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Background: The field of acceptability of health services is emerging and growing in coherence. But there are gaps, including relatively little integration of elements of acceptability. This study attempted to analyse collectively three elements of acceptability namely: patient-provider, patient-service organisation and patient-community interactions. Methods: Mixed methods were used to analyse secondary data collected as part of the Researching Equity in Access to Health Care (REACH) study of access to tuberculosis (TB) treatment, antiretroviral therapy (ART) and maternal health (MH) services in South Africa’s public health sector. Results: Provider acceptability was consistently high across all the three tracer services at 97.6% (ART), 96.6% (TB) and 96.4% (MH). Service acceptability was high only for TB tracer (70.1%). Community acceptability was high for both TB (83.6%) and MH (96.8%) tracers. Conclusion: Through mixed methods, this paper provides a nuanced view of acceptability of health services.
This study applies mixed methods to secondary quantitative and qualitative data collected as part of the Researching Equity in Access to Health Care (REACH) project, a multi-site, five year study in four of South Africa’s provinces. In this study, we draw on a sub-set of the REACH study population comprised of patients attending HIV, TB and Maternal Health Services from a sub-district in the City of Johannesburg. These data were collected between July 2008 and December 2010, a time of policy change directed towards expanding access to ART, with changes to clinical guidelines and the beginning of service roll-out from specialised community centres to primary health care clinics [26]. Since the study period, ART policy has become increasingly inclusive, culminating most recently in the adoption of WHO Universal Test and Treat Policy, which prescribes treatment on diagnosis, regardless of clinical indicators [27]. Therefore, while we recognize that this dataset may seem dated, given the sheer scale of South Africa’s ART programme and inclusive treatment policy environment [11, 27], we assert its continued importance for practice, policy and methodological development. The quantitative data consisted of patient exit interviews including socio-economic and demographic background, dwelling characteristics, household income, expenditure, household assets and acceptability of care. Furthermore patients’ self-reporting of their clinical conditions were considered: for ART services: buddy or support group, frequency of treatment collection, frequency of forgetting/not taking ART; for TB treatment: Directly Observed Treatment Short-course (DOTS) checked, frequency of TB medication collection, forgetting collecting/drinking TB medication and missing visit; and for MH: maternal parity, HIV status and type of delivery. The qualitative data consisted of in-depth interviews covering the participant’s illness (HIV and TB)/pregnancy and access stories, including exploration of the acceptability of care expected and received (MH). With regard to quantitative data, the three main acceptability constructs were developed, namely patient-provider interaction, patient-health service organization interaction and patient-community interaction. The acceptability variables were recoded in order to pool and categorise them on a binary scale with values coded “1” for a positive response and “0” for a negative response. With respect to qualitative data, in recognition that the researchers’ experience, background and expectations would affect, at least to some degree, the interpretation of the narratives from the in-depth interviews [28], we collectively developed a thematic coding system to ensure coding agreement. This coding system took into consideration the context in which those in-depth interviews took place. The acceptability themes included “perceived patient-health provider interaction”, “perceived patient-health care organization interaction” and “perceived patient-community support”. Quantitative data analysis was conducted using STATA version 14. Unit weighted composite scores were used to develop acceptability indices. A composite score was calculated as the average of coded responses in each of the three acceptability constructs. This method is regarded as an adequate method for developing a composite index [29, 30]. For ease of interpretation, the composite index was multiplied by 100 so that each composite index was expressed as percentages. The acceptability index was computed by dichotomising the composite indices as follows: the low acceptability index was defined as ranging from 0 to 66.66%, while the high acceptability index was above 66.66%. This cut off was also guided by acknowledging the patients’ fear to give a negative opinion about health provider or health services [31, 32]. Binary simple logistic regression was used to determine factors associated with the acceptability index. Then, all factors with a p-value less or equal to 0.20 in the univariate analysis were included in adjusted multiple logistic regression model. A p value < 0.05 was considered to indicate statistical significance. Regarding qualitative data, in-depth interview transcripts were imported into MAXQDA.12 to assist thematic content analysis. The narratives were reviewed and analysed deductively using the ‘acceptability themes’ from the conceptual framework (Fig. (Fig.1)1) and related to the ‘acceptability constructs’ used in quantitative analysis. Simultaneously, inductive analysis was done to consider the new themes emerging from the transcripts. To allow a deeper understanding of the results from this mixed study analysis, triangulation was used to integrate the findings from both quantitative and qualitative methods during the discussion of the results. Triangulation is a method that facilitates the verification of data through cross validation from more than two sources, and is recommended for mixed study analysis [33].
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