Magnitude of psychological distress and associated factors among war survivor women in Northern, Ethiopia, 2022: a community-based cross-sectional survey.

BACKGROUND: Civilian war and internal conflicts increase the incidences of mental health conditions among war survivors. It is crucial to assess war-related psychological consequences in war-affected areas in Ethiopia to intervene in the future. Thus, this study aimed to determine the magnitude of psychological distress and associated factors of psychological distress among war survivor women in Northern, Ethiopia. METHODS: A community-based cross-sectional survey was conducted, and 1596 war survivor women were recruited to participate using a face-to-face interviews with a census sampling technique from May 1-30, 2022. The psychological distress was assessed using a Kessler psychological distress scale (K10). Bi-variable and multi-variable logistic regression analyses were used, and variables with a p-value less than 0.05 in the multivariable analyses were considered statistically significant. RESULT: In this study, the response rate was 100% and the prevalence of psychological distress was 44.90% at a 95% CI: (42.40, 47.40). Psychological distress was significantly associated with the education of ability to read and write (AOR = 2.92; 95% CI: 2.12, 4.01), primary education and above (AOR = 3.08; 95% CI: 2.09, 4.54), housewife (AOR = 5.07; 95%CI: 2.64, 9.74), farmer (AOR = 8.92; 95%CI: 4.03, 19.70), emotional violence (AOR = 1.52; 95%CI: 1.05, 2.18), physical violence (AOR = 3.85; 95%CI: 2.37, 6.26) and sexual violence (AOR = 3.25; 95%CI: 1.98, 5.33) whereas being separate was protective for psychological distress (AOR = 0.38; 95%CI: 0.16, 0.92). CONCLUSION: The prevalence of psychological distress was found to be high. Therefore, women who are housewives, married, farmers, educated, and who have experienced violence must be the focus of governmental and private collaborative interventions to prevent war-related psychological morbidity and mortality.

Explore the practice and barriers of collaborative health policy and system research-priority setting exercise in Ethiopia.

INTRODUCTION: Collaboration is gaining prominence in the priority setting of Health Policy And System Research (HPSR). However, its practice and challenges are not well explored in Ethiopia. Understanding the practice and barriers of collaborative Health Policy and System Research will help design approaches and platforms for setting inclusive and participatory policy and system-level health research topics. This paper explores the practice and barriers of collaborative HPSR-priority setting exercise in Ethiopia. METHODS: This study investigates the practice and barriers of collaborative health policy and system research priority-setting exercises in Ethiopia. Utilizing a mixed-methods approach, we conducted Key Informant Interviews (KIIs) and an online self-administered survey with open-ended questionnaires to capture diverse perspectives from stakeholders involved in the research priority-setting process. Through conventional content analysis, we identified key contents related to current practices, challenges, and opportunities for enhancing collaboration in health policy and system research prioritization. RESULTS: Our findings reveal a complex landscape characterized by varying levels of stakeholder engagement, institutional capacity constraints, and competing priorities within the health research ecosystem. Despite notable efforts to foster collaboration, stakeholders identified persistent challenges such as limited resources, institutional fragmentation, and inadequate coordination mechanisms as barriers to effective priority-setting processes. The implications of our research extend beyond academic discourse, with direct relevance to health policy and system research practice in Ethiopia. By shedding light on the dynamics of collaborative priority-setting exercises, our findings offer valuable insights for policymakers, researchers, and practitioners seeking to enhance the effectiveness and inclusivity of health research prioritization processes. Addressing the identified barriers and leveraging existing strengths in the research ecosystem can contribute to more evidence-informed health policies and programs, ultimately improving health outcomes for Ethiopian populations. CONCLUSIONS: Most institutions do not apply health policy and system research-priority setting to conduct informed decision-making. The barriers explored were weak integration, lack of knowledge, system, and platforms for the priority setting of Health Policy and System Resreach. So, it is recommended to build skills of different actors in the Health Policy and System Research-priority setting exercise and design a system and platform to integrate different stakeholders for collaborative research topics priority setting.

Health policy and systems research priority-setting exercise in Ethiopia: a collaborative approach.

INTRODUCTION: Health policy and systems research (HPSR) is a multi-disciplinary approach of generating health system and policy-level evidence. Setting HPSR agendas is considered as an efficient strategy to map and identify policy and cost-effective research topics, but its practice in developing countries is limited. This paper aimed to conduct a collaborative health policy and system research priority-setting exercise in Ethiopia. METHOD: The WHO's plan, implement, publish, and evaluate (PIPE) framework and the Delphi technique were used to conduct the priority-setting exercise. The PIPE model was used to lead the priority-setting process from planning to evaluation, while the Delphi technique was used to run the rating and ranking exercise with the aim of reaching a consensus. Two rounds of expert panel workshops supplemented with an online survey were used for the HPSR agenda setting, rating and ranking purposes. Groups were formed using the WHO health system building blocks as a base framework to identify and prioritize the HPSR topics. RESULT: Under 8 themes, 32 sub-themes and 182 HPSR topics were identified. The identified research themes include leadership management and governance, health policy, health information system, healthcare financing, human resource for health, medical products and supply, service delivery and cross-cutting issues. CONCLUSIONS: Priority HPSR topics focussing on national health priority issues were identified. The identified topics were shared with policymakers and academic and research institutions. Evidence generation on the identified priority topics will guide future research endeavours and improve evidence-informed decision-making practice, health system performance and national health goals and targets.

Intention to Use an Electronic Community Health Information System Among Health Extension Workers in Rural Northwest Ethiopia: Cross-Sectional Study Using the Unified Theory of Acceptance and Use of Technology 2 Model.

BACKGROUND: IT has brought remarkable change in bridging the digital gap in resource-constrained regions and advancing the health care system worldwide. Community-based information systems and mobile apps have been extensively developed and deployed to quantify and support health services delivered by community health workers. The success and failure of a digital health information system depends on whether and how it is used. Ethiopia is scaling up its electronic community health information system (eCHIS) to support the work of health extension workers (HEWs). For successful implementation, more evidence was required about the factors that may affect the willingness of HEWs to use the eCHIS. OBJECTIVE: This study aimed to assess HEWs' intentions to use the eCHIS for health data management and service provision. METHODS: A cross-sectional study design was conducted among 456 HEWs in 6 pilot districts of the Central Gondar zone, Northwest Ethiopia. A Unified Theory of Acceptance and Use of Technology model was used to investigate HEWs' intention to use the eCHIS. Data were cleaned, entered into Epi-data (version 4.02; EpiData Association), and exported to SPSS (version 26; IBM Corp) for analysis using the AMOS 23 Structural Equation Model. The statistical significance of dependent and independent variables in the model was reported using a 95% CI with a corresponding P value of <.05. RESULTS: A total of 456 HEWs participated in the study, with a response rate of 99%. The mean age of the study participants was 28 (SD 4.8) years. Our study revealed that about 179 (39.3%; 95% CI 34.7%-43.9%) participants intended to use the eCHIS for community health data generation, use, and service provision. Effort expectancy (ß=0.256; P=.007), self-expectancy (ß=0.096; P=.04), social influence (ß=0.203; P=.02), and hedonic motivation (ß=0.217; P=.03) were significantly associated with HEWs' intention to use the eCHIS. CONCLUSIONS: HEWs need to be computer literate and understand their role with the eCHIS. Ensuring that the system is easy and enjoyable for them to use is important for implementation and effective health data management.

Effect of Performance-Based Nonfinancial Incentives on Data Quality in Individual Medical Records of Institutional Births: Quasi-Experimental Study.

BACKGROUND: Despite the potential of routine health information systems in tackling persistent maternal deaths stemming from poor service quality at health facilities during and around childbirth, research has demonstrated their suboptimal performance, evident from the incomplete and inaccurate data unfit for practical use. There is a consensus that nonfinancial incentives can enhance health care providers' commitment toward achieving the desired health care quality. However, there is limited evidence regarding the effectiveness of nonfinancial incentives in improving the data quality of institutional birth services in Ethiopia. OBJECTIVE: This study aimed to evaluate the effect of performance-based nonfinancial incentives on the completeness and consistency of data in the individual medical records of women who availed institutional birth services in northwest Ethiopia. METHODS: We used a quasi-experimental design with a comparator group in the pre-post period, using a sample of 1969 women's medical records. The study was conducted in the "Wegera" and "Tach-armacheho" districts, which served as the intervention and comparator districts, respectively. The intervention comprised a multicomponent nonfinancial incentive, including smartphones, flash disks, power banks, certificates, and scholarships. Personal records of women who gave birth within 6 months before (April to September 2020) and after (February to July 2021) the intervention were included. Three distinct women's birth records were examined: the integrated card, integrated individual folder, and delivery register. The completeness of the data was determined by examining the presence of data elements, whereas the consistency check involved evaluating the agreement of data elements among women's birth records. The average treatment effect on the treated (ATET), with 95% CIs, was computed using a difference-in-differences model. RESULTS: In the intervention district, data completeness in women's personal records was nearly 4 times higher (ATET 3.8, 95% CI 2.2-5.5; P=.02), and consistency was approximately 12 times more likely (ATET 11.6, 95% CI 4.18-19; P=.03) than in the comparator district. CONCLUSIONS: This study indicates that performance-based nonfinancial incentives enhance data quality in the personal records of institutional births. Health care planners can adapt these incentives to improve the data quality of comparable medical records, particularly pregnancy-related data within health care facilities. Future research is needed to assess the effectiveness of nonfinancial incentives across diverse contexts to support successful scale-up.

Health system responsiveness and its associated factors for intrapartum care in conflict affected areas in Amhara region, Ethiopia: a cross-sectional study.

OBJECTIVE: In Ethiopia, information about health system responsiveness (HSR) in conflict-affected areas is limited. No previous local study was conducted on the assessment of HSR at the community level. Hence, the study assessed HSR for intrapartum care in conflict-affected areas in Amhara region, Ethiopia. DESIGN: Community-based cross-sectional study design. SETTING: Wadila, Gayint and Meket districts, Amhara region, Ethiopia. PARTICIPANTS: The participants were 419 mothers who gave birth in conflict-affected areas within the last 6 months. The study included all mothers who gave birth at health facilities but excluded those who delivered at home, critically ill or unable to hear. OUTCOME: HSR was the outcome variable. In this regard, the study assessed how mothers were treated and the situation in which they were cared for in relation to their experience during the conflict. METHODS: We conducted the study in the community, where we analysed eight domains of HSR to identify 30 measurement items related to intrapartum care responsiveness. The domains we looked at were dignity (4), autonomy (4), confidentiality (2), communication (5), prompt attention (5), social support (3), choice (3) and basic amenities (4). We used a multiple linear regression model to analyse the data, and in this model, we used an unstandardized ß coefficient with a 95% CI and a p value of less than 0.05 to determine the factors significantly associated with HSR. RESULTS: The findings of our study revealed that the overall proportion of HSR in intrapartum care was 45.11% (95% CI: 40.38 to 49.92). The performance of responsiveness was the lowest in the autonomy, choice and prompt attention domains at 35.5%, 49.4% and 52.0%, respectively. Mothers living in urban areas (ß=4.28; 95% CI: 2.06 to 6.50), government employees (ß=4.99; 95% CI: 0.51 to 9.48), those mothers stayed at the health facilities before delivery/during conflict (ß=0.22; 95% CI: 0.09 to 0.35), those who were satisfied with the healthcare service (ß=0.69; 95% CI: 0.08 to 1.30) and those who perceived the quality of healthcare favourable (ß=0.96; 95% CI: 0.72 to 1.19) were more likely to rate HSR positively. On the other hand, joint decision-making for health (ß=-2.46; 95% CI: -4.81 to -0.10) and hospital delivery (ß=-3.62; 95% CI: -5.60 to -1.63) were negatively associated with HSR. CONCLUSION: In the Amhara region of Ethiopia, over 50% of mothers living in areas affected by conflict reported that health systems were not responsive with respect to intrapartum care. Therefore, all stakeholders should work together to ensure that intrapartum care is responsive to conflict-affected areas, with a focus on providing women autonomy and choice.

Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.

Introduction: Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications. Methods: The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model. Results: The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias. Conclusion: This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.