Utilization of routine health data and its determinants among healthcare workers in public health facilities of harari region, eastern Ethiopia.

BACKGROUND: Routine health information is the pillar of the planning and management of health services and plays a vital role in effective and efficient health service delivery, decision making, and program improvement. Little is known about evidence-based actions to successively advance the use of information for decision making. Therefore, this study aimed to assess the level and determinants of routine health data utilization among health workers in public health facilities in the Harari region, Ethiopia. METHODS: An institutional-based cross-sectional study design was used from June 1 to July 31, 2020. A total of 410 health care providers from two hospitals and five health centers were selected using a simple random sampling technique. Data were collected through a structured questionnaire complemented by an observational checklist. The collected data were thoroughly checked, coding, and entered into Epi-data version 4.6 before being transferred to Stata version 14 for analysis. Frequency and cross-tabulations were performed. To measure factors associated with routine use of health data, bivariate and multivariate logistic regression analyzes were performed. The odds ratio with a 95% CI was calculated, and then a p-value of less than 0.05 was considered significant. RESULT: The general utilization of routine health data was 65.6%. The use of routine health data was significantly associated with healthcare workers who had a positive attitude towards data [AOR = 4 (2.3-6.9)], received training [AOR = 2.1 (1.3-3.6)], had supportive supervision [AOR = 3.6 (2.1-6.2)], received regular feedback [AOR = 2.9 (1.7-5.0)] and perceived a culture of information use [AOR = 2.5 (1.3-4.6)]. CONCLUSIONS: Sixty percent of health professionals had used routine health data utilization. Training, supervision, feedback, and the perceived culture of information were independently associated with the use of routine health data utilization. Therefore, it is critical to focus on improving data utilization practices by addressing factors that influence the use of routine health data.

Does neonatal resuscitation associated with neonatal death in low-resource settings? A follow-up study.

BACKGROUND: The newborn period is the most vulnerable phase for a child's survival, with around half of all under-five deaths worldwide occurring during this time. Despite existing policies and measures, Ethiopia ranks among the top 10 African countries in terms of newborn mortality. In spite of many studies being carried out in the country, the incidence and predictors of neonatal mortality in the Pastoralist and agro-pastoralist parts of the country's southern still remain unidentified. Therefore, this study aimed to identify the predictors of neonatal mortality in selected public Hospitals in southern Ethiopia. MATERIALS AND METHODS: An institution-based retrospective cohort study was conducted among 568 neonates admitted to the neonatal intensive care unit at Bule Hora University teaching Hospital and Yabelo General Hospital, Southern Ethiopia from 1 January 2020-31 December 2021. A simple random sampling technique was used to select records of neonates. Data entry was performed using Epidata version 3.1 and the analysis was performed using STATA version 14.1 Kaplan Meir curve and Log-rank test were used to estimate the survival time and compare survival curves between variables. Hazard Ratios with 95% CI were computed and all the predictors associated with the outcome variable at p-value 0.05 in the multivariable cox proportional hazards analysis were declared as a significant predictor of neonatal death. RESULTS: Out of 565 neonates enrolled, 54(9.56%) neonates died at the end of the follow-up period. The overall incidence rate of death was 17.29 (95% CI: 13.24, 22.57) per 1000 neonatal days with a restricted mean follow-up period of 20 days. Of all deaths, 64.15% of neonates died within the first week of life. In the multivariable cox-proportional hazard model, neonatal age < 7 days (AHR: 9.17, 95% CI: (4.17, 20.13), place of delivery (AHR: 2.48, 95% CI: (1.38, 4.47), Initiation of breastfeeding after 1 h of birth (AHR: 6.46, 95% CI: (2.24, 18.59), neonates' body temperature <36.5 °C (AHR: 2.14, 95% CI: (1.19, 3.83), and resuscitated neonates (AHR: 2.15, 95% CI: (1.20, 3.82) were independent predictors of neonatal death. CONCLUSION: In the research setting, the incidence of neonatal death was high, especially during the first week of life. The study found that neonatal age < 7 days, place of delivery, Initiation of breastfeeding after 1 h of birth, neonates' body temperature <36.5 °C, and resuscitated neonates were predictors of neonatal death. To improve newborn survival, significant neonatal problems, improved resuscitation, and other relevant factors should be addressed.

Cost of maternal complications and its associated factors among mothers attending Hawassa public hospitals, Southern Ethiopia.

Background: The cost of maternal complications is considered as an important factor hindering the utilization of maternal health care services. However, information of estimate of spending on maternal complication was lacking. This study was aimed to estimate the cost of maternal complications and associated factors among mother's attending Hawassa public hospitals, Sidama Regional state, Ethiopia. Methods: A cross-sectional study design was conducted among 348 randomly selected mothers attending public hospitals in Hawassa from November 15 to December 15, 2021. Data was coded and entered into Epi Data version 3.1 and exported to STATA version 16.0 for analysis. Simple and multiple linear regression analysis was done. Correlation coefficient along with 95% CI was used to present the finding and p < 0.05 was used to declare statistical significance. Results: This study found that total median cost of maternal complications was 4895.5 (IQR = 3779) ETB. The total median direct medical cost was 1765.5 (IQR = 1649.5) ETB. Number of days absent [(R = 0.028; 95% CI: (0.023, 0.033)], distance from facility [(R = 0.001; 95% CI: (0.000, 0.002)], site of laboratory diagnosis [(R = 0.230; 95% CI: (0.140, 0.320)], number of laboratory test conducted [(R = 0.045; 95% CI: (0.021, 0.069)] were found to be significance predictors of maternal complications costs. Conclusions: Total median cost of maternal complications in current study was high. Respondents' site of diagnosis, number of days missed from work, number of laboratory tests, and distance from hospitals were independent predictors of maternal complications cost. Thus, we will recommend governments to introduce strategies that specifically help mothers with maternal complications.

Incidence and predictors of treatment interruption among patients on anti-tuberculosis treatment in Nekemte public healthcare facilities, Oromia, Western Ethiopia.

Introduction: Tuberculosis treatment interruption increases the risk of poor treatment outcomes and the occurrence of drug resistant Tuberculosis. However, data on the incidence and predictors of tuberculosis treatment interruption are still scarce in Ethiopia, as well as in the study area. Therefore, this study aimed to assess the incidence and predictors of treatment interruption among patients on tuberculosis treatment in Nekemte public healthcare facilities, Oromia region, Western Ethiopia, from July 1, 2017, to June 30, 2021. Methods: A retrospective cohort study design was conducted among 800 patients enrolled in anti-tuberculosis treatment during the study period. Data were collected from patient cards who were enrolled in treatment from July 1, 2017 to June 30, 2021. Epidata version 3.2 was used for data entry, and STATA version 14 was used for analysis. A multivariable Cox regression model with a 95% confidence interval (CI) and adjusted hazard ratio (AHR) was used to identify the significant predictors at a p value < 0.05. Finally, the log likelihood ratio, and a Cox-Snell residual graph was used to check the adequacy of the model. Results: A total of 800 patients were followed for a median time of 2.3 (95% CI: 2.20-2.36) months, and with a maximum follow-up time of 11.7 months. The overall incidence rate of treatment interruption was 27.4 per 1000 (95% CI: 22.8-32.8) person-month observations. Age 18-34 years (AHR = 1.8, 95% CI: 1.02-3.18), male (AHR = 1.63, 95% CI: 1.1-2.42), rural residence (AHR = 3, 95% CI: 1.98-4.64), presence of comorbidity (AHR = 10, 95% CI: 5.47-18.27) and lack of treatment supporters on the treatment follow-up (AHR = 2.82, 95% CI: 1.9-4.41) were found to be significant predictors of treatment interruption. Conclusion: A high incidence rate of interruption was observed among TB patients in public health facilities in Nekemte town. Health facilities should provide supportive care for patients with co-morbidities and consider interventions that target middle-aged patients from rural areas that reduce treatment interruptions.

Predictors of willingness to accept COVID-19 vaccine among adults.

Background: Vaccines are an effective and ultimate solution that can decrease the burden of coronavirus disease 2019 worldwide. However, poor knowledge and unwillingness to accept this vaccine are key barriers to manage the COVID-19 pandemic in different countries including Ethiopia. Control of the pandemic will depend on the acceptance of coronavirus disease vaccine. However, there is a paucity of evidence on coronavirus disease vaccine acceptance in the study area. The current study was aimed to assess willingness to accept the COVID-19 vaccine and associated factors among adult clients attending Bule Hora University Teaching Hospital, West Guji Zone, southern Ethiopia. Methods: An institution-based cross-sectional study was conducted among 385 study participants selected by a systematic random sampling technique. Data was collected through observation and structured questionnaires from April 10 to May 30, 2022. The collected data was cleaned and entered into EpiData 3.1 software before being exported to SPSS 25 statistical software for analysis. Bi-variable and multi-variable binary logistic regression model was used to identify the predictors of COVID-19 vaccine acceptance. The strength of association was measured using AOR with 95% confidence interval and significance was declared at p- value < 0.05. Result: Magnitude of willingness to accept coronavirus disease-19 vaccine was 67.5% (95%Cl: 63-72). Good knowledge [AOR = 2.07, (1.17-3.64)], history of chronic disease [AOR = 2.59, (1.4-4.78)], being a government employee [AOR = 2.35 (1.1-5)], having a favorable attitude [AOR = 14.15 (5.25-37.46)], and good adherence [AOR = 1.74 (1.02-2.97)] were factors that significantly associated with willingness to accept the coronavirus disease 2019 vaccine. Conclusion: Magnitude of willingness to accept the COVID-19 vaccine was considerable and needs to be improved. Knowledge, attitude, chronic illness, adherence, and being a government employee were factors that associated with willingness to accept the vaccine. Community awareness, advocacy, social mobilization and health education should be given at different levels.