Guidelines and Standard Frameworks for AI in Medicine: Protocol for a Systematic Literature Review.

BACKGROUND: Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation. OBJECTIVE: This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine. METHODS: We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented. RESULTS: The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023. CONCLUSIONS: Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks-as will be done in the proposed review-will provide comprehensive insights into current gaps and help to formulate future research directions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47105.

Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review.

BACKGROUND: Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains. OBJECTIVE: This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data. METHODS: The Arksey and O'Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. RESULTS: A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic. CONCLUSIONS: This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/22505.

Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling.

Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.

Approaches and Criteria for Provenance in Biomedical Data Sets and Workflows: Protocol for a Scoping Review.

BACKGROUND: Provenance supports the understanding of data genesis, and it is a key factor to ensure the trustworthiness of digital objects containing (sensitive) scientific data. Provenance information contributes to a better understanding of scientific results and fosters collaboration on existing data as well as data sharing. This encompasses defining comprehensive concepts and standards for transparency and traceability, reproducibility, validity, and quality assurance during clinical and scientific data workflows and research. OBJECTIVE: The aim of this scoping review is to investigate existing evidence regarding approaches and criteria for provenance tracking as well as disclosing current knowledge gaps in the biomedical domain. This review covers modeling aspects as well as metadata frameworks for meaningful and usable provenance information during creation, collection, and processing of (sensitive) scientific biomedical data. This review also covers the examination of quality aspects of provenance criteria. METHODS: This scoping review will follow the methodological framework by Arksey and O'Malley. Relevant publications will be obtained by querying PubMed and Web of Science. All papers in English language will be included, published between January 1, 2006 and March 23, 2021. Data retrieval will be accompanied by manual search for grey literature. Potential publications will then be exported into a reference management software, and duplicates will be removed. Afterwards, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 4 independent reviewers. Majority vote is required for consent to eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading will be performed independently by 2 reviewers and in the last step, key information will be extracted on a pretested template. If agreement cannot be reached, the conflict will be resolved by a domain expert. Charted data will be analyzed by categorizing and summarizing the individual data items based on the research questions. Tabular or graphical overviews will be given, if applicable. RESULTS: The reporting follows the extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses statements for Scoping Reviews. Electronic database searches in PubMed and Web of Science resulted in 469 matches after deduplication. As of September 2021, the scoping review is in the full-text screening stage. The data extraction using the pretested charting template will follow the full-text screening stage. We expect the scoping review report to be completed by February 2022. CONCLUSIONS: Information about the origin of healthcare data has a major impact on the quality and the reusability of scientific results as well as follow-up activities. This protocol outlines plans for a scoping review that will provide information about current approaches, challenges, or knowledge gaps with provenance tracking in biomedical sciences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31750.

Healthcare providers' acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model.

BACKGROUND: In almost all lower and lower middle-income countries, the healthcare system is structured in the customary model of in-person or face to face model of care. With the current global COVID-19 pandemics, the usual health care service has been significantly altered in many aspects. Given the fragile health system and high number of immunocompromised populations in lower and lower-middle income countries, the economic impacts of COVID-19 are anticipated to be worse. In such scenarios, technological solutions like, Telemedicine which is defined as the delivery of healthcare service remotely using telecommunication technologies for exchange of medical information, diagnosis, consultation and treatment is critical. The aim of this study was to assess healthcare providers' acceptance and preferred modality of telemedicine and factors thereof among health professionals working in Ethiopia. METHODS: A multi-centric online survey was conducted via social media platforms such as telegram channels, Facebook groups/pages and email during Jul 1- Sep 21, 2020. The questionnaire was adopted from previously validated model in low income setting. Internal consistency of items was assessed using Cronbach alpha (α), composite reliability (CR) and average variance extracted (AVE) to evaluate both discriminant and convergent validity of constructs. The extent of relationship among variables were evaluated by Structural equation modeling (SEM) using SPSS Amos version 23. RESULTS: From the expected 423 responses, 319 (75.4%) participants responded to the survey questionnaire during the data collection period. The majority of participants were male (78.1%), age <30 (76.8%) and had less than five years of work experience (78.1%). The structural model result confirmed the hypothesis "self-efficacy has a significant positive effect on effort expectancy" with a standardized coefficient estimate (ß) of 0.76 and p-value <0.001. The result also indicated that self-efficacy, effort expectancy, performance expectancy, facilitating conditions and social influence have a significant direct effect on user's attitude toward using telemedicine. User's behavioral intention to use telemedicine was also influenced by effort expectancy and attitude. The model also ruled out that performance expectancy, facilitating conditions and social influence does not directly influence user's intention to use telemedicine. The squared multiple correlations (r2) value indicated that 57.1% of the variance in attitude toward using telemedicine and 63.6% of the variance in behavioral intention to use telemedicine is explained by the current structural model. CONCLUSION: This study found that effort expectancy and attitude were significantly predictors of healthcare professionals' acceptance of telemedicine. Attitude toward using telemedicine systems was also highly influenced by performance expectancy, self-efficacy and facilitating conditions. effort expectancy and attitude were also significant mediators in predicting users' acceptance of telemedicine. In addition, mHealth approach was the most preferred modality of telemedicine and this opens an opportunity to integrate telemedicine systems in the health system during and post pandemic health services in low-income countries.

Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review.

BACKGROUND: Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing, reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic. OBJECTIVE: This paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain. METHODS: The Arksey and O'Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers. RESULTS: The results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021. CONCLUSIONS: We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/22505.

Data Quality and Cost-effectiveness Analyses of Electronic and Paper-Based Interviewer-Administered Public Health Surveys: Systematic Review.

BACKGROUND: A population-level survey (PLS) is an essential and standard method used in public health research that supports the quantification of sociodemographic events, public health policy development, and intervention designs. Data collection mechanisms in PLS seem to be a significant determinant in avoiding mistakes. Using electronic devices such as smartphones and tablet computers improves the quality and cost-effectiveness of public health surveys. However, there is a lack of systematic evidence to show the potential impact of electronic data collection tools on data quality and cost reduction in interviewer-administered surveys compared with the standard paper-based data collection system. OBJECTIVE: This systematic review aims to evaluate the impact of the interviewer-administered electronic data collection methods on data quality and cost reduction in PLS compared with traditional methods. METHODS: We conducted a systematic search of MEDLINE, CINAHL, PsycINFO, the Web of Science, EconLit, Cochrane CENTRAL, and CDSR to identify relevant studies from 2008 to 2018. We included randomized and nonrandomized studies that examined data quality and cost reduction outcomes, as well as usability, user experience, and usage parameters. In total, 2 independent authors screened the title and abstract, and extracted data from selected papers. A third author mediated any disagreements. The review authors used EndNote for deduplication and Rayyan for screening. RESULTS: Our search produced 3817 papers. After deduplication, we screened 2533 papers, and 14 fulfilled the inclusion criteria. None of the studies were randomized controlled trials; most had a quasi-experimental design, for example, comparative experimental evaluation studies nested on other ongoing cross-sectional surveys. A total of 4 comparative evaluations, 2 pre-post intervention comparative evaluations, 2 retrospective comparative evaluations, and 4 one-arm noncomparative studies were included. Meta-analysis was not possible because of the heterogeneity in study designs, types, study settings, and level of outcome measurements. Individual paper synthesis showed that electronic data collection systems provided good quality data and delivered faster compared with paper-based data collection systems. Only 2 studies linked cost and data quality outcomes to describe the cost-effectiveness of electronic data collection systems. Field data collectors reported that an electronic data collection system was a feasible, acceptable, and preferable tool for their work. Onsite data error prevention, fast data submission, and easy-to-handle devices were the comparative advantages offered by electronic data collection systems. Challenges during implementation included technical difficulties, accidental data loss, device theft, security concerns, power surges, and internet connection problems. CONCLUSIONS: Although evidence exists of the comparative advantages of electronic data collection compared with paper-based methods, the included studies were not methodologically rigorous enough to combine. More rigorous studies are needed to compare paper and electronic data collection systems in public health surveys considering data quality, work efficiency, and cost reduction. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/10678.

Cesarean delivery among women who gave birth in Dessie town hospitals, Northeast Ethiopia.

BACKGROUND: One of the key strategies for reducing maternal and perinatal morbidities and mortalities is the provision of skilled intrapartum care. While cesarean section is an important emergency obstetric intervention for saving the lives of mothers and newborns, a study comparing the prevalence of cesarean delivery is not sufficiently available in Ethiopia. This study aimed at assessing the prevalence and associated factors of cesarean delivery among women who gave birth at hospitals in Dessie town, Northeast Ethiopia. METHODS: A facility based cross-sectional study was conducted between July and October 2013. A total of 520 women who gave birth in four hospitals (public = 1, private = 3) were interviewed. Face-to-face interviews using a pre-tested and structured questionnaire were conducted for primary data collection. Additionally, patients' charts were reviewed to collect mothers' clinical data. Bivariate and multiple logistic regressions analyses were conducted. Odds ratios and 95% confidence intervals were computed and a P-value of less than 0.05 was taken to declare the level of significance. RESULTS: A total of 512 mothers were included in the final analysis (response rate = 98.4%), the prevalence of cesarean delivery was found to be 47.6% (95% CI: 44.3, 51.1), While 46 (18.2%) of the procedure conducted in public and 198 (76.1%) were in private hospitals. Partograph monitoring [AOR = 3.84 95%CI: 2.24, 6.59], oxytocin administration [AOR = 4. 80 95%CI: 2.87-8.02], previous cesarean delivery [AOR = 2. 86 95%CI: 1.64-5.01] and place of delivery being a private hospital [AOR = 6. 79 95%CI: 4.18-11.01)] were associated with cesarean delivery. CONCLUSION: The prevalence of cesarean delivery was found to be high, and was significantly higher in private hospitals than a public facility. There is a need to conduct cesarean delivery audits to appropriately utilize scarce resources. Further an in-depth exploration of the experiences of women with cesarean delivery is necessary.

Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation.

BACKGROUND: Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes. OBJECTIVE: This study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia. METHODS: A randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit-based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles. RESULTS: From the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. CONCLUSIONS: EDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site.

Data Quality and Cost-Effectiveness Analyses of Electronic and Paper-Based Interviewer-Administered Public Health Surveys: Protocol for a Systematic Review.

BACKGROUND: Population-level survey is an essential standard method used in public health research to quantify sociodemographic events and support public health policy development and intervention designs with evidence. Although all steps in the survey can contribute to the data quality parameters, data collection mechanisms seem the most determinant, as they can avoid mistakes before they happen. The use of electronic devices such as smartphones and tablet computers improve the quality and cost-effectiveness of public health surveys. However, there is lack of systematically analyzed evidence to show the potential impact on data quality and cost reduction of electronic-based data collection tools in interviewer-administered surveys. OBJECTIVE: This systematic review aims to evaluate the impact of interviewer-administered electronic device data collection methods concerning data quality and cost reduction in population-level surveys compared with the traditional paper-based methods. METHODS: We will conduct a systematic search on Medical Literature Analysis and Retrieval System Online, PubMed, CINAHL, PsycINFO, Global Health, Trip, ISI Web of Science, and Cochrane Library for studies from 2007 to 2018 to identify relevant studies. The review will include randomized and nonrandomized studies that examine data quality and cost reduction outcomes. Moreover, usability, user experience, and usage parameters from the same study will be summarized. Two independent authors will screen the title and abstract. A third author will mediate in cases of disagreement. If the studies are considered to be combinable with minimal heterogeneity, we will perform a meta-analysis. RESULTS: The preliminary search in PubMed and Web of Science showed 1491 and 979 resulting hits of articles, respectively. The review protocol is registered in the International Prospective Register of Systematic Reviews (CRD42018092259). We anticipate January 30, 2019, to be the finishing date. CONCLUSIONS: This systematic review will inform policymakers, investors, researchers, and technologists about the impact of an electronic-based data collection system on data quality, work efficiency, and cost reduction. TRIAL REGISTRATION: PROSPERO CRD42018092259; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID= CRD42018092259. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/10678.

Civil servants' demand for social health insurance in Northwest Ethiopia.

BACKGROUND: Absence of reliable health insurance schemes is a key challenge to meet the universal health coverage target of the Sustainable Development Goals (SDGs). Ethiopian health system is characterized by under financing, low protection mechanisms for the poor, and lack of mechanisms of risk pooling and cost sharing. Ethiopia is implementing social health insurance (SHI) scheme to reduce out of pocket payment (OOP) and improve access and use of healthcare. This study aimed to determine the demand for SHI among civil servants and associated factors in Northwest Ethiopia. METHODS: An institution-based cross-sectional study was conducted in Bahir Dar city from 557 randomly selected civil servants using structured and self-administered questionaire. The questionnaire included questions measuring demand for SHI and demographic, socio-economic, healthcare related and personal and behavioral factors. Data were first entered in Epi-Info version 7.0 and transferred to SPSS version 20 for analysis. Descriptive statistics, bivariate and multivariable logistic regression analysis were performed. RESULTS: From the total calculated sample size of 557, 488 respondents returned the questionnaire giving a response rate of 88%. Nearly three-fourth of the respondents, 355 (72.7%), reported their need to be enrolled in a SHI scheme. Two-third of the respondents 325 (66.6%) were willing to pay for their enrollment. Overall, three hundred and two (61.9%) were demanding SHI. Having good awareness about health insurance [AOR = 4.39, 95% CI = (1.82-12.89)] and trust on a health insurance agency [AOR = 3.0, 95% CI = (1.57-5.72)], were significantly associated with the demand for SHI among civil servants. CONCLUSION: The demand for SHI among civil servants were higher. The awareness towards SHI and trust on the SHI agency were significantly associated with demand for SHI. As Ethiopia aspires to insure all employees of the formal sector, and improving the awareness of civil servants about SHI and the agency providing the service could improve demand for SHI. Further research is important on healthcare organizational and professional readiness to handle the upcoming insurance driven quality health service need and health seeking behavioral change.

Trends of modern contraceptive use among young married women based on the 2000, 2005, and 2011 Ethiopian Demographic and Health Surveys: a multivariate decomposition analysis.

INTRODUCTION: Accessing family planning can reduce a significant proportion of maternal, infant, and childhood deaths. In Ethiopia, use of modern contraceptive methods is low but it is increasing. This study aimed to analyze the trends and determinants of changes in modern contraceptive use over time among young married women in Ethiopia. METHODS: The study used data from the three Demographic Health Surveys conducted in Ethiopia, in 2000, 2005, and 2011. Young married women age 15-24 years with sample sizes of 2,157 in 2000, 1,904 in 2005, and 2,146 in 2011 were included. Logit-based decomposition analysis technique was used for analysis of factors contributing to the recent changes. STATA 12 was employed for data management and analyses. All calculations presented in this paper were weighted for the sampling probabilities and non-response. Complex sampling procedures were also considered during testing of statistical significance. RESULTS: Among young married women, modern contraceptive prevalence increased from 6% in 2000 to 16% in 2005 and to 36% in 2011. The decomposition analysis indicated that 34% of the overall change in modern contraceptive use was due to difference in women's characteristics. Changes in the composition of young women's characteristics according to age, educational status, religion, couple concordance on family size, and fertility preference were the major sources of this increase. Two-thirds of the increase in modern contraceptive use was due to difference in coefficients. Most importantly, the increase was due to change in contraceptive use behavior among the rural population (33%) and among Orthodox Christians (16%) and Protestants (4%). CONCLUSIONS: Modern contraceptive use among young married women has showed a remarkable increase over the last decade in Ethiopia. Programmatic interventions targeting poor, younger (adolescent), illiterate, and Muslim women would help to maintain the increasing trend in modern contraceptive use.