Perceived quality of care among households ever enrolled in a community-based health insurance scheme in two districts of northeast Ethiopia: A community-based, cross-sectional study
Hussien, Mohammed; Azage, Muluken; Bayou, Negalign (2022), Perceived quality of care among households ever enrolled in a community-based health insurance scheme in two districts of northeast Ethiopia: A community-based, cross-sectional study, Dryad, Dataset, https://doi.org/10.5061/dryad.ncjsxksw5
Objectives: The purpose of this study was to examine how clients perceived the quality of health care they received and to identify associated factors both at the individual and facility levels.
Design: A community-based, cross-sectional study.
Setting: Two rural districts of northeast Ethiopia, Tehulederie and Kallu.
Participants: 1081 rural households who had ever been enrolled in community-based health insurance and visited a health center at least once in the previous 12 months. Furthermore, 194 health care providers participated in the study to provide cluster-level data.
Outcome measures: The outcome variable of interest was the perceived quality of care, which was measured using a 17-item scale. Respondents were asked to rate the degree to which they agreed on 5-point response items relating to their experiences with health care in the outpatient departments of nearby health centers. A multilevel linear regression analysis was used to identify predictors of perceived quality of care.
Results: The mean perceived quality of care was 70.28 (SD=8.39). Five dimensions of perceived quality of care were extracted from the factor analysis, with the patient-provider communication dimension having the highest mean score (M=77.84, SD=10.12), and information provision having the lowest (M=64.67, SD=13.87). Wealth status, current insurance status, perceived health status, presence of chronic illness, and time to a recent health center visit were individual level variables that showed a significant association with the perceived quality of care. At the cluster level, the work experience of health care providers, patient volume, and an interaction term between patient volume and staff job satisfaction also showed a significant association.
Conclusions: Much work remains to improve the quality of care, especially on information provision and access to care quality dimensions. A range of individual and cluster-level characteristics influence the perceived quality of care. For a better quality of care, it is vital to optimize the patient-provider ratio and enhance staff job satisfaction.
Individual-level data were collected through face-to-face interviews with household heads at their homes or workplace using a structured questionnaire via an electronic data collection platform. The data collectors submit the completed forms to a data aggregating server daily, which allowed us to review the submissions and streamline the supervision process. The Perceived Quality of Care (PQoC), which is the outcome variable of interest, was measured using a 17-item scale designed after a thorough review of validated tools. Respondents were asked to rate the extent to which they agreed on a set of items relating to their experiences with the health care they received in the outpatient departments of nearby health centers. Each item was designed on a 5-point response format with 1 - strongly disagree, 2 - disagree, 3 - neutral, 4 - agree and 5 - strongly agree. The summary scores for the PQoC and its dimensions were calculated for individual respondents by adding the scores of each item. This gives a scale ranging from 17 (1×17) to 85 (5×17) for the overall PQoC score. For quality dimensions consisting of three and four items, the scale ranges from 3 to 15 and 4 to 20, respectively. When reporting the results, the scores were arithmetically transformed to a scale of 20 to 100. This allows the comparison of mean scores of PQoC, its dimensions, and each measurement item on a common scale. Cluster-level data were collected from 12 health centers that provide health care for the population in the sampled Kebeles. Patient volume data were obtained by reviewing the monthly service delivery reports of health centers, while data related to work experience, affective commitment, and job satisfaction were collected through a self-administered questionnaire among health care providers who worked more than one year in the current facility.
The data which was stored in the online data aggregator (Kobo server) was downloaded, organized, cleaned, and edited using MS Excel spreadsheet. Then, it was exported to Stata version 17 for statistical analysis. Exploratory factor analysis was performed to assess the validity of the quality measurement scale. To compare mean scores of PQoC and its dimensions among subgroups, an independent t-test and a one-way analysis of variance (ANOVA) with Tukey's post-hoc test were used. Because the outcome variable was considered a continuous variable, a multilevel linear regression model was fitted to identify its predictors. The PQoC was assumed to be influenced by the characteristics of households (individual-level variables) as well as the characteristics of health centers (cluster-level variables). Cluster-level data were linked to individual-level data based on the usual source of health care for each study participant. Considering the hierarchical structure of the data, where patients are nested within health centers, a two-level linear regression model was applied. In this study, there were 12 health centers, hence the Restricted Maximum Likelihood estimation approach was employed because it is appropriate for smaller cluster sizes.
Data was analyzed using Stata 17 (StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX: StataCorp LLC.). Please refer to README file for more instruction