paper
我有数学建模竞赛经验和开发经验, 会使用 python, ts 等, 也有数学竞赛经验, 机器学习, 深度学习经验, 现在我需要编写本科论文, 在选题和初稿阶段, 就是题目还没选好, 我们的数据集以 Harmonized Data 为主
多模态生物医学数据驱动的老年人健康画像构建与预测
常规生物医学指标与中国老年人健康状况的相关性研究
Section titled “常规生物医学指标与中国老年人健康状况的相关性研究”- 第一章 dataset
- 第一节 介绍
- part 1 介绍
- part 2 Harmonized 概念
- part 3 Harmonized 问卷
- part 4 变量查询
- 第二节 Harmonized Core 问卷 与 选题
- part 1 问卷模块
- part 2 变量介绍 与 选题 思路
- part 3 Harmonized codebook
- 第三节 Harmonized end of life 问卷 与 选题
- part 1 临终访谈
- part 2 临终问卷模块
- part 3 变量介绍 与 选题 思路
- 第四节 Harmonized life history 问卷 与 选题
- part 1 生命历程访谈
- part 2 生命历程问卷模块
- part 3 变量介绍 与 选题 思路
- 第一节 介绍
- 第二章 各 dataset 及选题
- 第一节 CHARLS
- part 1 介绍
- part 2 采集流程
- part 3 原始问卷
- part 4 变量 与 选题
- part 5 权重介绍
- 第二节 HRS
- 第三节 ELSA
- …
- 第一节 CHARLS
- 第三章 数据处理与分析
- 第一节 描述性统计
- part 1 应用场景
- part 2 实战
- part 3 结果解读
- 第二节 回归分析: 广义线性, logistic, KM曲线+Cox
- part 1 三大回归分析应用场景
- part 2 实战
- part 3 结果解读
- 第三节 交互作用与中介效应
- part 1 应用场景
- part 2 实战
- part 3 结果解读
- 第四节 限制性立方样条 RCS
- 第五节 亚组分析与森林图
- part 1 介绍
- part 2 实战
- part 3 结果解读
- 第六节 轨迹模型
- 第七节 混合线性模型
- 第八节 高分队列研究套路1: 累计平均暴露\累计暴露\累积暴露时间进程
- part 1 介绍, 文献示例
- part 2 适用条件
- part 3 实战
- 第九节 高分队列研究套路2: 访视期间变异性\年度变化百分比\绝对年变化率
- part 1 介绍, 文献示例
- part 2 适用条件
- part 3 实战
- 第十节 高分队列研究套路3: 新发年龄\早发人群
- part 1 介绍, 文献示例
- part 2 适用条件
- part 3 实战
- 第十一节 高分队列研究套路4: 交叉滞后
- part 1 介绍, 文献示例
- part 2 适用条件
- part 3 实战
- 第一节 描述性统计
- 第四章 顶刊论文复现
- 第一节 如何进行研究设计及数据制备
- 第二节 如何进行数据分析
- 第三节 如何完成论文写作
数据集 Global Aging
Section titled “数据集 Global Aging”pip install pandas pyreadstat| 数据集名称 | 数据集描述 |
|---|---|
| CHARLS | 中国健康与养老追踪调查 (CHARLS) 是一项全国性、长期的纵向调查，旨在收集中国中老年人群体的健康、经济和社会信息。 |
| Health and Retirement Study, HRS | 健康与退休调查 (HRS) 是一项针对美国中老年人群体的纵向调查，旨在研究健康、经济和社会因素对退休决策的影响。 |
| English Longitudinal Study of Ageing, ELSA | 英国老年人纵向研究 (ELSA) 是一项针对英国中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
| Survey of Health, Ageing and Retirement in Europe, SHARE | 欧洲健康、老龄化与退休调查 (SHARE) 是一项针对欧洲中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
| Korea Longitudinal Study of Ageing, KLoSA | 韩国老龄化纵向研究 (KLoSA) 是一项针对韩国中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
| The Irish Longitudinal Study on Ageing, TILDA | 爱尔兰老龄化纵向研究 (TILDA) 是一项针对爱尔兰中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
| Mexico Health and Aging Study, MHAS | 墨西哥健康与老龄化研究 (MHAS) 是一项针对墨西哥中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
| Indonesia Family Life Survey, IFLS | 印尼家庭生活调查 (IFLS) 是一项针对印尼中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
| Japanese Study of Aging and Retirement, JSTAR | 日本老龄化与退休研究 (JSTAR) 是一项针对日本中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
| Study on Global Ageing and Adult Health, SAGE | 全球老龄化与成人健康研究 (SAGE) 是一项针对全球中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
| Global Ageing Data Repository, GADR | 全球老龄化数据存储库 (GADR) 是一项针对全球中老年人群体的纵向调查，旨在研究健康、经济和社会因素对老年人生活质量的影响。 |
Harmonized Data
Section titled “Harmonized Data”CHARLS 的 Harmonized Data 包含 4 波数据，从流行病学统计的角度来看，它既可以被视为一个队列研究，也可以在某些情况下被视为多个横断面研究
队列研究的角度
- 定义: 队列研究是指对一组特定人群（队列）进行长期随访，观察其暴露因素与结局之间的关系
- CHARLS 的特点:
- CHARLS 是一个纵向研究，对同一组受访者在多个时间点（波次）进行随访
- 通过多波数据，可以分析变量的动态变化（如健康状况、经济状况）以及这些变化与结局（如疾病发生）的关系
- 例如，研究 TyG-WHtR 的变化与 CVD 风险的关系时，CHARLS 的多波数据可以很好地支持这种动态分析
横断面研究的角度
- 定义: 横断面研究是在某一特定时间点对研究对象进行观察，分析暴露因素与结局的关系
- CHARLS 的特点:
- 每一波数据（如 Wave 1、Wave 2 等）都可以被视为一个独立的横断面数据集
- 如果只分析某一波的数据（如 Wave 1 的健康状况与经济状况的关系），那么这实际上是一个横断面研究
| 标题 | 翻译标题 | 自变量 | 因变量 | link | dataset | score |
|---|---|---|---|---|---|---|
| Association between triglyceride glucose body mass index and cardiovascular disease in adults: evidence from NHANES 2011-2020 | 甘油三酯葡萄糖体重指数与成人心血管疾病的关系：来自 NHANES 2011-2020 的证据 | TyG-bmi | CVD | NHANES | 3.9/Q2 | |
| Association between the cumulative average triglyceride glucose-body mass index and cardiovascular disease incidence among the middle-aged and older population: a prospective nationwide cohort study in China | 中老年人群积累平均甘油三酯葡萄糖体重指数与心血管疾病发生率的关系：中国前瞻性全国性队列研究 | cumulative TyG-bmi | CVD | CHARLS | 8.5/Q1 |
ID,householdID,communityID,hhid,hhidc,pnc,pn,ID_w1,householdID_w1,s1id,s2id,s3id,s4id,s1pn,s2pn,s3pn,s4pn,raspid1,raspid2,h1coupid,h2coupid,h3coupid,h4coupid,inw1,inw2,inw3,inw4,r1iwstat,r2iwstat,r3iwstat,r4iwstat,s1iwstat,s2iwstat,s3iwstat,s4iwstat,hacohort_c,r1wthh,r2wthh,r3wthh,r4wthh,r2wthhl,r1wthha,r2wthha,r3wthha,r4wthha,r1wtresp,r2wtresp,r3wtresp,r4wtresp,s1wtresp,s2wtresp,s3wtresp,s4wtresp,r2wtrespl,s2wtrespl,r1wtrespa,r2wtrespa,r3wtrespa,s1wtrespa,s2wtrespa,s3wtrespa,r1wtrespb,r2wtrespb,r3wtrespb,r4wtrespb,s1wtrespb,s2wtrespb,s3wtrespb,s4wtrespb,r1wtrespbioa,s1wtrespbioa,r1wtrespbiob,r2wtrespbiob,r3wtrespbiob,s1wtrespbiob,s2wtrespbiob,s3wtrespbiob,h1hhresp,h2hhresp,h3hhresp,h4hhresp,h1cpl,h2cpl,h3cpl,h4cpl,r1famr,r2famr,r3famr,r4famr,s1famr,s2famr,s3famr,s4famr,h1anyfam,h2anyfam,h3anyfam,h4anyfam,r1iwy,r2iwy,r3iwy,r4iwy,s1iwy,s2iwy,s3iwy,s4iwy,r1iwm,r2iwm,r3iwm,r4iwm,s1iwm,s2iwm,s3iwm,s4iwm,rabday,s1bday,s2bday,s3bday,s4bday,rafbday,s1fbday,s2fbday,s3fbday,s4fbday,rabmonth,s1bmonth,s2bmonth,s3bmonth,s4bmonth,rafbmonth,s1fbmonth,s2fbmonth,s3fbmonth,s4fbmonth,rabyear,s1byear,s2byear,s3byear,s4byear,rafbyear,s1fbyear,s2fbyear,s3fbyear,s4fbyear,rafbdate,s1fbdate,s2fbdate,s3fbdate,s4fbdate,raidbyear,s1idbyear,s2idbyear,s3idbyear,s4idbyear,raidbmonth,s1idbmonth,s2idbmonth,s3idbmonth,s4idbmonth,raidbday,s1idbday,s2idbday,s3idbday,s4idbday,rafidbdate,s1fidbdate,s2fidbdate,s3fidbdate,s4fidbdate,radyear,s1dyear,radmonth,s1dmonth,r1agey,r2agey,r3agey,r4agey,s1agey,s2agey,s3agey,s4agey,ragender,s1gender,s2gender,s3gender,s4gender,rafgendr,s2fgendr,s3fgendr,s4fgendr,raeduc_c,s1educ_c,s2educ_c,s3educ_c,s4educ_c,raeducl,s1educl,s2educl,s3educl,s4educl,r1mstat,r2mstat,r3mstat,r4mstat,s1mstat,s2mstat,s3mstat,s4mstat,r1mstath,r2mstath,r3mstath,r4mstath,s1mstath,s2mstath,s3mstath,s4mstath,r1mnev,r2mnev,r3mnev,r4mnev,s1mnev,s2mnev,s3mnev,s4mnev,r1mrct,r2mrct,s1mrct,s2mrct,r1mcurln,r2mcurln,s1mcurln,s2mcurln,rabplace_c,s1bplace_c,s2bplace_c,s3bplace_c,s4bplace_c,r1hukou,r2hukou... (line truncated to 2000 chars)些数据集通常都有 Harmonized Data（标准化数据）。Harmonized Data 是对原始数据进行标准化和一致化处理后的数据集，目的是方便跨国家或跨地区的比较研究。这种数据处理方式特别适用于像你提到的这些关于老龄化、健康和退休的纵向调查数据
Harmonized Data 是通过对不同数据集的变量进行标准化处理后生成的统一数据集。它的主要特点包括：
- 变量一致性
- 跨数据集的可比性
- 便于分析
Gateway to Global Aging Data
Section titled “Gateway to Global Aging Data”官网地址：https://g2aging.org/
Gateway to Global Aging Data 是由 加州大学洛杉矶分校（UCLA） 提供的一个平台，专注于全球老龄化相关数据的整合和标准化。它为多个国家的老龄化研究数据（如 CHARLS、HRS、ELSA 等）提供了 Harmonized Data，以便研究者进行跨国家和跨地区的比较研究
Preface 前言
Section titled “Preface 前言”The China Health and Retirement Longitudinal Study (CHARLS) is a longitudinal study of individuals over age 45 in China. It was designed to better understand the socioeconomic determinants and consequences of aging. The survey includes a rich set of questions regarding economic standing, physical and psychological health, demographics, and social networks of aged persons. The survey is designed to ensure comparability with the Health and Retirement Survey (HRS) in the United States and related aging surveys, such as the English Longitudinal Study of Aging (ELSA) in England and the Survey of Health, Aging and Retirement in Europe (SHARE) in Europe and Israel. Part of the reason for the close connection is to allow cross-country comparisons using these data.
In order to make the data more accessible to researchers and to facilitate such comparisons, we the USC Gateway to Global Aging Data team, created the Harmonized CHARLS, a user- friendly version of a subset of the CHARLS interviews. The Harmonized CHARLS initiative is part of a larger set of projects. With funding and support from the National Institute on Aging, we have also created Harmonized HRS (USA), Harmonized MHAS (Mexico), Harmonized ELSA (England), Harmonized SHARE (Europe and Israel), Harmonized CRELES (Costa Rica), Harmonized KLoSA (South Korea), Harmonized JSTAR (Japan), Harmonized TILDA (Ireland), Harmonized LASI (India), and Harmonized MARS (Malaysia). Further information about these Harmonized data files with questionnaires and other searchable metadata is available on our website, https://g2aging.org/.
In creating the Harmonized data files, we have followed the RAND HRS and Harmonized HRS conventions of variable naming and data structure. The RAND HRS is a user-friendly version of a subset of the HRS that the RAND Center for the Study of Aging created to increase usability. The Harmonized HRS is a supplementary dataset to the RAND HRS, which also include a subset of the HRS data, that the Gateway to Global Aging Data team has created to increase usability of a greater number of HRS variables. The Harmonized CHARLS includes variables with a similar naming convention that mimics the RAND HRS, Harmonized HRS, and other Harmonized variables. This document describes these data.
We are grateful for the continuing support of and funding from NIA. In working with the CHARLS data, we greatly benefited from the help from Dr. Yaohui Zhao, Dr. Yafeng Wang, Dr. John Strauss, Dr. Albert Park and the CHARLS team members. We have greatly benefited from discussions with and the suggestions from our colleagues Arie Kapteyn, Marco Angrisani, Erik Meijer, and Bas Weerman. We would also like to acknowledge our current and former colleagues at the Gateway to Global Aging Data: Sidney Beaumaster, Sandy Chien, Samuel Lau, and Ashley Lin.
Requested Acknowledgment 要求致谢
Section titled “Requested Acknowledgment 要求致谢”We ask all users of the Harmonized CHARLS to please inform our team of any written analysis using data from the Harmonized CHARLS or information from the Harmonized CHARLS Codebook by sending an email to papers@g2aging.org. We also ask users to include the following acknowledgement in their written work:
“This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version D as of June 2021 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Aging (R01 AG030153, RC2 AG036619, R03 AG043052). For more information, please refer to https://g2aging.org/.”
CHARLS Version and Acknowledgment CHARLS 版本和致谢
Section titled “CHARLS Version and Acknowledgment CHARLS 版本和致谢”This document used CHARLS Waves 1 through 4 as of June 2021. CHARLS is supported by Peking University, the National Natural Science Foundation of China, the National Institute on Aging, and the World Bank.
Contents
Section titled “Contents”- PREFACE … 1
- REQUESTED ACKNOWLEDGMENT … 2
- CHARLS VERSION AND ACKNOWLEDGMENT … 2
- WHAT’S NEW IN VERSION D OF THE HARMONIZED CHARLS? … 5
- INTRODUCTION AND OVERVIEW … 9
- 1.1. Gateway to Global Aging Data …10
- 1.2. Unit of Observation …11
- 1.3. Data File Structure …11
- 1.4. Variable Naming Convention …11
- 1.5. Missing Values, Nonresponse, and Imputations …13
- 1.6. Weighting and Accounting for Survey Design …13
- 1.7. Availability of Stress Measures …14
- WEALTH AND INCOME VARIABLES … 15
- 2.1 Units of Observation and financial respondent…15
- 2.2. Currency …16
- STRUCTURE OF CODEBOOK … 17
- DISTRIBUTION AND TECHNICAL NOTES … 21
- DATA CODEBOOK … 22
- SECTION A: DEMOGRAPHICS AND IDENTIFIERS … 23
- SECTION B: HEALTH … 76
- SECTION C: HEALTH CARE UTILIZATION AND INSURANCE … 173
- SECTION D: COGNITION … 203
- SECTION E: FINANCIAL AND HOUSING WEALTH … 223
- SECTION F: INCOME AND CONSUMPTION … 290
- SECTION G: FAMILY STRUCTURE … 383
- SECTION H: EMPLOYMENT HISTORY … 496
- SECTION I: RETIREMENT … 538
- SECTION J: PENSION … 556
What’s New in Version D of the Harmonized CHARLS?
Section titled “What’s New in Version D of the Harmonized CHARLS?”Version D incorporates the latest released version of CHARLS data, and adds variables for Wave 4. It contains 25,586 observations or rows. It is a Respondent-level file so each row represents a unique Respondent. It also adds new variables and makes adjustments and corrections.
We have added the following new sections to the file:
Physical Measures:
- We created the Physical Measures section, which contains variables pertaining to timed walk measurements, blood pressure measurements, hand grip strength measurements, height and weight measurements, waist measurements, lung function measurements, balance tests, and chair stand tests.
Assistance and Caregiving:
- We created the Assistance and Caregiving section, which contains variables pertaining to receiving any care for individual ADLS, using assistive devices for ADLS, receiving any care for individual IADLS, future ADL needs, receiving any care for ADLs or IADLs, receiving any informal care, receiving informal care from spouse, receiving informal care from children or grandchildren, receiving informal care from non-relatives, receiving any formal care, receiving formal care from paid professionals, receiving formal care from unpaid professionals, paid help, providing care for parents and grandchildren (previously in Family Structure section), and providing informal care to sick or disabled persons.
Stress:
- We created the Stress section, which contains variables pertaining to childhood/lifetime stressful events and self-rated childhood health and finances.
Psychosocial:
- We created the Psychosocial section, which contains variables pertaining to depressive symptoms using CES-D (previously in the Health section) and single life satisfaction question. This work was done in collaboration with Jacqui Smith and Lindsay Ryan of the University of Michigan.
We have added the following variables to the file:
Demographics and Identifiers:
- We added HACOHORT_C, which is the cohort in which the household was originally sampled.
- We added RAFBYEAR, RAFBMONTH, and RAFBDAY, indicating the wave at which the respondent reported the values used in RABYEAR, RABMONTH, and RABDAY, respectively.
- We added RwNHMLIV for all available waves, indicating whether the respondent lives in an institution.
- We added RwRXHIBP, RwRXHIBP_C, RwRXDIABI, RwRXDIAB, RwRXDIAB_C, RwCNCRMEDS, RwCNCRMEDS_C, RwCNCRCHEM, RwCNCRSURG, RwCNCRRADN, RwRXLUNG, RwRXLUNG_C, RwRXHEART, RwRXHEART_C, RwRXSTROK, RwRXSTROK_C, RwTRPSYCH, RwRXARTHR, RwRXARTHR_C, RwRXDYSLIP, RwRXDYSLIP_C, RwRXLIVER, RwRXLIVER_C, RwRXKIDNEY, RwRXKIDNEY_C, RwRXDIGEST, RwRXDIGEST_C, RwRXMEMRY, and RwRXMEMRY_C for all available waves, indicating whether and how the respondent was treated for high blood pressure, diabetes, cancer, lung condition, heart problems, stroke, psychiatric condition, arthritis, dyslipidemia, liver disease, kidney disease, digestive disease, and memory problems.
- We added RwDRINKR_C at all available waves, indicating the highest range of the number of drinks per day that the respondent reports for any one of three types of alcohol (liquor, beer, or wine).
- We added RwDRINKNR at all available waves, indicating whether the respondent has ever taken a drink first thing in the morning to steady nerves or get rig of a hangover.
Financial and Housing Wealth:
- We added HwABORR at all available waves, indicating the total amount of personal loans owed to others by the respondent and spouse.
Family Structure:
- We added RAMOMEDUCL and RADADEDUCL, indicating the highest level of education completed by the respondent’s mother and father. These variables use a harmonized scale that is a simplified version of the 1997 International Standard Classification of Education (ISCED) codes.
- We added RAMOMOCCUP_C and RADADOCCUP_C, indicating the occupation of the respondent’s mother and father before the respondent was age 17.
Employment History:
- We added RwJHOURTOT at all available waves, indicating the total number of hours the respondent works per week on their main job and other jobs.
- We added RwJGOVTEMP at all available waves, indicating whether the respondent is employed by the government.
- We added RwJSALARY_C at all available waves, indicating whether the respondent is paid salary or wages in their current job.
- We added RwJBONUS at all available waves, indicating the amount of bonuses the respondent received in the past year.
- We added RwJMSICK at all available waves, indicating whether the respondent missed work in the past year due to health problems.
- We added RwJNSICKAGR, RwJNSICKEMP, and RwJNSICKSF at all available waves, indicating the number of days of agricultural work, employed work, and self-employed work the respondent missed in the past year due to health problems.
- We added RwJBGYR and RwJBGMO at all available waves, indicating the year and month the respondent began their current job.
- We added RwJRSLEFT and RwJRSLEFT_C at all available waves, indicating the reason the respondent left their last employer, if they are currently not working.
Retirement:
- We added RwFRETF_C at all available waves, indicating whether the respondent’s previous report that they completed retirement or receding position procedures is disputed at the current interview or a later interview.
- We added RwWKAFTRET at all available waves, indicating whether the respondent worked after they completed the retirement process.
- We added RwRETRS_C at all available waves, indicating the respondent’s main reason for processing early or internal retirement.
We have made the following adjustments, improvements, and corrections to the data and
documentation:
All Sections:
- In version C of the Harmonized CHARLS, we had named the 2015 wave of CHARLS Wave 4, coinciding with that wave’s naming in the CHARLS documentation. Since CHARLS now refers to the 2015 wave as Wave 3 of CHARLS, we have renamed all variables for this wave to pertain to Wave 3, and now refer to the 2018 wave as Wave 4.
Demographics and Identifiers:
- We renamed HHID_W1 to HOUSEHOLDID_W1, indicating the 9-character household identifier at Wave 1.
- We adjusted RABYEAR, RABMONTH, and RABDAY so that Chinese lunar calendar birth dates are converted to solar (Gregorian) calendar birth dates. This change impacts the value of RwAGEY.
- We adjusted RADYEAR and RADMONTH so that Chinese lunar calendar death dates are converted to solar (Gregorian) calendar death dates.
Health:
- We moved RwBMI, RwHEIGHT, and RwWEIGHT, indicating the respondent’s body mass index, height in meters, and weight in kilograms, respectively, to the new Physical Measures section. Also, we renamed RwBMI to RwMBMI, RwHEIGHT to RwMHEIGHT, and RwWEIGHT to RwMWEIGHT to identify that these values were derived from measurements conducted by CHARLS as opposed to self-reported values.
- We renamed RwDRINK to RwDRINKEV to align better with ever drinking variables in other Harmonized dataets.
Health Care Utilization and Insurance:
- We renamed RwORDOC1M to RwTRDMED1M, indicating whether the respondent visit a Chinese medicine hospital in the past month.
- We renamed RwORDTIM1M to RwTRDMED1M, indicating the number of times the respondent visited a Chinese medicine hospital in the past month.
Financial and Housing Wealth:
- We updated the values of CyyyyCPINDEX based on updated values from the OECD. We continue to use 2010 as the base year.
Family Structure:
- We renamed HwPCNTF to HwPCNT, indicating whether the respondent and spouse had any weekly contact with parents/parents-lin-law in person, to avoid confusion with non-comparable HwPCNTF variables in other Harmonized datasets.
- We moved HwGRCARE and HwGKCARE, indicating whether the respondent gave care to parents/parents-in-law and grandchildren, respectively, to the new Assistance and Caregiving section. Also, we renamed HwGRCARE to HwGPCARE.
- Financial support to and from others at Wave 3, H3FOAMT, H3FOANY, H3TOAMT, and H3TOANY (previously named H4FOAMT, H4FOANY, H4TOAMT, and H4TOANY), were adjusted to also include support to and from siblings.
Employment History:
- We adjusted RwLBRF_C at all waves, indicating the respondent’s labor force status, to include both an agricultural employed work and an agricultural self- employed work category instead of one general agricultural work category.
Retirement:
- We removed RwRPLYNR, indicating the year when the respondent plans to stop working.
Introduction and Overview (介绍和概述)
Section titled “Introduction and Overview (介绍和概述)”This report documents the Harmonized CHARLS data files, a streamlined collection of variables derived from the China Health and Retirement Longitudinal Study (CHARLS). CHARLS is a panel survey of people aged 45 and over and their partners regardless of age in China. The main goal is to provide a high quality nationally representative sample of Chinese residents’ data to serve the needs of scientific research on health, economic position, and quality of life as people age. The survey elicits information about demographics, income, assets, health, cognition, family structure and connections, health care use and costs, housing, job status and history, expectations, biomarkers, and insurance.
CHARLS is supported by Peking University, the National Natural Science Foundation of China, the Behavioral and Social Research Division of the National Institute on Aging, and the World Bank. CHARLS shares the same basic guidelines as the Health and Retirement Study (HRS) and related aging surveys, (such as the English Longitudinal Study of Aging (ELSA) in England) and the Survey of Health, Aging and Retirement in Europe (SHARE) in Europe and Israel.
The first wave of CHARLS was conducted between June 2011 and March 2012. The sample population was selected as part of a stratified, multistage probability design. The first component of this sampling framework was the probability proportion to size (PPS) sampling of all county-level unit except for Tibet after stratifying by region, characteristic of the county (urban or rural), and per-capita gross domestic product (GDP). Households were selected within PSUs using a CHARLS-designed mapping/listing software (CHARLS-GIS) that uses Google Earth images to list all dwelling units in all residential buildings to create sampling frames. If the sampled household had occupants older than 40, one of them was randomly selected. If the selected person was aged 45 or older, they became a respondent. If the selected person was between age 40 and 44, they were reserved as a refreshment sample. This initial sample included 17,708 respondents in 10,257 households in 450 villages/urban communities in 150 counties/districts in 28 provinces. The second wave was conducted between July 2013 and January 2014 and included a refreshment sample consisting of individuals aged between 43 and 44 at Wave 1 and their partners. The third wave was conducted between July 2015 and January 2016 and included a refreshment sample consisting of individuals aged between 41 and 42 at Wave 1 and their partners. The fourth wave was conducted between July and November 2018 and included a refreshment sample consisting of individuals who were 40-years-old at Wave 1 and their partners.
The data include any individual interviewed at least once. This includes respondents and current and former spouses regardless of age.
The Harmonized CHARLS data file incorporates the demographic background data, family information data, family transfer data, health care and insurance data, health status and function data, household income data, household roster data, housing characteristics data, individual income data, weight data, and work, retirement and pension data. It does not include any data which is not for public release.
Gateway to Global Aging Data
Section titled “Gateway to Global Aging Data”The Health and Retirement Study (HRS) has achieved remarkable scientific success, as demonstrated by an impressive number of users, research studies, and publications using it. Its success has generated substantial interest in collecting similar data as population aging has progressed in every region of the world.
The result has been a number of surveys designed to be comparable with the HRS: the Mexican Health & Aging Survey (MHAS), the English Longitudinal Study of Ageing (ELSA), the Survey of Health, Ageing and Retirement in Europe (SHARE), the Korean Longitudinal Study of Aging (KLoSA), the Japanese Study on Aging Retirement (JSTAR), the Irish Longitudinal Study on Ageing (TILDA), the China Health and Retirement Longitudinal Study (CHARLS), Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI), the Brazilian Longitudinal Study of Ageing (ELSI), Healthy Ageing in Scotland (HAGIS), the Northern Ireland Cohort Longitudinal Study of Ageing (NICOLA), the Longitudinal Aging Study in India (LASI), and the Malaysia Ageing and Retirement Survey (MARS). The overview of this family of surveys, including their research designs, samples, and key domains can be found in Lee, Phillips, and Wilkens (2019).
As these surveys were designed with harmonization as a goal, they provide remarkable opportunities for cross-country studies. The value of comparative analyses, especially the opportunities they offer for learning lessons resulting from policies adopted elsewhere, is widely recognized. Yet there are only a limited number of empirical studies exploiting such opportunities. This is partly due to the difficulty associated with learning multiple surveys and the policies and institutions of each country.
Identifying comparable questions across surveys is the first step toward cross-country analyses. The Gateway to Global Aging Data (Gateway) helps users understand and use these large-scale population surveys on health and retirement. The Gateway includes several tools to facilitate cross-national health and retirement research. It includes a digital library of survey questions for all participating surveys. Its search engine enables users to find relevant survey questions. The Gateway also includes a concordance with information comparing measures within and across surveys over time. Using these tools, researchers can identify all questions related to particular key words or within a domain. The Gateway also includes population and sub- population estimates for key harmonized variables and presents them in graphs and tables that can be downloaded.
The Gateway can be accessed at https://g2aging.org/. For more information about using the Gateway visit the Help page. For more information about obtaining the Harmonized CHARLS or downloading the Stata file used to create the Harmonized CHARLS using the Gateway see “Chapter 4.Distribution and Technical Notes.”
Unit of Observation
Section titled “Unit of Observation”Like in the HRS, an age-eligible individual is sampled and then this individual and their spouse or partner regardless of age is interviewed, but no other household members, even if they are age-eligible. We distinguish between three units of observation: individual, couple, and household. A “couple” is a “single individual or individual with his/her spouse. A “household” includes the single individual, spouse, and all other household members.
Data File Structure
Section titled “Data File Structure”The Harmonized CHARLS data contain four waves of CHARLS. The data are stored in a “fat format” where each observation represents one respondent. The unit of observation is the individual. Each individual is uniquely identified by the unique identifier “ID”. Households are identified by HHID. Couples are identified by wave-specific HwCOUPID where “w” refers to the specific wave. Households are identified by the unique identifier “HOUSEHOLDID”. It is important to note that unlike the HRS, households in the CHARLS might include multiple couples and other household members.
Variable Naming Convention
Section titled “Variable Naming Convention”With few exceptions, variable names in the Harmonized CHARLS Data follow a consistent pattern. The first character indicates whether the variable refers to the reference person (“R”), spouse (“S”), a financial unit couple (“H”), and the full household (“HH”).1 2 The second character indicates the wave to which the variable pertains: “1”, “2”, “3”, “4”, or “A”. The “A” indicates “all,” i.e., the variable is not specific to any single wave. An example is RABDATE, the birth date of the respondent. The remaining characters describe the concept that the variable captures. For example:
S1HLTHLM
S: Spouse
1: Wave 1 (2011)
HLTHLM: Health problem limiting work
Variable S1HLTHLM captures whether the spouse of the reference person experiences an impairment or health problem that limits the kind or amount of paid work he/she can do. The name of the variable does not indicate who provided the information. For example, the spouse’s health problem may have been reported by the spouse himself or herself, or it may have been reported by the reference person as a proxy.
In the text below, we may refer to variables such as SwHLTHLM for example, without specifying the wave. This reference points at the group of variables S1HLTHLM, S2 HLTHLM, S3 HLTHLM, and S4 HLTHLM.
