Background: Using reliable information from routine health information systems is vital for planning, monitoring, and evaluation thereby improving health outcomes. However, in developing countries including Ethiopia, the level of use of routine health information for decision making is low and the insufficient quality of the data produced limits their usefulness. Objective: To assess routine health information use for decision-making and associated factors among health care providers of Awi Zone, 2020. Methods: Institution-based cross-sectional study design was conducted at public health institutions of Awi Zone, Northwest Ethiopia. A total of 562 study participants were included through the stratified sampling technique. Data were anal (Preprint)

Abstract

Health information is the foundation of the overall building blocks of health systems strengthening (1). Availability of information that will enable to utilize for better decision making is a crucial step for strengthening the system (2, 3). Information use exists when relevant information is available and decision-makers are aware of the decisions they are about to make or question to be answered (3). Information use is the use of information in one or more steps and/or processes of policymaking, program planning, managing, monitoring and evaluation, and improving service provision at community, institution, and administrative levels (1, 4, 5). At the institution level information is used to monitor service coverage and quality, resources availability, clients’ satisfaction, and/or informs planning and managing health services, program performance, and resources (1, 3, 6). One of the most persistent characters of the information age is that health institutions have focused too much on mastering transaction data and not enough on turning it into information and knowledge that can lead to results (7). Significant human and financial resources have been invested worldwide in the collection of data in health facilities and communities; Health workers collect data on patients and routinely report on all the activities within the health institutions (8). Although there is a slight improvement in data monitoring and evaluation, information use for decisions has often been negligible among stakeholders (9). As a result, many health systems fail to fully link evidence to decisions and suffer from inadequate ability to respond to priority health needs at all levels of the health system (2, 10). Ideally, Public health officials and the communities they serve need to: identify priority health problems; formulate effective health policies; respond to public health emergencies; select, implement, and evaluate cost-effective interventions to prevent and control disease and injury; and allocate human and financial resources (1). Many public health decisions appear to be made intuitively or politically. The goals of data-driven decision-making are to strengthen the capacity of decision-makers to identify data needs for solving problems, to interpret and use data appropriately for public health decisions. It is also used to enhance the capacity of technical advisors to provide valid, essential, and timely data for decision-makers clearly and effectively. Strengthening health information systems (HISs) to facilitate the collection, analysis, reporting, presentation, and use at local, district, regional, and national levels are the ultimate goals (11). In few CDC participating countries, the use of routine health information for decision-making strategy improved evidence-based health provisions. Subsequently, information-driven decision-making concepts and practices have been institutionalized (11). Problem-driven implementation plans with data-based solutions as objectives were developed(9). Interdisciplinary in-service training programs for mid-level policymakers, program managers, and technical advisors in applied epidemiology, management and leadership, communications, economic evaluation, and HISs were designed and implemented in some countries well practicing routine information use for decision making(8). The associated factors behind the poor routine health information use for decision making are poor knowledge of health technology, the inadequacy of information inputs such as computers, behavioral factors of health care providers, and inadequate support and/ or feedback (12-14).

This study aims to determine routine health information use for decision-making and associated factors among health care providers

Design and Setting of the Study Study design and setting An institution-based cross-sectional study was conducted from September 2020 to December 2020 In the Awi Zone, North West Ethiopia. Awi Zone has located 445 km from Addis Ababa, the Capital city of Ethiopia, and 112 km from Bahir Dar, the Capital city of Amhara regional state. According to the zonal health department, the study area has a total population of 1322692 (43, 44). Study population, sample size, and sampling procedure All health care providers at Awi Zone health institutions were selected through stratified sampling then proportionate allocation. The sample size was determined using the single population proportion formula, considering the following assumptions; Proportion =79% (health information utilization at a district level in North Gondar Zone(39) Level confidence = 95% Margin of error =5% Design effect =2 Non-response rate = 10% n=(za/2)2p(1-p)/d2. So n= (1.96)2x0.79 (1-0.79)/ (0.05)2=255. The non-response rate was taken 10% (26), so the sample is 255+255*0.1=281. Final sample size (considering design effect of 2) was 281*2=562. Data collection tool and procedure The data collection tool is the customized PRISM assessment tool, a self-administered structured questionnaire that is carefully reviewed, pretested, and revised before final data collection. The research instrument was an English version and translated to Amharic and back to English for analysis. In the conceptual framework, technical, organizational, and individual factors are determinants of the use of routine health information for decision-making. To ensure the quality of data, before the actual data collection time, the questionnaire was pre-tested for the relevant modification on the sample size 26 (5%). The reliability of the tool for individual, institutional and technical factors was checked using Cronbach’s alpha reliability test with a score of 0.767, 0.898, and 0.77 respectively. Additionally, the data collectors and supervisors were provided with necessary information and instruction. Questionnaires were reviewed and checked regularly for completeness, accuracy, and consistency by the supervisor and the investigator. Operational definition and Terms Use of routine health information for decision making-using routinely collected health information for decisions on planning, monitoring, and evaluation, outbreak investigation, and management, medical supplies management, staffing decisions, service improvement, resource mobilization. Level of routine health information use- is the use of routine health information for informed decision making assessed using information use index based on 6 areas and 22 criteria (planning 4 verifications, monitoring, and evaluation 5 verifications, outbreak investigation and management 4 verifications, medical supplies management 3 verifications, staffing decision 3 verifications and service improvement with 3 verifications). Respondents self-rated result was calculated out of 22 and the extent to which they use routine health information use for decision making was dichotomized as poor level (0-50%) and good level (51-100%) for analysis purposess. Health care provider: Any staff engaged in an institution providing clinical or public health care. It includes health professionals, health administrators, and officers of health institutions (i.e. zone health department officers, woreda health officers, hospital staff, health center staff, and health extension workers). Routine health information system (RHIS): is a system that provides information at regular intervals of a week, month, quarter, bi-annual, or year to meet predictable information needs. These may be paper-based or electronic health records and facility or district-level management information systems (4). Educational level: is the respondents’ educational background which is expressed as below level IV (health extension workers, others), level IV (diploma), Bachelors’ degree (BSc.), masters’ degree (MSc.), and specialty. Data processing and analysis Questionnaires were coded after cleaning. This was pre-tested by entering questionnaires. After validation, data is entered into epi-data. Then it was exported to SPSS version 25 for statistical analysis. Data analysis was made by SPSS, for testing association and other statistical computations using the binary logistic regression model. Descriptive statistics such as frequencies, means, and proportions were computed using tables and figures to summarize the variables. Variables with a p-value of less than 0.2 in the bi-variable analysis were entered into the multivariable logistic regression analysis. Both Crude Odds Ratio (COR) and Adjusted Odds Ratio (AOR) with 95% confidence intervals were computed to show the strengths of associations with the technique of backward regression method. A p-value of less than 0.05 at the multivariable logistic regression analysis was used to identify variables significantly associated with the use of a routine health information system. Hosmer and Lemeshow’s goodness of fit test was used and is greater than 0.05. The multicollinearity assumption was checked through Variance Inflation Factor (VIF) and was below 10.

Socio-demographic characteristics A total of 555 respondents participated in the study with a response rate of 98.8%. The majority of respondents’ age, 376 (70.1%) was 20-29 years. Among the study participants more than half, 297 (53.9%), were male. One-third, 159 (29.2%), of respondents were nurses. More than half, 272 (51.5%), of respondents, were diploma (level 4). For details of the socio-demographic characteristics, please see the table below. Table 1: Socio-demographic characteristic of the study participants, Awi zone, 2020. Variables Sex Category Male Frequency 297 Percent 53.9 Female 254 46.1 Age 20-29 376 70.1 30-39 134 25 40-49 19 3.5 50-60 7 1.4 Profession Doctors 20 3.7 Nurses 159 29.2 Midwives 80 14.7 HO 49 9.0 HEW 96 17.6 Others* 141 25.9 Education Certificate (Level 3) 28 5.3 Diploma (Level 4) 272 51.5 BSc and above 228 43.2 Experience Fresh level (0-2 years) 122 22.4 Junior level (2-5 years) 167 30.7 Senior-level (>5 years) 255 46.9 Health institution Health post 95 17.1 Health Center 221 39.8 Hospital 199 35.9 WoHO 40 7.2 Department Health post 95 17.8 OPD 110 20.6 IPD 21 3.9 MCH 71 13.3 Emergency 18 3.4 Others** 220 41.1 Salary Lower paid 38 9.3 Middle paid 342 83.6 Higher paid 29 7.1 *pharmacy, laboratory, anesthetist, biomedical, psychiatry… ** NICU, laboratory, pharmacy, office… Level of routine information use for decision making Use of routine health information for decision making was assessed using information use index (mean) established from a set of six (planning, monitoring and evaluation, outbreak investigation and management, medical supplies and drugs management, staffing decisions, and service improvement) areas of information use based on criteria set by PRISM assessment tool. The overall RHI use was calculated by taking the mean of all six dimensions which was found 55.93%. The table below summarizes all dimensions of information use indexes Table 2: Overall extent of RHI use for decision making, Awi Zone, 2020. UDDM areas Sum of scores UDDM Remark Planning (N=546) 421.7 76.81% M and E (N=550) 356.5 64.23% Outbreak investigation and management (N=546) 363.9 66.16% Medical supplies drugs management (N=545) 343.4 62.89% Staffing decisions (N=441) 172.3 39.07% Service improvement (N=445) 212.7 47.80% Overall RHIS use for decision (N=555) 311.75 55.93% Technical factors influencing health information use Training and skills The training in aspects of information management (HMIS or DHIS2, analysis and presentation, and computer skills) was determined, and reported that only 33%, 29.4%, and 18.4 were trained respectively. Table 3: Extent of Training provided for HCP, Awi zone, North West Ethiopia, 2020 Training areas Frequency Percent HMIS (DHIS2) 184 33.3 Data analysis 162 29.4 Basic computer skill 102 18.4 The ability to carry out the data manipulation concerning data management shows that most of the respondents have good skills. Table 4: Skills of healthcare providers on data quality, Awi zone, 2020. Skills Frequency Percent I can check data accuracy 453 82.2 I can calculate rates 436 79.3 I can plot information on an appropriate chart/graph 448 81.2 I can explain findings and their implications 393 71.3 I can use the information to identify gaps and set targets 448 81.2 Organizational factors influencing health information use Access to functional HMIS materials Information use is determined by access to functional resources and materials. Among the health workers who participated in study 349(64.3%) have access to routine health data. And 114 (20.7%) reported having access to the internet in the office or institution. Table 5: Access to health information, Awi zone Health care providers, 2020. HMIS materials Frequency Percent Access routine data 349 64.3 Computer 241 43.7 Printer 205 37.1 Data backup units (flash disc, CD or external hard disk) 198 36 Access to internet 114 20.7 Level of support Among the respondents, only 106 (19.2%) received adequate support from in-charges. The job descriptions regarding health information use were noted among 219 (39.8%). Figure 1: Level of support from in-charge, Awi zone, 2020 Table 6: the table shows the extent of the job description on HMIS, Awi zone, 2020 Variables Extent Never Sometimes Always (adequate) Clearly defined in your job description in terms of HMIS 92(16.7%) 239(43.5%) 219(39.8%) Frequency of display key performance indicators 81(14.8%) 264(48.2%) 203(37%) Individual factors influencing health information use Quality of data collected Significant respondents, 70 (12.8%), reported that the data collected was not timely. Surprisingly 118 (21.7%), 59 (10.7%), and 65 (11.9%) reported that the data collected was incredible, irrelevant, and incomplete respectively. Figure 2: Bar graph depicting data quality level, Awi zone, 2020 Factors associated with the use of routine health information for decision making On bivariate logistic regression analysis; professional training, health institution type, level of education, experience, training (on DHIS2 or HMIS, data analysis, and basic computer skill), skills and abilities of data manipulation (checking the accuracy, calculating findings, plotting appropriate chart, using findings and ability to explain findings), access to RHIS, availability of back up materials, availability of printer, access to the internet, support from in-charge, job description regarding HMIS, frequency of reviewing key performance indicators, routine data appropriateness (timeliness, completeness, accuracy, reliability, relevance, and credibility), and level of motivation met the requirement to proceed to multivariable logistic regression analysis. After multivariable analysis; type of health institution, experience, training on data analysis, ability to calculate findings, access to HMIS materials, frequency of reviewing key performance indicators, timeliness of data, and credibility of data showed statistically significant association with the use of routine health information for decision making. Those health care provides who are working in health centers were found to increase the odds of use of health information for decision making by 5.6 times as compared to health extensions (AOR=5.61: 2.23-14.08), whereas those who are from hospitals 4.2 times and WoHO 7.1 times increase the odds of use of health information for decision making (AOR=4.19:1.59-11.07) and AOR=7.14:1.84-27.75) respectively. Those health care providers who did not take data analysis training were found to decrease by 63% compared to trained health care providers (AOR=0.37: 0.20-0.71). The healthcare providers who cannot calculate findings tend to decrease by 53% (AOR=0.47: 0.26-0.85) compared to those who can compute. Good in timeliness data increases 4.1 times and very good 4.4 times the odds of routine health information use for decision compared to poor in timeliness data (AOR=4.11: 1.70-9.97) and 4.43:1.53-12.81) respectively. Where credible data increases odds by 9.3 times (AOR=9.33:4.23-20.55). Those health care providers who are working on health institutions always reviewing performance indicators increase odds of routine health information use by 3.5 times (AOR=3.49: 1.46-8.38) compared to not reviewing. Having no access to HMIS materials tends to decrease odds of routine health information use for decision by 54%, compared to those who have access to HMIS materials. Table 7: Multivariate logistic regression table showing the significant variables associated with routine health information use, Awi Zone, 2020. Variable Level of UDDM P- value AOR (95% CI) Good Poor Health institution type Health post 56(58.95%) 39(41.05%) 1 Health centre 126(57.27%) 94(42.73%) 0.000 5.606(2.232,14.079) * Hospital 96(48%) 104(52%) 0.004 4.190(1.586,11.066) * WoHO 24(60%) 16(40%) 0.005 7.141(1.84, 27.748) * Training on data analysis Yes 118(72.84%) 44(27.16%) 1 No 183(47.04%) 206(52.96%) 0.003 0.374(0.196,0.713) * I can calculate the rate Yes 251(57.57%) 185(42.43%) 1 No 48(42.11%) 66(57.89%) 0.012 0.470(0.261,0.847) * Access to HMIS Yes 220(63.04%) 129(36.96%) No 80(41.24%) 114(58.76%) 0.027 0.535(0.307, 0.931) * Frequency of reviewing indicators No 36(44.44%) 45(55.56%) Sometimes 104(39.39%) 160(60.61%) 0.519 0.782(0.370,1.652) Always 160(78.82%) 43(21.08%) 0.005 3.492(1.455, 8.380) * Timeliness Poor 18(25.71%) 52(74.29%) 1 Good 166(49.55%) 169(50.45%) 0.002 4.114(1.698, 9.968) * Very good 116(80.56%) 28(14.44%) 0.006 4.425(1.528, 12.814) * Credibility Poor 25(21.19%) 93(78.81%) Good 155(58.71%) 109(41.29%) 0.000 9.326(4.232,20.551) * Very good 116(71.61%) 46(28.39%) 0.017 3.559(1.259,10.064) * DISCUSSION The study found that the use of routine health information for decision-making among health care providers was 55.93% at 95% CI (53.71-58.15). The study finding is in line with a study conducted in Gamo Gofa Zone (58.2%) (20). This finding is higher than that of the study conducted in East Gojjam Zone 45.8% (19) and Jimma Zone 32.9% (18), (46). The deviation might be due to the attention given by the government to health information included in the health sector transformation agenda 2015(26) and the information use training manual is published (1). However, this finding is lower than a study done in North Gondar Zone, Ethiopia (78.5%) (27), and Mombasa, Kenya (69.6%)(38). The variation for the former could be due to different institutions (it is studied from hospitals and health centers only). The difference for the Kenyan study might be a variation of the health care system index for Ethiopia (52.12%) and Kenya (55.83%). In addition, Accuracy and completeness in filling out reports for Ethiopia are low (37.5%) and high in Kenya (63.93)(21). According to the multivariable logistic regression analysis, the higher odds of routine health information use for decision making was noted among health professionals who are working in health centers 5.6 times, hospitals 4.2 times, and WoHO 7.14 compared to the health post. This might be due to the difference in motivation which is better in respective institutions. Hospitals have several departments equipped to treat a wide array of medical issues and admit patients for treatment. They offer a variety of opportunities for clinical work, as well as positions in research, education, and management that motivates and is the preferred workplace (47). Those health care providers who did not take training on HMIS were found to decrease by 63%. That health care provider who cannot calculate rates tends to decrease by 53% when compared with those who can calculate. The possible reason might be training increases knowledge and skill there by practice. Effective training lead to quality work and thus to a higher level of achievement(22). The health care providers who have access to timely health information use 4.1 times more data for decision-making than those who haven’t. Health care providers in health institutions that have credible data can use the information for decision-making 9.5 times more than incredible data. The possible reason for this may be that access to timely and credible health information (including the internet) can enhance fact-driven decision-making significantly (48). Those healthcare providers who are working on health institutions that are always reviewing key performance indicators increase odds of routine health information use by 3.1 times compared to not reviewing. This might be due to the positive race and winning spirit. In addition, it often generates written reports that contribute to transparency and accountability and allows for lessons to be shared more easily (2).

CONCLUSIONS Based on the findings of this study, it was concluded that the use of routine health information for decision-making is below expected. This means there are areas to be improved on health institutions and individuals when we compare the result with other studies. Health workers working in health centers, hospitals, and woreda health offices are better at using routine health information than health extension workers. Training and skills in data analysis increase routine health information use. The health workers who can calculate findings and have access to timely and credible data can use routine health information compared to counterparts. Reviewing key indicators always increases routine health information use. Training, generating timely and credible data, reviewing indicators regularly, and giving attention to health extension workers are highly recommended for improving routine health information use.