Routine health data use for decision making and its associated factors among primary healthcare managers in dodoma...

Data demand and use culture have a tremendous impact on the proper allocation of scarce resources and evidence-based decision making. However, primary healthcare managers in the majority of Sub-Saharan African countries continue to struggle with using routine health data for decision-making.

This study aimed to assess routine health data use for decision making among primary healthcare managers in Dodoma region.

Cross-sectional study design involved 188 primary healthcare managers from Dodoma City Council, Kondoa Town Council and Bahi District Council was conducted. A self-administered questionnaire adapted from the Performance of Routine Information System Management (PRISM) tools was used to collect the data. Data was analysed by using the Statistical Package for Social Science (SPSS) program. Principal Component Analysis was used to find the level of routine health data use, binary logistic regression analysis was used to determine factors associated with routine health data use for decision making among primary healthcare managers. The study was conducted from May to June, 2022.

The level of adequate routine health data use for decision making among healthcare managers was 63.30%. Factors associated with adequate routine health data use for decision making among healthcare managers were; respondents characteristics: years of working experience (OR = 1.955, 95% CI= [0.892,4.287]), district surveyed (OR = 4.760, 95%CI= [1.412,16.049]), level of health facility (OR = 3.867, 95%CI= [1.354,7.122]) and male gender (OR = 1.901, 95%CI= [1.027,3.521]). Individual factors: comparing data with strategic objectives (OR = 2.986, 95%CI= [1.233-7.229]), decision based on health needs (OR = 7.330, 95%CI= [1.968-27.295]) and decision based on detection of outbreak (OR = 3.769, 95%CI= [1.091-13.019]). Technical factors: ability to check data accuracy (OR = 3.120, 95%CI= [1.682-5.789]), ability to explain findings and its implication (OR = 2.443, 95%CI= [1.278-4.670]) and ability to use information to identity gaps and targets (OR = 2.621, 95%CI= [1.381-4.974]). Organizational factors: organizational support (OR = 3.530, CI= [1.397-8.919]), analyse data regularly (OR = 2.026, 95%CI= [1.075-3.820]) and displays information on key performance indicators (OR = 3.464, 95%CI= [1.525-7.870]).

The level of routine health data use for decision making among primary healthcare managers was found to be modest. The level of data demand and use culture may increase more quickly if capacity building is strengthened and issues that de-motivate primary health care managers from using data are addressed.