Blood pressure and hypertension prevalence among oldest-old in China for 16 year: based on CLHLS

Prevalence and risk factors for isolated systolic hypertension among the oldest-old population in southwestern China: A community-based cross-sectional study

The prevalence of isolated systolic hypertension (ISH) has doubled between 2002-2005 and 2014 among the oldest-old population in China. However, the prevalence and characteristics of ISH among the oldest-old population in southwestern China remain less known. This study aimed to investigate the prevalence of ISH among the oldest-old population in Chengdu and identify associated factors to provide valuable information for disease etiology and prevention. We recruited 1,312 participants aged over 80 years by using a stratified cluster sampling method between September 2015 and June 2016, from three districts (Jinjiang, Qingyang, and Longquanyi) of Chengdu, the largest city of southwest China. A structured questionnaire, anthropometric data, and blood pressure were collected according to the standard method. Blood pressure was measured three times by using a standardized mercury sphygmomanometer after a 10-minute seated rest. Of 1312 participants, 53.0% (n = 695) had ISH. The prevalence of ISH in men and women was 54.7% and 51.3%, respectively, with no significant sex difference (P = .222). The prevalence of ISH increased with advanced age in men (P for trend = 0.029), 52.5% for the 80-84 years group, 55.2% for the 85-89 years group, and 70.4% for the 90-98 years group, respectively. Multivariable logistic regression analyses found that drinking (OR = 1.85, 95%CI = 1.26-2.71), being overweight (OR = 1.88, 95%CI = 1.19-2.96), and having a higher heart rate (OR = 0.66, 95%CI = 0.51-0.86) were associated with ISH. Stratified by sex, these three factors remained significant in men. Our work highlights that the burden of ISH is substantial among the oldest-old population in southwestern China.

Combined effects of heatwaves and air pollution, green space and blue space on the incidence of hypertension: A national cohort study

Extreme heat exposure has been associated with hypertension. However, its interactive influences with air pollution, green and blue spaces are unclear. This study aimed to explore the interaction between heatwaves, air pollution, green and blue spaces on hypertension. Cohort data enrolled 6448 Chinese older adults aged 65 years and over were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 2008 and 2018. Nine heatwave definitions, combining three heat thresholds (92.5th, 95th, and 97.5th percentiles of daily maximum temperature) and three durations (≥2, 3 and 4 days) were used as time-varying variables in the analysis and were the one-year exposure before survival events. Fine particulate matter (PM ≤2.5 μm in aerodynamic diameter (PM_2.5)), the Normalized Difference Vegetation Index (NDVI) and the average proportion of open water bodies were used to reflect the air pollution, green and blue space exposures, respectively. PM_2.5, green and blue space exposures were time-varying indicators and contemporaneous with heatwaves. Mixed Cox models with time-varying variables were fitted to assess the multiplicative and additive interaction of heatwaves, PM_2.5, and green and blue spaces on hypertension, measured by a traditional product term with the ratio of hazard ratio (HR) and relative risk due to interaction (RERI), respectively. A positive multiplicative (HRs >1) and additive interaction (RERIs >0) between heatwaves and higher PM_2.5 levels was observed. There was a synergistic effect between heatwaves and decreasing greenness levels on hypertension incidence on additive and multiplicative scales. No significant interaction between heatwaves and blue space was observed in the analysis. The combined effects of heatwaves, air pollution, green and blue space exposures on the risk of hypertension varied with age, gender, and educational attainment. This study's findings complemented the existing evidence and revealed synergistic harmful impacts for heatwaves with air pollution and lack of green space on hypertension incidence.

Turning Point of Cognitive Decline for Chinese Older Adults from a Longitudinal Analysis: Protective Factors and Risk Factors

OBJECTIVES

To explore the turning point of cognitive decline in Chinese older adults and to explore the influencing factors including covariates.

PARTICIPANTS

Aged 65 and older whose cognitive function was normal at their first test.

METHODS

a secondary analysis that identified participants from the database of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Cohort-sequential design was used to categorize the data by age (rather than study wave), including the follow-up data of Chinese older adults aged 65-79 years and spanning 14 years. Cognitive function in 1278 participants was assessed using the Chinese Mini-Mental State Examination (CMMSE) in five waves over 14 years. Piecewise latent growth curve modeling was used to analyze the data.

RESULTS

(1) The turning point of cognitive decline in Chinese older adults occurs between the ages of 68 and 70. (2) There are statistically significant individual differences in the initial level of cognitive function and the growth rate of cognitive function before and after the transition stage. (3) Factors influencing cognitive function include residence, education level, smoking, drinking, exercise, leisure activities, social activities, Activities of Daily Living (ADL), and Instrumental Activities for Daily Living (IADL). (4) Exercise and ADL are the main protective factors, while smoking and drinking are the main risk factors.

CONCLUSIONS

There is a transition stage (68-70) in the decline of cognitive function in Chinese older adults and four main factors (such as smoking, drinking, exercise and ADL) have impacts on the cognitive decline. We should strengthen these protective factors (exercise and ADL) for the cognitive decline of older adults and avoid these risk factors (smoking, drinking). To prevent the decline of the cognitive function of older adults, the government should build more places conducive to activities for older adults and actively encourage older adults to improve their physical activity level. Given our findings, public health interventions centered on alcohol and tobacco cessation in older adults should be governmentally endorsed.

Hypermixed Convolutional Neural Network for Retinal Vein Occlusion Classification

Retinal vein occlusion (RVO) is one of the most common retinal vascular diseases leading to vision loss if not diagnosed and treated in time. RVO can be classified into two types: CRVO (blockage of the main retinal veins) and BRVO (blockage of one of the smaller branch veins). Automated diagnosis of RVO can improve clinical workflow and optimize treatment strategies. However, to the best of our knowledge, there are few reported methods for automated identification of different RVO types. In this study, we propose a new hypermixed convolutional neural network (CNN) model, namely, the VGG-CAM network, that can classify the two types of RVOs based on retinal fundus images and detect lesion areas using an unsupervised learning method. The image data used in this study is collected and labeled by three senior ophthalmologists in Shanxi Eye Hospital, China. The proposed network is validated to accurately classify RVO diseases and detect lesions. It can potentially assist in further investigating the association between RVO and brain vascular diseases and evaluating the optimal treatments for RVO.

Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults

Background

Multimorbidity presents an enormous problem to societal and healthcare utilization under the context of aging population in low- and middle-income countries (LMICs). Currently, systematic studies on the profile of multimorbidity and its characteristics among Chinese elderly are lacking. We described the temporal and spatial trends in the prevalence of multimorbidity and explored chronological changes of comorbidity patterns in a large elderly population survey.

Methods

Data were extracted from the Chinese Longitudinal Healthy Longevity Study (CLHLS) conducted between 1998 and 2018 in a random selection of half of the counties and city districts. All the elderly aged 65 and older were included in the survey of eight waves. We used 13 investigated chronic diseases to measure the prevalence of multimorbidity by means of geography, subpopulation, and chronological changes. The patterns of multimorbidity were assessed by computing the value of relative risk (RR indicates the likelihood of certain diseases to be associated with multimorbidity) and the observed-to-expected ratio (O/E indicates the likelihood of the coexistence of a multimorbidity combination).

Results

From 1998 to 2018, the prevalence of multimorbidity went from 15.60 to 30.76%, increasing in the fluctuation across the survey of eight waves (p_{for trend} = 0.020). Increasing trends were observed similarly in a different gender group (p_male = 0.009; p_female = 0.004) and age groups among female participants (p_~80 = 0.009; p₈₁₋₉₀ = 0.004; p₉₁₋₁₀₀ = 0.035; p_101~ = 0.018). The gap in the prevalence of multimorbidity between the north and the south was getting narrow across the survey of eight waves. Hypertension was the highest prevalent chronic condition while diabetes was most likely to coexist with other chronic conditions in the CLHLS survey. The most frequently occurring clusters were hypertension and heart disease, hypertension and cataract, and hypertension and chronic lung disease. And, the cancer, TB, and Parkinson's disease cluster took the domination of O/E rankings over time, which had a higher probability of coexistence in all the multimorbidity combinations.

Conclusions

The prevalence of multimorbidity has been increasing nationwide, and more attention should be paid to a rapid growth in the southern part of China. It demands the effective diagnosis and treatment adopted to the highly prevalent comorbidities, and strategies and measures were adjusted to strongly relevant clusters.