The level of routine health information system data quality and associated factors at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023: A mixed study

The level of routine health information system data quality and associated factors at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023: A mixed study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The level of routine health information system data quality and associated factors at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023: A mixed study Nigussie Dukamo, Alemu Tamiso, Adugnaw Adane, Abriham Asefa, Samuel Misganaw, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5347454/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Routinely collected data of poor quality can compromise the validity of effectiveness evaluations and lead to poor decision-making, inappropriate resource allocation, and a loss of trust in the health system. Routine health information system data are seen as poor in quality, are not used for decisions in Ethiopia, and continue to be a significant problem. Methods and materials: A facility-based mixed-method study with an embedded design was conducted. A total of four public hospitals, 32 departments or units, 605 healthcare workers, and 12 key informant interviews were selected. Simple random sampling and purposive sampling techniques were used for selecting study participants in the quantitative and qualitative studies, respectively. The data were entered into Epi-data version 4.4, Open Code version 4.03, and exported to SPSS version 26, and descriptive statistics were used to assess the level of data quality. Binary logistic regression and thematic analysis were run to identify factors affecting data quality. Adjusted odds ratios with 95% confidence intervals and themes or subthemes were reported. Results The overall facility data quality level was 90.84%; and the completeness and consistency of the data were 85.5% and 85.3%, respectively. Easy understandability of registration and report formats [AOR 1.92; CI 1.11–3.33], receiving training [AOR 1.62; CI 1.07–2.44], receiving supervision [AOR 1.66; CI 1.05–2.61], providing regular feedback [AOR 1.72; CI 1.07–2.75], the team's work being appreciated and valued by supervisors [AOR 1.61; CI 1.04–2.75] and making decisions and follow-up actions identified in performance monitoring team meetings [AOR 1.73; CI 1.12–2.67] were significantly associated with data quality; and thematic analysis was performed and categorized into four themes and twelve subthemes. Conclusion and recommendation: The level of data quality at the public hospital in the Silte Zone is almost equal to the national expected level of data quality, but the completeness and consistency of the data were lower than the national expected level. The Minister of Health and other supporting organizations should intervene in the identified gaps, especially to reduce incompleteness and inconsistency of data. Data quality Routine Health information System Accuracy Completeness Timeliness Consistency Central region Ethiopia Figures Figure 1 Introduction According to the World Health Organization (WHO) definition the Health Information System (HIS) is a system that integrates data collection, processing, reporting, and use of the information necessary for improving health service effectiveness and efficiency through better management at all levels of the health system ( 1 ). A routine health information system (RHIS) is a system designed to gather, process, use, and disseminate health-related data to enhance the management of programs, resources, and health care outcomes ( 2 ). Information from many parts of the health system is gathered via a routine health information system ( 3 ). It has been used globally for more than a century. Recently, the developing world has begun to place greater emphasis on RHIS, and it has become more well known in the developing world ( 4 ). In 2008, Ethiopia began using the Health Management Information System (HMIS), which is meant to generate routine data for decision-making at various levels of the health system ( 5 ). The information revolution is one of the major agendas of Ethiopia’s health sector transformation plan II (HSTP-II), and it involves phenomenal advancements in the methods and practices of collecting, analyzing, presenting, and disseminating information ( 6 ). The quality of routine health information system data is vital for the health system to function well and for policymakers to be able to evaluate the effects of health system efforts to improve the health of the population ( 7 ). Improved health system performance is directly linked with the quality and use of routine data in a country’s HIS ( 8 ). There is no agreement on the dimensions of data quality, and there are cross-cutting dimensions identified in the literature: completeness, timeliness, consistency, accuracy, reliability and precision ( 9 ). Specifically, the dimensions such as: completeness, accuracy, consistency and timeliness were the most commonly reviewed dimensions in the literature ( 10 ). The quality of routine RHIS data in low- and middle-income countries remains quite low in the global health system ( 10 ). In India, Nepal, and Pakistan, studies have shown that the overall health data quality is much lower than the national standard ( 11 ). In Ethiopia, according to a study performed at Addis Ababa, the overall data quality was 76.22%, and at Dire Dawa, the overall data quality in the unit or department was found to be 75.3%; previous evidence in Ethiopia, suggested that the level of data quality was recorded as below the national threshold ( 2 , 12 , 13 ). The core determinants of routine health information system data quality and utilization are technical, behavioral and organizational factors ( 14 , 15 ). Incomplete registers and a low level of data accuracy are common types of data quality problems ( 16 , 17 ). Even though supportive supervision, short-term training, and data owners have been assigned, routine health system data quality is still a major problem in low- and middle-income countries, including Ethiopia. This affects health system performance and the health of society ( 18 ). This study attempted to address the data quality level at public hospitals by including various program areas and applying four dimensions to assess the data quality in each program area. Objectives of the study General objective To assess the level and associated factors of routine health information system data quality and explore the factors affecting data quality at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023. Specific objectives To assess the quality of routine health information system data at public hospitals, 2023. To identify factors affecting routine health information system data quality at public hospitals, 2023. To explore the factors that affect the quality of routine health information system data at public hospitals, 2023. Methods and materials Study area This study was conducted in the Central Region of Ethiopia, the Silte Zone, at the public hospital. The Silte Zone is located in the Central Region of Ethiopia and is 173 K.M away from Addis Ababa, the capital city of Ethiopia. The zone has 212 kebeles, 12 districts and 3 administrative towns. According to the 2018 Central Statistical Agency (CSA) reports and regional government reports, the average estimated population of the zone is 1,017,557. Currently, the Silte Zone has four hospitals (Werabe Comprehensive Specialized Hospital, Kibet Primary Hospital, Tora Primary Hospital, Alem Gebeya Primary Hospital), and there are 1184 health care workers in different disciplines in the Silte Zone at public hospitals. Study design and period A facility-based mixed method with an embedded study design was employed at a public hospital in the Silte Zone Central Region of Ethiopia from March 18 to April 30, 2023. A cross-sectional study design was used for the quantitative part, and a phenomenology study design was used for the qualitative part. Population Source population All public hospitals in the Silte Zone were included in the source population. Study population The selected units or departments at public hospitals in the Silte Zone were included. Study unit For the quantitative study The HMIS registration books or report formats, as well as individual medical records and health care workers chosen randomly from particular departments or selected units, were examined. For the qualitative study The key informants selected purposeful at public hospitals were considered. Eligibility criteria Inclusion criteria For the quantitative study Health care workers who have worked for more than six months at this institution and three consecutive months of data that were registered in 2023 on the HMIS registration book were included in this study. For the qualitative study The key informants, such as HMIS officers, quality officers, and performance monitoring team (PMT) members, were included in this study. Exclusion criteria Health care workers who were on leave for three selected months for various reasons were excluded from the study. Sample size determination Sample size for objective one The WHO recommends to use five units with core indicators for regular data quality review ( 19 ). Additionally, countries may select other indicators on the basis of their needs and the resources available. One indicator from each unit or department was selected. Therefore, eight units or departments at each hospital were included. There were four public hospitals, so the total sample size used to assess the data quality was 32 units. Sample size for objective two The sample size for objective two is calculated via Epi–info from the findings of previous similar studies. The calculations are separately made for three potential determinants (training, supervision and feedback), which are consistently significant in many studies. To determine the appropriate sample size, the percent of outcome from the unexposed group, two-sided confidence level, power of the study and relatively least extreme odds ratio were detected were used. Therefore, the maximum calculated sample size is 605. (Table.1) Table 1: Sample size calculation for objective-two. Sample size for objective three For the qualitative study, 12 participants (3 from each hospital) were purposefully selected for key informant interviews (KIIs). Sampling technique and sampling procedure Health care workers for the self-administered questionnaire were selected by a simple random sampling technique. The participants in the qualitative study were selected purposefully to explore the factors affecting data quality at public hospitals. The three-month documents of registrations and reports were reviewed to check the accuracy, completeness, timeliness, and consistency of the data at each department or unit. The first month was selected randomly by lottery methods, and then data registered and reported for three consecutive months were included for chart or document review. The selected indicators from departments or units were maternal health (ANC1), laboratory (malaria tested case), immunization (Penta3/PCV3), ART (currently on ART), tuberculosis (new or relapsed TB case), inpatient departments (IPD data), emergency departments (emergency data), and outpatient departments (OPD data). To select health care workers, the sample size was allocated to each hospital and then to departments or units on the basis of the proportional allocation formula (nj = n/N* Nj). Finally, the study participants were selected by a simple random sampling method as described below (Fig. 1). Figure 1: Sampling procedure to obtain study participants from each hospital and the final sample size. Variables Dependent variables Data quality (good or poor). Independent variables Sociodemographic factors sex, age, work experience, professional category, and level of education, salary and position in hospitals. Organizational related feedback, supervision, training and data use, appreciation and performance monitoring meetings and accountability for poor performance. Technical related complexity or user-friendliness of the reporting or register formats and trained person ability to fill the format. Behavioral-related Data manipulation for competition, negligence, sense of responsibility, and Perception of staff on data, active engagement and knowledge of routine health information data. Data collection tool and procedure For the quantitative study The data were collected by a structured questionnaire, which was adapted from a review of different studies and the PRISM tool ( 15 ). The self-administered questionnaire contains four parts: sociodemographic characteristics, technical factors, organizational factors, behavioral factors, and knowledge assessment. Two supervisors and six data collectors (two BSc nursing graduates who have received HMIS training) and two with Masters of Public Health were assigned.to collect the data from each respondent, and review the documents, registration, and individual patient charts. The data were collected by visiting all the hospitals, explaining the aim of the study, ensuring the confidentiality of the data, and obtaining consent from each participant. For the qualitative study After convenient time and place, the participants were interviewed via semi structured interview guide questionnaires. Two Master of Public Health data collectors who were previously exposed to qualitative data collection were used to collect the data. The data collectors conducted the interviews via an audio recording device for 20–40 minutes while simultaneously taking field notes. Data quality assurance For the quantitative study An amendment to the tool was made after pretesting on 5% ( 30 ) of health care workers or 5% ( 2 ) of charts and the HMIS register of the total sample at Adare General Hospital. Before data collection, two days of training were provided on the purpose, how to collect data, and ethical issues, emphasizing the importance of the safety of the participants and the quality of the data. The data collectors were supervised, and onsite technical assistance was given. Moreover, data completeness and consistency were evaluated on a daily basis, and corrective steps were implemented promptly. Finally, prior to data entry, each questionnaire was coded. For the qualitative study The trustworthiness of the data was evaluated via the following criteria: Credibility During in-depth interviews, enough time was given to participants to respond their perceptions and experiences, the participants were interviewed in comfortable places, and data were collected. Dependability To ensure the consistency of the data from all KIIs, the same data collectors used the same KII guide. After data collection, the raw or recorded data were transcribed verbatim and then translated into English. Transferability Nominated samples were used for in-depth interviews to be representative of the source population. Conformability To avoid researcher bias throughout data collection, coding, and analysis, the KIs' own words were used instead of the researchers' opinions and expectations. Data processing and analysis For the quantitative study The data were checked for completeness and consistency and then entered into Epi-data version 4.4 and exported to SPSS version 26 for statistical analysis. Descriptive statistics means, frequencies, and tables were used to summarize and describe the data. The mean scores are used as cutoff points to split the data into different scale measures to dichotomize the variables. Binary logistic regression was performed, and the variables with a p value < 0.25 were entered into the multivariable logistic regression analysis. The Hosmer–Lemeshow statistical test was used to assess the model’s goodness of fit (fit if the p value was greater than 0.05), and in this study, the Hosmer–Lemeshow statistical test was 0.43, and the value of the variation inflation factor (VIF) was between 1 and 2 for each independent variable, which indicates that there was no multicollinearity effect. Finally, the adjusted odds ratio (AOR) with its 95% CI was reported. Variables with a 95% confidence interval that did not include one in the multivariable logistic regression analysis were significantly associated with routine health information system data quality. For the qualitative study The data were collected from key informants via audio recording and note-taking, and then transcribed after being listened to repeatedly. They were translated verbatim from Amharic to English via by experts with a health professional background and prior translation experience. Ultimately, it was saved in plain text format. After they reviewed and familiarized themselves with the responses from key informant interviews (KIIs), the data were coded, categorized, and analyzed via thematic analysis by open-code software. Themes and subthemes were then identified. Finally, the themes and subthemes were presented separately in the results and integrated with the quantitative findings in the discussion in a narrative manner. Operational Definitions Technical determinants Factors related to the availability of HMIS tools, the complexity or user friendliness of formats, and the trained person's ability to fill out the format to perform routine health information tasks in organizations ( 20 ). Organizational determinants Factors related to organizations, such as feedback, supervision, training, data use and performance monitoring teams, contribute to improving the RHIS process ( 20 ). Performance monitoring team (PMT) Performance monitoring teams (PMTs) are health care workers who review and analyze a hospital's performance and develop action plans for course correction ( 21 ). Behavioral determinants Individual-level factors affect the practice of routine health information system tasks ( 22 ). Completeness Is the average of the source document or registration content completeness and report content completeness, the data are complete if the average is ≥ 90% ( 13 , 23 ). Data accuracy was measured by calculating the total number of diseases or services from the source document or register divided by the total number from the report submitted to the next level. The data were considered accurate if the average was within the acceptable limit (0.90–1.10 or 90–110%), and 10% tolerance for data accuracy was used ( 13 , 24 ). Report timeliness was measured by the number of reports delivered up to the deadline for the health management information system (HMIS) unit divided by the number of reports expected to come. The data are reported in a timely manner if the average is ≥ 90% ( 23 ). Consistency was measured by the data on the register with data on individual medical records. By dividing those individual medical record numbers (MRNs) with matched data elements written on the register by the total number of sampled MRNs, the data are considered consistent if the consistency score is ≥ 90 ( 23 ). Matched Matched is defined as when all of the selected data elements that are recorded on the registers for the sampled individual are also recorded on the individual medical records. Not matched is defined as when at least one or more of the selected data elements that are recorded on the registers for the sampled individual are not exactly the same as what is recorded on the individual medical records. In addition, if the individual medical record is not physically available, then it is also considered not matched ( 2 ). RHIS data quality was measured by calculating the sum of the four dimensions of data quality measured and then taking the average of the scores ( 2 ). Good data quality The data’s average scores of the four dimensions ≥ 90 ( 2 , 25 ). Poor data quality The data’s average scores of four dimensions < 90% ( 22 , 26 ). Level of knowledge A health care worker is said to have good knowledge if they respond to knowledge questions above the respondent’s mean score. Results Sociodemographic characteristics of the respondents A total of 605 respondents participated in this study, for a response rate of 100%. Eight indicators in the departments or units were included. Among all the facilities included, 575 (95%) were health care workers working at staff positions, and 389 (64.3%) of the respondents were under 31 years of age. Among the respondents, 313 (51.7%) were male. Regarding the distribution of levels of education, 474 (78.3%) were degree holders. Approximately 258 (42.6%) of the respondents were nurses, and most of the respondents had less than five years of experience, with 331 (54.7%) (Table 2). Table 2 Sociodemographic characteristics of respondents at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023. Level of data quality The study's overall data quality was 90.84%, with a 95% confidence interval of [88.9–92.76]. The lowest data quality was found at Tora PH, whereas the highest percentage was found at Alem Gebeya Primary Hospital, at 89.83% and 93.2%, respectively. (Table 3). Table 3 The level of data quality at each public hospital in the Silte Zone, Central Region, Ethiopia, in 2023. Facility level data quality with respect to their indicators From the list of the ten most common diseases, the top six were selected, and the data accuracy at each hospital was checked. In WCSH, a total of 3291 diseases or services were reviewed. Among these data, 99.1% (111/112) of the IPD data, 97.9% (576/588) of the emergence data and 99.4% (1338/1346) of the OPD data were within the acceptable limits, whereas 0.89% of the IPD data, 2.04% of the emergence data and 0.59% of the OPD data were overreported. The 4920 clients were registered at various departments or units. Of those, 246 (or 5% of the register) client data were checked for consistency and completeness of data at each department or unit, and the three-month report submission dates were checked at each department or unit, ensuring that all reports were sent on time to the HMIS unit or higher level. From Tora Primary Hospital, a total of 2252 diseases or services were checked; 98.05% (453/462) of the emergence data, 97.1% (34/35) of the TB data, 97.4% (221/227) of the OPD data, and 93.8% (61/65) of the malaria data were reported within the acceptable limits, whereas 1.95% of the emergence data, 2.86% of the TB data, 2.64% of the OPD data and 6.15% of the malaria data were over reported. The 2420 clients were registered at various units or departments for three particular months, in which 121 (5% of the total register) clients’ data registers were checked. One-month emergence and OPD reports were not submitted in a timely manner. In Kibet primary hospital, 1939 diseases or services were counted and checked for data accuracy, of which 97.87% (46/47) of the TB data, 94.34% (100/106) of the IPD data, 96.55% (112/116) of the Penta3 vaccine data, 96.18% (126/131) of the OPD data, and 97.78% (309/316) of the emergence data were within the acceptable range, whereas 2.13% of the TB data, 5.66% of the IPD data, and 3.45% of the Penta 3 vaccine data were overreported. A total of 92.5% (431/401) of the ANC1 data were reported at acceptable limits, whereas 7.48% of the ANC1 data and 0.12% of the malaria data were underreported. In addition, 2540 clients were registered at different selected departments or units, in which 127 (5% of the register) client data registers were checked for data completeness and consistency. One-month laboratory (malaria) and emergence reports were not submitted in a timely manner. From the service or disease register (2651) of three selected months at Alem Gebeya Primary Hospital, 97.78% (44/45) of the IPD data and 99.91% (1072/1074) of the malaria data were reported at the accepted limit, whereas 2.22% of the IPD data and 0.19% of the malaria data were overreported. A total of 97.85% (285/279) of the emergence data and 96.98% (648/629) of the ANC1 service data were reported with acceptable limits, but 2.15% of the emergence data, and 3.02% of the ANC1 data were under reported in Alem Gebeya Primary Hospital, 3040 clients were registered at different selected departments or units, and 152 (5% of the register) client registers were checked for completeness and consistency of the data at each department or unit, and one-month OPD reports were not submitted in a timely manner. (Table 4). Table 4 The level of data quality at each hospital with respect to its indicators in the Silte Zone, Central Region, Ethiopia, 2023. Factors related to routine health information system data quality. The Likert scale (with five scales were used), ranges from strongly disagree to strongly agree. These responses were dichotomized into disagree if the answers were 1 to 3 and agree, if the answers were 4 to 5. (Table 5). Table 5. Organizational, behavioral and technical factors related to RHIS data quality at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023. Bivariate and multivariable analysis In the bivariate logistic regression analysis, educational status, knowledge of RHIS, the presence of a standardized set of indicators, registration and reporting formats easily understandable, trained staff ability, receiving HMIS-related training, receiving supervision from higher officials, providing regular feedback to staff, checking data quality, collecting data are appreciated and valued by supervisors, active staff engagement in all activities, making decisions and following up on actions identified in PMT meetings, encouraging the gathering of data, ensuring data importance for monitoring facility service performance and accounting for poor performance had a p-value of less than 0.25 and were included in the multivariable model. In the multivariable logistic regression, the registration and report format are user-friendly or easily understandable; receiving training on HMIS-related activities; receiving supervision from higher officials; providing regular feedback to staff; collecting data is appreciated and valued by supervisors; and decisions made and follow-up actions identified in PMT meetings are significantly associated with routine health information system data quality. The participants agreed that having a registration and reporting format that is easy to use or understand is a 1.9-fold greater probability of having good data quality (AOR = 1.92; 95% CI: 1.11–3.33; P = 0.020) than other. Healthcare workers who received HMIS-related training had a 1.6 times greater probability of having good data quality than those who did not. (AOR = 1.62; 95% CI: 1.07–2.44 with P = 0.022), and those who received supervision from higher officials had a 1.7 times greater likelihood of having good data quality than those who did not receive supervision (AOR = 1.66; 95% CI: 1.05–2.61 with P = 0.029). Those who received regular feedback from supervisors through reports were 1.7 times more likely to have good data quality than those who did not receive regular feedback (AOR = 1.72; 95% CI: 1.08–2.75 with P = 0.024). Health care workers who agreed on the impact of decisions made and follow-up actions identified in PMT meetings on the basis of the presented data were 1.7 times more likely to have good data quality than others (AOR = 1.73; 95% CI: 1.12–2.67, P = 0.013). Health care workers whose work was appreciated by supervisors and coworkers were 1.6 times more likely to have good data quality than others (AOR = 1.61; 95% CI: 1.04–2.47 with P = 0.031). (Table 6). Table 6: Bivariate and multivariable logistic regression results for public hospitals in the Silte zone, Central Region, Ethiopia, 2023. Thematic findings Thematic analysis was performed by open-code software, and the data were categorized into four themes and twelve subthemes. These themes were data collection tools and their impact, data quality challenges and assurance mechanisms, supervision and feedback on data quality, and training and its impact on data quality. Theme one: Data quality challenge and assurance mechanism Challenge of data quality A majority of in-depth interviewees mentioned that there was incomplete information in the register, the hospital did not use HMIS data, negligent or carelessness, and that improper disease classification is a major challenge that affects data quality. The key informants were asked about the reason for the incompleteness of information and responded as follows: “ Most of the time, some registered data are incomplete; because there are some careless or negligent health professionals; who do not complete the data in the registry. To register indicators correctly and obtain quality data, incomplete registration is the main problem affecting data quality [KII 2, HMIS officer]” . “ Many times, there is something that makes it difficult for us to maintain data quality; some health professionals do not register information, not only one person who is trained in the health management information system but also all the health professionals who are assigned to do it in turn. This is a problem; sometimes they may write a diagnosis that is not correct, and the register may be written in illegible handwriting. The other is incompleteness, which is similar to another challenge; the registration book should have complete things, such as writing one by one, but some staff may fill up by jumping. Many times, when trainings are given at the institutional level to solve this problem, we are told to provide complete information; few of them are filled with complete information, but when we visit the unit, what the situation we see is an incomplete card or register [KII 9, Quality directorate]” . The majority of individuals pointed out the challenge related to improper disease classification as follows: “ Regarding the disease registration; many clinicians expected to write HMIS disease codes, but with us, the doctor only writes a clinical diagnosis, When the data owner makes a monthly report, to match it with the National Classification of Disease (NCoD), he or she round it off [KII 2, HMIS officer]” . The key informants respond to the challenge related to the hospital in which HMIS data are not used and for negligence or carelessness as follows: “ As a hospital, there are some gaps; instead of using data, if we use the data to improve our services, it is very good, but in reality, the hospital did not use them according to our plan [KII 12, Quality officer]” . “ I do not think that negligence has anything positive effect, because it affects the data quality very much. There are some health professionals who do not register what they have done; and some who start and make incomplete registers; for example, some professionals who are in dire need of emergencies do not record the time of patient arrival. Because of that, sometimes it is difficult to know patients who have been there for more than 24 hours. Because of this, negligence strongly affects the data quality [KII 2, HMIS officer] . Assurance mechanism of data quality A majority of in-depth interviewees mentioned that there was an assigned HMIS focal person, a strong PMT monthly meeting, and the LQAS (lot quality assurance sampling) method was used to assure data quality. One participant explained that one of the data quality assurance mechanisms was checking by the LQAS method, described as follows: “ With respect to data quality assurance methods, there is a registration book, there is a tally sheet and there is a monthly report. We will check with LQAS; if they are under LQAS, they will work again. If it is correct, it will be sent to HMIS, and then DHSI 2 will be entered. The quality assurance mechanism in our institution is the LQAS method [KII 4, PMT member]” . The key informant responded that one of the data quality assurance mechanisms was assigning an HMIS focal person and strengthening PMT monthly meetings. “ There was a matter of not registering the patient as soon as it came and not registering it for different reasons, but now, they have been assigned an independent focal person who follows it, and now, whether it is an elective or emergency case, there is an assigned nurse to register [KII 1, Dept. coordinator]” . “ There are performance monitoring teams (PMTs), so PMT check reports periodically, monthly and weekly reports at the case team level, especially monthly report data, are evaluated at the case team level by PMT before being sent to our hospital HMIS unit. After they are confirmed or after the quality of the data items is confirmed, they are sent to the HMIS unit, which is one way to assure data quality [KII 7, Quality officer]” . Theme two: Data collection tools and their impact A majority of in-depth interviewees mentioned that tool availability and friendliness affect RHIS data quality and stated as follow: “ The register tool and tally sheet as well as the report format are easy to understand. I do not think there is a problem with the data register tool because the tool is easy to understand, if you need an explanation, you will find it written below in the registration or report format. Tool availability and easy understandability have positive effects. Currently, we have a tool that we can use to do all staff to report and register; that has a positive effect on data quality [KII 10, Nursing & Midwifery Service Director]” . Theme three: supervision and feedback on data quality In most of the in-depth interviews, supervision and feedback were given, which helped to prepare an action plan and to correct the gap. Stated as follows: “ By the way, feedback is very good because they see things that you do not see. When they give you such a comment, if there is something good, it helps us to continue, and if there is a gap, it helps us to fix it, and it helps us to make an action plan to solve the gap on that basis [KII 1, Dept. coordinator]” . “ They provide feedback as soon as they are supervised. If they come every three months, they will give feedback; if they come once every six months, they will also give feedback. When the performance monitoring team reviews the monthly report every time and if the data are good, positive feedback continues. I think having supervision and giving feedback is very beneficial for staff to improve data quality [KII 4, Hospital PMT member]” . Theme four: training and its impact on data quality Many of the participants in the in-depth interviews mentioned that the training identified different indicators that are new; this in turn, enhances data quality. Explained as follow. “ I think training is a must, like our hospital, because the indicators change every time, when the indicator changes, the registrations also change because the data sources are registration points for many indicators. Therefore, it reduces our outcome and data quality [KII 12, Quality officer]” . “ Training is very important. Now, we have given disease classification; just as it was changed in July, we have given training to the data owner. Currently, the reporting format is better, that is, after we have given training. After we provide HMIS training and before we provide it, different indicators are very clear to them, which means that after training is complete [KII 2, HMIS officer]” . “ Training, when you take it, it will strengthen more because every time you take training, you will get something new, and with that training, you will make a correct and valid report, which means that it will be make the data accurate at the end [ KII 4, PMT member ] ” . Discussion This study attempted to assess the level of data quality and explore the factors affecting routine health information system data quality. The results indicated that the average data accuracy and consistency were approximately 98% and 85%, respectively. Overall, the level of data quality was about 91%, with a 95% CI [88.9–92.76]. In this study, approximately 44% of health workers received training, around 67% of health professionals were supervised by higher officials, and about 66% of health workers received feedback from higher officials. In this study area, Werabe Comprehensive Specialized Hospital and Alem Gebeya Primary Hospital were supported by the CBMP/DUP Project of HIS Implementation. The average data quality in terms of accuracy, completeness, timeliness, and consistency was about 98%, 86%, 95% and 85%, respectively. The level of data quality at WCSH was around 90%, that at Tora Primary Hospital was approximately 89%, that at Kibet Primary Hospital was 90%, and that at Alem Gebeya Primary Hospital was about 93%, which was calculated by using the average of the four dimensions. A study conducted in the Addis Ababa City Administration revealed that the overall data quality of health centers was 76%, whereas a study in the Hadiya Zone, Southern Ethiopia, reported that the overall data quality was 82%, and a study performed in the Harari Region, Ethiopia, reported that 51% of departments had good data quality. These studies’ data quality results were lower than that of this study, which was conducted at public hospitals in the Silte Zone, and the average data quality in this study was in line with the expected data quality at the national level. A possible justification might be the implementation of a Capacity Building and Mentorship Project (CBMP) in half of the study areas where regular technical and capacity-building support is provided for more than two years in an attempt to strengthen HIS in the region and information diffusion to the remaining hospitals as a result of these hospitals being supported by the project. Another possible justification might be that in this study, only public hospitals were included, but most of the studies listed above included health posts and health centers. In addition, in this study, additional dimensions were used, the duration of the study, and the variation in sample size might be possible reasons for the difference ( 23 , 24 , 27 ). The accuracy of the data from this health facility was 98%, which is in line with the results of the study conducted in Mozambique, which revealed that the indicators used for their study had an accuracy range of 91–97%, whereas the results of the study conducted in the Hadiya Zone were 76% accurate, and those of the study conducted in the West Gojjam Zone of Northwest Ethiopia were 74% accurate. The accuracies of the studies conducted in the Harari Region, Ethiopia, were 58%, and those of the studies in the Addis Ababa City Administration (69%) and Nigeria (76%) were lower ( 28 ). The differences might be due to variations in the types of facilities, the duration of the studies, and the indicators selected to measure data accuracy. The overall Silte Zone public hospital data completeness score was 86%, which is lower than that reported in a study conducted in Addis Ababa (94%) and its source document completion (96%). The Addis Ababa study might have used the DHIS2-generated report completeness score, whereas studies in India (71%) and the Harari Region, Ethiopia (60%), reported lower data completeness rates than this study did. Possible explanations for these differences might be the varying durations of the studies and the different indicators used to measure completeness ( 2 , 23 , 29 ). Another dimension examined was consistency. In this study, the consistency of data elements between registers and individual medical records was 85%, whereas in the study conducted in Addis Ababa, approximately 97% of the reports from health centers were consistent, which is higher than that in this study. The difference might be attributed to their use of aggregated data, different measurement methods, and variations in the duration of reported data ( 2 ). In this study, 95% of the data were reported in a timely manner, which was close to the 94% reported in a study conducted in the Harari Region but higher than the timeliness reported from other parts of Ethiopia: 70% in East Wollega and in the Addis Ababa City Administration, where the median report timeliness score was 33% (ranging from 0–100%). These differences might be due to varying methods of assessing timeliness, such as the use of DHIS2-generated timeliness reports and the consideration of the number of reports reviewed ( 30 ). In this study, approximately 44% of health workers received training regarding HMIS activities. Another study conducted in Hadiya, Ethiopia, reported that approximately 52% of health workers received training regarding HMIS activities ( 27 ). Participants who receive training in HMIS-related activities have a 1.6-fold greater probability of having good data quality than participants who do not receive training, which is important for creating awareness and having skilled human resources. Another study conducted in eastern Ethiopia reported that trained staff have a 2.3-fold greater probability of having good data quality than those who are not trained ( 27 ). This is supported by the qualitative study results, as follows: “… data quality training is very effective because if health professionals do not know about the data quality, the data they bring from their unit cannot be correct [ KII 3, HMIS officer]”. Concerning supervision, regular supportive supervision with feedback is essential for resolving problems with data quality and enhancing HMIS's overall performance, particularly with respect to improved data quality. In this study, 67% of hospitals had health professionals supervised by higher officials, whereas a study conducted in the Hadiya Zone and Harari region, Ethiopia, revealed that more than half (63%) and 66%, respectively, were supervised by their respective higher levels in the last two quarters, whereas a study conducted in Kenya found that 79% were supervised. The findings of this study were lower than those of a study conducted in Kenya. The possible reason for the variation might be that the Kenyan study incorporated different types of health facilities, including health posts( 16 , 23 , 27 ). Receiving supervision from higher officials results in 1.7 times greater probability of having good data quality than not receiving supervision, whereas in this study, 66% of hospital health professionals received regular feedback from higher officials. Feedback and supervision remain essential for achieving and maintaining improvements in data quality( 2 ). Providing regular feedback to their staff on the basis of evidence through regular reports from supervisors was associated with 1.7-fold greater odds of having good data quality than others. This is supported by the qualitative study results of this study. “ …if they do a thorough assessment, they will give us feedback after seeing the details; detailed feedback is given to us many times, and I think the feedback they give us is very good for increasing data quality [KII 12, Quality officer]” . Another participant emphasized this by saying “ ...When performance monitoring reviews the monthly report every time, if the data are good, positive feedback continues. I think having supervision and giving feedback is very beneficial for staff [KII 4, PMT member]”. According to the findings of this study, 61% of the study participants agreed that decisions and follow-up actions identified in PMT meetings on the basis of the presented data increase the quality of routine health information system data, but only 79% of service delivery points establish performance monitoring teams. A study performed in Addis Ababa reported that all sampled health centers had PMT. However, there were gaps in the consistency of the meetings, and all the sampled health centers had PMTs ( 2 ), healthcare workers agreed on decisions made, and follow-up actions identified in PMT meetings on the basis of presented data were associated with 1.7 times greater odds of having good data quality than others. This is supported by the qualitative results, as follows: “…especially those monthly reports are evaluated at the case team level by PMT before being sent to our hospital or HMIS unit. After they are confirmed, or after the quality of the data items is confirmed, they are sent to the HMIS unit, which is one of the ways to maintain data quality [ KII 7, Quality officer]. In this study, healthcare workers were agreed had 1.9 times more likely to have good data quality than those who disagreed on a registration and reported a format that was user-friendly or easily understandable. This finding is consistent with a study conducted in the West Gojjam Zone, Northwest Ethiopia: those health workers who agreed that the complexity of the RHIS format affects data quality had higher odds of good data quality than those who disagreed with the complexity of the RHIS format ( 30 ). This is supported by the qualitative results of the study: “ …if the report or register format is not friendly, and someone does not understand or know it, the thing here will be damaged [KII 6, PMT member ] ” . In terms of motivation, appreciation may motivate health professionals toward RHIS activities, which in turn affects the quality of the RHIS data. In this study, approximately 60% of health workers agreed on the effect of appreciation by supervisors or coworkers on data quality. This is supported by the literature on the study conducted at the Addis Ababa City Administration. The results indicated that the motivations of service providers and health center data quality were strongly positively correlated ( 2 ). The findings of this study showed that those who are appreciated and valued by supervisors and coworkers have 1.6 times higher odds of having good data quality than others do. Conclusion and recommendation Conclusion The level of data quality at the public hospital in the Silte Zone was approximately 91%, and from the four dimensions of data quality, completeness and consistency were less than 90%, whereas data accuracy and timeliness were greater than 90%. The registration and reporting formats easily understandability, receive training, receive supervision, provide regular feedback, teams that are appreciated and valued by supervisors and make decisions and follow up actions identified in PMT meetings on the basis of presented data were factors that affect the quality of routine health information system data. The data collection tools and their impact, data quality challenges and assurance mechanisms, supervision and feedback on data quality, and training and its impact on data quality were the four themes identified during thematic analysis. Recommendation The MOH and other supporting organizations intervene in identified gaps, especially to reduce the incompleteness and inconsistency of data, which in turn increases data quality. The regional and zonal health bureaus should increase supportive supervision and regular feedback to health professionals and work on identified gaps. The health facility level of managers should consider staff motivation and make sense of the owner ship as well as the use of data at the hospital and national levels. Strengths and limitations of the study Strength This study was conducted by using both quantitative and qualitative data collection and was triangulated in the discussion section of the study. This study used four dimensions to state the level of data quality and attempted to include additional indicators to assess dimensions of data quality. Limitations The study was not able to include health posts and health centers to state overall zonal health facility data quality. Since this study was a cross-sectional study, it was challenging to prove the temporal correlation in a cause-effect relationship. Abbreviations Ethical Considerations Ethical approval was obtained from the Institutional Review Board (IRB) of Hawassa University College of Medicine (Ref. No: IRB/184/15) . The letter was submitted to the Silte Zone health bureau and then to the public hospital, and official permission was written from them and submitted to each department or unit’s head to obtain permission for data collection. The study participants were informed about the purpose of the study, and informed consent was obtained from the study participants in accordance with the Declaration of Helsinki. Consent for publication Not applicable Data and materials availability We described all the relevant information in the manuscript, but the refined dataset can be obtained from the corresponding author upon reasonable request. Competing interests All the authors declare that no conflicts of interest exist. Funding There was funding obtained from DUP/DDCF project for this study. The funders had no role in study design, data collection and analysis. Acknowledgments We would like to acknowledge Hawassa University, College of Medicine and Health Sciences, DUP/DDCF project, Silte Zone Health Bureau; Hospital administrative body and the data collectors for their valuable involvement and contributions. Contributions of authors ND: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing - original manuscript draft, writing - review & editing, visualization, supervision, project administration. AT: Took part in methodology, software, validation, formal analysis, investigation, writing, review & editing, visualization. SM: Took part in methodology, software, validation, formal analysis, investigation, writing, review & editing, visualization. AA: Took part in methodology, software, formal analysis, investigation, writing - review & editing, visualization. MB: Methodology, software, validation, formal analysis, investigation, writing - review & editing, visualization. AB: Methodology, software, validation, formal analysis, investigation, writing, review & editing, visualization. All authors reviewed and approved the final manuscript prior to submission. Declarations Ethical Considerations Ethical approval was obtained from the Institutional Review Board (IRB) of Hawassa University College of Medicine (Ref. No: IRB/184/15) . The letter was submitted to the Silte Zone health bureau and then to the public hospital, and official permission was written from them and submitted to each department or unit’s head to obtain permission for data collection. The study participants were informed about the purpose of the study, and informed consent was obtained from the study participants in accordance with the Declaration of Helsinki. Consent for publication Not applicable Data and materials availability We described all the relevant information in the manuscript, but the refined dataset can be obtained from the corresponding author upon reasonable request. Competing interests All the authors declare that no conflicts of interest exist. Funding There was funding obtained from DUP/DDCF project for this study. The funders had no role in study design, data collection and analysis. Acknowledgments We would like to acknowledge Hawassa University, College of Medicine and Health Sciences, DUP/DDCF project, Silte Zone Health Bureau; Hospital administrative body and the data collectors for their valuable involvement and contributions. Contributions of authors ND: Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing - original manuscript draft, writing - review & editing, visualization, supervision, project administration. AT: Took part in methodology, software, validation, formal analysis, investigation, writing, review & editing, visualization. SM: Took part in methodology, software, validation, formal analysis, investigation, writing, review & editing, visualization. AA: Took part in methodology, software, formal analysis, investigation, writing - review & editing, visualization. MB: Methodology, software, validation, formal analysis, investigation, writing - review & editing, visualization. AB: Methodology, software, validation, formal analysis, investigation, writing, review & editing, visualization. All authors reviewed and approved the final manuscript prior to submission. References Organization WH. Monitoring the building blocks of health systems: a handbook of indicators and their measurement strategies. World Health Organization; 2010. Haftu B. Assessment of routine health information system (RHIS) data quality and factors affecting it, Addis Ababa City Administration, Ethiopia, 2020: Addis Ababa University; 2020. Kumar M, Gotz D, Nutley T, Smith JB. Research gaps in routine health information system design barriers to data quality and use in low-and middle‐income countries: A literature review. Int J Health Plann Manag. 2018;33(1):e1–9. 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World Health Organization. 2017 [ https://www.healthdatacollaborative.org/fileadmin/uploads/hdc/Documents/Working_Groups/Data_Quality_Review_1.pdf . Dagnew E, Woreta SA, Shiferaw AM. Routine health information utilization and associated factors among health care professionals working at public health institution in North Gondar, Northwest Ethiopia. BMC Health Serv Res. 2018;18:1–8. JSI Research & Training Institute. I. a. t. M. o. H. M. (2019). Optimizing Performance Monitoring Teams to Improve Data Quality and Use in Ethiopia Hospitals. Deng Z, Lu Y, Wei KK, Zhang J. Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China. Int J Inf Manag. 2010;30(4):289–300. Shama AT, Roba HS, Abaerei AA, Gebremeskel TG, Baraki N. Assessment of quality of routine health information system data and associated factors among departments in public health facilities of Harari region, Ethiopia. BMC Med Inf Decis Mak. 2021;21:1–12. Mathewos T, Worku A. Community health management information system Performance and factors associated with at health post of Gurage zone, SNNPR, Ethiopia. University of Gondar; 2015. Directorate E. Health data quality training module participant manual. Mathewos T, collaboration. 2018. Kebede M, Adeba E, Chego M. Evaluation of quality and use of health management information system in primary health care units of east Wollega zone, Oromia regional state, Ethiopia. BMC Med Inf Decis Mak. 2020;20:1–12. Getachew N, Erkalo B, Garedew MG. Data quality and associated factors in the health management information system at health centers in Shashogo district, Hadiya zone, southern Ethiopia, 2021. BMC Med Inf Decis Mak. 2022;22(1):154. Adejumo A. An assessment of data quality in routine health information systems in Oyo State, Nigeria. 2017. Sharma A, Rana SK, Prinja S, Kumar R. Quality of health management information system for maternal & child health care in Haryana state, India. PLoS ONE. 2016;11(2):e0148449. Shiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inf Decis Mak. 2017;17:19. Tables Table 1: Sample size calculation for objective-two. S . N Variable CI Percentage of outcome from Unexposed AOR Power Total Sample Non response rate (5%) References 1 Training 95% 46% 2.47 80 201 211 (13) 2 Supervision 95% 57.3% 1.71 80 576 605 (23) 3 Feed back 95% 33% 3.08 80 131 138 (13) Table 2: Sociodemographic characteristics of respondents at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023 (n=605). Variables Category Frequency Percent Age in year =31 216 35.7 Sex Male 313 51.7 Female 292 48.3 Year of service in Year =11 59 9.8 Position Health care workers (staff) 575 95.0 Case team leader 26 4.3 HMIS officer 4 0.7 Educational status Diploma 116 19.2 BSC degree 474 78.3 Master’s Degree 15 2.5 Salary in ETB 1046-4095 3 0.5 4096-12695 601 99.3 >12695 1 0.2 Table 3: The level of data quality at each public hospital in the Silte Zone, Central Region, Ethiopia, 2023 (n = 32) Name of facility Accuracy Completeness Timelines Consistence Data quality WCSH 98.71 79.54 100 81.95 90.05 TPH 97.53 85.18 91.67 84.95 89.83 Kibet PH 96.30 89.10 87.51 88.19 90.28 Alemgebeya PH 98.54 88.28 100 85.99 93.20 Over all 97.77 85.52 94.79 85.26 90.84 Table 4: The level of data quality at each hospital with respect to its indicators in the Silte Zone, Central Region, Ethiopia, 2023 (n=32). Units or departments with its respective indicators Facility Level of data quality with respect to their indicators WCSH TPH KPH AGPH TB unit (New or relapsed case) 90.75 91.53 90.18 92.70 Laboratory unit (Malaria tested) 90.3 90.93 88.13 90.80 Maternity unit (ANC1) 90.45 90.93 90.18 91.28 Immunization (Penta3/PCV3) 94.33 93.88 95.18 98.28 ART unit (New HIV) 95.10 99.75 100 99.50 IPD department (IPD data) 90.05 90.95 90.28 91.00 Emergency department (eme data) 77.13 80.00 77.82 91.60 OPD department (OPD data) 92.30 80.65 90.45 90.45 Overall result (%) 90.05 89.83 93.20 93.20 Table 5. Organizational, behavioral and technical factors related to RHIS data quality at the public hospital in the Silte Zone, Central Region, Ethiopia, 2023 (n=605). Variables Categories Frequency Percent Education Diploma 116 19.2 BSC 474 78.3 Masters 15 2.5 Standardized set of indicators Disagree 216 35.7 Agree 472 64.3 Understandability of registration format Disagree 133 22.0 Agree 472 78.0 Trained staff able to fill out format Disagree 250 41.3 Agree 355 58.7 Received training No 264 43.6 Yes 341 56.4 Getting supervision Yes 405 66.9 No 200 33.1 Providing regular feedback Disagree 206 34.0 Agree 399 66.0 Supervisors check data quality Disagree 194 32.1 Agree 411 67.9 My work is appreciated by supervisors Disagree 245 40.5 Agree 360 59.5 Staffs engage actively Disagree 207 34.2 Agree 398 65.8 Decisions are made in PMT meetings Disagree 236 39.0 Agree 369 61.0 Institution encourages Disagree 233 38.5 Agree 372 61.5 Staffs are accountable Disagree 246 40.7 Agree 359 59.3 knowledge of RHIS Poor 285 47.1 Good 320 52.9 Table 6: Bivariate and multivariable logistic regression results for public hospitals in the Silte Zone, Central Region, Ethiopia, 2023 (n=605). Variables Categories Data quality COR (95% CI) AOR (95%CI) Good n (%) Poor n (%) Education Diploma 95 (81.9) 21 (18.1) 1 1 BSC 347(73.2) 127(26.8) 1.7(0.99,2.77) 1.69 (0.99,2.92) Masters 11 (73.3) 4 (26.7) 1.6(0.48,5.67) 2.33 (0.60,9.01) Presence indicators Disagree 178(82.4) 38 (17.6) 1 1 Agree 275(70.7) 114(29.3) 1.9(1.28,2.93) 1.36(0.87,2.12) Registration understandability Disagree 113 (84.9) 20 (15.1) 1 1 Agree 340 (72) 132 (28) 2.19(1.31,3.68) 1.92(1.11,3.33) * Staff ability Disagree 190 (76) 60 (24) 1 1 Agree 263 (74.1) 92 (25.9) 1.1(0.76,1.61) 0.99(0.66,1.49) Received training Yes 239(70.1) 102(29.9) 1.83(1.24,2.68) 1.62(1.07,2.44) * No 214(81.1) 50(18.9) 1 1 Supervision Yes 287(70.9) 118 (29.1) 2.0(1.30,3.08) 1.66(1.05,2.61) * No 166(83) 34(17) 1 1 Regular feedback Disagree 173(83.9) 33 (16.1) 1 1 Agree 280 (70.2) 119(29.8) 2.2(1.45,3.42) 1.72(1.07,2.7) * Supervisor check Disagree 154 (79.4) 40(20.6) 1 1 Agree 299(72.8) 112(27.2) 1.4(0.96,2.17) 1.1(0.72,1.78) Appreciation Disagree 202(82.4) 43 (17.6) 1 1 Agree 251 (69.7) 109(30.3) 2.0(1.37,3.04) 1.61(1.05,2.48) * Staff engagement Disagree 165 (79.7) 42(20.3) 1 1 Agree 288 (72.4) 110(27.6) 1.5(1.00,2.25) 1.21(0.78,1.88) Decisions in PMT Meetings Disagree 194 (82.2) 42(17.8) 1 1 Agree 259 (70.2) 110 (29.8) 1.97(1.31,2.93) 1.73(1.12,2.67) * Staff encourages Disagree 184 (78.9) 49 (21.1) 1 1 Agree 269(72.3) 103(27.7) 1.44(0.98,2.12) 1.04(0.68,1.59) Accountability Disagree 191 (77.6) 55 (22.4) 1 1 Agree 262 (72.9) 97 (27.1) 1.29(0.88,1.88) 1.13(0.75,1.71) Knowledge Poor 222 (77.9) 63(22.1) 1 1 Good 231 (72.2) 89 (27.8) 1.36(0.94,1.97) 1.19(0.80,1.7) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5347454","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371580291,"identity":"cf23694e-3031-4ebe-b557-2c829ef2adef","order_by":0,"name":"Nigussie Dukamo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACZhDBxmAAJBkfAAkePlK0MIMIHjbirIJoYZOAsAkAc3b2a5I/yg4b8087fKzya46dDBsD88NHN/BosWzmKZPmOXfYTOJ2Wtpt2W3JQIexGRvn4NFicJgnTZqx7bANw+0cs9uS25iBWnjYpAlpkfwJ1CIP1FIsua2eGC3sxyR42w6bGQC1MH7cdpgoW5itec6lGxveTkuWZtx2nIeNmZBfzh9/ePNHmbXhvNvJBz/+3FZtz8/e/PAxPi3AuDOAM5l5wCRe5SDA/gDOZPxBUPUoGAWjYBSMRAAALwVC6+eLnykAAAAASUVORK5CYII=","orcid":"","institution":"Hawassa University","correspondingAuthor":true,"prefix":"","firstName":"Nigussie","middleName":"","lastName":"Dukamo","suffix":""},{"id":371580292,"identity":"4d09028c-ff6d-41d1-aa2a-5404fe27b710","order_by":1,"name":"Alemu Tamiso","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Alemu","middleName":"","lastName":"Tamiso","suffix":""},{"id":371580293,"identity":"08159c22-a26b-43e5-b896-782fd81d5f2d","order_by":2,"name":"Adugnaw Adane","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Adugnaw","middleName":"","lastName":"Adane","suffix":""},{"id":371580294,"identity":"c59db3da-ebe2-4421-bb06-add7cdb3cf74","order_by":3,"name":"Abriham Asefa","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Abriham","middleName":"","lastName":"Asefa","suffix":""},{"id":371580295,"identity":"71417962-a031-4bc1-8b1f-ec961516997d","order_by":4,"name":"Samuel Misganaw","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Misganaw","suffix":""},{"id":371580296,"identity":"1d8e7436-49ca-496e-9efc-23a347d25068","order_by":5,"name":"Mehretu Belayneh","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Mehretu","middleName":"","lastName":"Belayneh","suffix":""}],"badges":[],"createdAt":"2024-10-28 13:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5347454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5347454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69420907,"identity":"29e77473-d30c-4e71-b347-b71810ff457d","added_by":"auto","created_at":"2024-11-20 07:46:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":364024,"visible":true,"origin":"","legend":"\u003cp\u003eSampling procedure to obtain study participants from each hospital and the final sample size.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5347454/v1/3c3dadfb77f36fbac8de02ba.png"},{"id":69422293,"identity":"955fabc1-e64e-4232-ab90-0ab6e2796075","added_by":"auto","created_at":"2024-11-20 07:54:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1815324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5347454/v1/2a16cf2c-0aae-46a8-89da-697397f72825.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The level of routine health information system data quality and associated factors at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023: A mixed study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the World Health Organization (WHO) definition the Health Information System (HIS) is a system that integrates data collection, processing, reporting, and use of the information necessary for improving health service effectiveness and efficiency through better management at all levels of the health system (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA routine health information system (RHIS) is a system designed to gather, process, use, and disseminate health-related data to enhance the management of programs, resources, and health care outcomes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Information from many parts of the health system is gathered via a routine health information system (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It has been used globally for more than a century. Recently, the developing world has begun to place greater emphasis on RHIS, and it has become more well known in the developing world (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2008, Ethiopia began using the Health Management Information System (HMIS), which is meant to generate routine data for decision-making at various levels of the health system (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The information revolution is one of the major agendas of Ethiopia\u0026rsquo;s health sector transformation plan II (HSTP-II), and it involves phenomenal advancements in the methods and practices of collecting, analyzing, presenting, and disseminating information (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The quality of routine health information system data is vital for the health system to function well and for policymakers to be able to evaluate the effects of health system efforts to improve the health of the population (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImproved health system performance is directly linked with the quality and use of routine data in a country\u0026rsquo;s HIS (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). There is no agreement on the dimensions of data quality, and there are cross-cutting dimensions identified in the literature: completeness, timeliness, consistency, accuracy, reliability and precision (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Specifically, the dimensions such as: completeness, accuracy, consistency and timeliness were the most commonly reviewed dimensions in the literature (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The quality of routine RHIS data in low- and middle-income countries remains quite low in the global health system (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In India, Nepal, and Pakistan, studies have shown that the overall health data quality is much lower than the national standard (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In Ethiopia, according to a study performed at Addis Ababa, the overall data quality was 76.22%, and at Dire Dawa, the overall data quality in the unit or department was found to be 75.3%; previous evidence in Ethiopia, suggested that the level of data quality was recorded as below the national threshold (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe core determinants of routine health information system data quality and utilization are technical, behavioral and organizational factors (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Incomplete registers and a low level of data accuracy are common types of data quality problems (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Even though supportive supervision, short-term training, and data owners have been assigned, routine health system data quality is still a major problem in low- and middle-income countries, including Ethiopia. This affects health system performance and the health of society (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). This study attempted to address the data quality level at public hospitals by including various program areas and applying four dimensions to assess the data quality in each program area.\u003c/p\u003e\n\u003ch3\u003eObjectives of the study\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeneral objective\u003c/h2\u003e \u003cp\u003eTo assess the level and associated factors of routine health information system data quality and explore the factors affecting data quality at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpecific objectives\u003c/h3\u003e\n\u003cp\u003eTo assess the quality of routine health information system data at public hospitals, 2023.\u003c/p\u003e \u003cp\u003eTo identify factors affecting routine health information system data quality at public hospitals, 2023.\u003c/p\u003e \u003cp\u003eTo explore the factors that affect the quality of routine health information system data at public hospitals, 2023.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Central Region of Ethiopia, the Silte Zone, at the public hospital. The Silte Zone is located in the Central Region of Ethiopia and is 173 K.M away from Addis Ababa, the capital city of Ethiopia. The zone has 212 kebeles, 12 districts and 3 administrative towns. According to the 2018 Central Statistical Agency (CSA) reports and regional government reports, the average estimated population of the zone is 1,017,557. Currently, the Silte Zone has four hospitals (Werabe Comprehensive Specialized Hospital, Kibet Primary Hospital, Tora Primary Hospital, Alem Gebeya Primary Hospital), and there are 1184 health care workers in different disciplines in the Silte Zone at public hospitals.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and period\u003c/h3\u003e\n\u003cp\u003eA facility-based mixed method with an embedded study design was employed at a public hospital in the Silte Zone Central Region of Ethiopia from March 18 to April 30, 2023. A cross-sectional study design was used for the quantitative part, and a phenomenology study design was used for the qualitative part.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePopulation\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSource population\u003c/h2\u003e \u003cp\u003eAll public hospitals in the Silte Zone were included in the source population.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe selected units or departments at public hospitals in the Silte Zone were included.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy unit\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eFor the quantitative study\u003c/h2\u003e \u003cp\u003eThe HMIS registration books or report formats, as well as individual medical records and health care workers chosen randomly from particular departments or selected units, were examined.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFor the qualitative study\u003c/h2\u003e \u003cp\u003eThe key informants selected purposeful at public hospitals were considered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEligibility criteria\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eInclusion criteria\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003eFor the quantitative study\u003c/h2\u003e \u003cp\u003eHealth care workers who have worked for more than six months at this institution and three consecutive months of data that were registered in 2023 on the HMIS registration book were included in this study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFor the qualitative study\u003c/h2\u003e \u003cp\u003eThe key informants, such as HMIS officers, quality officers, and performance monitoring team (PMT) members, were included in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eExclusion criteria\u003c/h2\u003e \u003cp\u003eHealth care workers who were on leave for three selected months for various reasons were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSample size determination\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eSample size for objective one\u003c/h2\u003e \u003cp\u003eThe WHO recommends to use five units with core indicators for regular data quality review (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additionally, countries may select other indicators on the basis of their needs and the resources available. One indicator from each unit or department was selected. Therefore, eight units or departments at each hospital were included. There were four public hospitals, so the total sample size used to assess the data quality was 32 units.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSample size for objective two\u003c/h2\u003e \u003cp\u003eThe sample size for objective two is calculated via Epi\u0026ndash;info from the findings of previous similar studies. The calculations are separately made for three potential determinants (training, supervision and feedback), which are consistently significant in many studies. To determine the appropriate sample size, the percent of outcome from the unexposed group, two-sided confidence level, power of the study and relatively least extreme odds ratio were detected were used. Therefore, the maximum calculated sample size is 605. (Table.1)\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;1: Sample size calculation for objective-two.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSample size for objective three\u003c/h2\u003e \u003cp\u003eFor the qualitative study, 12 participants (3 from each hospital) were purposefully selected for key informant interviews (KIIs).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSampling technique and sampling procedure\u003c/h2\u003e \u003cp\u003eHealth care workers for the self-administered questionnaire were selected by a simple random sampling technique. The participants in the qualitative study were selected purposefully to explore the factors affecting data quality at public hospitals. The three-month documents of registrations and reports were reviewed to check the accuracy, completeness, timeliness, and consistency of the data at each department or unit. The first month was selected randomly by lottery methods, and then data registered and reported for three consecutive months were included for chart or document review. The selected indicators from departments or units were maternal health (ANC1), laboratory (malaria tested case), immunization (Penta3/PCV3), ART (currently on ART), tuberculosis (new or relapsed TB case), inpatient departments (IPD data), emergency departments (emergency data), and outpatient departments (OPD data).\u003c/p\u003e \u003cp\u003e To select health care workers, the sample size was allocated to each hospital and then to departments or units on the basis of the proportional allocation formula (nj\u0026thinsp;=\u0026thinsp;n/N* Nj). Finally, the study participants were selected by a simple random sampling method as described below (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eFigure 1: Sampling procedure to obtain study participants from each hospital and the final sample size.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eDependent variables\u003c/h2\u003e \u003cp\u003eData quality (good or poor).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eIndependent variables\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eSociodemographic factors\u003c/strong\u003e \u003cp\u003esex, age, work experience, professional category, and level of\u003c/p\u003e \u003c/p\u003e \u003cp\u003eeducation, salary and position in hospitals.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOrganizational related\u003c/strong\u003e \u003cp\u003efeedback, supervision, training and data use, appreciation and performance monitoring meetings and accountability for poor performance.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTechnical related\u003c/strong\u003e \u003cp\u003ecomplexity or user-friendliness of the reporting or register formats and trained person ability to fill the format.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBehavioral-related\u003c/strong\u003e \u003cp\u003eData manipulation for competition, negligence, sense of responsibility, and\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePerception of staff on data, active engagement and knowledge of routine health information data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eData collection tool and procedure\u003c/h2\u003e \u003cdiv id=\"Sec28\" class=\"Section4\"\u003e \u003ch2\u003eFor the quantitative study\u003c/h2\u003e \u003cp\u003eThe data were collected by a structured questionnaire, which was adapted from a review of different studies and the PRISM tool (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The self-administered questionnaire contains four parts: sociodemographic characteristics, technical factors, organizational factors, behavioral factors, and knowledge assessment. Two supervisors and six data collectors (two BSc nursing graduates who have received HMIS training) and two with Masters of Public Health were assigned.to collect the data from each respondent, and review the documents, registration, and individual patient charts. The data were collected by visiting all the hospitals, explaining the aim of the study, ensuring the confidentiality of the data, and obtaining consent from each participant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eFor the qualitative study\u003c/h2\u003e \u003cp\u003eAfter convenient time and place, the participants were interviewed via semi structured interview guide questionnaires. Two Master of Public Health data collectors who were previously exposed to qualitative data collection were used to collect the data. The data collectors conducted the interviews via an audio recording device for 20\u0026ndash;40 minutes while simultaneously taking field notes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData quality assurance\u003c/h3\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eFor the quantitative study\u003c/h2\u003e \u003cp\u003eAn amendment to the tool was made after pretesting on 5% (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) of health care workers or 5% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) of charts and the HMIS register of the total sample at Adare General Hospital. Before data collection, two days of training were provided on the purpose, how to collect data, and ethical issues, emphasizing the importance of the safety of the participants and the quality of the data. The data collectors were supervised, and onsite technical assistance was given. Moreover, data completeness and consistency were evaluated on a daily basis, and corrective steps were implemented promptly. Finally, prior to data entry, each questionnaire was coded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eFor the qualitative study\u003c/h2\u003e \u003cp\u003eThe trustworthiness of the data was evaluated via the following criteria:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCredibility\u003c/strong\u003e \u003cp\u003e During in-depth interviews, enough time was given to participants to respond their perceptions and experiences, the participants were interviewed in comfortable places, and data were collected.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDependability\u003c/strong\u003e \u003cp\u003eTo ensure the consistency of the data from all KIIs, the same data collectors used the same KII guide. After data collection, the raw or recorded data were transcribed verbatim and then translated into English.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTransferability\u003c/strong\u003e \u003cp\u003eNominated samples were used for in-depth interviews to be representative of the source population.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConformability\u003c/strong\u003e \u003cp\u003eTo avoid researcher bias throughout data collection, coding, and analysis, the KIs' own words were used instead of the researchers' opinions and expectations.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eData processing and analysis\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eFor the quantitative study\u003c/h2\u003e \u003cp\u003eThe data were checked for completeness and consistency and then entered into Epi-data version 4.4 and exported to SPSS version 26 for statistical analysis. Descriptive statistics means, frequencies, and tables were used to summarize and describe the data. The mean scores are used as cutoff points to split the data into different scale measures to dichotomize the variables. Binary logistic regression was performed, and the variables with a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were entered into the multivariable logistic regression analysis.\u003c/p\u003e \u003cp\u003eThe Hosmer\u0026ndash;Lemeshow statistical test was used to assess the model\u0026rsquo;s goodness of fit (fit if the p value was greater than 0.05), and in this study, the Hosmer\u0026ndash;Lemeshow statistical test was 0.43, and the value of the variation inflation factor (VIF) was between 1 and 2 for each independent variable, which indicates that there was no multicollinearity effect. Finally, the adjusted odds ratio (AOR) with its 95% CI was reported. Variables with a 95% confidence interval that did not include one in the multivariable logistic regression analysis were significantly associated with routine health information system data quality.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eFor the qualitative study\u003c/h3\u003e\n\u003cp\u003eThe data were collected from key informants via audio recording and note-taking, and then transcribed after being listened to repeatedly. They were translated verbatim from Amharic to English via by experts with a health professional background and prior translation experience. Ultimately, it was saved in plain text format. After they reviewed and familiarized themselves with the responses from key informant interviews (KIIs), the data were coded, categorized, and analyzed via thematic analysis by open-code software. Themes and subthemes were then identified. Finally, the themes and subthemes were presented separately in the results and integrated with the quantitative findings in the discussion in a narrative manner.\u003c/p\u003e\n\u003ch3\u003eOperational Definitions\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eTechnical determinants\u003c/strong\u003e \u003cp\u003eFactors related to the availability of HMIS tools, the complexity or user friendliness of formats, and the trained person's ability to fill out the format to perform routine health information tasks in organizations (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOrganizational determinants\u003c/strong\u003e \u003cp\u003eFactors related to organizations, such as feedback, supervision, training, data use and performance monitoring teams, contribute to improving the RHIS process (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePerformance monitoring team (PMT)\u003c/strong\u003e \u003cp\u003ePerformance monitoring teams (PMTs) are health care workers who review and analyze a hospital's performance and develop action plans for course correction (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBehavioral determinants\u003c/strong\u003e \u003cp\u003eIndividual-level factors affect the practice of routine health information system tasks (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompleteness\u003c/strong\u003e \u003cp\u003eIs the average of the source document or registration content completeness and report content completeness, the data are complete if the average is \u0026ge;\u0026thinsp;90% (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData accuracy\u003c/strong\u003e \u003cp\u003ewas measured by calculating the total number of diseases or services from the source document or register divided by the total number from the report submitted to the next level. The data were considered accurate if the average was within the acceptable limit (0.90\u0026ndash;1.10 or 90\u0026ndash;110%), and 10% tolerance for data accuracy was used (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReport timeliness\u003c/strong\u003e \u003cp\u003ewas measured by the number of reports delivered up to the deadline for the health management information system (HMIS) unit divided by the number of reports expected to come. The data are reported in a timely manner if the average is \u0026ge;\u0026thinsp;90% (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsistency\u003c/strong\u003e \u003cp\u003ewas measured by the data on the register with data on individual medical records. By dividing those individual medical record numbers (MRNs) with matched data elements written on the register by the total number of sampled MRNs, the data are considered consistent if the consistency score is \u0026ge;\u0026thinsp;90 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMatched\u003c/strong\u003e \u003cp\u003eMatched is defined as when all of the selected data elements that are recorded on the registers for the sampled individual are also recorded on the individual medical records.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNot\u003c/b\u003e matched is defined as when at least one or more of the selected data elements that are recorded on the registers for the sampled individual are not exactly the same as what is recorded on the individual medical records. In addition, if the individual medical record is not physically available, then it is also considered not matched (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRHIS data quality\u003c/strong\u003e \u003cp\u003ewas measured by calculating the sum of the four dimensions of data quality measured and then taking the average of the scores (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGood data quality\u003c/strong\u003e \u003cp\u003eThe data\u0026rsquo;s average scores of the four dimensions\u0026thinsp;\u0026ge;\u0026thinsp;90 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePoor data quality\u003c/strong\u003e \u003cp\u003eThe data\u0026rsquo;s average scores of four dimensions\u0026thinsp;\u0026lt;\u0026thinsp;90% (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLevel of knowledge\u003c/strong\u003e \u003cp\u003eA health care worker is said to have good knowledge if they respond to knowledge questions above the respondent\u0026rsquo;s mean score.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics of the respondents\u003c/h2\u003e \u003cp\u003eA total of 605 respondents participated in this study, for a response rate of 100%. Eight indicators in the departments or units were included. Among all the facilities included, 575 (95%) were health care workers working at staff positions, and 389 (64.3%) of the respondents were under 31 years of age. Among the respondents, 313 (51.7%) were male. Regarding the distribution of levels of education, 474 (78.3%) were degree holders. Approximately 258 (42.6%) of the respondents were nurses, and most of the respondents had less than five years of experience, with 331 (54.7%) (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e \u003cp\u003eSociodemographic characteristics of respondents at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003eLevel of data quality\u003c/h2\u003e \u003cp\u003eThe study's overall data quality was 90.84%, with a 95% confidence interval of [88.9\u0026ndash;92.76]. The lowest data quality was found at Tora PH, whereas the highest percentage was found at Alem Gebeya Primary Hospital, at 89.83% and 93.2%, respectively. (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e \u003cp\u003eThe level of data quality at each public hospital in the Silte Zone, Central Region, Ethiopia, in 2023.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003eFacility level data quality with respect to their indicators\u003c/h2\u003e \u003cp\u003eFrom the list of the ten most common diseases, the top six were selected, and the data accuracy at each hospital was checked. In WCSH, a total of 3291 diseases or services were reviewed. Among these data, 99.1% (111/112) of the IPD data, 97.9% (576/588) of the emergence data and 99.4% (1338/1346) of the OPD data were within the acceptable limits, whereas 0.89% of the IPD data, 2.04% of the emergence data and 0.59% of the OPD data were overreported. The 4920 clients were registered at various departments or units. Of those, 246 (or 5% of the register) client data were checked for consistency and completeness of data at each department or unit, and the three-month report submission dates were checked at each department or unit, ensuring that all reports were sent on time to the HMIS unit or higher level.\u003c/p\u003e \u003cp\u003eFrom Tora Primary Hospital, a total of 2252 diseases or services were checked; 98.05% (453/462) of the emergence data, 97.1% (34/35) of the TB data, 97.4% (221/227) of the OPD data, and 93.8% (61/65) of the malaria data were reported within the acceptable limits, whereas 1.95% of the emergence data, 2.86% of the TB data, 2.64% of the OPD data and 6.15% of the malaria data were over reported. The 2420 clients were registered at various units or departments for three particular months, in which 121 (5% of the total register) clients\u0026rsquo; data registers were checked. One-month emergence and OPD reports were not submitted in a timely manner.\u003c/p\u003e \u003cp\u003eIn Kibet primary hospital, 1939 diseases or services were counted and checked for data accuracy, of which 97.87% (46/47) of the TB data, 94.34% (100/106) of the IPD data, 96.55% (112/116) of the Penta3 vaccine data, 96.18% (126/131) of the OPD data, and 97.78% (309/316) of the emergence data were within the acceptable range, whereas 2.13% of the TB data, 5.66% of the IPD data, and 3.45% of the Penta 3 vaccine data were overreported. A total of 92.5% (431/401) of the ANC1 data were reported at acceptable limits, whereas 7.48% of the ANC1 data and 0.12% of the malaria data were underreported. In addition, 2540 clients were registered at different selected departments or units, in which 127 (5% of the register) client data registers were checked for data completeness and consistency. One-month laboratory (malaria) and emergence reports were not submitted in a timely manner. From the service or disease register (2651) of three selected months at Alem Gebeya Primary Hospital, 97.78% (44/45) of the IPD data and 99.91% (1072/1074) of the malaria data were reported at the accepted limit, whereas 2.22% of the IPD data and 0.19% of the malaria data were overreported. A total of 97.85% (285/279) of the emergence data and 96.98% (648/629) of the ANC1 service data were reported with acceptable limits, but 2.15% of the emergence data, and 3.02% of the ANC1 data were under reported in Alem Gebeya Primary Hospital, 3040 clients were registered at different selected departments or units, and 152 (5% of the register) client registers were checked for completeness and consistency of the data at each department or unit, and one-month OPD reports were not submitted in a timely manner. (Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e \u003cp\u003eThe level of data quality at each hospital with respect to its indicators in the Silte Zone, Central Region, Ethiopia, 2023.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFactors related to routine health information system data quality.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Likert scale (with five scales were used), ranges from strongly disagree to strongly agree. These responses were dichotomized into disagree if the answers were 1 to 3 and agree, if the answers were 4 to 5. (Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;5. Organizational, behavioral and technical factors related to RHIS data quality at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBivariate and multivariable analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the bivariate logistic regression analysis, educational status, knowledge of RHIS, the presence of a standardized set of indicators, registration and reporting formats easily understandable, trained staff ability, receiving HMIS-related training, receiving supervision from higher officials, providing regular feedback to staff, checking data quality, collecting data are appreciated and valued by supervisors, active staff engagement in all activities, making decisions and following up on actions identified in PMT meetings, encouraging the gathering of data, ensuring data importance for monitoring facility service performance and accounting for poor performance had a p-value of less than 0.25 and were included in the multivariable model.\u003c/p\u003e \u003cp\u003eIn the multivariable logistic regression, the registration and report format are user-friendly or easily understandable; receiving training on HMIS-related activities; receiving supervision from higher officials; providing regular feedback to staff; collecting data is appreciated and valued by supervisors; and decisions made and follow-up actions identified in PMT meetings are significantly associated with routine health information system data quality. The participants agreed that having a registration and reporting format that is easy to use or understand is a 1.9-fold greater probability of having good data quality (AOR\u0026thinsp;=\u0026thinsp;1.92; 95% CI: 1.11\u0026ndash;3.33; P\u0026thinsp;=\u0026thinsp;0.020) than other.\u003c/p\u003e \u003cp\u003eHealthcare workers who received HMIS-related training had a 1.6 times greater probability of having good data quality than those who did not. (AOR\u0026thinsp;=\u0026thinsp;1.62; 95% CI: 1.07\u0026ndash;2.44 with P\u0026thinsp;=\u0026thinsp;0.022), and those who received supervision from higher officials had a 1.7 times greater likelihood of having good data quality than those who did not receive supervision (AOR\u0026thinsp;=\u0026thinsp;1.66; 95% CI: 1.05\u0026ndash;2.61 with P\u0026thinsp;=\u0026thinsp;0.029). Those who received regular feedback from supervisors through reports were 1.7 times more likely to have good data quality than those who did not receive regular feedback (AOR\u0026thinsp;=\u0026thinsp;1.72; 95% CI: 1.08\u0026ndash;2.75 with P\u0026thinsp;=\u0026thinsp;0.024).\u003c/p\u003e \u003cp\u003eHealth care workers who agreed on the impact of decisions made and follow-up actions identified in PMT meetings on the basis of the presented data were 1.7 times more likely to have good data quality than others (AOR\u0026thinsp;=\u0026thinsp;1.73; 95% CI: 1.12\u0026ndash;2.67, P\u0026thinsp;=\u0026thinsp;0.013). Health care workers whose work was appreciated by supervisors and coworkers were 1.6 times more likely to have good data quality than others (AOR\u0026thinsp;=\u0026thinsp;1.61; 95% CI: 1.04\u0026ndash;2.47 with P\u0026thinsp;=\u0026thinsp;0.031). (Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;6: Bivariate and multivariable logistic regression results for public hospitals in the Silte zone, Central Region, Ethiopia, 2023.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThematic findings\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThematic analysis was performed by open-code software, and the data were categorized into four themes and twelve subthemes. These themes were data collection tools and their impact, data quality challenges and assurance mechanisms, supervision and feedback on data quality, and training and its impact on data quality.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme one: Data quality challenge and assurance mechanism\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eChallenge of data quality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA majority of in-depth interviewees mentioned that there was incomplete information in the register, the hospital did not use HMIS data, negligent or carelessness, and that improper disease classification is a major challenge that affects data quality.\u003c/p\u003e \u003cp\u003eThe key informants were asked about the reason for the incompleteness of information and responded as follows: \u003cb\u003e\u0026ldquo;\u003c/b\u003eMost of the time, some registered data are incomplete; because there are some careless or negligent health professionals; who do not complete the data in the registry. To register indicators correctly and obtain quality data, incomplete registration is the main problem affecting data quality \u003cb\u003e[KII 2, HMIS officer]\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026ldquo;\u003c/b\u003eMany times, there is something that makes it difficult for us to maintain data quality; some health professionals do not register information, not only one person who is trained in the health management information system but also all the health professionals who are assigned to do it in turn. This is a problem; sometimes they may write a diagnosis that is not correct, and the register may be written in illegible handwriting. The other is incompleteness, which is similar to another challenge; the registration book should have complete things, such as writing one by one, but some staff may fill up by jumping. Many times, when trainings are given at the institutional level to solve this problem, we are told to provide complete information; few of them are filled with complete information, but when we visit the unit, what the situation we see is an incomplete card or register \u003cb\u003e[KII 9, Quality directorate]\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe majority of individuals pointed out the challenge related to improper disease classification as follows: \u003cb\u003e\u0026ldquo;\u003c/b\u003eRegarding the disease registration; many clinicians expected to write HMIS disease codes, but with us, the doctor only writes a clinical diagnosis, When the data owner makes a monthly report, to match it with the National Classification of Disease (NCoD), he or she round it off \u003cb\u003e[KII 2, HMIS officer]\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe key informants respond to the challenge related to the hospital in which HMIS data are not used and for negligence or carelessness as follows: \u003cb\u003e\u0026ldquo;\u003c/b\u003eAs a hospital, there are some gaps; instead of using data, if we use the data to improve our services, it is very good, but in reality, the hospital did not use them according to our plan \u003cb\u003e[KII 12, Quality officer]\u0026rdquo;\u003c/b\u003e. \u003cb\u003e\u0026ldquo;\u003c/b\u003eI do not think that negligence has anything positive effect, because it affects the data quality very much. There are some health professionals who do not register what they have done; and some who start and make incomplete registers; for example, some professionals who are in dire need of emergencies do not record the time of patient arrival. Because of that, sometimes it is difficult to know patients who have been there for more than 24 hours. Because of this, negligence strongly affects the data quality \u003cb\u003e[KII 2, HMIS officer]\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssurance mechanism of data quality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA majority of in-depth interviewees mentioned that there was an assigned HMIS focal person, a strong PMT monthly meeting, and the LQAS (lot quality assurance sampling) method was used to assure data quality. One participant explained that one of the data quality assurance mechanisms was checking by the LQAS method, described as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026ldquo;\u003c/b\u003eWith respect to data quality assurance methods, there is a registration book, there is a tally sheet and there is a monthly report. We will check with LQAS; if they are under LQAS, they will work again. If it is correct, it will be sent to HMIS, and then DHSI 2 will be entered. The quality assurance mechanism in our institution is the LQAS method \u003cb\u003e[KII 4, PMT member]\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe key informant responded that one of the data quality assurance mechanisms was assigning an HMIS focal person and strengthening PMT monthly meetings. \u003cb\u003e\u0026ldquo;\u003c/b\u003eThere was a matter of not registering the patient as soon as it came and not registering it for different reasons, but now, they have been assigned an independent focal person who follows it, and now, whether it is an elective or emergency case, there is an assigned nurse to register \u003cb\u003e[KII 1, Dept. coordinator]\u0026rdquo;\u003c/b\u003e. \u003cb\u003e\u0026ldquo;\u003c/b\u003eThere are performance monitoring teams (PMTs), so PMT check reports periodically, monthly and weekly reports at the case team level, especially monthly report data, are evaluated at the case team level by PMT before being sent to our hospital HMIS unit. After they are confirmed or after the quality of the data items is confirmed, they are sent to the HMIS unit, which is one way to assure data quality \u003cb\u003e[KII 7, Quality officer]\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme two: Data collection tools and their impact\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA majority of in-depth interviewees mentioned that tool availability and friendliness affect RHIS data quality and stated as follow: \u003cb\u003e\u0026ldquo;\u003c/b\u003eThe register tool and tally sheet as well as the report format are easy to understand. I do not think there is a problem with the data register tool because the tool is easy to understand, if you need an explanation, you will find it written below in the registration or report format. Tool availability and easy understandability have positive effects. Currently, we have a tool that we can use to do all staff to report and register; that has a positive effect on data quality \u003cb\u003e[KII 10, Nursing \u0026amp; Midwifery Service Director]\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme three: supervision and feedback on data quality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn most of the in-depth interviews, supervision and feedback were given, which helped to prepare an action plan and to correct the gap. Stated as follows: \u003cb\u003e\u0026ldquo;\u003c/b\u003eBy the way, feedback is very good because they see things that you do not see. When they give you such a comment, if there is something good, it helps us to continue, and if there is a gap, it helps us to fix it, and it helps us to make an action plan to solve the gap on that basis \u003cb\u003e[KII 1, Dept. coordinator]\u0026rdquo;\u003c/b\u003e. \u003cb\u003e\u0026ldquo;\u003c/b\u003eThey provide feedback as soon as they are supervised. If they come every three months, they will give feedback; if they come once every six months, they will also give feedback. When the performance monitoring team reviews the monthly report every time and if the data are good, positive feedback continues. I think having supervision and giving feedback is very beneficial for staff to improve data quality \u003cb\u003e[KII 4, Hospital PMT member]\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheme four: training and its impact on data quality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMany of the participants in the in-depth interviews mentioned that the training identified different indicators that are new; this in turn, enhances data quality. Explained as follow.\u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026ldquo;\u003c/b\u003eI think training is a must, like our hospital, because the indicators change every time, when the indicator changes, the registrations also change because the data sources are registration points for many indicators. Therefore, it reduces our outcome and data quality \u003cb\u003e[KII 12, Quality officer]\u0026rdquo;\u003c/b\u003e. \u003cb\u003e\u0026ldquo;\u003c/b\u003eTraining is very important. Now, we have given disease classification; just as it was changed in July, we have given training to the data owner. Currently, the reporting format is better, that is, after we have given training. After we provide HMIS training and before we provide it, different indicators are very clear to them, which means that after training is complete \u003cb\u003e[KII 2, HMIS officer]\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026ldquo;\u003c/b\u003eTraining, when you take it, it will strengthen more because every time you take training, you will get something new, and with that training, you will make a correct and valid report, which means that it will be make the data accurate at the end [\u003cb\u003eKII 4, PMT member\u003c/b\u003e]\u003cb\u003e\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study attempted to assess the level of data quality and explore the factors affecting routine health information system data quality. The results indicated that the average data accuracy and consistency were approximately 98% and 85%, respectively. Overall, the level of data quality was about 91%, with a 95% CI [88.9\u0026ndash;92.76]. In this study, approximately 44% of health workers received training, around 67% of health professionals were supervised by higher officials, and about 66% of health workers received feedback from higher officials. In this study area, Werabe Comprehensive Specialized Hospital and Alem Gebeya Primary Hospital were supported by the CBMP/DUP Project of HIS Implementation.\u003c/p\u003e \u003cp\u003eThe average data quality in terms of accuracy, completeness, timeliness, and consistency was about 98%, 86%, 95% and 85%, respectively. The level of data quality at WCSH was around 90%, that at Tora Primary Hospital was approximately 89%, that at Kibet Primary Hospital was 90%, and that at Alem Gebeya Primary Hospital was about 93%, which was calculated by using the average of the four dimensions. A study conducted in the Addis Ababa City Administration revealed that the overall data quality of health centers was 76%, whereas a study in the Hadiya Zone, Southern Ethiopia, reported that the overall data quality was 82%, and a study performed in the Harari Region, Ethiopia, reported that 51% of departments had good data quality. These studies\u0026rsquo; data quality results were lower than that of this study, which was conducted at public hospitals in the Silte Zone, and the average data quality in this study was in line with the expected data quality at the national level. A possible justification might be the implementation of a Capacity Building and Mentorship Project (CBMP) in half of the study areas where regular technical and capacity-building support is provided for more than two years in an attempt to strengthen HIS in the region and information diffusion to the remaining hospitals as a result of these hospitals being supported by the project. Another possible justification might be that in this study, only public hospitals were included, but most of the studies listed above included health posts and health centers. In addition, in this study, additional dimensions were used, the duration of the study, and the variation in sample size might be possible reasons for the difference (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe accuracy of the data from this health facility was 98%, which is in line with the results of the study conducted in Mozambique, which revealed that the indicators used for their study had an accuracy range of 91\u0026ndash;97%, whereas the results of the study conducted in the Hadiya Zone were 76% accurate, and those of the study conducted in the West Gojjam Zone of Northwest Ethiopia were 74% accurate. The accuracies of the studies conducted in the Harari Region, Ethiopia, were 58%, and those of the studies in the Addis Ababa City Administration (69%) and Nigeria (76%) were lower (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The differences might be due to variations in the types of facilities, the duration of the studies, and the indicators selected to measure data accuracy.\u003c/p\u003e \u003cp\u003eThe overall Silte Zone public hospital data completeness score was 86%, which is lower than that reported in a study conducted in Addis Ababa (94%) and its source document completion (96%). The Addis Ababa study might have used the DHIS2-generated report completeness score, whereas studies in India (71%) and the Harari Region, Ethiopia (60%), reported lower data completeness rates than this study did. Possible explanations for these differences might be the varying durations of the studies and the different indicators used to measure completeness (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Another dimension examined was consistency. In this study, the consistency of data elements between registers and individual medical records was 85%, whereas in the study conducted in Addis Ababa, approximately 97% of the reports from health centers were consistent, which is higher than that in this study. The difference might be attributed to their use of aggregated data, different measurement methods, and variations in the duration of reported data (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, 95% of the data were reported in a timely manner, which was close to the 94% reported in a study conducted in the Harari Region but higher than the timeliness reported from other parts of Ethiopia: 70% in East Wollega and in the Addis Ababa City Administration, where the median report timeliness score was 33% (ranging from 0\u0026ndash;100%). These differences might be due to varying methods of assessing timeliness, such as the use of DHIS2-generated timeliness reports and the consideration of the number of reports reviewed (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, approximately 44% of health workers received training regarding HMIS activities. Another study conducted in Hadiya, Ethiopia, reported that approximately 52% of health workers received training regarding HMIS activities (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Participants who receive training in HMIS-related activities have a 1.6-fold greater probability of having good data quality than participants who do not receive training, which is important for creating awareness and having skilled human resources. Another study conducted in eastern Ethiopia reported that trained staff have a 2.3-fold greater probability of having good data quality than those who are not trained (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This is supported by the qualitative study results, as follows: \u003cb\u003e\u0026ldquo;\u0026hellip;\u003c/b\u003edata quality training is very effective because if health professionals do not know about the data quality, the data they bring from their unit cannot be correct [\u003cb\u003eKII 3, HMIS officer]\u0026rdquo;.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eConcerning supervision, regular supportive supervision with feedback is essential for resolving problems with data quality and enhancing HMIS's overall performance, particularly with respect to improved data quality. In this study, 67% of hospitals had health professionals supervised by higher officials, whereas a study conducted in the Hadiya Zone and Harari region, Ethiopia, revealed that more than half (63%) and 66%, respectively, were supervised by their respective higher levels in the last two quarters, whereas a study conducted in Kenya found that 79% were supervised. The findings of this study were lower than those of a study conducted in Kenya. The possible reason for the variation might be that the Kenyan study incorporated different types of health facilities, including health posts(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Receiving supervision from higher officials results in 1.7 times greater probability of having good data quality than not receiving supervision, whereas in this study, 66% of hospital health professionals received regular feedback from higher officials. Feedback and supervision remain essential for achieving and maintaining improvements in data quality(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Providing regular feedback to their staff on the basis of evidence through regular reports from supervisors was associated with 1.7-fold greater odds of having good data quality than others. This is supported by the qualitative study results of this study. \u003cb\u003e\u0026ldquo;\u003c/b\u003e\u0026hellip;if they do a thorough assessment, they will give us feedback after seeing the details; detailed feedback is given to us many times, and I think the feedback they give us is very good for increasing data quality \u003cb\u003e[KII 12, Quality officer]\u0026rdquo;\u003c/b\u003e. Another participant emphasized this by saying \u003cb\u003e\u0026ldquo;\u003c/b\u003e...When performance monitoring reviews the monthly report every time, if the data are good, positive feedback continues. I think having supervision and giving feedback is very beneficial for staff \u003cb\u003e[KII 4, PMT member]\u0026rdquo;.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccording to the findings of this study, 61% of the study participants agreed that decisions and follow-up actions identified in PMT meetings on the basis of the presented data increase the quality of routine health information system data, but only 79% of service delivery points establish performance monitoring teams. A study performed in Addis Ababa reported that all sampled health centers had PMT. However, there were gaps in the consistency of the meetings, and all the sampled health centers had PMTs (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), healthcare workers agreed on decisions made, and follow-up actions identified in PMT meetings on the basis of presented data were associated with 1.7 times greater odds of having good data quality than others. This is supported by the qualitative results, as follows: \u0026ldquo;\u0026hellip;especially those monthly reports are evaluated at the case team level by PMT before being sent to our hospital or HMIS unit. After they are confirmed, or after the quality of the data items is confirmed, they are sent to the HMIS unit, which is one of the ways to maintain data quality [\u003cb\u003eKII 7, Quality officer].\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, healthcare workers were agreed had 1.9 times more likely to have good data quality than those who disagreed on a registration and reported a format that was user-friendly or easily understandable. This finding is consistent with a study conducted in the West Gojjam Zone, Northwest Ethiopia: those health workers who agreed that the complexity of the RHIS format affects data quality had higher odds of good data quality than those who disagreed with the complexity of the RHIS format (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This is supported by the qualitative results of the study: \u003cb\u003e\u0026ldquo;\u003c/b\u003e\u0026hellip;if the report or register format is not friendly, and someone does not understand or know it, the thing here will be damaged \u003cb\u003e[KII 6, PMT member\u003c/b\u003e]\u003cb\u003e\u0026rdquo;\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn terms of motivation, appreciation may motivate health professionals toward RHIS activities, which in turn affects the quality of the RHIS data. In this study, approximately 60% of health workers agreed on the effect of appreciation by supervisors or coworkers on data quality. This is supported by the literature on the study conducted at the Addis Ababa City Administration. The results indicated that the motivations of service providers and health center data quality were strongly positively correlated (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The findings of this study showed that those who are appreciated and valued by supervisors and coworkers have 1.6 times higher odds of having good data quality than others do.\u003c/p\u003e"},{"header":"Conclusion and recommendation","content":"\n\u003ch3\u003eConclusion\u003c/h3\u003e\n\u003cp\u003eThe level of data quality at the public hospital in the Silte Zone was approximately 91%, and from the four dimensions of data quality, completeness and consistency were less than 90%, whereas data accuracy and timeliness were greater than 90%. The registration and reporting formats easily understandability, receive training, receive supervision, provide regular feedback, teams that are appreciated and valued by supervisors and make decisions and follow up actions identified in PMT meetings on the basis of presented data were factors that affect the quality of routine health information system data.\u003c/p\u003e \u003cp\u003eThe data collection tools and their impact, data quality challenges and assurance mechanisms, supervision and feedback on data quality, and training and its impact on data quality were the four themes identified during thematic analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRecommendation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe MOH and other supporting organizations intervene in identified gaps, especially to reduce the incompleteness and inconsistency of data, which in turn increases data quality.\u003c/p\u003e \u003cp\u003eThe regional and zonal health bureaus should increase supportive supervision and regular feedback to health professionals and work on identified gaps. The health facility level of managers should consider staff motivation and make sense of the owner ship as well as the use of data at the hospital and national levels.\u003c/p\u003e\n\u003ch3\u003eStrengths and limitations of the study\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eStrength\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study was conducted by using both quantitative and qualitative data collection and was triangulated in the \u003cspan refid=\"Sec41\" class=\"InternalRef\"\u003ediscussion\u003c/span\u003e section of the study.\u003c/p\u003e \u003cp\u003eThis study used four dimensions to state the level of data quality and attempted to include additional indicators to assess dimensions of data quality.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study was not able to include health posts and health centers to state overall zonal health\u003c/p\u003e \u003cp\u003efacility data quality.\u003c/p\u003e \u003cp\u003eSince this study was a cross-sectional study, it was challenging to prove the temporal correlation in a cause-effect relationship.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Review Board (IRB) of Hawassa University College of Medicine \u003cstrong\u003e(Ref. No: IRB/184/15)\u003c/strong\u003e. The letter was submitted to the Silte Zone health bureau and then to the public hospital, and official permission was written from them and submitted to each department or unit\u0026rsquo;s head to obtain permission for data collection. The study participants were informed about the purpose of the study, and informed consent was obtained from the study participants in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eData and materials availability\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eWe described all the relevant information in the manuscript, but the refined dataset can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eAll the authors declare that no conflicts of interest exist.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThere was funding obtained from DUP/DDCF project for this study.\u003c/p\u003e\n\u003cp\u003eThe funders had no role in study design, data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Hawassa University, College of Medicine and Health Sciences,\u003c/p\u003e\n\u003cp\u003eDUP/DDCF project, Silte Zone Health Bureau; Hospital administrative body and the data collectors for their valuable involvement and contributions.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eContributions of authors\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eND: \u003c/strong\u003eConceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing - original manuscript draft, writing - review \u0026amp; editing, visualization, supervision, project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAT: \u003c/strong\u003eTook part in methodology, software, validation, formal analysis, investigation, writing,\u003c/p\u003e\n\u003cp\u003ereview \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSM: \u003c/strong\u003eTook part in methodology, software, validation, formal analysis, investigation, writing,\u003c/p\u003e\n\u003cp\u003ereview \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAA: \u003c/strong\u003eTook part in methodology, software, formal analysis, investigation, writing - review \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMB: \u003c/strong\u003eMethodology, software, validation, formal analysis, investigation, writing - review \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAB: \u003c/strong\u003eMethodology, software, validation, formal analysis, investigation, writing, review \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript prior to submission.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Review Board (IRB) of Hawassa University College of Medicine \u003cstrong\u003e(Ref. No: IRB/184/15)\u003c/strong\u003e. The letter was submitted to the Silte Zone health bureau and then to the public hospital, and official permission was written from them and submitted to each department or unit\u0026rsquo;s head to obtain permission for data collection. The study participants were informed about the purpose of the study, and informed consent was obtained from the study participants in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eData and materials availability\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eWe described all the relevant information in the manuscript, but the refined dataset can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eAll the authors declare that no conflicts of interest exist.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThere was funding obtained from DUP/DDCF project for this study.\u003c/p\u003e\n\u003cp\u003eThe funders had no role in study design, data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Hawassa University, College of Medicine and Health Sciences,\u003c/p\u003e\n\u003cp\u003eDUP/DDCF project, Silte Zone Health Bureau; Hospital administrative body and the data collectors for their valuable involvement and contributions.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eContributions of authors\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eND: \u003c/strong\u003eConceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing - original manuscript draft, writing - review \u0026amp; editing, visualization, supervision, project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAT: \u003c/strong\u003eTook part in methodology, software, validation, formal analysis, investigation, writing,\u003c/p\u003e\n\u003cp\u003ereview \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSM: \u003c/strong\u003eTook part in methodology, software, validation, formal analysis, investigation, writing,\u003c/p\u003e\n\u003cp\u003ereview \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAA: \u003c/strong\u003eTook part in methodology, software, formal analysis, investigation, writing - review \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMB: \u003c/strong\u003eMethodology, software, validation, formal analysis, investigation, writing - review \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAB: \u003c/strong\u003eMethodology, software, validation, formal analysis, investigation, writing, review \u0026amp; editing, visualization.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript prior to submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOrganization WH. 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Int J Environ Res Public Health. 2014;11(5):5170\u0026ndash;207.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarhajuddin S, Khan S, Ramesh K, Khushk I, Saima H, Assad H. Assessment of completeness and timeliness of district health information system at first level care facilities in a rural district of Sindh, Pakistan. Pakistan J Public Health. 2015;5(3):28\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdabarora E, Chipps JA, Uys L. Systematic review of health data quality management and best practices at community and district levels in LMIC. Inform Dev. 2014;30(2):103\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolomon M, Addise M, Tassew B, Balcha B, Abebe A. Data quality assessment and associated factors in the health management information system among health centers of Southern Ethiopia. PLoS ONE. 2021;16(10):e0255949.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoomaney RA, Pillay-van Wyk V, Awotiwon OF, Nicol E, Joubert JD, Bradshaw D, et al. Availability and quality of routine morbidity data: review of studies in South Africa. J Am Med Inform Assoc. 2017;24(e1):e194\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAqil A, Lippeveld T, Hozumi D. PRISM framework: a paradigm shift for designing, strengthening and evaluating routine health information systems. Health Policy Plann. 2009;24(3):217\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiba Y, Oguttu MA, Nakayama T. Quantitative and qualitative verification of data quality in the childbirth registers of two rural district hospitals in Western Kenya. Midwifery. 2012;28(3):329\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Hagan R, Marx MA, Finnegan KE, Naphini P, Ng'ambi K, Laija K, et al. National assessment of data quality and associated systems-level factors in Malawi. Global Health: Sci Pract. 2017;5(3):367\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBram JT, Warwick-Clark B, Obeysekare E, Mehta K. Utilization and monetization of healthcare data in developing countries. Big data. 2015;3(2):59\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. \u0026thinsp;2017 [ \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.healthdatacollaborative.org/fileadmin/uploads/hdc/Documents/Working_Groups/Data_Quality_Review_1.pdf\u003c/span\u003e\u003cspan address=\"https://www.healthdatacollaborative.org/fileadmin/uploads/hdc/Documents/Working_Groups/Data_Quality_Review_1.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDagnew E, Woreta SA, Shiferaw AM. Routine health information utilization and associated factors among health care professionals working at public health institution in North Gondar, Northwest Ethiopia. BMC Health Serv Res. 2018;18:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJSI Research \u0026amp; Training Institute. I. a. t. M. o. H. M. (2019). Optimizing Performance Monitoring Teams to Improve Data Quality and Use in Ethiopia Hospitals.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng Z, Lu Y, Wei KK, Zhang J. Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China. Int J Inf Manag. 2010;30(4):289\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShama AT, Roba HS, Abaerei AA, Gebremeskel TG, Baraki N. Assessment of quality of routine health information system data and associated factors among departments in public health facilities of Harari region, Ethiopia. BMC Med Inf Decis Mak. 2021;21:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathewos T, Worku A. Community health management information system Performance and factors associated with at health post of Gurage zone, SNNPR, Ethiopia. University of Gondar; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDirectorate E. Health data quality training module participant manual. Mathewos T, collaboration. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKebede M, Adeba E, Chego M. Evaluation of quality and use of health management information system in primary health care units of east Wollega zone, Oromia regional state, Ethiopia. BMC Med Inf Decis Mak. 2020;20:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGetachew N, Erkalo B, Garedew MG. Data quality and associated factors in the health management information system at health centers in Shashogo district, Hadiya zone, southern Ethiopia, 2021. BMC Med Inf Decis Mak. 2022;22(1):154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdejumo A. An assessment of data quality in routine health information systems in Oyo State, Nigeria. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma A, Rana SK, Prinja S, Kumar R. Quality of health management information system for maternal \u0026amp; child health care in Haryana state, India. PLoS ONE. 2016;11(2):e0148449.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inf Decis Mak. 2017;17:19.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Sample size calculation for objective-two.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.81541%;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003cp\u003e. N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4093%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.54414%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2986%;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003cp\u003eof outcome\u0026nbsp;from\u003c/p\u003e\n \u003cp\u003eUnexposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003eAOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003ePower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.557%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3724%;\"\u003e\n \u003cp\u003eNon\u0026nbsp;response\u003c/p\u003e\n \u003cp\u003erate\u0026nbsp;(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.81541%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.54414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e46%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.47\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.557%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e201\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3724%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e211\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e\u0026nbsp;(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.81541%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupervision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.54414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e57.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.71\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.557%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e576\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3724%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e605\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e(23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.81541%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4093%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed\u0026nbsp;back\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.54414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.557%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e131\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3724%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e138\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.66774%;\"\u003e\n \u003cp\u003e(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;2:\u003c/strong\u003e Sociodemographic characteristics of respondents at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023 (n=605).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;in\u0026nbsp;year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026lt;31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026gt;=31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eYear\u0026nbsp;of\u0026nbsp;service\u0026nbsp;in\u003c/p\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026lt;=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e6-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026gt;=11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eHealth\u0026nbsp;care\u0026nbsp;workers\u0026nbsp;(staff)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e95.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eCase\u0026nbsp;team leader\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eHMIS\u0026nbsp;officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eEducational\u0026nbsp;status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eBSC\u0026nbsp;degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e78.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s\u0026nbsp;Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSalary\u0026nbsp;in\u0026nbsp;ETB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1046-4095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e4096-12695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e99.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026gt;12695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3:\u003c/strong\u003e The level of data quality at each public hospital in the Silte Zone, Central Region, Ethiopia, 2023 (n = 32)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2449%;\"\u003e\n \u003cp\u003eName\u0026nbsp;of\u0026nbsp;facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9766%;\"\u003e\n \u003cp\u003eCompleteness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0406%;\"\u003e\n \u003cp\u003eTimelines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003eConsistence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003eData\u0026nbsp;quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2449%;\"\u003e\n \u003cp\u003eWCSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e98.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9766%;\"\u003e\n \u003cp\u003e79.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0406%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e81.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e90.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2449%;\"\u003e\n \u003cp\u003eTPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e97.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9766%;\"\u003e\n \u003cp\u003e85.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0406%;\"\u003e\n \u003cp\u003e91.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e84.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e89.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2449%;\"\u003e\n \u003cp\u003eKibet PH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e96.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9766%;\"\u003e\n \u003cp\u003e89.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0406%;\"\u003e\n \u003cp\u003e87.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e88.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e90.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2449%;\"\u003e\n \u003cp\u003eAlemgebeya\u0026nbsp;PH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e98.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9766%;\"\u003e\n \u003cp\u003e88.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0406%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e85.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e93.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.2449%;\"\u003e\n \u003cp\u003eOver\u0026nbsp;all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e97.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9766%;\"\u003e\n \u003cp\u003e85.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0406%;\"\u003e\n \u003cp\u003e94.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e85.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9126%;\"\u003e\n \u003cp\u003e90.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4: The level of data quality at each hospital with respect to its indicators in the Silte Zone, Central Region, Ethiopia, 2023 (n=32).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnits\u0026nbsp;or\u0026nbsp;departments with\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;its respective indicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 52.0408%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Level of data quality with respect to their indicators\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWCSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTPH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKPH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGPH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eTB\u0026nbsp;unit (New\u0026nbsp;or relapsed\u0026nbsp;case)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e91.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e92.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eLaboratory\u0026nbsp;unit\u0026nbsp;(Malaria\u0026nbsp;tested)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e88.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e90.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eMaternity\u0026nbsp;unit\u0026nbsp;(ANC1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e91.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eImmunization\u0026nbsp;(Penta3/PCV3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e94.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e93.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e95.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e98.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eART\u0026nbsp;unit (New HIV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e95.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e99.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e99.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eIPD\u0026nbsp;department (IPD data)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e91.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eEmergency\u0026nbsp;department (eme data)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e77.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e80.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e77.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e91.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eOPD\u0026nbsp;department (OPD data)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e92.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e80.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e90.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47.9592%;\"\u003e\n \u003cp\u003eOverall\u0026nbsp;result\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e90.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e89.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2857%;\"\u003e\n \u003cp\u003e93.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e93.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable\u0026nbsp;5.\u003c/strong\u003e Organizational, behavioral and technical factors related to RHIS data quality at the public hospital in the Silte Zone, Central Region, Ethiopia, 2023 (n=605).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e78.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eStandardized\u0026nbsp;set\u0026nbsp;of\u0026nbsp;indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eUnderstandability\u0026nbsp;of\u0026nbsp;registration format\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eTrained\u0026nbsp;staff\u0026nbsp;able\u0026nbsp;to\u0026nbsp;fill\u0026nbsp;out\u0026nbsp;format\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eReceived\u0026nbsp;training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e43.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e56.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eGetting\u0026nbsp;supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e66.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eProviding\u0026nbsp;regular\u0026nbsp;feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e66.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eSupervisors\u0026nbsp;check\u0026nbsp;data\u0026nbsp;quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e32.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eMy\u0026nbsp;work\u0026nbsp;is appreciated by\u0026nbsp;supervisors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eStaffs\u0026nbsp;engage\u0026nbsp;actively\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eDecisions\u0026nbsp;are\u0026nbsp;made\u0026nbsp;in PMT\u0026nbsp;meetings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e61.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eInstitution\u0026nbsp;encourages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eStaffs\u0026nbsp;are\u0026nbsp;accountable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e59.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 298px;\"\u003e\n \u003cp\u003eknowledge\u0026nbsp;of\u0026nbsp;RHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;52.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u0026nbsp;\u003c/strong\u003eBivariate and multivariable logistic regression results for public hospitals in the Silte Zone, Central Region, Ethiopia, 2023 (n=605).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;Categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Data quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;COR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;AOR (95%CI)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Good n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Poor n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e95 (81.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e21 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eBSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e347(73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e127(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.7(0.99,2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.69 (0.99,2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e11 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.6(0.48,5.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e2.33 (0.60,9.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003ePresence indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e178(82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e38 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e275(70.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e114(29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.9(1.28,2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.36(0.87,2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eRegistration understandability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e113 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e20 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e340 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e132 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.19(1.31,3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.92(1.11,3.33) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eStaff ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e190 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e60 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e263 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e92 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.1(0.76,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.99(0.66,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eReceived training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e239(70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e102(29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.83(1.24,2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.62(1.07,2.44) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e214(81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e50(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSupervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e287(70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e118 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.0(1.30,3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.66(1.05,2.61) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e166(83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e34(17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eRegular feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e173(83.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e33 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e280 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e119(29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.2(1.45,3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;1.72(1.07,2.7) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSupervisor check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e154 (79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e40(20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e299(72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e112(27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.4(0.96,2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.1(0.72,1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eAppreciation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e202(82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e43 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e251 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e109(30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.0(1.37,3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.61(1.05,2.48) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eStaff engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e165 (79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e42(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e288 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e110(27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.5(1.00,2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.21(0.78,1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eDecisions in PMT\u003c/p\u003e\n \u003cp\u003eMeetings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e194 (82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e42(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e259 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e110 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.97(1.31,2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.73(1.12,2.67) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eStaff encourages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e184 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e49 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e269(72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e103(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.44(0.98,2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.04(0.68,1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eAccountability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e191 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e55 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e262 (72.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e97 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.29(0.88,1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.13(0.75,1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e222 (77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e63(22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e231 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e89 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.36(0.94,1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.19(0.80,1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Data quality, Routine Health information System, Accuracy, Completeness, Timeliness, Consistency, Central region, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-5347454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5347454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRoutinely collected data of poor quality can compromise the validity of effectiveness evaluations and lead to poor decision-making, inappropriate resource allocation, and a loss of trust in the health system. Routine health information system data are seen as poor in quality, are not used for decisions in Ethiopia, and continue to be a significant problem.\u003c/p\u003e\u003ch2\u003eMethods and materials:\u003c/h2\u003e \u003cp\u003eA facility-based mixed-method study with an embedded design was conducted. A total of four public hospitals, 32 departments or units, 605 healthcare workers, and 12 key informant interviews were selected. Simple random sampling and purposive sampling techniques were used for selecting study participants in the quantitative and qualitative studies, respectively. The data were entered into Epi-data version 4.4, Open Code version 4.03, and exported to SPSS version 26, and descriptive statistics were used to assess the level of data quality. Binary logistic regression and thematic analysis were run to identify factors affecting data quality. Adjusted odds ratios with 95% confidence intervals and themes or subthemes were reported.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall facility data quality level was 90.84%; and the completeness and consistency of the data were 85.5% and 85.3%, respectively. Easy understandability of registration and report formats [AOR 1.92; CI 1.11\u0026ndash;3.33], receiving training [AOR 1.62; CI 1.07\u0026ndash;2.44], receiving supervision [AOR 1.66; CI 1.05\u0026ndash;2.61], providing regular feedback [AOR 1.72; CI 1.07\u0026ndash;2.75], the team's work being appreciated and valued by supervisors [AOR 1.61; CI 1.04\u0026ndash;2.75] and making decisions and follow-up actions identified in performance monitoring team meetings [AOR 1.73; CI 1.12\u0026ndash;2.67] were significantly associated with data quality; and thematic analysis was performed and categorized into four themes and twelve subthemes.\u003c/p\u003e\u003ch2\u003eConclusion and recommendation:\u003c/h2\u003e \u003cp\u003eThe level of data quality at the public hospital in the Silte Zone is almost equal to the national expected level of data quality, but the completeness and consistency of the data were lower than the national expected level. The Minister of Health and other supporting organizations should intervene in the identified gaps, especially to reduce incompleteness and inconsistency of data.\u003c/p\u003e","manuscriptTitle":"The level of routine health information system data quality and associated factors at public hospitals in the Silte Zone, Central Region, Ethiopia, 2023: A mixed study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 07:46:19","doi":"10.21203/rs.3.rs-5347454/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-29T06:32:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-29T03:06:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-29T03:05:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2024-10-28T13:11:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"542a7005-0352-44e4-bad2-0ca66647baba","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T13:09:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 07:46:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5347454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5347454","identity":"rs-5347454","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}