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. 2023 Mar 9;2(2):94–111. doi: 10.1002/hcs2.32

To what extent do disparities in economic development and healthcare availability explain between‐province health inequalities among older people in China?

Sol Richardson 1,✉, Zhihui Li 1
PMCID: PMC11080879  PMID: 38938765

Abstract

Background

Uneven economic development has led to substantial health inequalities between Chinese provinces. The extent of, and factors underlying, between‐province health inequalities have received little attention.

Methods

Data from 15,278 respondents in Wave 2 (2013) of the China Health and Retirement Longitudinal Study (CHARLS) were used to investigate inequalities among people aged ≥50 years in five health outcomes between 27 Chinese province‐level administrative units. After characterizing the between‐province differences and the relevance of province effects, proportional change in variance between unadjusted and adjusted models was calculated to determine the percentage of between‐province variance in health outcomes explained by province‐level variables including measures of economic development and healthcare availability.

Results

Although province effects explained <10% of overall variance in health outcomes, they underpinned large between‐province inequalities among people aged ≥50 years. Gross Regional Product per capita was more important than doctor density in explaining between‐province variance in health outcomes, particularly depression symptoms and instrumental activities of daily living impairment.

Conclusion

Policy efforts, including more equal distribution of healthcare personnel, may be warranted to reduce between‐province health inequalities.

Keywords: inequalities, multilevel modelling, depression, wellbeing, disability, overweight, lung function, China

The results of this study, which analyzed decomposition of variance in five health outcomes between Chinese provinces using nationally representative panel data, highlight the wide health inequalities among people aged ≥50 years. For example, comparing similar individuals from randomly selected provinces, the median impact of between‐province residual heterogeneity (i.e., comparing higher and lower risk provinces) on depression symptom outcomes or instrumental activities of daily living is comparable to that of being divorced, separated, or widowed versus being married or cohabiting with a partner. Province‐level per capita economic output explained a greater proportion of the differences in these health outcomes between provinces than access to healthcare as measured by licensed doctors per capita.

graphic file with name HCS2-2-94-g002.jpg

Abbreviations

95% CI

95% confidence intervals

BMI

body mass index

CES‐D

Center for Epidemiologic Studies Depression Scale

CHARLS

China Health and Retirement Longitudinal Study

COPD

chronic obstructive pulmonary disease

GRP

gross regional product

IADL

instrumental activities of daily living

ICC

intracluster correlation coefficient

MOR

median odds ratio

NBS

National Bureau of Statistics (China)

OR

odds ratio

PCV

proportional change in variance

PEF

peak expiratory flow

1. INTRODUCTION

China has experienced rapid economic development since 1978 and a transition from a primarily agricultural to an industrial economy [1]. This process has been accompanied by rising inequalities in wealth and income from the household level to the province level, however. Economic disparities between export‐oriented coastal provinces and inland provinces in terms of gross regional product (GRP) increased markedly from around 1990 until the mid‐2000s [2]. Between‐province differences in GRP per capita and household income have since started to decline since then, however [3].

Economic changes have been accompanied by a shift in China's population age structure due to declines in both fertility and mortality, and growth in the proportion of people aged ≥50 years among the general population partially as a consequence of the One‐Child Policy [4]. At the same time, noncommunicable diseases have become a significant policy concern even since the early 1990s [5], driven by changes in diet, physical activity, smoking prevalence, environmental pollutants, and other factors [4, 6].

Economic liberalization and uneven development have contributed to increasing inequity in access to public services. Following initial economic reforms, financing of health was devolved from central to provincial governments, and rural health services organized by agricultural collectives were dissolved [7]. This has resulted in an increasing concentration of health service expenditure and utilization in more developed provinces [8], greater between‐province inequality in health resources such as health workers and beds, and wide disparities in physician pay [3, 9].

The relative extent to which inequalities in population health status is explained by economic inequalities and inequalities in healthcare access has yet to be elucidated, and both have contributed to growing between‐province health inequalities. This is particularly the case for noncommunicable diseases. Regarding specific outcomes, although prevalence of depression is comparable with other developing countries, there are wide disparities in prevalence between different regions of China [10, 11]. Prevalence of impairment in instrumental activities of daily living (IADL) has decreased over time but remains higher in rural areas [12]. Prevalences of overweight and chronic obstructive pulmonary disease have risen markedly in recent decades and vary significantly across Chinese regions [13, 14].

To my knowledge, no systematic attempt has been made to quantify the degree of between‐province health inequalities in China, or to identify variables underlying these inequalities. While previous studies have investigated province‐level effects in China, these failed to quantify either the magnitude of between‐province inequalities or the proportion of variance explained by province‐level variables [15, 16]. Partitioning of variance within a multilevel framework has been employed in studies in other contexts. For example, one study has investigated the proportion of between‐country variance in well‐being change following exit from paid employment explained by national‐level welfare policies across European countries [17].

The objectives of this study were to:

  • (1)

    Characterize prevalence of five health outcomes (depression symptoms, IADL impairment, limitation in physical functioning, overweight, and lung function impairment), and per capita economic output and doctor density, by province, and between‐province differences.

  • (2)

    Investigate the fixed‐effects individual‐ and province‐level predictors of each health outcome measure.

  • (3)

    Estimate and interpret the proportion of overall variance in each health outcome attributable to province effects, and the proportion of province effects attributable to per capita economic output and doctor density.

2. METHODS

2.1. Data sources

Data were obtained from Wave 2 (2013) of the China Health and Retirement Longitudinal Study (CHARLS); although more recent waves are currently available, data from this wave were used due to the availability of health outcomes in the “biomarkers” module (absent from Wave 4) and the low degree of missingness in important individual‐level variables. This biannual, nationally representative sample of the middle‐aged and elderly population of China, with self‐reported and objective assessments of respondents’ social, economic and health circumstances, encompasses 27 Chinese provincial‐level administrative units (provinces, municipalities, and autonomous regions) excluding Hainan, Ningxia, Shaanxi, Tibet Autonomous Region, Macau Special Administrative Region, Hong Kong Special Administrative Region, and Taiwan [18]. The analytic sample included respondents aged ≥50 years (any outcome, n = 15,278)1. CHARLS has been approved by the ethics committee of Peking University Health Science Center and all participants gave written informed consent before participation. No further ethical approval or participant consent was required for this study as it was based on a secondary analysis of existing data.

2.2. Variable definitions

We defined six health outcome measures, operationalized as binary variables. These included depression symptoms based on the CES‐D‐10 instrument [19], which consists of 10 Likert‐type items and yields scores ranging from 0 to 30.2 A cutoff score of 10, which has shown high specificity in samples of older people, was used to define probable depressive cases [20, 21]. Limitation in physical functioning3 and IADL impairment4 were defined using self‐reports of difficulties in performing physical and functional tasks. Overweight was defined using a body mass index (BMI) cutoff of ≥24, which is the typical cutoff employed in China [22], based on recordings of height and weight taken during the nurse visit. Lung function impairment was defined as having a peak expiratory flow (PEF) of <70% of an individual's expected value based on a formula including age, sex, and height [23, 24]5.

Two province‐level variables for 2013 were extracted from the National Bureau of Statistics (NBS) China Statistical Yearbook to measure economic development and healthcare availability [28]. GRP per capita, a province‐level measure of economic output equivalent to Gross Domestic Product per capita, was denominated in 2015 Yuan (¥) per inhabitant by increments of ¥1,000 (or $282 in 2015 Purchasing Power Parity adjusted United States Dollars) [29]. Doctor density was measured in doctors per 10,000 inhabitants. Doctors are defined as those who have passed a licencing examination and registered at a county or higher level as physicians or assistant physicians [9].

Individual‐level covariates included sex (male or female), age (years), partnership status (married and cohabiting, married but living apart, never married, or divorced, separated or widowed), ethnicity (Han or other), residence (urban or rural), health insurance (uninsured, enhanced government‐sponsored insurance, basic government‐sponsored insurance or private insurance)6, employment status (working or not working), quintile of gross square root equivalized household income, level of education (less than elementary school, elementary school, middle school, high school or vocational college and university), smoking status (nonsmoker or current smoker) and alcohol intake (none, once per month or >once per month). A 0–7 point index of housing quality was specified as a proxy for household wealth and based on the presence of electricity, an indoor toilet, central heating, internet, running water, sufficient living space (<2.5 people per room), and brick or concrete as primary building materials (Cronbach's α = 0.81).

2.3. Analytic methods

All analyses were performed in Stata 14 [30]. We used logistic random‐effects models for binary outcomes using the xtmelogit command to investigate the odds of respondents meeting the criteria for each of the six health outcome measures. Observations were nested within province‐level units and considered nonindependent due to spatial dependence. All models fitted random intercepts for each province. For models with covariate adjustment, covariates were fitted as fixed effects only. Analysis of variance components involved estimations of three random‐effects statistics: intraclass‐correlation coefficient (ICC), median odds ratio (MOR), and proportional change in variance (PCV).

ICC is defined as “the proportion of the variance explained by the grouping structure” [31]. Calculations of ICC are based on both individual‐level and area‐level variance. In multilevel logistic regression, area‐level variance is calculated on a logistic scale while individual‐level variance is on a probability scale. This study employed the latent variable method to convert individual‐level variance to the logistic scale [31].

In logistic random‐effects models for binary outcomes, ICC is calculated using the latent variable method by the following equation [32]:

ICC=VA(VA+3.29), (1)

where VA represents residual area‐level variance. The unobserved individual variable follows a logistic distribution with variance equal to π2/3 (3.29). The latent variable method assumes that ICC is a function only of the area‐level variance and is independent of the prevalence of the outcome.

The simulation method, proposed by Merlo et al. [33], was not used as it was considered unecessary. First, although estimation of random‐effects parameters may be biased when there are fewer than 20 area‐level units, this study employed data from 27 province‐level administrative units of China. Second, the assumption of the latent variable method that an individual's propensity to be positive for a given outcome is a continuous latent variable underlying the binary response variable was likely to have been met as all outcomes were derived from continuous measures and coded using cutoffs.

MOR, a measure of residual heterogeneity between areas, translates area‐level variance into an odds ratio (OR). It is defined as the median OR for a given outcome between the area at highest risk and the area at lowest risk when randomly selecting two areas, and is statistically independent of the prevalence of the phenomenon in question [33]. MOR can be directly compared with fixed‐effects OR estimates from the same model when considering the relevance of residual between‐area heterogeneity. The MOR is based on estimates of area‐level variance (VA ) and was calculated using the following formula:

MOR=exp[(2×VA)×0.6745]. (2)

In this context, 0.6745 is the 75th centile of the cumulative distribution function of the normal distribution with mean 0 and variance 1.

PCV is calculated by comparing variance estimates from an “unconditional” or “empty” intercept‐only model (without adjustment) with those from a “conditional” model (adjusted for individual‐ and/or province‐level covariates) [34]. It is defined as the percentage difference in area‐level variance between the empty and conditional models, and describes the proportion of area‐level variance explained after adjusting for (individual or groups of) individual or area‐level variables in the conditional model.

Between‐area differences can be attributable to compositional (i.e., differences in their population characteristics) and contextual factors (i.e., between‐area differences). PCV, expressed as a percentage of level‐2 variance explained by compositional and/or contextual factors, was estimated using the following formula:

PCV=(VA−VB)VA×100. (3)

where VA represents an estimate of area‐level variance from the “empty” (unconditional) intercept‐only model without adjustment for either individual‐ or province‐level variables (Model 2 in this study) and VB residual area‐level variance from the “conditional” model (Models 3–6) with adjustment for individual‐ and province‐level covariates.

2.4. Descriptive analysis

We estimated the prevalence of each of the six health outcomes in 2013, both overall and by province, using cross‐sectional survey weights for individual respondents. Provinces were categorized by quartile of prevalence of each health outcome, per capita GRP, and doctor density. Individual‐level characteristics of the analytic sample were then described for each outcome.

2.5. Statistical analysis

Six models were fitted for each health outcome. Model 1 was an unconditional model fitted for random‐effects only and without adjustment for individual or province‐level variables. It used the full available data sample without excluding observations with missing covariate data. We then fitted another empty model for random‐effects only using the analytic sample after dropping observations with missing data without adjustment for any individual or area‐level variables (Model 2). Between‐province variance parameter estimates from Model 2 were then compared with subsequent models with adjustment for individual‐level (Model 3) and province‐level variables (Models 4–6) to calculate the percentage of between‐province variance in the empty model attributable to compositional and contextual factors. The meresc package was used to rescale the results of xtmelogit models to the same scale as unconditional intercept‐only models and allow comparison of variance components across models [35]. We analyzed complete cases only as meresc does not support missing data techniques such as multiple imputation.

Model 3 was fitted as a conditional model with individual‐level variables (compositional factors), and fixed‐effects OR and 95% confidence intervals (95% CI) were estimated to show associations between individual‐level variables and each health outcome. Model 4 was fitted as a conditional model with adjustment for individual‐level variables and province‐level GRP per capita while Model 5 was adjusted for individual‐level variables and doctor density. Model 6 fitted all individual level covariates in addition to both province‐level GRP per capita and doctor density. Fixed‐effects associations between province‐level variables and health outcomes, in addition to PCV compared with Model 2, were estimated for Models 4–6.

3. RESULTS

3.1. Descriptive analysis

Table 1 shows GRP per capita and doctor density by province and categorizes them by quartile. Province‐level GRP per capita ranged from ¥23,151 (Guizhou) to ¥100,105 (Tianjin). Mean GRP across the 27 provinces was ¥49,248. Mean doctor density was 2.2 doctors per 10,000 inhabitants, with a range of 1.6 (Anhui and Jiangxi) to 4.3 (Beijing). Table 2 shows the prevalence of each health outcome, in addition to GRP per capita and doctor density, by province, and categorizes provinces by quartile. There was an east‐to‐west gradient in prevalence of depression symptoms, limitation in physical functioning and IADL impairment, and a south‐to‐north gradient in overweight. Table 3 shows the individual‐level characteristics of the analytic sample for each outcome measure.

Table 1.

China National Bureau of Statistics (NBS) estimates of gross regional product (GRP) per capita and doctor density by province in 2013

Province Total (27 provinces) GRP per capita in 2013 (2015 ¥) Mean 49,248 Quartile Doctors per 10,000 inhabitants in 2013 Mean 2.2 Quartile
Anhui 32,001 1st 1.6 1st
Beijing 94,648 4th 4.3 4th
Chongqing 43,223 3rd 2.0 2nd
Fujian 58,145 3rd 2.0 2nd
Gansu 24,539 1st 1.8 1st
Guangdong 58,833 3rd 2.0 2nd
Guangxi 30,741 1st 1.8 1st
Guizhou 23,151 1st 1.8 1st
Hebei 38,909 2nd 2.2 3rd
Heilongjiang 37,697 2nd 2.0 2nd
Henan 34,211 2nd 1.9 1st
Hubei 42,826 2nd 2.1 2nd
Hunan 36,943 2nd 2.0 2nd
Inner Mongolia 64,836 4th 2.5 3rd
Jiangsu 75,354 4th 2.4 3rd
Jiangxi 31,930 1st 1.6 1st
Jilin 47,428 3rd 2.5 3rd
Liaoning 61,996 3rd 2.5 3rd
Qinghai 36,875 2nd 2.3 3rd
Shandong 56,885 3rd 2.4 3rd
Shanghai 90,993 4th 2.8 4th
Shanxi 43,117 3rd 2.3 3rd
Sichuan 32,617 1st 2.1 2nd
Tianjin 100,105 4th 2.6 4th
Xinjiang 37,553 2nd 2.1 2nd
Yunnan 25,322 1st 1.7 1st
Zhejiang 68,805 4th 2.9 4th
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Table 2.

Percentage prevalence of six health outcomes by province for individuals aged 50 years and over in China Health and Retirement Longitudinal Study (CHARLS) Wave 2 (2013)

Province Depression symptoms (CES‐D‐10) Limitation in physical functioning IADL impairment Overweight Lung function impairment
% 95% confidence interval (CI) Quartile % 95% CI Quartile % 95% CI Quartile % 95% CI Quartile % 95% CI Quartile
Total (27 provinces) 31.3 30.3, 32.3 60.5 59.5, 61.5 24.6 23.8, 25.5 31.1 30.0, 32.1 51.7 50.6, 52.9
Anhui 36.9 32.8, 41.0 3rd 67.1 63.4, 70.8 4th 29.5 25.8, 33.2 4th 25.5 0.22, 0.29 1st 59.4 54.7, 64.1 4th
Beijing 4.5 0, 9.5 1st 59.6 47.8, 71.3 2nd 20.1 8.4, 31.8 1st 85.1 0.78, 0.93 4th 42.6 27.4, 57.9 1st
Chongqing 41.8 33.5, 50.0 4th 64.7 57.8, 71.6 3rd 28.2 21.4, 35.0 3rd 31 0.24, 0.38 3rd 65.2 56.9, 73.5 4th
Fujian 35.5 29.8, 41.3 3rd 57.5 52.3, 62.6 1st 20.5 16.3, 24.6 1st 30.4 0.26, 0.35 3rd 57.6 52.0, 63.2 3rd
Gansu 52.2 45.8, 58.7 4th 66.6 61.0, 72.1 4th 39.3 33.3, 45.2 4th 27.2 0.22, 0.33 1st 58.6 52.0, 65.2 4th
Guangdong 24.6 17.8, 31.4 2nd 50.6 43.1, 58.2 1st 14.1 10.9, 17.3 1st 47 0.40, 0.54 4th 48.7 40.6, 56.8 2nd
Guangxi 34.4 29.5, 39.2 3rd 59.1 54.7, 63.5 1st 25.4 21.4, 29.4 2nd 32.1 0.28, 0.36 3rd 49 44.0, 54.1 2nd
Guizhou 35.9 27.3, 44.6 3rd 59.3 51.6, 67.0 2nd 29 21.8, 36.2 3rd 37.7 0.30, 0.45 3rd 44.7 34.6, 54.8 1st
Hebei 32.9 28.7, 37.1 2nd 64.4 60.5, 68.2 3rd 23.6 20.1, 27.0 2nd 28.2 0.24, 0.32 2nd 53 47.7, 58.2 3rd
Heilongjiang 25.8 20.1, 31.5 2nd 62.5 56.8, 68.2 2nd 26.7 20.7, 32.6 3rd 47.7 0.42, 0.54 4th 28.8 22.2, 35.4 1st
Henan 24.2 21.3, 27.1 1st 66.1 62.9, 69.3 3rd 28.8 24.9, 32.7 3rd 28.1 0.25, 0.31 2nd 45.4 41.0, 49.8 1st
Hubei 38 33.2, 42.8 4th 66.1 61.7, 70.4 3rd 27.7 23.5, 31.8 3rd 39.5 0.35, 0.44 3rd 49.8 44.4, 55.1 3rd
Hunan 35.9 31.6, 40.3 3rd 66.4 62.4, 70.4 3rd 22 18.3, 25.6 2nd 29.6 0.26, 0.34 2nd 48.9 44.0, 53.7 2nd
Inner Mongolia 32.9 28.7, 37.0 2nd 64.6 60.5, 68.7 3rd 30.5 26.6, 34.3 4th 41.3 0.37, 0.45 3rd 44.6 40.0, 49.3 1st
Jiangsu 27 22.9, 31.1 2nd 53.3 49.2, 57.5 1st 19.4 15.7, 23.1 1st 26.3 0.23, 0.30 1st 46.7 40.3, 53.1 2nd
Jiangxi 34.1 30.1, 38.1 3rd 62.8 59.0, 66.6 2nd 25.5 22.1, 29.0 2nd 30 0.27, 0.33 2nd 67.3 62.9, 71.7 4th
Jilin 24.4 19.1, 29.6 2nd 61.5 56.1, 66.8 2nd 23.5 18.9, 28.2 2nd 32 0.27, 0.37 3rd 46.8 38.0, 55.5 2nd
Liaoning 25.2 21.0, 29.5 2nd 63.9 59.4, 68.3 2nd 25.5 21.3, 29.7 2nd 27.6 0.23, 0.32 2nd 36 30.1, 41.9 1st
Qinghai 51 41.7, 60.3 4th 73.5 65.5, 81.5 4th 35.5 26.7, 44.3 4th 11.3 0.06, 0.17 1st 62.3 50.6, 74.1 4th
Shandong 22.8 20.3, 25.4 1st 52.2 49.3, 55.1 1st 17.7 15.4, 20.0 1st 22.7 0.20, 0.25 1st 41.3 37.8, 44.8 1st
Shanghai 9.8 2.7, 16.8 1st 28.6 17.7, 39.4 1st 16.1 6.6, 25.6 1st 47.6 0.36, 0.59 4th 48.5 16.9, 80.1 2nd
Shanxi 33.5 29.7, 37.3 3rd 64.2 60.8, 67.6 3rd 28.2 24.9, 31.5 3rd 24 0.21, 0.27 1st 51.9 47.8, 56.0 3rd
Sichuan 38.9 35.8, 42.1 4th 67.7 64.9, 70.4 4th 31.5 28.6, 34.4 4th 29.3 0.27, 0.32 2nd 56.6 53.2, 59.9 3rd
Tianjin 22.5 12.9, 32.2 1st 70.1 61.3, 78.9 4th 24.9 15.9, 33.9 2nd 47.1 0.37, 0.57 4th 56.2 41.9, 70.4 3rd
Xinjiang 17 8.3, 25.7 1st 88.8 81.9, 95.6 4th 46.3 34.4, 58.1 4th 44.2 0.32, 0.56 4th 55.3 42.6, 68.0 3rd
Yunnan 40.9 37.2, 44.7 4th 60 56.5, 63.4 2nd 27.1 23.9, 30.2 3rd 25.7 0.23, 0.29 1st 69.9 66.0, 73.8 4th
Zhejiang 19 15.0, 23.0 1st 44.6 40.1, 49.0 1st 14.7 11.3, 18.2 1st 27.4 0.23, 0.31 2nd 45.8 40.6, 51.0 2nd
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Cross‐sectional survey weights ere applied when estimating prevalence of health outcomes.

Table 3.

Characteristics of analytic samples for six health outcome measures, China Health and Retirement Longitudinal Study (CHARLS) Wave 2 (2013)

Depression symptoms (CES‐D‐10) (n = 8204) Limitation in physical functioning (n = 8874) Instrumental activities of daily living (IADL) impairment (n = 8874) Overweight (n = 8874) Lung function impairment (n = 6692)
Variable Categories n % n % n % n % n %
Outcome No 5615 68.4 3539 39.9 6929 78.1 6894 77.7 3338 49.9
Yes 2589 31.6 5335 60.1 1945 21.9 1980 22.3 3354 50.1
Sex Male 4154 50.6 4408 49.7 4408 49.7 4408 49.7 3300 49.3
Female 4050 49.4 4466 50.3 4466 50.3 4466 50.3 3392 50.7
Partnership status Married (living together) 6938 84.6 7432 83.8 7432 83.8 7432 83.8 5644 84.3
Married (living apart) 138 1.7 153 1.7 153 1.7 153 1.7 97 1.4
Divorced/separated/widowed 1062 12.9 1214 13.7 1214 13.7 1214 13.7 903 13.5
Never married 66 0.8 75 0.8 75 0.8 75 0.8 48 0.7
Ethnicity Han Chinese 7629 93.0 8254 93.0 8254 93.0 8254 93.0 6232 93.1
Other 575 7.0 620 7.0 620 7.0 620 7.0 460 6.9
Residence Rural 5215 63.6 5676 64.0 5676 64.0 5676 64.0 4398 65.7
Urban 2989 36.4 3198 36.0 3198 36.0 3198 36.0 2294 34.3
Employment status Not working 2811 34.3 3146 35.5 3146 35.5 3146 35.5 2259 33.8
Working 5393 65.7 5728 64.5 5728 64.5 5728 64.5 4433 66.2
Health insurance Uninsured 260 3.2 290 3.3 290 3.3 290 3.3 212 3.2
Govt. scheme, basic coverage 6834 83.3 7425 83.7 7425 83.7 7425 83.7 5664 84.6
Govt. scheme, enhanced coverage 977 11.9 1014 11.4 1014 11.4 1014 11.4 721 10.8
Private or other 133 1.6 145 1.6 145 1.6 145 1.6 95 1.4
Quintile of equivalized gross household income 1 (poorest) 1647 20.1 1845 20.8 1845 20.8 1845 20.8 1409 21.1
2 1749 21.3 1928 21.7 1928 21.7 1928 21.7 1482 22.1
3 1672 20.4 1802 20.3 1802 20.3 1802 20.3 1388 20.7
4 1598 19.5 1689 19.0 1689 19.0 1689 19.0 1260 18.8
5 1538 18.7 1610 18.1 1610 18.1 1610 18.1 1153 17.2
Level of education Less than elementary school 3724 45.4 4188 47.2 4188 47.2 4188 47.2 3170 47.4
Elementary school 1799 21.9 1915 21.6 1915 21.6 1915 21.6 1488 22.2
Middle school 1688 20.6 1752 19.7 1752 19.7 1752 19.7 1303 19.5
High school 648 7.9 664 7.5 664 7.5 664 7.5 486 7.3
Vocational college/university 345 4.2 355 4.0 355 4.0 355 4.0 254 3.8
Smoking status Nonsmoker 3393 41.4 3622 40.8 3622 40.8 3622 40.8 2715 40.6
Current smoker 4811 58.6 5252 59.2 5252 59.2 5252 59.2 3977 59.4
Alcohol intake None 2266 27.6 2377 26.8 2377 26.8 2377 26.8 1794 26.8
Once per month 606 7.4 653 7.4 653 7.4 653 7.4 508 7.6
More than once per month 5332 65.0 5844 65.9 5844 65.9 5844 65.9 4 90 65.6
Median Median Median Median Median
Age Years 61 61 61 61 61
Housing quality Scale (0–7) 4 4 4 4 4
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3.2. Fixed‐effects

Tables 4 and 5 show the fixed‐effects estimates for the associations between individual‐level variables and each health outcome (Model 3). Age and high alcohol intake (>once per month) were positively and significantly associated with higher odds of all health outcomes, except for overweight for which p > 0.05. Higher housing quality was negatively associated with all health outcomes except lung function impairment and overweight. Higher household income was protective against all outcomes except lung function impairment but predictive of overweight. Smoking status was associated with lung function impairment. Age was associated with lower odds of depression symptoms and overweight but positively associated with higher odds of other outcomes. Associations between other individual‐level variables and each of the six health outcomes varied.

Table 4.

Results of conditional logistic random‐effects models for depression caseness, functional disability and instrumental activities of daily living (IADL) impairment with adjustment for individual‐level variables (Model 3)

Depression symptoms (CES‐D‐10) (n = 8204) Limitation in physical functioning (n = 8874) IADL impairment (n = 8874)
Variable Categories OR 95% confidence interval (CI) p OR 95% CI p OR 95% CI p
Sex Male Ref Ref Ref
Female 1.55 1.35, 1.79 <0.001 1.85 1.06, 1.53 0.01 1.11 0.95, 1.30 0.195
Age Years 0.99 0.98, 1.00 0.001 1.02 0.95, 0.97 <0.001 1.03 1.02, 1.04 <0.001
Partnership status Married (living together) Ref Ref Ref
Married (living apart) 1.43 1.00, 2.04 0.48 1.17 1.11, 2.57 0.014 0.91 0.57, 1.46 0.694
Divorced/separated/widowed 1.33 1.15, 1.53 <0.001 1.05 1.13, 1.61 0.001 1.01 0.87, 1.18 0.860
Never married 1.30 0.79, 2.12 0.299 0.82 1.16, 3.31 0.012 0.89 0.51, 1.53 0.667
Ethnicity Han Chinese Ref Ref Ref
Other 0.78 10.63, 0.96 0.016 1.05 0.76, 1.26 0.858 0.89 0.70, 1.12 0.311
Residence Rural Ref Ref Ref
Urban 0.86 0.76, 0.97 0.012 0.97 0.91, 1.24 0.445 0.87 0.76, 1.00 0.053
Employment status Not working Ref Ref Ref
Working 0.80 0.72, 0.90 <0.001 0.62 0.75, 1.01 0.059 0.44 0.39, 0.50 <0.001
Health insurance Uninsured 1.00 0.77, 1.30 0.999 0.93 1.21, 2.15 0.001 0.94 0.70, 1.26 0.677
Govt. scheme, basic coverage Ref Ref Ref
Govt. scheme, enhanced coverage 0.93 0.76, 1.13 0.478 0.75 0.69, 1.22 0.556 0.72 0.57, 0.91 0.006
Private or other 1.11 0.70, 1.74 0.658 0.98 0.42, 1.78 0.690 0.96 0.58, 1.56 0.856
Quintile of equivalized gross household income 1 (poorest) 1.14 0.99, 1.32 0.074 1.20 1.11, 1.61 0.002 0.99 0.84, 1.17 0.888
2 1.17 1.01, 1.35 0.031 1.11 1.10, 1.59 0.003 1.14 0.97, 1.34 0.100
3 Ref Ref Ref
4 0.90 0.77, 1.04 0.151 0.94 0.59, 0.90 0.003 0.87 0.73, 1.04 0.140
5 0.69 0.58, 0.82 <0.001 0.81 0.57, 0.92 0.008 0.71 0.57, 0.87 0.001
Housing quality Scale (0–7) 0.85 0.81, 0.89 <0.001 0.89 0.75, 0.84 <0.001 0.85 0.80, 0.89 <0.001
Level of education Less than elementary school Ref Ref Ref
Elementary school 0.83 0.73, 0.94 0.004 0.94 0.67, 0.93 0.006 0.70 0.60, 0.80 <0.001
Middle school 0.80 0.69, 0.92 0.002 0.87 0.68, 0.99 0.043 0.65 0.54, 0.77 <0.001
High school 0.78 0.63, 0.96 0.020 0.76 0.59, 1.03 0.082 0.49 0.37, 0.65 <0.001
Vocational college/university 0.77 0.56, 1.05 0.101 0.89 0.54, 1.36 0.52 0.50 0.33, 0.74 0.001
Smoking status Nonsmoker Ref Ref Ref
Current smoker 1.01 0.88, 1.15 0.875 1.02 0.87, 1.24 0.665 1.01 0.87, 1.17 0.946
Alcohol intake None Ref Ref Ref
Once per month 1.21 0.99, 1.48 0.060 1.29 1.23, 2.03 <0.001 1.19 0.94, 1.51 0.150
More than once per month 1.19 1.05, 1.34 0.007 1.17 1.01, 1.41 0.041 1.20 1.04, 1.39 0.015
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Table 5.

Results of conditional logistic random‐effects models for overweight and lung function impairment with adjustment for individual‐level variables (Model 3)

Overweight (n = 8874) Lung function impairment (n = 6692)
Variable Categories OR 95% confidence interval (CI) p OR 95% CI p
Sex Male Ref Ref
Female 0.81 0.70, 0.94 0.005 1.35 1.16, 1.56 <0.001
Age Years 1.01 1.00, 1.01 0.055 1.04 1.03, 1.05 <0.001
Partnership status Married (living together) Ref Ref
Married (living apart) 1.56 1.09, 2.24 0.016 0.76 0.50, 1.15 0.192
Divorced/separated/widowed 1.23 1.06, 1.43 0.006 1.01 0.87, 1.18 0.863
Never married 1.36 0.81, 2.28 0.246 1.60 0.88, 2.90 0.122
Ethnicity Han Chinese Ref Ref
Other 1.27 1.03, 1.56 0.025 1.23 0.99, 1.53 0.059
Residence Rural Ref Ref
Urban 1.17 1.03, 1.32 0.016 0.85 0.75, 0.96 0.008
Employment status Not working Ref Ref
Working 0.59 0.52, 0.66 <0.001 0.85 0.76, 0.96 0.008
Health insurance Uninsured 1.68 1.30, 2.17 <0.001 1.05 0.80, 1.39 0.712
Govt. scheme, basic coverage Ref Ref
Govt. scheme, enhanced coverage 1.23 1.02, 1.47 0.026 0.73 0.60, 0.89 0.002
Private or other 1.69 1.17, 2.45 0.005 0.47 0.30, 0.75 0.002
Quintile of equivalized gross household income 1 (poorest) 1.11 0.94, 1.31 0.211 1.03 0.89, 1.20 0.680
2 0.98 0.83, 1.15 0.764 1.00 0.86, 1.16 0.968
3 Ref Ref
4 1.03 0.87, 1.22 0.731 0.96 0.82, 1.12 0.603
5 1.48 1.24, 1.76 <0.001 0.90 0.76, 1.07 0.226
Housing quality Scale (0–7) 1.03 0.98, 1.08 0.242 0.96 0.92, 1.00 0.079
Level of education Less than elementary school Ref Ref
Elementary school 0.91 0.79, 1.05 0.212 0.93 0.82, 1.06 0.285
Middle school 1.07 0.92, 1.25 0.391 0.88 0.76, 1.02 0.087
High school 1.23 0.99, 1.52 0.057 0.75 0.60, 0.93 0.008
Vocational college/university 1.21 0.92, 1.58 0.181 0.74 0.54, 1.01 0.058
Smoking status Nonsmoker Ref Ref
Current smoker 0.96 0.83, 1.10 0.524 1.24 1.08, 1.42 0.002
Alcohol intake None Ref Ref
Once per month 0.84 0.68, 1.05 0.119 0.95 0.78, 1.17 0.648
More than once per month 0.95 0.84, 1.09 0.469 1.19 1.05, 1.35 0.007
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Table 6 shows fixed‐effects associations between province‐level variables and each outcome (Models 4–6). GRP per capita was significantly associated with lower odds of reported depression symptoms (OR: 0.86, 95% CI: 0.81, 0.92, p < 0.001) and IADL impairment (OR: 0.94, 95% CI: 0.87, 1.00, p = 0.049), and higher odds of overweight (OR: 1.01, 95% CI: 1.00, 1.02, p= 0.039), but not other outcomes (Model 4). Doctor density was associated with lower odds of depression symptoms (OR: 0.54, 95% CI: 0.40, 0.75, p = 0.002) (Model 5). After mutual adjustment, only GRP per capita was found to have a significant association with the health outcomes: there was a borderline significant association between GRP per capita and lower odds of depression symptoms, and a significant association between GRP per capita and lower odds of IADL impairment (Model 6).

Table 6.

Summary of analyses of between‐province variance and area level effects for depression, functional disability, instrumental activities of daily living (IADL) impairment, overweight, and lung function impairment outcomes

Model Measures of variation and clustering Depression symptoms (CES‐D‐10) Limitation in physical functioning IADL impairment Overweight Lung function impairment
Empty model, full sample (Model 1) Sample size 12,670 15,255 15,255 16,377 10,469
Area‐level variance 0.22 0.12 0.09 0.24 0.13
Intraclass‐correlation coefficient (ICC) 0.065 0.036 0.027 0.068 0.039
Median odds ratio (MOR) 1.58 1.39 1.33 1.59 1.41
Empty model, analytic sample (Model 2) Sample size 8204 8874 8874 8874 6692
Area‐level variance 0.20 0.08 0.09 0.22 0.14
proportional change in variance (PCV) (%) 0.058 0.023 Ref Ref Ref
ICC 1.53 1.30 0.026 0.064 0.040
MOR Ref Ref 1.33 1.57 1.42
Model with individual‐level variables, analytic sample (Model 3) Sample size 8204 8874 8874 8874 6692
Area‐level variance 0.11 0.07 0.09 0.11 0.12
ICC (rescaled) 0.032 0.021 0.025 0.032 0.036
MOR 1.36 1.28 1.32 1.38 1.39
PCV (%) 46.5 9.4 3.3 50.0 10.8
Model 3 + GRP per capita, analytic sample (Model 4) Sample size 8204 8874 8874 8874 6692
Gross regional product (GRP) per capita OR (95% confidence interval [CI]), p 0.86 (0.81, 0.92), p < 0.001 0.97 (0.91, 1.03), p = 0.258 0.94 (0.87, 1.00), p = 0.049 1.01 (1.00, 1.02), p = 0.039 0.98 (0.91, 1.07), p = 0.674
Area‐level variance 0.06 0.06 0.06 0.11 0.12
ICC (rescaled) 0.017 0.019 0.019 0.029 0.034
MOR 1.25 1.27 1.27 1.35 1.39
PCV (%) 72.3 17.1 28.2 55.4 14.1
Model 3 + doctor density, analytic sample (Model 5) Sample size 8204 8874 8874 8874 6692
Doctor density OR (95% CI), p 0.54 (0.40, 0.75), p = 0.002 0.98 (0.76, 01.27), p = 0.908 0.95 (0.70, 1.28), p = 0.716 1.28 (0.94, 1.75), p = 0.119 0.84 (0.61, 1.15), p = 0.279
Area‐level variance 0.06 0.07 0.08 0.11 0.11
ICC (rescaled) 0.019 0.020 0.025 0.032 0.032
MOR 1.27 1.28 1.32 1.37 1.37
PCV (%) 67.9 9.64 5.5 50.84 20.4
Model 3 + GRP per capita and doctor density, analytic sample (Model 6) Sample size 8204 8874 8874 8874 6692
GRP per capita OR (95% CI), p 0.99 (0.98, 0.99), p = 0.052 0.99 (0.98, 1.00), p = 0.114 0.99 (0.98, 1.00), p = 0.014 1.01 (1.00, 1.02), p = 0.181 1.00 (0.99, 1.02), p = 0.617
Doctor density OR (95% CI), p 0.77 (0.49, 1.27), p = 0.263 1.25 (0.85, 1.83), p = 0.263 1.40 (0.92, 2.16), p = 0.115 1.01 (0.62, 1.60), p = 0.996 0.76 (0.46, 1.24), p = 0.277
Area‐level variance 0.06 0.06 0.06 0.10 0.11
ICC (rescaled) 0.017 0.18 0.019 0.029 0.032
MOR 1.26 1.26 1.27 1.35 1.37
PCV (%) 71.7 21.5 27.4 55.4 19.27
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3.3. Random‐effects

Random‐effects area‐level variance parameter estimates for each health outcome are shown in Table 6. Variance parameter estimates were similar between Models 1 and 2 for all outcomes; this implies that estimates of province effects were unaffected by loss of respondents with missing covariate observations from the analytic sample.

ICC estimates for Model 2 showed that province effects accounted for 5.8% of variance in depression symptoms, 6.4% in overweight, and 4.0% in lung function impairment, but only 2.3% and 2.6% of variance in limitation in physical functioning and IADL impairment in the analytic sample (Model 2). Figure 1(a–e) shows random‐effects residuals for each health outcome by province transformed into ORs with 95% CIs relative to the grand mean for the 27 province‐level units (Model 2). MORs ranged from 1.53 for depression symptoms and 1.57 for overweight to 1.30 and 1.33 for limitation in physical functioning and IADL impairment.

Figure 1.

Figure 1

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Random‐intercepts residual plots of province‐level effects for depression symptoms (CES‐D‐10), (a) limitation in physical functioning, (b) instrumental activities of daily living (IADL) impairment, (c) overweight (d) and lung function impairment (E) derived from unconditional models (Model 2) expressed as odds ratios with 95% confidence intervals

The results of Model 3 show random‐effects statistics after adjustment for individual‐level variables (compositional effects). PCV estimates show that these accounted for 46.5% and 50.% of between‐province variance in depression symptoms and overweight but only 9.4% and 3.3% for limitation in physical functioning and IADL impairment.

After adjustment for GRP per capita, Model 4 explained the largest proportions of between‐province variance in depression symptoms, IADL impairment, and overweight (72.3%, 28.2% and 55.4%). After adjustment for doctor density, meanwhile, Model 5 explained 67.9% and 50.8% of between‐province variance in depression symptoms and overweight, but only 9.6% and 5.5% of variance for limitation in physical functioning and IADL impairment. When models mutually adjusted for GRP per capita and doctor density (Model 6), PCV estimates for depression symptoms, limitation in physical functioning and IADL impairment were 71.7%, 21.5%, and 27.4%.

4. DISCUSSION

After mutual adjustment for GRP per capita and doctor density, only GRP per capita both independently predicted depression symptom outcomes and limitation in physical functioning. Within‐province differences (Model 2) still explained the majority of variance across all outcomes (93.6%–97.4%). The scale of province‐level differences, as shown by MORs, was still large across all outcomes. The impact of residual between‐province residual heterogeneity on depression symptom outcomes (MOR: 1.36) is comparable to that of being divorced, separated, or widowed (OR: 1.33, 95% CI: 1.15, 1.53) (Model 3). Although 46.5% and 50.0% of between‐province variance in depression symptoms and overweight outcomes were explained by compositional effects (Model 3), PCVs were ≤10.6% for all other outcomes. GRP per capita was important in explaining between‐province variation in depression symptom outcomes (Model 4, PCV: 72.3%).

To my knowledge, this study is the first to quantify the relevance of between‐province heterogeneity in health outcomes in China, and to estimate the degree to which these are explained by province‐level variables. Its methods present a new approach to investigating health inequalities at the national and subnational levels. The results highlight the wide inequalities among people aged ≥50 years in multiple health outcomes between Chinese provinces as found in previous studies [10, 13, 14, 15]. As Zhou et al. [6] conclude: “China in epidemiological terms is not one nation, but five: rapid transitions are occurring in all of them, but the most important health problems and the challenges imposed on the health system by demographic and epidemiological change are different.”

The results underscore that there is relatively limited potential for reduction in between‐province health inequalities through ensuring more equal distribution of healthcare personnel, with GRP per capita being more important in explaining between‐province differences in health outcomes (particularly for depression and IADL impairment outcomes). However, availability and distribution of qualified healthcare personnel can be improved through expansion of training, standardization of credentials and specific initiatives such as exchange programmes to increase equity between provinces [36, 37].

4.1. Strengths and limitations

Strengths of the study include its large sample size and CHARLS’ representativeness of the Chinese population. The available data provide near‐complete coverage of Chinese provinces, in addition to a wide range of health outcomes encompassing psychosocial and physical functioning, and objective measures of BMI and lung function.

CHARLS only samples official residents with valid Hukou status and excludes individuals residing in institutions such as nursing homes, (the latter represent only a small proportion of older people in China) [18]. Non‐Hukou residents are more likely to reside in provinces with higher GRP per capita, and may experience disparities in health and access to healthcare compared with permanent legal (Hukou‐holding) residents [3]. This may have undermined representativeness of province‐level samples, and inflated GRP per capita estimates for wealthier provinces [38]. Doctor density does not account for distribution of personnel across primary and secondary care facilities or numbers of specialists.

The results of this study do not imply causation as to the province‐level determinants of the health outcomes analyzed but provides a description of the degree to which they are explained by the former. If causal associations exist, these may be mediated by other factors; for example, the association between province‐level GRP and overweight may be mediated by factors such as nutrition and sedentary lifestyle.

5. CONCLUSION

Ongoing reforms are targeted at equalizing investment and public services across provinces [7], in addition to achieving universal and equitable coverage of basic healthcare for all Chinese citizens [35]. As all provinces of China complete their epidemiological transition, tackling the noncommunicable disease burden will be key to reducing both social and health inequalities [4]. Success in reducing between‐province health inequalities requires a coordinated health policy approach across national and province‐level governments. Localized approaches to healthcare delivery are needed to address different provinces’ diverse challenges, however [6].

AUTHOR CONTRIBUTIONS

Sol Richardson: Conceptualization (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing (lead). Zhihui Li: Writing – review and editing (supporting).

CONFLICT OF INTEREST

The authors declare no conflict of interest.

ETHICS STATEMENT

Ethical approval for all the CHARLS waves was granted from the Institutional Review Board at Peking University. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052‐11015; the IRB approval number for biomarker collection was IRB00001052‐11014.

INFORMED CONSENT

All CHARLS participants who provided data for this study provided written informed consent at the time of participation.

ACKNOWLEDGMENTS

The authors would like to thank Shaoru Chen, Vanke School of Public Health, Tsinghua University, for her assistance in preparing the manuscript for publication. The authors received no specific funding for this work. CHARLS was supported by the Behavioral and Social Research division of the National Institute on Aging (grant numbers 1‐R21‐AG031372‐01, 1‐R01‐AG037031‐01, and 3‐R01AG037031‐03S1); the Natural Science Foundation of China (grant numbers 70910107022, 71130002 and 71273237); the World Bank (contract numbers 7145915 and 7159234); the China Medical Board, and Peking University.

Richardson S, Li Z. To what extent do disparities in economic development and healthcare availability explain between‐province health inequalities among older people in China? Health Care Sci. 2023;2:94–111. 10.1002/hcs2.32

Footnotes

1

This this age cutoff was selected as it corresponds to the minumim age for inclusion in comparable panel studies (such as the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA), is consistent with typical definitions of an “older person” employed in previous studies, and, in China, is the minimum statutory retirement age (for women).

2

Depression symptom outcomes were measured using the 10‐item Center for Epidemiological Studies Depression Scale (CES‐D‐10). This instrument was based on a 10‐item scale comprising symptoms includeing feeling “bothered,” “trouble concentrating,” “depressed,” “effort,” “fearful,” “restless sleep,” “hard to get going,” “hopeful,” and “happy” within the last week. Each item was scored on a four‐point scale ranging from 0 (“rarely or none of the time”) to 3 (“most or all of the time”), and the former two positively worded items were reverse‐coded before calculating summary scores. Summary scores ranged from 0 to 30 and cutoff score of 10 was used to define probable depressive cases.

3

Respondents were categorized as having a limitation in physical functioning if they self‐reported difficulties in performing any of the following tasks: sitting, climbing stairs, kneeling, reaching or extending, lifting objects, picking up objects. This variable was not based on a validated scale but has been employed in previous studies on functional abilities of older people. While some studies 25  refer to these as “limitation in physical functioning,” other studies have also used the term “limitation in mobility” or “limitation in mobility activities”. 26 , 27

4

Respondents were defined as having an IADL impairment if they reported problems carrying out any of the following: managing finances, managing transportation, shopping, (hot) meal preparation, household chores, communication, and managing medications.

5

Respondents' peak expiratory flow (PEF), defined using the highest of three measures taken with a peak flow meter during the nurse visit, was compared with their expected value based on their gender, age, and height calculated using the equations below (see Nunn and Gregg [22]):

PEF(male)=e((0.544*ln(Age))−(0.0151*Age)−(74.7/Height)+5.48)
PEF(female)=e((0.376*ln(Age))−(0.012*Age)−(58.8/Height)+5.63)

Respondents whose actual PEF was <70% of their predicted value were categorized as having lung function impairment. This cutoff represents a highly sensitive measure of severe COPD.

6

CHARLS Wave 2 respondents were invited to self‐report their type of health insurance coverage from a list of 10 options. In this study, respondents were categorized by their type of health insurance coverage using the four categories below. Categorization was hierarchical, and respondents who reported having more than one type of health insurance coverage were assigned to the highest (lowest‐numbered) category:

1. Government scheme, enhanced coverage: Government employee medical insurance (公费医疗), urban employee medical insurance (职工医保)

2. Government scheme, basic coverage: New cooperative medical insurance (新型农村合作医疗), rural and urban resident medical insurance (城乡居民基本医疗保险)

3. Private or other: Any other insurance scheme not included in the questionnaire

4. Uninsured: No membership of any health insurance scheme reported.

DATA AVAILABILITY STATEMENT

The CHARLS data employed in this study are available free of charge to registered users through the CHARLS website at http://charls.pku.edu.cn/en.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The CHARLS data employed in this study are available free of charge to registered users through the CHARLS website at http://charls.pku.edu.cn/en.

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