Policy interventions
To improve care for patients with chronic diseases, a recent policy initiative in Thailand focuses on strengthening primary care with the two-pronged strategy. Since 2016, the first strategy had been applied to an accumulated number of 1137 primary care units (PCU) or 11.6% of the total of 9777 in 74 provinces. [ http://pcc.moph.go.th/pcc/dashboard/?p=teamCount_rpt]. A physician trained in family medicine and new medical equipment (such as ultrasonography, ECG monitor) were distributed to each of the upgraded PCUs. The physician is assigned to provide full-time clinical services of 3–5 days a week to the upgraded PCU in addition to outpatient care services in the referral hospital of the PCU. In contrast, patients seeking care at ordinary PCUs have only one day per week to receive care from the team and a physician with or without training in family medicine.
The training strategy was applied to 21 of the upgraded PCUs in July 2019. From each of the 21 upgraded PCUs, the head and 2–3 clinicians attended two consecutive training workshops (1 and a half days each). The first one started with a didactic lecture addressing the concepts of the strategy and tools for translating the concepts into practices i.e., system thinking and design thinking. Two small group sessions followed the lecture to discuss experiences and ideas related to the translation of the knowledge tailored to specific settings. Reading materials focusing on WHO’s Integrated People-centered Health Service (IPCHS) and CCM were shared with the participants. The second workshop followed one month after the first to explore the feasibility and barriers of implementing the strategy making use of the participant experiences. The participants were expected to transfer the knowledge and skills to the rest of the team members in each upgraded PCU. To ensure fidelity of the implementation theory, follow up support and encouragement throughout the study period were carried out by two implementation support practitioners. They paid a visit to each team of the participants aiming at activating implementation-relevant knowledge, skills, and attitudes, and to operationalize and apply these in the context of the participants. In doing so, they aimed to trigger both relational and behavioral outcomes. For instance, the application of the concept of risk stratification of the patients was encouraged in order to customized clinical transactions according to the needs of specific patients instead of treating all patients similarly which usually results in superficial provider-patient dialogue and refilling medications over a period of just 3–5 minutes for each patient. Nevertheless, there was no systematic check of the fidelity.
Patient survey
Population and samples
In order to examine the effects of provider training and local health systems settings (using facility type as proxy), the following number of PCUs and hospital NCD clinics in the same district agreed to participate: 7 trained upgraded PCUs, 6 untrained upgraded PCU, 6 ordinary PCU and 13 hospital NCD clinics (Fig. 1). The hospital NCD clinics were included since they were supposed to care for complicated patients referred from PCUs. Patients with the chronic conditions attended the participating facilities on their regular visits were asked to take part in the study. In total, 4071 patients gave informed consent and were interviewed at home using the PACIC + questionnaire by trained field workers during September 2019.
Data collection
The PACIC + questionnaire was adopted from Thai version of the Patient Assessment of Chronic Illness Care (PACIC) validated in outpatient clinic of a university hospital in Thailand with high reliability (Cronbach’s alpha per subscale varied from 0.58 to 0.81 and that of the summary scores were 0.89 and 0.91). The PACIC + contains 26 items. Twenty items are from the original PACIC, which measure different parts of the CCM, and an additional 6 items assess the 5A Model. Each item asks the patient to evaluate the care they have received in the past 6 months on a 5-point scale: 1 (Almost never), 2 (Usually not), 3 (Sometimes), 4 (Mostly) and 5 (Almost always). It takes approximately 5–10 min to complete. The items of the PACIC + are grouped into CCM subscales and 5A Model subscales. The CCM subscales constitute: Patient Activation (items 1–3); Delivery System (items 4–6); Goal Setting (items 7–11); Problem solving (items 12–15); Follow-up (items 16–20). The 5A Model consists: Assess (items 1,11, 15, 20, 21); Advise (items 4, 6, 9, 19, 24); Agree (items 2, 3, 7, 8, 25); Assist (items 10, 12, 13, 14, 26); and Arrange (items 16, 17, 18, 22, 23). Furthermore, summary scores can be calculated for the PACIC (items 1–20) and the items related to the 5A Model (items 1–4, 6–26). Each subscale is scored by averaging the answers of each item in the subscale. Subscales take values between 1 (Almost never) and 5 (Almost always).
In our study, there were 3 sections in the questionnaire: 1) personal information of the respondents; 2) perceptions of the interactions with providers, and 3) PACIC + items. Supplement provided details of the 3 sections. In brief, the personal information consists of demographic profile, type of chronic conditions, duration of the conditions and health insurance status. Section 2 explored perception about: channel of contact with providers, receiving care from the same provider on repeated visits.
Statistical analysis
We constructed 2 set of latent variables as subscale from the PACIC + items to reflect the components of chronic care model and 5 A model. The subscales for components of chronic care model (CCM) included patient activation, delivery system, goal setting, problem solving and follow-up and for the 5 A model components included assess, advise, agree, assist, and arrange,. Confirmatory factor analysis was performed using structural equation modeling to evaluate the fitness of the data to the PACIC + scale structure. The extent to which the items loaded on to the hypothesized variables and the correlation (Table A1, A2 in supplement) were examined. For CCM subscales, almost all the factors have factor loadings of 0.60 or greater, only 4 items had standardized factor loadings less than 0.6. The goodness of fit for the overall model was moderate, and the value of RMSEA and CFI were 0.092 and 0.863, respectively. For 5A the overall goodness of fit was slightly lower than that of CCM.
To examine the individual factor and type of primary care setting that associated with patient perception measures, PACIC, each subscale was categorized in to binary variable cut at percentile 75th (0 = low score, and 1 = high score) and treated as the outcome variable. Chi-square’s test was performed to explore the association between each independent variable and each outcome and variables that provided p-value of less than 0.10 were included in the multivariable regression analysis. The multilevel regression analysis was considered, as the first level was individual and the second level was the primary care cluster. Mixed effects logistic regression model was used to examine the association between each subscale and the explanatory variables with a random intercept for “primary care unit (PCU)” level to take into account the correlation among patients in the same PCU. Independent variables that were in the multivariable regression analysis included individual level: sex, age, education (primary, secondary and bachelor), chronic diseases (DM, HT, and both), duration of the chronic disease condition, know about family doctor (yes/no) know health care provider’s name (yes/no), type of contact channel (no, mobile phone, Line application, and others), seeing same doctor on repeated visits, (yes/no) and insurance scheme (universal health, social security, civil servant, and others) and type of primary care setting (trained PCU, upgraded PCU, ordinary PCU, and NCD clinic in hospital). Odds ratio and 95% CI were calculated and reported. Stata version 16 (StataCorp. 2019. College Station, TX) was used for the statistical analysis.