Development and validation of a Malawian version of the primary care assessment tool

Abstract

BackgroundMalawi does not have validated tools for assessing primary care performance from patients’ experience. The aim of this study was to develop a Malawian version of Primary Care Assessment Tool (PCAT-Mw) and to evaluate its reliability and validity in the assessment of the core primary care dimensions from adult patients’ perspective in Malawi.MethodsA team of experts assessed the South African version of the primary care assessment tool (ZA-PCAT) for face and content validity. The adapted questionnaire underwent forward and backward translation and a pilot study. The tool was then used in an interviewer administered cross-sectional survey in Neno district, Malawi, to test validity and reliability. Exploratory factor analysis was performed on a random half of the sample to evaluate internal consistency, reliability and construct validity of items and scales. The identified constructs were then tested with confirmatory factor analysis. Likert scale assumption testing and descriptive statistics were done on the final factor structure. The PCAT-Mw was further tested for intra-rater and inter-rater reliability.ResultsFrom the responses of 631 patients, a 29-item PCAT-Mw was constructed comprising seven multi-item scales, representing five primary care dimensions (first contact, continuity, comprehensiveness, coordination and community orientation). All the seven scales achieved good internal consistency, item-total correlations and construct validity. Cronbach’s alpha coefficient ranged from 0.66 to 0.91. A satisfactory goodness of fit model was achieved (GFI = 0.90, CFI = 0.91, RMSEA = 0.05, PCLOSE = 0.65). The full range of possible scores was observed for all scales. Scaling assumptions tests were achieved for all except the two comprehensiveness scales. Intra-class correlation coefficient (ICC) was 0.90 (n = 44, 95% CI 0.81–0.94, p < 0.001) for intra-rater reliability and 0.84 (n = 42, 95% CI 0.71–0.96, p < 0.001) for inter-rater reliability.ConclusionsComprehensive metric analyses supported the reliability and validity of PCAT-Mw in assessing the core concepts of primary care from adult patients’ experience. This tool could be used for health service research in primary care in Malawi.Electronic supplementary materialThe online version of this article (10.1186/s12875-018-0763-0) contains supplementary material, which is available to authorized users.

Evaluating the performance of South African primary care: a cross-sectional descriptive survey

“…14 The PCAT has since been adapted for use in a number of countries including low-and middle-income countries in South America and more recently in South Africa and Malawi . 13,15,16 In the South African adaptation and cross-cultural validation process (ZA PCAT), conducted under the supervision of the original PCAT author, a dimension to measure PHC team availability was added. PHC team functioning has since been added as a second PHC team subdomain.…”

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“…Descriptive statistics with crosstabulation was firstly generated for all questionnaire items. Following the procedures of testing measurement instruments recommended by Dullie and colleagues [39], the data set was randomly and equally divided into two similar subsamples [i.e., a derivation sample (Sample 1) and a validation sample (Sample 2)] to derive and validate the latent variables. Prior to performing the factor analyses, the overall Kaiser-Meyer-Olkin (KMO) statistic and Bartlett's test for sphericity were calculated to evaluate whether the sample was suitable for performing a factor analysis.…”

“…An insignificant chi-square statistic (p>0.05) is commonly used to indicate a wellfitting model. However, a large sample size makes it usually impossible to occur [39]. Therefore, three other common goodness-of-fit indicators, including comparative fit index (CFI), goodness-of-fit index (GFI) and the root-mean-square error of approximation (RMSEA), were used to compare the hypothesized structure identified by the EFA with the research data.…”

“…Therefore, three other common goodness-of-fit indicators, including comparative fit index (CFI), goodness-of-fit index (GFI) and the root-mean-square error of approximation (RMSEA), were used to compare the hypothesized structure identified by the EFA with the research data. A model can be considered as a good fit when both CFI and GFI were more than 0.90, and RMSEA was less than 0.08 [39].…”

“…Thus, a significant chi square test (p<0.05) only indicates a need to modify the model for a better fit to the data [35]. Furthermore, based on three other criteria recommended by Dullie and his colleagues [39], the hypothesized four-factor structure was found to have an acceptable fit to the data (Figure 3), since RMSEA value (0.07) was below 0.08, both CFI (0.92) and GFI (0.91) were above 0.90. In addition, all the structure loading ranged between 0.52 and 0.89 and were significant, indicating a good relationship between the observed variable and latent variable.…”