experiences and provide technical guidance. The expected outcome was to build a common understanding on decision-making process for resuming non-essential travel during COVID-19.

International Public Health Laboratory Twinning: An Innovative Approach to Strengthen the National Health Laboratory System in Uganda, 2014-2017.

International initiatives to strengthen national health laboratory systems in resource-poor countries are often hampered by unfamiliarity with the country's health laboratory environment and turnover of international partners during the initiative. This study provides an overview of, and lessons learned from, the use of a laboratory long-term partnership approach (ie, "twinning") to strengthen the national public health laboratory system in an international setting. We focused on the partnering of the Uganda Ministry of Health Central Public Health Laboratory (CPHL) with the New Mexico State Public Health Laboratory to help the CPHL become Uganda's national public health reference laboratory (Uganda National Health Laboratory Services [UNHLS] Institute) and leader of its nascent Uganda National Health Laboratory Network (UNHLN). Via twinning, CPHL leadership received training on laboratory leadership and management, quality systems, facility management, and the One Health environmental strategy (ie, that the health of persons is connected to the health of animals and the environment), and drafted a National Health Laboratory Policy, UNHLS Institute business plan, and strategic and operating plans for the UNHLS Institute and UNHLN. The CPHL is now responsible for the UNHLS Institute and coordinates the UNHLN. Lessons learned include (1) twinning establishes stable long-term collaborations and (2) success requires commitment to a formal statement of activities and objectives, as well as clear and regular communication among partners.

Comparative Effectiveness of Coalitions Versus Technical Assistance for Depression Quality Improvement in Persons with Multiple Chronic Conditions.

Significance: Prior research suggests that Community Engagement and Planning (CEP) for coalition support compared with Resources for Services (RS) for program technical assistance to implement depression quality improvement programs improves 6- and 12-month client mental-health related quality of life (MHRQL); however, effects for clients with multiple chronic medical conditions (MCC) are unknown. Objective: To explore effectiveness of CEP vs RS in MCC and non-MCC subgroups. Design: Secondary analyses of a cluster-randomized trial. Setting: 93 health care and community-based programs in two neighborhoods. Participants: Of 4,440 clients screened, 1,322 depressed (Patient Health Questionnaire, PHQ8) provided contact information, 1,246 enrolled and 1,018 (548 with ≥3 MCC) completed baseline, 6- or 12-month surveys. Intervention: CEP or RS for implementing depression quality improvement programs. Outcomes and Analyses: Primary: depression (PHQ9 <10), poor MHRQL (Short Form Health Survey, SF-12<40); Secondary: mental wellness, good physical health, behavioral health hospitalization, chronic homelessness risk, work/workloss days, services use at 6 and 12 months. End-point regressions were used to estimate intervention effects on outcomes for subgroups with ≥3 MCC, non-MCC, and intervention-by-MCC interactions (exploratory). Results: Among MCC clients at 6 months, CEP vs RS lowered likelihoods of depression and poor MHRQL; increased likelihood of mental wellness; reduced work-loss days among employed and likelihoods of ≥4 behavioral-health hospitalization nights and chronic homelessness risk, while increasing faith-based and park community center depression services; and at 12 months, likelihood of good physical health and park community center depression services use (each P<.05). There were no significant interactions or primary outcome effects for non-MCC. Conclusions: CEP was more effective than RS in improving 6-month primary outcomes among depressed MCC clients, without significant interactions.

Community Partners in Care: 6- and 12-month Outcomes of Community Engagement versus Technical Assistance to Implement Depression Collaborative Care among Depressed Older Adults.

Objective: Community Partners in Care, a community-partnered, cluster-randomized trial with depressed clients from 95 Los Angeles health and community programs, examined the added value of a community coalition approach (Community Engagement and Planning [CEP]) versus individual program technical assistance (Resources for Services [RS]) to implement depression collaborative care in underserved communities. This exploratory subanalysis examines 6- and 12-month outcomes among CPIC participants aged >50 years. Design: Community-partnered, cluster-randomized trial conducted between April 2010 and March 2012. Setting: Hollywood-Metropolitan (HM) and South Los Angeles (SLA) Service Planning Areas (SPAs), Los Angeles, California. Participants: 394 participants aged >50 years with depressive symptoms (8-item Patient Health Questionnaire score ≥ 10). Intervention: A community-partnered multi-sector coalition approach (Community Engagement and Planning [CEP]) vs individual program technical assistance (Resources for Services [RS]) to implement depression collaborative care. Main Outcome Measures: Depressive symptoms (PHQ-8 score), mental health-related quality of life (MHRQL), community-prioritized outcomes including mental wellness, homelessness risk and physical activity, and services utilization. Results: At 6 months, CEP was more effective than RS at improving MHRQL and mental wellness among participants aged >50 years; no differences were found in the effects of CEP vs RS on other outcomes. No significant outcome differences between CEP and RS were found at 12 months. Conclusions: A multisector community coalition approach may offer additional benefits over individual program technical assistance to improve outcomes among depressed adults aged >50 years living in underserved communities.