### Study design and study population

A cross-sectional study was conducted among PLWHIV aged more than 16 years old in the 10 municipalities in Yunnan province, China from October 2019 to May 2020. A convinced sampling method was used. The sample size was calculated by the Yaro Yamane’s approach for finite population using the formula of ({text{n}} = N/left( {1 + N(e)^{2} } right)) [20]. In our study, n stands for the expected sample size. *N* stands for the finite population that the sample derived from. We set a number of the total PLWHIV estimation of the ten selected areas. e stands for the level of significance and we set 0.05. Finally, the estimated sample size was 354. Based on the reported number of PLWHIVs of each municipality, a convenient sample of 150–200 was included for each selected area. We also excluded respondents with a cognitive impairment who were unwilling to finish the investigation. Our study total included 1997 participants. All investigators from local Center for Disease Control and Prevention (CDC) and social organizations were trained strictly to implement the investigation face to face.

### Data collection

#### Health-related quality of life

Individual-level HRQoL data were measured using the SF-12 and EQ-5D-5L. The 12-item Short Form Health Survey (SF-12), which is the shortened version of 36-item Short Form Health Survey (SF-36) could explain at least 90% of the accuracy of the SF-36 [21]. The SF-12 consists of eight domains to generate two separate summary scores, physical functional scores (PCS) and mental functional scores (MCS) ranging from 0 to 100. Higher scores indicated better HRQoL. Cronbach’s α = 0.89. We also used EQ-5D-5L to measure HRQoL simultaneously. The EQ-5D-5L could define the 3125 possible health states by the different combinations. We adopted the Chinese population-based preference trade-off time (TTO) to transform the measures into utility index (UI, Table 1), thereby producing a single preference-based index ranging from -0.391 to 1.000, where 0 was equal to death and − 0.391 meant worse than death. For example, when we calculated a combination of “21145”, the UI equalled to 1 − 0.066 − 0 − 0 − 0.252 − 0.258 = 0.424 [22]. Cronbach’s α = 0.79.

#### Demographic and HIV diagnosis variables

All demographic data, including age, race/ethnicity, education level, marital status, household income per year, whether infection status was known to others, and HIV diagnosis variables including initial infection status, transmission model, duration of ART and the most recent CD4 counts were obtained using self-designed questionnaires.

#### Social support, depression and anxiety

We used the Social Support Rating Scale (SSRS) established by Xiao Shuiyuan in 1986 primarily for the Chinese population [23]. It comprised ten items and three dimensions. A respondent’s social support was measured on three scales: objective social support, subjective social support and support utilization. The final social support was obtained by averaging all item scores from three dimensions. A higher total score demonstrated a higher level of perceived social support. Cronbach’s α = 0.68. Anxiety and depression were measured using Chinese version of the Hospital Anxiety and Depression Scale (HADS) [24], which is a short scale with 14 items designed for anxiety and depression diagnose in nonpsychiatric patients. Anxiety and depression were assessed using seven items respectively. Higher scores demonstrated more serious depression or anxiety symptoms. Cronbach’s α = 0.85.

#### Area-level data collection

We assembled municipal-level social economic data from the Yunnan Statistical Yearbook in 2020 carried out by the Statistical Bureau of Yunnan Province [25]. We used gross domestic product (GDP) per capita, employment rate, birth rate, mortality rate and natural growth rate to calculate the municipal-level social economic effect, which was encouraged to measure the social economic status of the areas. Other municipal-level data about prevention strategy came from the evaluation system for the quality of strategy implemented, which was designed by Yunnan CDC, which included epidemic surveillance, the high-risk behaviour intervention, PLWHIV management, and follow-up and experimental management to construct the prevention strategy. The strategy could be formed different models including of the good quality strategy (stategy 1), the traditional strategy with advantage (strategy 2), the advanced strategy (strategy 3) and the general strategy (strategy 4).

### Data analysis

For the statistical descriptive, we used the mean (standard deviation) and median (interquartile range) to describe the total HRQoL measured using EQ-5D-5L and PCS-12 and MSC-12, respectively.

For the statistical analysis, our study first used five indicators (GDP per capita, employment rate, birth rate, mortality rate and natural growth rate) to demonstrate the social economic effect. All six indicators (the epidemic surveillance score, the comprehensive score of female sex workers intervention, the comprehensive score of men has sex with men intervention, the comprehensive score of PLWHIV management and follow-up and the score of HIV laboratory testing quality) demonstrated the strategy implemented effect of each area. We used principal component analysis to build the social economic and strategy effects of each area. In view of the sensitivity to the dimensions for principal component analysis, all of the calculated indicators were adjusted between 0 and 1 using min–max standardization to eliminate the influence of dimension inconformity [12]. The standardization equation is shown as flowing.

$$S_{ij} = frac{{x_{ij} – x_{ij(min )} }}{{x_{ij(max )} – x_{ij(min )} }}$$

*S*_{ij} demonstrated the transferred *i* indicator of area *j*, *x*_{ij} demonstrated the original *i* indicator of area *j*, and *x*_{ij(min)} and *x*_{ij(max)} demonstrated the max and min *i* indicators in all areas.

We defined the principal components with reference to the variation greater than 80%, and also explanatory variables according to the practice for the social economic effect and strategy effect. In our study, for the social economic and strategy effect, the first and second component scores were calculated as follows:

The first component score = − 0.170 × GDP per capita − 0.229 × employment rate + 0.387 × birth rate + 0.222 × mortality rate + 0.362 × natural growth rate

The second component score = 0.488 × GDP per capita + 0.434 × employment rate + 0.183 × birth rate + 0.322 × mortality rate + 0.112 × natural growth rate

For the strategy practice effect, the first and second component scores were calculated as follows:

The first component score = 0.127 × epidemic surveillance score + 0.343 × the comprehensive score of female sex workers intervention + 0.372 × the comprehensive score of men has sex with men intervention + 0.379 × the comprehensive score of PLWHIV management and follow-up + 0.062 × the score of HIV laboratory testing quality

The second component score = 0.428 × epidemic surveillance score − 0.324 × the comprehensive score of female sex workers intervention + 0.033 × the comprehensive score of men has sex with men intervention + 0.011 × the comprehensive score of PLWHIV management and follow-up + 0.648 × the score of HIV laboratory testing quality

Second, one-way ANOVA was used to perform univariate analysis to identify the predictors with significant differences. Candidates for multivariate analysis included the variables: (1) professionals associated with HRQoL among PLWHIV; (2) social support, anxiety and depression; and (3) variables at the level of *P* less than 0.1 in one-way ANOVA.

Our study used a multilevel model (MLM) to explore the personality, social economic and strategy effects on health-related quality of life among PLWHIV [13, 26]. We set the individual-level as level-1 and the area-level as level-2. Based on the social economic models and strategy practice models by component analyses, we primarily examined the strategy effect as the area-level variables to predict HRQoL, with age, race/ethnicity, marital status, education level, occupation, household income per year, other know HIV status, initial infectious status, transmission model, duration of ART, the most recent CD4 counts, social support score, anxiety score and depression score as individual-level variables to predict the HRQoL. We adapted the random coefficient model to fit. Let *y*_{ij} be the score of HRQoL for individual *i* from area *j*. We used one individual-level and one area-level predictor to keep the notation simple and without loss of generality, indicated by *x*_{ij} and *z*_{j}. Respectively. We listed the traditional single-level model as

$$y_{ij} = beta_{0} + beta_{1} x_{ij} + beta_{2} z_{j} + varepsilon_{ij}$$

Casual heterogeneity was expressed by adding an interaction term, (beta_{3} x_{ij} z_{j}), to the model.

For the MLM, the individual-level model includes only individual-level predictors and its regression coefficients were not fixed but varied across areas and fitted into an area-level model.

Individual-level model: (y_{ij} = beta_{0j} + beta_{1j} x_{ij} + varepsilon_{ij}).

Area-level intercept model: (beta_{0j} = beta_{00} + beta_{01} z_{j} + mu_{0j}).

Area-level slope model: (beta_{1j} = beta_{10} + beta_{11} z_{j} + mu_{1j}).

The area-level errors (left( {mu_{0j} mu_{1j} } right)sim Nleft( {0,sum { = left[ begin{gathered} tau_{00} tau_{01} hfill \ tau_{01} tau_{11} hfill \ end{gathered} right]} } right)) and were assumed to be independent from the individual-level errors (varepsilon_{ij} sim Nleft( {0,sigma^{2} } right)). Both the intercept and slope of the individual-level model were determined by the area-level variable. The main effect of area-level variables and the causal heterogeneity were determined by examining the intercept and slope of the individual-level model respectively.

The MLM divided the total variance of HRQoL into between-country (i.e., Σ) and within-country (i.e., σ^{2}) variance.

We could also include multiple independent variables in the full MLM such as the multivariate models. In our study, the individual-level independent variables were age, race/ethnicity, education level, household income per year, recent CD4 + T counts, transmission model, duration of ART, social support, anxiety and depression. The area-level predictors in the study were social economic effect and strategy effect.

We used STATA version 14.0 (StataCorp LLC, College Station, TX) to perform all the statistical analysis.

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