Temporal trends and disparities of population attributable fractions of modifiable risk factors for dementia in China: a time-series study of the China health and retirement longitudinal study (2011-2018)

Abstract

Background: In China, dementia poses a significant public health challenge, exacerbated by an ageing population and lifestyle changes. This study assesses the temporal trends and disparities in the population-attributable fractions (PAFs) of modifiable risk factors (MRFs) for new-onset dementia from 2011 to 2018.

Methods: We used data from the China Health and Retirement Longitudinal Study (CHARLS), covering 75,214 person-waves. We calculated PAFs for 12 MRFs identified by the Lancet Commission (including six early-to mid-life factors and six late-life factors). We also determined the individual weighted PAFs (IW-PAFs) for each risk factor. Subgroup analyses were conducted by sex, socio-economic status (SES), and geographic location.

Findings: The overall PAF for dementia MRFs had a slight increase from 45.36% in 2011 to 52.46% in 2018, yet this change wasn't statistically significant. During 2011-2018, the most contributing modifiable risk was low education (average IW-PAF 11.3%), followed by depression, hypertension, smoking, and physical inactivity. Over the eight-year period, IW-PAFs for risk factors like low education, hypertension, hearing loss, smoking, and air pollution showed decreasing trends, while others increased, but none of these changes were statistically significant. Sex-specific analysis revealed higher IW-PAFs for traumatic brain injury (TBI), social isolation, and depression in women, and for alcohol and smoking in men. The decline in IW-PAF for men's hearing loss were significant. Lower-income individuals had higher overall MRF PAFs, largely due to later-life factors like depression. Early-life factors, such as TBI and low education, also contributed to SES disparities. Rural areas reported higher overall MRF PAFs, driven by factors like depression, low education, and hearing loss. The study also found that the gap in MRF PAFs across different SES groups or regions either remained constant or increased over the study period.

Interpretation: The study reveals a slight but non-significant increase in dementia's MRF PAF in China, underscoring the persistent relevance of these risk factors. The findings highlight the need for targeted public health strategies, considering the demographic and regional differences, to effectively tackle and reduce dementia risk in China's diverse population.

Funding: This work was supported by the PKU Young Scholarship in Global Health and Development.

Keywords: China; Dementia; Disparity; Population attributable fractions; Temporal trend.

Fig. 1

Overall temporal trends in population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018. Average percentage change (APC) was used to quantify the temporal trend in population attributable fraction (PAF, as %), extracted from linear regression with PAF as the outcome and continuous form of the year as the predictor. The APC indicates the extent to which the percentage points of PAF vary with each passing year.

Fig. 2

Temporal trends in population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018, by risk factor. Average percentage change (APC) was used to quantify the temporal trend in population attributable fraction (PAF, as %), extracted from linear regression with PAF as the outcome and continuous form of the year as the predictor. The APC indicates the extent to which the percentage points of PAF vary with each passing year. The same figure but with the range of the Y-axis adjusted accordingly based on the observations for each factor, can be found in Supplementary Fig. S1.

Fig. 3

Temporal trends in population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018, by sex. Average percentage change (APC) was used to quantify the temporal trend in population attributable fraction (PAF, as %), extracted from linear regression with PAF as the outcome and continuous form of the year as the predictor. The APC indicates the extent to which the percentage points of PAF vary with each passing year.

Fig. 4

Temporal trends in population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018, by sex and risk factor. Average percentage change (APC) was used to quantify the temporal trend in population attributable fraction (PAF, as %), extracted from linear regression with PAF as the outcome and continuous form of the year as the predictor. The APC indicates the extent to which the percentage points of PAF vary with each passing year. The same figure but with the range of the Y-axis adjusted accordingly based on the observations for each factor, can be found in Supplementary Fig. S2.

Fig. 5

Temporal trends in population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018, by socio-economic status. Average percentage change (APC) was used to quantify the temporal trend in population attributable fraction (PAF, as %), extracted from linear regression with PAF as the outcome and continuous form of the year as the predictor. The APC indicates the extent to which the percentage points of PAF vary with each passing year.

Fig. 6

Temporal trends in population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018, by socio-economic status and risk factor. Average percentage change (APC) was used to quantify the temporal trend in population attributable fraction (PAF, as %), extracted from linear regression with PAF as the outcome and continuous form of the year as the predictor. The APC indicates the extent to which the percentage points of PAF vary with each passing year. The same figure but with the range of the Y-axis adjusted accordingly based on the observations for each factor, can be found in Supplementary Fig. S3.

Fig. 7

Temporal trends in population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018, by rural vs urban. Average percentage change (APC) was used to quantify the temporal trend in population attributable fraction (PAF, as %), extracted from linear regression with PAF as the outcome and continuous form of the year as the predictor. The APC indicates the extent to which the percentage points of PAF vary with each passing year.

Fig. 8

Temporal trends in population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018, by rural-urban and risk factor. Average percentage change (APC) was used to quantify the temporal trend in population attributable fraction (PAF, as %), extracted from linear regression with PAF as the outcome and continuous form of the year as the predictor. The APC indicates the extent to which the percentage points of PAF vary with each passing year. The same figure but with the range of the Y-axis adjusted accordingly based on the observations for each factor, can be found in Supplementary Fig. S4.

Fig. 9

Provincial distribution of population attributable fraction of 12 modifiable risk factors for dementia, 2011–2018. The map only shows areas on land. Grey areas represent unavailable data for the corresponding areas. The table form can be found in Supplementary Table S4.