Translation and validation of the PACIC+ questionnaire: the Thai version

The prevalence of diabetes in Thailand is increasing with a prevalence of 2.3% in 1991 and a prevalence of 6.9% in 2009 for people who are 15 years or older [1] and it was estimated to be responsible for 4% of all deaths in Thailand in 2014 [2]. This makes diabetes one of the major public health burdens in Thailand. Treatment is complex for several reasons [3]. For example, chronic conditions require continuous medical care over a patient’s life time. Additionally, many healthcare providers participate in care, which poses challenges for communication and coordination of services across sectors. The increasing prevalence, complexity of care and resulting financial burden to the health system are all factors driving initiatives to improve the management of diabetes in Thailand [4].

Currently, diabetic patients in Thailand can visit a primary care facility for treatment if they have comprehensive health insurance, otherwise out-of-pocket payments are required. 99.84% of Thai citizens were covered by a form of health insurance in 2014 [5]. The major components are as follow: 73.80% were covered by Universal Coverage Scheme, 16.90% by Social Security Scheme and 7.39% by Civil Servant Medical Benefit Scheme. These insurances cover both primary and hospital-level care. All insurance schemes mentioned cover essential drugs, the Civil Servant Medical Benefit Scheme additionally covers nonessential drugs. The 2014 National Health Security Office Clinical Practice Guideline for Diabetes in Thailand (only available in Thai) [6] includes similar recommendations to the 2012 Position Statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) [7, 8], which includes, for example, planned follow-up visits every 3 months after the target HbA1c level has been achieved and an annual ophthalmologist visit.

Of the many initiatives to assess and improve the quality of care of chronic illnesses, such as diabetes [9], a well-established evidence-based approach is the Chronic Care Model (CCM) [10], which was developed at the MacColl Center for Health Care Innovation [11, 12]. This model identifies six domains that are essential to provide good quality of care for chronic illnesses: the community, the health system, self-management support, delivery system design, decision support and clinical information systems [10]. However, due to societal and cultural differences between Thailand and Western countries some aspects of the CCM are more difficult to fulfill e.g. “community domain” availability of diabetes support groups in smaller communities in Thailand.

Several tools have been developed to enable evaluation of CCM to assess the extent to which chronic illness management aligns with the Chronic Care Model [13]. The earliest example is the Assessment of Chronic Illness Care (ACIC) [14], which measures quality of care at the level of the healthcare provider. However, the ACIC is problematic. It has proved inappropriate for widespread use and is prone to clinician over-reporting [15]. Another CCM evaluation tool is the Patient Assessed Chronic Illness Care (PACIC) [13]. The PACIC is a 20-item survey which measures the patient’s perceived quality of care retrospectively for 6 months. There are variations to this instrument, the PACIC-S and the PACIC+. The PACIC-S is a short form of the PACIC, containing 11 items, which aims to provide an alternative instrument with a lower burden for the patients [16]. The PACIC+ additionally addresses the evidence-based 5A model for behavioral changes [17] and was developed in order to fill the same gap for the 5A model that existed for the CCM [15]. The 20 items from the PACIC are complemented by another 6 items in order to improve content validity and to enable the assessment of factors related to the 5A model, a counseling model for behavioral changes [18]. The 5A model was first developed to help people quit smoking and was later refined to be applicable to any behavioral changes. The model has 5 aspects: Assess, Advise, Agree, Assist and Arrange, which each addresses different components of a patient’s self-management. A major advantage of the PACIC over the ACIC is the integration of the patient perspective.

ACIC and PACIC were developed and have been validated in the English language. The ACIC has been translated to Thai and validated [19]. However, at the time of the reported study there was no published Thai version of the PACIC available. This study aimed to translate the PACIC+ tool from English into Thai and to test its psychometric properties to enable different aspects of the CCM and the 5A model to be assessed for Thai speakers.

The Thai PACIC+ questionnaire demonstrated good internal consistency for the majority of the subscales. This meant that the items in the subscales correlated substantially with each other. The test-retest reliability showed low correlation but a good mean difference. This indicated that the overall care patients had received had not changed, but on the individual level, perceived care changed between the baseline assessment and follow-up. The low test-retest reliability could be due to a number of factors that occurred to the participants between assessments, which might have changed their answers. A large proportion of the participants used the extreme high answering category in several items. This indicated that the patients were extremely satisfied with care regarding these items. On the other hand, summary scores did not show ceiling effects, which suggests that use of the Thai PACIC+ would allow identification of care domains that might be improved.

Data were suitable for performing an EFA, with the KMO of both models graded as “meritorious” [25] and the Bartlett’s value for sphericity was significant. The factor loading in rotated factor solution showed an overall respectable concordance for the subscales in the CCM. The factor structure of the 5A model has a poorer concordance than that of the CCM. The structures of “Assess” and “Advice” were the most poorly represented. This is congruent with the results of the internal consistency analysis as the subscales “Advise” and “Assess” also demonstrated the poorest Cronbach’s α. Since the PACIC was developed first with the CCM in mind and the PACIC+ later included 6 items to assess the 5A Model [15], the fact that the CCM items shows an overall better factor structure than the 5A Model could be anticipated, as the original 20 questions were not designed to specifically reflect the 5A Model. Despite all this, the EFA demonstrates a satisfactory factor structure for the CCM and an overall reasonable factor structure for the 5A model.

Compared to the original validation PACIC study [13], Cronbach’s alpha values in this study are lower. However, they are comparable to those of other translated versions such as the Spanish version [26] or the Dutch version [27]. In addition, the original PACIC study did not report any ceiling effects for the items and only floor effects for a few of the items that were expected to have a low score [13]. As in other studies, such as the French version [28], the Danish version [29] or study in the United Kingdom [30], the Thai PACIC+ also showed floor and ceiling effects for several items, but not the subscales.

Furthermore, test-retest reliability of the overall score of the Thai PACIC+ after 4 weeks were comparable to the original PACIC at the 3-month retest [13] and the PACIC short form retest at 8 months [31]. Reliability of the Thai PACIC+ was lower than that of the Spanish version of the PACIC at the two to four months retest and the original PACIC at two weeks [26]. The test-retest reliability of the Thai PACIC+ subscales were lower than those in the above named studies. The KMO and the Bartlett’s test for sphericity was comparable to the Dutch PACIC, which also had a “meritorious” KMO and a p < 0.001 [27]. The factor structure of the Thai PACIC+ was also comparable to the reported factor structure of the Dutch [27] and the Slovenian PACIC [32], where the “Patient activation” and “Delivery system” subscales have an excellent concordance. Concordance of other 3 subscales were poorer and comparable to the results of the Thai PACIC+. Two Australian studies also showed similar results where items 9 and 10 in the “Goal setting” subscale also deviated from the factor structure, however, their “Problem solving” and “Follow up” subscales had a better factor structure [33] than those in our study.

The 5A Model in the PACIC+ is more difficult to compare as the original publication did a confirmatory factor analysis [15] and there are not many validation studies done for this instrument, and to date those that we found used methods other than an EFA.

No bias due to gender, age or level of education was identified, which is congruent with the notes of the developers of the instrument [15]. This validation study of the Thai PACIC was conducted in one healthcare center. Further studies should be repeated in other Thai healthcare facilities to see if results are transferable. A limitation in this validation is that 3 items of Thai PACIC+ showed floor effects with a high proportion of extreme low answering categories (see Table 2: questions 10, 17, 26). This could be due to the fact that in Thailand these procedures (e.g. support groups, log books) are not commonly performed. It should be considered whether, as part of the cultural adaptation of the Thai PACIC+, these items should be excluded if they continue to show floor effects in future studies. Nevertheless, comprehensibility of these questions, was not problematic during the forward and backward translation process and the think-aloud protocol.

In summary, the Thai PACIC+ has good psychometric properties. Data were suitable for performing an EFA, which demonstrated a factor structure consistent with the original PACIC and its theoretical model. Through this validation study, the Thai PACIC+ can be implemented to measure patients’ perceived care during treatment. Further studies are recommended in other Thai healthcare facilities to evaluate transferability and to analyze the correlation between the PACIC score and patient outcomes for validation purposes.