Based on four national social surveys on elderly population in China, CLHLS, SSAPUR, CHARLS, and CLASS, this paper compared and harmonized the Katz ADL index, assessed their reliability and validity, and analyzed prevalence of physical disability. Its main merit is to avoid the bias in estimation of physical disability prevalence based on one single data source, which is critical to understand the real level of physical disability and develop aged care policies like the on-going long-term care insurance pilots in China.
The physical disability prevalence estimated using data from four surveys significantly vary, even after we used the survey data in most similar years, harmonized the ADL index, and standardized the crude physical disability prevalence using national sex and age structure. Such results are consistent with previous studies [9, 11, 13, 23, 25]. The factors contribute to the disparities in physical disability prevalence across surveys at least include the following aspects. Firstly, difference exists in the design of the four social surveys. CLHLS adopts a targeted random-sample method and is not nationally representative [11], while other three surveys are designed to be representative of elderly population in China. Secondly, despite our best effort, differences still exist in the ADL index across four surveys. Unlike in other three surveys, the second options in CLHLS for eating, dressing, toileting, getting in and out of bed, and bathing are “some difficulties”, from which we can’t tell if a help from others is needed to perform these tasks. To cope with this, we analyzed two situations, SSAPUR-1 and SSAPUR-2 regarding those choosing “some difficulties” as disabled and nondisabled with these tasks, respectively. The physical disability prevalence based on SSAPUR-1 (18.07%) is expected to overestimate the real level of physical disability and SSAPUR-2 underestimate (11.99%). A long-standing duration of the ability restriction, “three months”, is required in CHARLS. CLHLS specify “at least the last six months” in a question before ADL index, but not explicitly indicate the duration in the following ADL index. Both CLASS and SSAPUR did not have any time limitation, which may result in overestimation of physical disability prevalence [7]. The order and description of ADL items also vary across surveys, whereas it’s difficult to determine their potential impact on physical disability prevalence estimation.
The real level of physical disability prevalence among elderly Chinese might be between 10.21–18.07%. All the four social surveys, i.e. CLHLS, CLASS, CHARLS and SSAPUR, have good data quality [13, 14, 19, 20] and the ADL index in four surveys passed through reliability and validity examination. On the other hand, all of the four surveys have some disadvantages in measuring ADL disability. CLHLS is not nationally representative, SSAPUR and CLASS don’t set a time restriction for the ability assessment, and CHARLS has a small size of oldest-old and urban residents. Considering these above factors, we can’t conclude any of the survey data better than others in estimating physical disability prevalence. We also found that the variation in physical disability prevalence was higher with respect to older age, rural residence, and mild disability.
Last but not least, researchers should be careful in using CLHLS, SSAPUR, CHARLS, and CLASS in providing evidence for aged care policies. Firstly, considering the disparities in physical disability prevalence in these four social surveys, analysis using multiple data sources probably produce more robust results than that using single data source. For example, our results show that the number of severely disabled older population is between 2.09 million to 5.10 million in China, which is still a wide range for long-term care policies. Any recommendation based on single data source may introduce bias into policymaking. Secondly, the physical disability assessment tools are different between social surveys and long-term care insurance practices in China. Katz ADL is used in all the four social surveys, and the Barthel index is used in most long-term care insurance pilots [29]. These two tools probably produce different physical functional assessment results [7, 30]. Additionally, at least six months of physical disability is also required for eligibility of long-term care insurance benefits. However, in CLASS and SSAPUR, the duration of ability restriction is not indicated in the questionnaire, which may include some short-term physical disability and overestimate the needs for long-term care. In the future, social surveys could consider including physical disability assessment tool used in long-term care insurance into the questionnaire, which will increase the implication for policy practices.
This study has some limitations. Firstly, despite our best effort, the physical disability prevalence measured with ADL index in the four social surveys is not completely comparable. This is because we aren’t able to change the survey designs and questionnaire setting after the data had been collected. Secondly, as required to be stored in China Research Center on Ageing, the SSAPUR data was analyzed in a slightly different way with other data source. We used statistical software PASW (version 18) to analyze the SSAPUR data and Stata MP (version 15.0) for other three sources. Six resamples of 25% SSAPUR respondents were drawn to perform the factor analysis due to the large sample size of SSAPUR and the limited computing capability of computers. We estimated the tetrachoric correlation matrix in PASW (version 18) according to Lorenzo-Seva and Ferrando [31] and using “tetrachoric” module in Stata MP (version 15.0). Considering the consistence between the two statistical softwares and stable results across SSAPUR resamples, the bias introduced by the different analysis approaches could be neglected.
After we improved the comparability by drawing data from the surveys in most similar years, harmonizing the Katz ADL index, and standardizing the crude physical disability prevalence, the prevalence of physical disability among elderly people still significantly differed across four national social surveys in China, ranging from 10.21% (9.47%-10.95%) in CLHLS to 18.07% (17.88%-18.27%) in SSAPUR-1. All the indexes had satisfying reliability and validity assessment results and surveys had good data quality, whereas all the surveys had some disadvantages in measuring physical disability. Therefore, it’s difficult to conclude the best data source to estimate physical disability prevalence. We suggest that future studies on, such as elderly physical disability and long-term care need, use multiple data sources at the same time to produce robust results. Considering that China has the largest number of elderly people in the World, this study may shed some lights on assessing global elderly disability. Furthermore, researchers in other countries also need to pay attention on the potential differences in physical disability prevalence estimated from different data sources.