The distribution of social-demographic characteristics of respondents who participated in this study are presented in table 1 and table 2

Data presented in table 1 shows that 61 data managers working in western province districts health centers were voluntarily recruited to participate in this study. Majority (55.7%) of data managers are male, 55.7% were found in ages category from 33-42 years, 55.8% have A1 education level, 59.0% did health sciences and 49.2% have experience which is less than five years.

Data presented in table 2 shows that 12 responsible of maternity services were voluntarily recruited to participate in this study to collect study qualitative data. Majority (58.3%) of maternity responsible were found female, 75.0% were found in ages category from 33-42 years, 91.7% were found with A1 education level, all of them (100%) did health sciences and 75% have experience which is less than five years.

Quarterly median of four MNH indicators reported by western province districts health centers is presented in table 3. The researcher used the RDQA tool to calculate the score for each indicator. To assess quarterly data quality index, researcher used RDQA tool to get the score of each indicator. Based on score of each indicator, researcher calculated the mean of score attributed to these four MNH indicators and this was called data quality index.

The results presented in table 3 reveal that the median data quality index for three out of four MNH indicators (75.0%) is over 95 accepted by the MoH, while one indicator (25%) is below 95 (66.7). Indicators number 3 (Number of newborns not breathing at birth who were resuscitated) is the one which was founded with lowest data quality index.

Common reasons for insufiscient data quality index of four MNH indicators were collected in selected health centers of western province. These reasons are presented in figure 1.

Study findings presented in figure 1 shows that 36.4% of HCs has insufficient DQI due to incompleteness of data collection tools, 33.3% due to lack of monthly data analysis report, 24.2% due to lack of validation report prior data entry in HMIS, and 6.1% due to data discrepancies for few randomly selected indicators.

The main objective of this research was to assess the data quality of MNH indicators reported in HMIS by western province health centers. Findings from this research reveals that data quality index of one out of four (25%) MNH indicators was found below 95%. Indicators number 3 (Number of newborns not breathing at birth who were resuscitated) is the one which was founded with low data quality index. There are no equivalent findings from studies conducted in similar circumstances found to compare the similarity. In contrast, our results differ from ones of study conducted in Ethiopia, which found data quality index ranged from 32 percent to 75 percent 21. Our results might be different from the one of Ethiopia as referred study examined eight indicators while we examined four MNH indicators. This indicator was found with insufiscient data quality index because it might be not well calculated as health care providers affected in maternity might subjectively assess APGAR

Normally the hospital evaluation team conduct evaluation sessions each quarter at the HC level. It seems that this evaluation is not contributing to the increase of quality of data as it is mandate. This is very crucial because normally this supportive supervision might be well prepared and conducted with purpose of educating HC staff as they are conducted by advanced skilled staff. Once conducted with routine, they are not contributing to the strengthening of health system or to solve identified gaps. Another way of solving data quality issues in health centers is data validation meetings which are mandate to be chaired by HCs heads and responsible of services each month. The role of data validation meetings to solve data quality issues is clearly defined in HMIS SOP document. The problem is that health centers data validation meetings are not chaired at regular basis. In the HCs which conducted these meetings, the minute shown gaps and the agenda seems to be not really the discussions and validation of data but what to show to evaluators in qualitative evaluation. The role of leadership of HC and hospital is very important to discourage this attitude of not focusing on data.

The main reasons of insufficient data quality, lack of monthly data analysis, validation meeting prior data entry in HMIS (56.5%) and incompleteness of data collection tools (36.4%), were found as main reasons of insufficient data quality. These results are completed and confirmed by key informants from HCs with insufficient DQI who highlighted that insufficient knowledge of MNH indicators, incompleteness of maternity registers, and shortage of staff, supervision which doesn t go in deep for verification of data validity, HCs staff and leadership who do not take data validation meetings as significant, as main challenges. Our findings are similar with the study conducted in Benin which revealed that insufficient quality of data was associated to the incompleteness of reporting forms 22, misunderstanding of HMIS tools/indicators and insufficient skills, workload, and incentives 1. Our findings were found differ from one of study conducted in South Africa which revealed that the poor quality of the data was attributed to the insufficient competencies of health information personnel 23 and the one conducted in Iran on data quality and utilization found that poor quality was linked to the volume of data gathered, the frequency of HIS training, supervision and feedback, and an incentive mechanism 24. This is linked to the skills of nurses and midwives affected in maternity to provide maternal and newborn services who founded in report with insufficient knowledge to calculate APGAR score 19. Data from health centers with insufficient data quality index shown that lack of training, poor and delay of documenting primary data sources, shortage of staff, health care providers and leadership who don t give values reported data and validation meetings are the main challenges that these health centers met to have poor data quality report. We can t ignore the role of training for health care providers who don t have knowledge on indicators to report.

As health care providers misunderstood MNH indicators, they can make errors in counting data. Once these providers don t have knowledge of indicators, don t know how numerator/ denominator, this result to the leaving empty reporting cages and contribute to the insufficient data quality. Ignoring filling reporting tools while these are quarterly checked in qualitative and quantitative evaluation is a problem.

The overloaded health care providers, cannot properly complete reporting tools, can commit errors in data counting, transcript. The problem that these health centers with insufficient data quality index is that they experienced the problem of fillings reporting tools, and data analysis and validation meeting prior data submission in HMIS. These problems which affect quality of data normally might be solved by validation meetings, but these are not taken by leadership as serious meeting. Again, supervisions from hospital level which were supposed to educate health care providers and data managers to play their role and responsibility don t go in deep for verification of data validation. Hospital M&E teams or qualitative evaluators are mandate to pass at the HC level at quarterly basis for their routine activities of supporting health care providers. The problem is that these supervisions are not conducted just to report that they have been done for PBF not in educative way as highlighted by health care providers.