Data Quality in Voluntary Medical Male Circumcision Program in Selected Health Facilities in Siaya County, Kenya
Loading...
Date
2025-11
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Kenyatta University
Abstract
Since 2008, Kenya has implemented the voluntary medical male circumcision program as a strategy for preventing HIV. Siaya County's male circumcision rate was 56%, well below the recommended 80% according to the Kenya Demographic Health Survey, 2015 data. From 2008 to June 2019, 299,261 circumcisions were performed in Siaya County, according to the Kenya Health Information System. The completion, timeliness, and accuracy reporting rates for the VMMC program in KHIS were consistently lower than the required standard of 80%. Even though the VMMC program has been in operation since 2008, little research has been done on the data's quality. The goal of the study was to evaluate the organizational, technological, and behavioral aspects that affect Siaya County's VMMC program data quality. Data accuracy, timeliness, and completeness were three characteristics that were evaluated. By stating whether a value for a certain data element from a particular facility was or was not available in the information system, completeness was evaluated. Timeliness was measured by the date when the data was submitted to the information system compared to the anticipated submission date. Data from individual facility registers and data stored in KHIS were compared to determine the correctness of the data. Out of the 224 health facilities sampled for this research, only 202 answered (90.1% response rate). The personnel in charge of VMMC data collecting and reporting were subjected to the study questionnaire and the records checklist through the online open data toolkit (ODK). The County Health Management Team was the subject of key informant interviews. SPSS was used to analyse quantitative data. Measures of dispersion (standard deviation and range) and measures of central tendency (mode, mean, and median) were used to summarize the data. With a 95% confidence interval and a P-value of >=0.05, Pearson chi-square was utilized to analyze the relationships between the technological, organizational and behavioral factors and the data quality index. After being categorized into themes using the objectives and responses into three thematic areas as behavioral, technological, and organizational, qualitative data was analysed thematically and verbally conveyed. The scores for accuracy, completeness, and timeliness were summed up to create the Data Quality Index. For all three aspects associated with good data quality, the response was 1=Yes. For each of the three aspects of data quality, bad data scored a 0-No value. It was evident that 44.1% of the facilities had accurate data, 77.2% was timely and complete in 80.2% of the facilities. Only 29.7% of the 202 facilities had good data quality, according to the data quality index. There was a significant relationship between good data and education level (χ2 =5.25, df =3, p-value =0.02), cadres (χ2 =7.22, df =4, p-value =0.01), and length of stay at the health facility (χ2 =6.48, df =2, p- value =0.04). None of the behavioral variables had a strong correlation with good data quality. The proportion of respondents (64.2%) who agreed that staff members receive training on the national EMR system were substantially more likely to have good quality data than those who disagreed (χ2 =9.10, df =1, p-value =0.01). The respondents who agreed that systems are put in place to identify and address data quality (49%), were significantly related with good data (χ2 =13.85, df =1, p-value =0.01) for organizational characteristics. The percentage of respondents (50.6%) who agreed that VMMC data is used to guide facility decision-making had a significant association with good data quality (χ2 =131.67, df =1, p- value =0.01). The study recommends Siaya County integrate VMMC supervision and DQA with other health services in an effort to improve the quality of the data. It also recommends the national VMMC program to ensure that all VMMC indicators are incorporated in the national EMR and that all staff members be educated to improve reporting. For the purpose of informing choices about policies and other programmatic matters, the Ministry of Health would gather information from the various data sources and build a consensus based on data from the national surveys.