Edited by: Steven A. Cohen, University of Rhode Island, United States
Reviewed by: Xiang Qi, New York University, United States; Tingting Ye, Monash University, Australia
This article was submitted to Aging and Public Health, a section of the journal Frontiers in Public Health
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This study aims to identify the dynamic changes in cognitive performance differentials between urban and rural older adults in China from 2008 to 2018 and decomposes determinants affecting such changes.
Two waves (2008 and 2018) of data were extracted from the Chinese Longitudinal Healthy Longevity Survey. The cognitive function was tested using the Chinese Mini-Mental State Examination (MMSE). The effects of the explanatory variables (demographic, economic, neighborhood, environmental events and social and cultural domains) on the changes in the urban-rural inequality of cognitive performance were divided into two components using the Juhn–Murphy–Pierce (JMP) decomposition: quantity effect and price effect.
A total of 14,628 (urban respondents: 5,675, rural respondents: 8,953) and 10,311 older adults (urban respondents: 5,879, rural respondents: 4,432) for 2008 and 2018, respectively, were included in our study. A narrowing of 0.071 in the urban-rural disparity in cognitive function score of the older adults from 2008 to 2018 was identified. Quantity and price effects of explanatory variables contributed 65.21 and 46.84%, respectively, to the observed components in explaining the narrowed disparity. Quantity effects of age (35.71%), exercise (56.72%), self-rated economic status (33.19%) and price effect of homeownership (54.97%) contributed significantly to the reduced urban-rural gap. Contrastingly, inequality in pension (−27.31%) and social security (−23.11%) between urban and rural widened cognitive performance differentials. Furthermore, effects of hunger in childhood (−10.53%) and less years of schooling (−77.20%) on the increase in urban-rural inequality seemed to be stronger over time.
Economic development and reform of the rural health system are responsible for the decline in the urban-rural disparity in the cognitive performance of older adults. Equalizing the distribution of social security and welfare between urban and rural must be highlighted for eliminating cognitive ability disparity. Additionally, rural older adults who endured hunger and poor education in childhood also deserve further policy interventions.
Cognitive impairment is a syndrome that reduces mind or intellectual activity. Given that late life cognitive changes may not initially appear to directly affect daily living, the degeneration of cognitive function may be left undiagnosed or be diagnosed at later stages (
To make matters worse, the urban-rural inequality in cognitive health has been plaguing the Chinese government. Previous studies have demonstrated a widened urban-rural disparity in cognitive performance amongst older population in China. For example, Zhang et al. found that rural older adults suffered from more severe cognitive impairment due to inadequate access to healthcare (
It is well known that China is a huge country with significant urban-rural differences in terms of social and economic circumstances. Large academic medical centers, tertiary hospitals and skilled medical practitioners are concentrated in urban areas, which resulted in the inequality of health services between rural older adults and their urban counterpart (
To eliminate the urban-rural disparity in healthcare, many strategies have been taken at the national level over the past decade. For example, healthcare resources were redistributed across rural areas since the new round of healthcare system reforms in 2009 (
This study aims to identify the dynamic changes in cognitive performance differentials between urban and rural older adults over a 10-year period (2008–2018) in China and further decomposes determinants affecting this change. To our knowledge, this is first study in China that focuses on the dynamic changes of urban-rural disparity in the cognitive ability of older adults. Findings of the study will shed some light on future priorities to further narrow the rural-urban disparity in health outcomes.
Data used in this study were extracted from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort study on the health status of older adults (≥65 years) people in China and its social, behavioral and biological determinants. CLHLS started in 1998, and eight waves of surveys have been completed up to date. In each wave, those lost persons were recorded and new participants from the neighboring households were added to ensure the sample is nationally representative. During investigation, a multistage, stratified cluster sampling design was applied to recruit participants from 23 of the 31 provinces in China. CLHLS randomly selected 631 cities and counties representing roughly 85% of the Chinese population. The questionnaire for CLHLS includes many variables, such as basic information, health status, family status, lifestyle, healthcare services and so on. CLHLS provides high-quality and nationally representative information for studying the health status of older adults in China, and more details can be found elsewhere (
In this study, two waves of data were selected to meet the needs of dynamic decomposition: the most recent one collected in 2018 was compared with the one collected in 2008. These two waves of data were chosen for the following reasons. First, China has experienced rapid urbanization and a series of social reform from 2008 to 2018. Decomposition using these two waves of data reflects the impact of social change. Second, data collected prior to 2008 in CLHLS does not include some variables (for example, trip, social services and social security) we need in this study.
Due to some samples containing missing values on the outcome and explanatory variables, 1,083 respondents in 2008 and 1063 respondents in 2018 were excluded for data analysis. After filtering the data, 14,628 for the 2008 cohort and 10,311 for the 2018 cohort were included in our study. The basic characteristics (including age, gender, marital status, economic status) of the respondents did not change significantly before and after the sample was screened.
In CLHLS, the cognitive performance of older adults was assessed using the Chinese version of Mini Mental State Examination (MMSE), which has been proved to be reliable and valid for older Chinese adults (
Following the conceptual framework developed by Lund et al. (
Variable construction by domain based on Lund's conceptual framework.
The demographic domain includes gender, age and marital status. The economic domain includes homeownership, employment status, pension and self-rated economic status. In the neighborhood domain, three variables were selected: regular exercise, number of trips organized and number of social services in the community. The environmental events contained two variables related to the respondent's experience of hunger and access to healthcare in childhood. Social and cultural domains include living arrangements, years of schooling and amount of social security and commercialized insurance.
Firstly,
On the basis of the O–B method, MMSE scores of the differences between urban and rural in 2008 and 2018 were decomposed to compare the cognitive ability disparity and contributions of explanatory variables of the two cohort samples. This technology was widely used because it can partition the gap in an outcome variable between two groups into an explained component and an unexplained component (
Where
To further decompose the changes in rural-urban disparity in the MMSE scores of older adults over a 10-year period, JMP decomposition was adopted. Unlike O–B decomposition, this method allows us to estimate the dynamic differences in an unobserved component. By applying this decomposition technique, the changes in the differences in the cognitive function of the older adults between urban and rural over time can be divided into two parts: changes in characteristics and coefficient effects (
The change in the cognitive performance gap over time can be written as:
Where “[(
Statistical analyses were performed using STATA 14.0. A
The differences in outcome and explanatory variables between urban and rural in 2008 and 2018 were tested in
Rural-urban difference in MMSE scores and explanatory variables in 2008 and 2018.
MMSE (SD) | 16.15 (7.52) | 15.24 (7.92) | <0.001 | 19.12 (7.84) | 18.29 (7.65) | <0.001 |
0.007 | <0.001 | |||||
Male (%) | 2,516 (44.33) | 3,767 (42.08) | 2,648 (45.04) | 1,817 (41.00) | ||
Female (%) | 3,159 (55.67) | 5,186 (57.92) | 3,231 (54.96) | 2,615 (59.00) | ||
<0.001 | 0.816 | |||||
65-75 (%) | 1,222 (21.53) | 1,749 (19.54) | 1,582 (26.91) | 1,179 (26.60) | ||
76-85 (%) | 1,167 (20.56) | 1,994 (22.27) | 1,514 (25.75) | 1,142 (25.77) | ||
86-95 (%) | 1,862 (32.81) | 2,803 (31.31) | 1,386 (23.58) | 1,024 (23.10) | ||
≥96 (%) | 1,424 (25.09) | 2,407 (26.88) | 1,397 (23.76) | 1,087 (24.53) | ||
<0.001 | <0.001 | |||||
Married and living with spouse (%) | 1,886 (33.23) | 2,655 (29.65) | 2,527 (42.98) | 1,730 (39.03) | ||
Separated/divorced/never married (%) | 1,24 (2.19) | 258 (2.88) | 157 (2.67) | 112 (2.53) | ||
Widowed (%) | 3,665 (64.58) | 6,040 (67.46) | 3,195 (54.35) | 2,590 (58.44) | ||
<0.001 | <0.001 | |||||
Own (%) | 4,767 (84.00) | 8,696 (97.13) | 4932 (83.89) | 4243 (95.74) | ||
Not own (%) | 908 (16.00) | 255 (2.85) | 947 (16.11) | 189 (4.26) | ||
<0.001 | <0.001 | |||||
Not working (%) | 1,577 (27.79) | 379 (4.23) | 2,074 (35.28) | 278 (6.27) | ||
Working (%) | 4,098 (72.21) | 8,577 (95.80) | 3,805 (64.72) | 4,154 (93.73) | ||
Pension | <0.001 | <0.001 | ||||
No (%) | 3,573 (62.96) | 8,425 (94.10) | 3,431 (58.36) | 4,070 (91.83) | ||
Yes (%) | 2,102 (37.04) | 528 (5.90) | 2,448 (41.64) | 362 (8.17) | ||
<0.001 | <0.001 | |||||
Rich (%) | 909 (16.02) | 1,014 (11.33) | 1,303 (22.16) | 675 (15.23) | ||
Fair (%) | 4,045 (71.28) | 6,000 (67.02) | 4,095 (69.65) | 3,190 (71.98) | ||
Poor (%) | 721 (12.70) | 1,939 (21.66) | 481 (8.18) | 567 (12.79) | ||
<0.001 | <0.001 | |||||
Yes (%) | 2,242 (39.51) | 1,800 (20.10) | 2,188 (37.22) | 1,081 (24.39) | ||
No (%) | 3,433 (60.49) | 7,153 (79.90) | 3,691 (62.78) | 3,351 (75.61) | ||
<0.001 | <0.001 | |||||
0 times (%) | 5,215 (91.89) | 8,714 (97.33) | 4,842 (82.36) | 4,083 (92.13) | ||
≥1 times (%) | 460 (8.11) | 239 (2.67) | 1,037 (17.64) | 349 (7.87) | ||
<0.001 | 0.004 | |||||
0 (%) | 3,832 (67.52) | 6,799 (75.94) | 2,085 (35.47) | 1,702 (38.40) | ||
1 (%) | 746 (13.15) | 1,264 (14.12) | 1,109 (18.86) | 839 (18.93) | ||
≥2 | 1,097 (19.33) | 891 (9.95) | 2,685 (45.67) | 1,891 (42.67) | ||
<0.001 | 0.953 | |||||
Yes (%) | 2,308 (40.67) | 2,544 (28.42) | 510 (8.67) | 383 (8.64) | ||
No (%) | 3,367 (59.33) | 6,409 (71.58) | 5,369 (91.33) | 4,049 (91.36) | ||
<0.001 | <0.001 | |||||
Yes (%) | 3,761 (66.27) | 7,197 (80.39) | 3,936 (66.95) | 3,538 (79.83) | ||
No (%) | 1,914 (33.73) | 1,756 (19.61) | 1943 (33.05) | 894 (20.17) | ||
0.009 | 0.224 | |||||
With household member (%) | 4,824 (85.00) | 7,441 (83.11) | 4,884 (83.08) | 3,625 (81.79) | ||
Alone (%) | 686 (12.09) | 1,399 (15.62) | 788 (13.40) | 634 (14.31) | ||
In an institution (%) | 165 (2.91) | 113 (1.26) | 207 (3.52) | 173 (3.90) | ||
<0.001 | <0.001 | |||||
0 (%) | 3,014 (53.11) | 6,150 (68.69) | 2,469 (42.00) | 2,548 (57.49) | ||
1-5 (%) | 1,332 (23.47) | 1,842 (26.49) | 1,290 (21.94) | 1,044 (23.56) | ||
≥6 (%) | 1,329 (23.42) | 961 (10.73) | 2,120 (36.06) | 840 (18.95) | ||
<0.001 | <0.001 | |||||
0 (%) | 1,370 (24.14) | 2,017 (22.52) | 359 (6.11) | 313 (7.06) | ||
1-2 (%) | 3,757 (66.20) | 6,503 (72.63) | 5,070 (86.24) | 3,989 (90.00) | ||
≥3 (%) | 548 (9.66) | 433 (5.00) | 450 (7.65) | 130 (2.93) |
O-B decomposition of the urban-rural disparity in cognitive performance in 2008 and 2018.
Difference | 0.904 |
100.00 | 0.833 |
100 |
Explained | 0.954 |
105.00 | 0.681 |
81.75 |
Unexplained | −0.050 | −5.00 | 0.152 | 18.25 |
Female | 0.022 |
2.31 | 0.020 |
2.94 |
76-85 | 0.021 |
2.20 | 0.001 | 0.15 |
86-95 | −0.067 | −7.02 | −0.018 | −2.64 |
≥96 | 0.157 |
16.46 | 0.069 | 10.13 |
Separated/divorced/ never married | 0.003 | 0.31 | −0.001 | −0.15 |
Widowed | 0.028 |
2.94 | 0.042 |
6.17 |
Not own | −0.022 | −2.31 | −0.071 |
−10.43 |
Working | −0.087 | −9.12 | −0.089 | −13.07 |
Yes | 0.078 | 8.18 | 0.039 | 5.73 |
Fair | −0.021 |
−2.20 | 0.019 |
2.79 |
Poor | 0.166 |
17.40 | 0.109 |
16.01 |
No | 0.303 |
31.76 | 0.150 |
22.03 |
≥1 times | 0.054 |
5.66 | 0.035 |
5.14 |
1 | 0.003 | 0.31 | 0.001 | 0.15 |
≥2 | 0.061 |
6.39 | 0.004 | 0.59 |
No | 0.045 |
4.72 | 0.001 | 0.15 |
No | 0.071 |
7.44 | 0.079 |
11.60 |
Alone | −0.027 |
−2.83 | −0.012 | −1.76 |
In an institution | −0.003 | −0.31 | −0.003 | −0.44 |
1-5 | 0.037 |
3.88 | −0.033 | −4.85 |
≥6 | 0.182 |
19.08 | 0.327 |
48.02 |
1-2 | −0.025 |
−2.62 | −0.012 | −1.76 |
≥3 | −0.026 |
−2.73 | 0.024 | 3.52 |
Comparing the two cohort samples, contributions of age (aged over 95) and exercise decreased from 16.46 and 31.76% to 10.13 and 22.03% in 2008 and 2018, respectively, whereas years of schooling (over 5 years) made a greatly increased contribution in this period (19.08-48.02%). In addition to the above variables, other variables explained the cognitive ability disparity between urban and rural older adults to a certain degree. For example, the contributions of experience of hunger in explaining urban-rural disparity accounted for 7.44 and 11.60% in the two cohort samples.
A narrowing of ~0.071 in the urban-rural disparity in the cognitive ability of older adults was found during the period 2008-2018 as whole (
JMP decomposition of the change in urban-rural disparity in cognitive performance.
Changes in rural-urban disparity | −0.071 | 100 |
Difference in predicted gap | −0.365 | 514.08 |
Quantity effect | −0.238 | 65.21 |
Price effect | −0.171 | 46.84 |
Quantity effect * price effect | 0.044 | 12.05 |
Difference in residual gap | 0.295 | −415.49 |
In terms of quantity effects, the reduced disparity in the proportion of people who aged over 95 (35.71%), had self-rated poor economic status (33.19%) and had no exercise (56.72%) between urban and rural were mainly responsible for the reduced cognitive ability differentials (
Quantity and priced effects of explanatory variables on the change in urban-rural disparity in cognitive performance.
Female | 0.015 | −6.30 | −0.011 | 6.43 |
76-85 | −0.016 | 6.72 | −0.003 | 1.75 |
86-95 | −0.014 | 5.88 | 0.007 | −4.09 |
≥96 | −0.085 | 35.71 | 0.008 | −4.68 |
Separated/divorced/ |
−0.008 | 3.36 | −0.007 | 4.09 |
Widowed | 0.009 | −3.78 | −0.002 | 1.17 |
Not own | −0.003 | 1.26 | −0.094 | 54.97 |
Working | 0.039 | −16.39 | −0.006 | 3.51 |
Yes | 0.065 | −27.31 | −0.016 | 9.36 |
Fair | 0.001 | −0.42 | −0.012 | 7.02 |
Poor | −0.079 | 33.19 | 0.019 | −11.11 |
No | −0.135 | 56.72 | −0.144 | 84.21 |
≥1 times | 0.039 | −16.39 | −0.032 | 18.71 |
1 | −0.028 | 11.76 | −0.018 | 10.52 |
≥2 | −0.018 | 7.56 | −0.012 | 7.02 |
No | −0.027 | 11.34 | −0.019 | 11.11 |
No | −0.003 | 1.26 | 0.046 | −26.90 |
Alone | 0.011 | −4.62 | 0.003 | −1.75 |
In an institution | −0.022 | 9.24 | 0.002 | −1.17 |
1-5 | −0.022 | 9.24 | 0.037 | −21.64 |
≥6 | 0.005 | −2.10 | 0.095 | −55.56 |
1-2 | −0.015 | 6.30 | −0.024 | 14.04 |
≥3 | 0.055 | −23.11 | 0.012 | −7.02 |
Priced effects revealed that the effects of urban and rural differentials in homeownership (54.97%), regular exercise (84.21%) and number of trips (18.71%) on cognitive function disparity weakened over time. However, the effects of other explanatory variables, such as hunger in childhood (−26.90%) and years of schooling (−77.20%) strengthened during the 2008–2018 period, thereby worsening the urban and rural gap of cognitive performance.
This study reveals a reduction in the cognitive performance of older adults' differentials between urban and rural older adults in China from 2008 to 2018. This change is attributable to the quantity and priced effects of various explanatory factors.
The change in the proportion of people ≥ 96 years between urban and rural significantly contributed to the decline in cognitive performance disparity. The underlying reason may be because the considerable investment in the rural health system in recent 10 years has increased the life expectancy of the rural population in China (
Interestingly, an effect of homeownership gap on cognitive ability disparity in urban and rural seemingly decreased in the 2018 wave. The association of homeownership with cognitive health is mainly due to the mediation effects of the perceived sense of control, community trust and residential stability (
Another encouraging finding is that the reduction in self-rated poor economic status amongst rural older adults positively contributed to the narrowed cognitive disorder differential. The rapid socioeconomic development in China over the past few decades significantly increased income growth in rural regions, and the establishment of a universal medical insurance system also decreased the out-of-pocket payment ratio for medical care (
Our findings also reveal that an increased share of rural older adults who exercise regularly reduced the cognitive function inequality greatly between urban and rural. These findings were supported by previous studies reporting physical exercises can increase production of neurotrophic factors and cerebral blood flow, as well as further prevent degeneration of the brain's cognitive function with aging (
A gap in proportion to the older adults who still worked during retirement age between urban and rural exacerbated the widening cognitive function disparity. A potential explanation can be attributed to the urban-rural gap in social security and welfare benefits. On the one hand, social security coverage in rural areas is much lower than that in urban areas, even though the Chinese government proposed the establishment of a rural endowment insurance system in 2009. On the other hand, the level of social security and welfare benefits rural older adults can receive is also quite limited because of the great urban-rural disparity in economic development (
Individual experiences in childhood can influence health and well-being in the late periods of life. Exposure to fetal malnutrition has considerable and long-lasting impacts on physical health and cognitive abilities (
This study holds some limitations the must be acknowledged. Firstly, community- and provincial-level variables were not used to explore their contributions to the changes in the cognitive performance differences between urban older adults and rural older adults due to limited data, even though a comprehensive set of individual level variables was included on the basis of Lund's conceptual framework. Secondly, survival bias may exist, because the CLHLS focuses on the long lived, and the survey may exclude people who did not live long lives. Thus, a healthy sample can be presented, resulting in an underestimated cognitive impairment. Thirdly, data in CLHLS were collected by self-report. Recall bias and measurement bias should be paid an attention.
Although the urban-rural disparity in the cognitive performance of older adults in China narrowed from 2008 to 2018, it remains a significant problem. The rapid economic development and reform of the rural health system significantly contributed to the narrowed gap. However, then unbalanced distribution in social security and welfare between urban and rural regions resulted in an expanding trend in the urban-rural inequality of cognitive function. Additionally, future policy interventions must highlight rural older adults who suffered from hunger and limited education during childhood, because the negative effects of these hardships on cognitive function intensify late in life.
The original contributions presented in the study are included in the article/
The studies involving human participants were reviewed and approved by the Institutional Review Board, Duke University (Pro00062871), and the Biomedical Ethics Committee, Peking University (IRB00001052–13074). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.
TZ was responsible for the study design, implementation, and writing. BL collected and analyzed the data and reviewed and edited original draft. XW revised and critically commented the manuscript. All authors made significant contributions to this study and have read and approved the final manuscript.
This work was supported by National Natural Science Foundation of China (grant number: 71974050) and the Scientific Research Foundation for Scholars of HZNU (grant number: 4265C50221204120).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
We are grateful to the respondents of CLHLS and to Peking University for making the CLHLS dataset publicly available.
The Supplementary Material for this article can be found online at: