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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2017 Mar 28;72(4):665–679. doi: 10.1093/geronb/gbx030

Physical Function in an Aging Population in Rural South Africa: Findings From HAALSI and Cross-National Comparisons With HRS Sister Studies

Collin F Payne 1,✉, Francesc Xavier Gómez-Olivé 1,2, Kathleen Kahn 2,3, Lisa Berkman 1
PMCID: PMC6075193  PMID: 28369527

Abstract

Objectives:

We use recently-collected data from the Health and Aging in Africa: a Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) cohort from Agincourt, South Africa, to describe physical functioning in this aging population, and place the overall level and age-trajectories of physical health in the context of other Health and Retirement Study (HRS) sister studies in low- and middle-income countries (LMICs).

Method:

We conduct multiple regression to estimate associations of physical functioning assessed from both self-report (activities of daily living [ADL] limitation, self-reported health) and performance (grip strength, gait speed) with socio-demographic and health characteristics in HAALSI, and use fully-interacted regression models to compare age-patterns of physical functioning outcomes cross-nationally.

Results:

Gender differences in self-reported health are minimal, and men had 30% higher odds of being ADL limited controlling for socio-demographic and health characteristics. Measured physical performance is closely tied with socioeconomic conditions, but self-reported measures have a much smaller or weaker socioeconomic gradient. In international age-adjusted comparisons, the HAALSI sample had lower physical performance outcomes than most comparison populations.

Discussion:

As the first HRS sister study undertaken in Africa, HAALSI adds vital information on population aging and health in the region. Continuing waves of HAALSI data will be a key resource for understanding differences in the complex processes of disability across LMIC contexts.

Keywords: Africa, Cross-national comparisons, Disability, Low- and middle-income countries, Physical functioning

Information on physical function and health among the older population in South Africa is key for understanding the current level of disability and ability of older men and women to perform basic care as well as engage fully in their families and communities. With the exception of the WHO-SAGE South Africa study (Kowal et al., 2012), few extant studies contain the sorts of standardized and validated measures needed to conduct meaningful comparisons across populations. The health and wellbeing of the growing older adult population in South Africa is changing rapidly, and will continue to do so in the coming decades as increasing numbers of people survive into old age (Tollman et al., 2008). But current data on the physical and functional ability of this aging population is limited, even as the population of older adults (defined here as aged 40+) is growing rapidly (US Census Bureau, 2016).

In this article, we use recently-collected data from the Health and Aging in Africa: a Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) cohort from Agincourt, South Africa. HAALSI is the first Health and Retirement Study (HRS) sister study conducted in Africa. We use this data to describe the prevalence and distribution of self-reported measures of activities of daily living (ADL) and general health and performance-based measurements of physical ability in this aging population. In addition, we use standardized outcomes to place the overall level and age-trajectories of physical health and physical performance in the context of other HRS sister studies in low- and middle-income countries (LMICs).

Background

Current information on physical function and disability among older adults in sub-Saharan Africa (SSA) is very limited, though available evidence suggests that this population faces a substantial burden of physical limitation in later life (Gómez-Olivé, Thorogood, Clark, Kahn, & Tollman, 2013; Payne, Mkandawire, & Kohler, 2013; Xavier Gómez-Olivé, Thorogood, Clark, Kahn, & Tollman, 2010). Evidence from Western and often more industrialized countries is not sufficient for understanding these emerging issues of disability and functional limitation in LMICs, where both definitions of disability and consequences of limitations in functioning may have very different consequences. Although national health sector strategic plans highlight the need for policies to prevent disabilities and ensure access to curative and rehabilitative care among older individuals in South Africa (South African Ministry of Health, 2013) and other SSA LMICs (African Union, 2007), there is a dearth of understanding among national and international decision-makers about the magnitude of the aging problem, the scope of old age-related health needs, and the patterns of health and disability at older ages (Aboderin, 2016; Aboderin & Beard, 2015).

Physical functioning in older ages can be conceptualized as a composite of “the overall effect of medical conditions, lifestyle, and age-related physiologic changes” in the context of an individual’s environment (Reuben et al., 2004). Declines in physical functioning are a core determinant of quality of life in old age, and even small declines in functioning associate with increased mortality, need for caregiving assistance, and health-related expenditures (Cooper, Kuh, Hardy, & Group, 2010). The sources of these declines are complex and multifaceted, resulting from the complex interplay between age-related biological changes (Visser et al., 2005), life-course social and economic conditions (Gómez-Olivé et al., 2014; Guralnik, Land, Blazer, Fillenbaum, & Branch, 1993; Stuck et al., 1999), injuries, occupational and environmental exposures (Balfour & Kaplan, 2002; Schoeni, Freedman, & Martin, 2008), and allostatic load (Karlamangla, Singer, McEwen, Rowe, & Seeman, 2002). Though physical function has become a common measure for social, epidemiologic, and clinical research in high-income countries, it remains understudied in LMIC contexts, particularly in SSA, largely due to a lack of high-quality data on older populations.

Most research frameworks relating to disability start with an early-stage disease process that flows to influence functioning and ultimately leads to disability. We recognize that multiple frameworks of disability—including the disablement process (Verbrugge & Jette, 1994) and the International Classification of Functioning, Disability, and Health (World Health Organization, 2001)—hold value and should be subject to empirical investigation. In this study, we take a more agnostic, less causal approach, in which we examine a host of environmental and contextual conditions that may influence physical functioning, performance, and disability, and seek to understand the overall age patterns of these conditions. This strategy, in large part, follows from the cross-sectional nature of our data—we are limited in investigating specific disease processes or environmental constraints leading to disease and disability. Furthermore, our approaches incorporate an ecosocial framework for population health that fully incorporates the potential for sociostructural and environmental conditions to shape a number of outcomes, in this case physical functioning and disability (Krieger, 2001). We therefore focus primarily on describing cross-sectional patterns of physical and functional outcomes (for which we have stronger data) in HAALSI, situating these patterns within a wider set of HRS sister studies. We recognize that disability is not purely a health-based measure, and is a function of the complex interactions between health conditions, environmental factors, and personal factors (World Health Organization, 2001; Schneidert et al., 2003). We thus embed our descriptive findings of prevalence in analyses incorporating socio-demographic characteristics that may shape both functional abilities and access to resources for compensating for limitations.

Self-reported measures of functional ability (such as the Activities of Daily Living scale (Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963) and the Nagi Index (Nagi, 1976)) are designed to measure gaps between the demands of a given activity in a given environment and an individual’s capacity to perform that activity, and have proven broadly useful for comparing disability across contexts (Minicuci et al., 2004; Payne, 2015). These self-reported measures have deservedly gained widespread use in studies of functional aging—they are simple and cost-effective to administer, and have been shown to predict a host of outcomes such as physical decline and mortality. However, there is a growing recognition that more discriminating assessment tools may be needed (Reuben et al., 2004), particularly tools that allow for assessment of smaller-scale changes in physical ability that may not be evident from self-reported scales.

Increasingly, studies of aging are incorporating measures of physical performance such as gait speed and hand grip strength (Leong et al., 2015). These performance assessments are largely determined by physiological functions, and the decline in these physiological functions with age is a substantial contributor to frailty and disability in later life (Cooper et al., 2010). The HAALSI study collects two primary measures of physical performance: walking speed, which reflects lower limb strength and mobility, and grip strength, which acts a marker of overall muscle strength (Goldman, Glei, Rosero-Bixby, Chiou, & Weinstein, 2014; Goldman et al., 2014). These performance assessments are considered to have greater face validity and reliability than self-reports, are more likely to be comparable across contexts, and capture a wide range of physical ability, including early stages of impairment that may not be captured by self-reported measures (Goldman et al., 2014; Guralnik et al., 1994; Reuben et al., 2004). These physical performance measures add a finer grain of detail than self-reported measurements, including smaller physiological changes and measurement of biological processes underlying physical decline, such as age-related declines in muscle mass and strength (Visser et al., 2005). As we obtain richer data and are able to contrast self-reports and perceptions of functioning with performance based assessments, more nuanced inquiry is possible into how social and physical environments may frame older adults’ perceptions and capacity to function in daily life.

As the first HRS sister study undertaken in Africa, HAALSI fills an important gap in the knowledge base on population aging and health in the region. In this article, we employ a series of self-reported and performance-based measures of physical functioning and disability that are both well-validated and highly predictive of subsequent mortality and progression (Cooper et al., 2010). The physical functioning measures collected in HAALSI were designed to be directly comparable with other large scale international studies, including the United States HRS and other HRS sister studies. With regard to results presented here, HAALSI collected detailed information on the self-reported general health and ADL as well as performance measures of upper and lower body function. Neither self-reported measures of ability nor physical performance assessments tell the whole story of disability, conceptualized here as a gap between environmental demands and intrinsic capabilities (Verbrugge & Jette, 1994). The meaning of self-reports may differ across contexts, and may often be more aligned with normative expectations and cultural values than physical ability to complete certain tasks (Capistrant, Glymour, & Berkman, 2014). Assessing both performance measures and experienced limitations in ADL is crucial to understanding the true burden of physical disability in a population, as these measures tap into very different constructs and are not directly interchangeable (Jette, 1994).

Methods

Data

The HAALSI study is a population-based survey that aims to examine and characterize a population of older men and women in rural South Africa with respect to health, well-being, and physical and cognitive function, as well as the social, environmental, and biological factors affecting these domains.

Setting

The study was conducted in the Agincourt sub-district in the Mpumalanga Province, South Africa, where the MRC/Wits Rural Public Health and Health Transitions Research Unit has been running the Agincourt Health and Demographic Surveillance System site since 1992 (Kahn et al., 2012). In 2015 the study area covered around 450sq km including 32 villages with a population of approximately 110,000 people. The primary health care system consists of six clinics and two health centers. Three hospitals covering the district are 45 to 60 km from the study site. The social situation of this community has improved in the past 20 years but there are still huge gaps in availability of electricity, water and tarred road coverage. Unemployment rates are high, putting stress on families and leading to high rates of work migration and reliance on remittances as an important source of income.

Sampling

Participants were sampled from the existing framework of the Agincourt Health and Socio-Demographic Surveillance System (AHDSS) site in Mpumalanga province (Kahn et al., 2012). Individuals 40 years and older as of July 1, 2014 and permanently living in the study site during the 12 months previous to 2013 census round were eligible for this study. Using the full 2013 Census data, we identified a sampling frame of 8,974 women and 3,901 men aged 40 and older who met the residence criteria. Our target sample size was approximately 5,000 completed interviews. Assuming an 80% response rate, we selected a total of 6,281 women and men for the main household study.

Data Collection

All sampled individuals were visited at home from November 2014 to November 2015. Trained, local fieldworkers collected survey data electronically using Computer Assisted Personal Interviews (CAPI). Surveys were conducted in the local Shangaan language, with instruments translated from English and back-translated to ensure reliability. Out of the 6,281 selected for the study, 391 had moved outside of the study site or were deceased. Out of the remaining 5,890 eligible individuals, 5,059 (86%) participated in the baseline survey. A table comparing the demographic characteristics between the surveyed individuals, the selected sample, and the eligible sample is included as Supplementary Table 1. Overall, the HAALSI interviewees were slightly older than those in the AHDSS sample frame, and the gender balance was fairly skewed in the AHDSS sample frame compared to the HAALSI sample or the interviewees (as men were oversampled to obtain gender balance). Additional waves of longitudinal follow up of the cohort are planned every 3 years. The data and questionnaires for HAALSI are publically available at http://haalsi.org/.

Ethics and Grant Information

Ethical approval for HAALSI was obtained from the University of the Witwatersrand Human Research Ethics Committee (#M141159), the Harvard T.H. Chan School of Public Health Office of Human Research Administration (#13–1608), and the Mpumalanga Provincial Research and Ethics Committee.

International Comparisons

To place the HAALSI cohort in international context, we compare the functioning and self-reported physical health of the HAALSI sample to the community-dwelling sample of the U.S. Health and Retirement Study (HRS) (Health and Retirement Study, n.d.) and other HRS sister studies in Mexico (MHAS) (MHAS Mexican Health and Aging Study, 2012, n.d.), and China (CHARLS) (CHARLS China Health and Retirement Longitudinal Survey, 2013), the China Health and Retirement Longitudinal Study. The HRS is a nationally-representative sample survey of more than 35,000 individuals over age 50 in the United States, and has become the model for a harmonized international network of longitudinal studies of aging (Sonnega et al., 2014). The HRS questionnaire and data collection procedures for physical performance were used as the initial structure for HAALSI. The MHAS is a nationally-representative survey of adults aged 50+ in Mexico, and was designed to facilitate cross-national comparisons with the HRS and other HRS sister studies and address the dynamics of health and ageing in Mexico (Wong, Michaels-Obregon, & Palloni, 2015). Data collection started in 2001, and the 2012 wave of data collection (used in this study) included over 14,000 respondents. The CHARLS is, similarly, a nationally-representative survey of persons aged 45+ in China, examining the social, economic, and health circumstances of China’s rapidly aging population (Zhao, Hu, Smith, Strauss, & Yang, 2014). The CHARLS questionnaire and related data collection procedures were harmonized with the HRS and other HRS sister studies to maximize the international comparability of the data. In each of these studies, we use the full non-institutionalized population as a benchmark for comparing population-level physical performance and self-reported limitations. Grip strength and gait speed were collected on selected sub-samples in HRS and MHAS. Our use of standardized outcomes enables the comparison of performance of sample members in each site to other international studies, including populations from both developed (HRS) and developing (CHARLS, MHAS) countries. These analyses are critical to understand global aging and to inform surveillance efforts.

Measures

Activities of Daily Living

Our major outcome reflecting functional limitations was the assessment of ADL (Katz et al., 1963). The questions ask the respondent to report difficulty or inability to do ADL on a regular basis due to health or memory problems, excluding any limitations expected to last less than 3 months. Respondents were asked to report difficulty or inability to do any of the following five activities: bathing, eating, getting in/out of bed, toileting, and walking across a room. For the analysis, a dichotomous variable was generated, which took the value of 1 if the respondent reported difficulty on one or more ADLs (1+ ADL) and 0 otherwise. In the cross-national analyses, the CHARLS study did not ask about walking across a room; we instead use the response to the question “Do you have difficulty walking 100 meters.” The distribution of those reporting difficulty by item is reported in Supplementary Table 2.

Self-Rated Health

Self-reported health is based on the question “In general, how would you rate your health today?”, with responses of very good, good, moderate, bad, and very bad (for HAALSI) and excellent, very good, good, fair, and poor (for HRS, MHAS, and CHARLS). For this analysis, we treat self-rated health as a continuous outcome variable, with “very good/excellent” health coded as 5 and “very bad/poor” health coded as 1.

Timed Walk

Gait speed correlates with many indicators of poor health among older adults, including worse self-reported health (Jylhä, Guralnik, Balfour, & Fried, 2001), disability (Guralnik et al., 1994), falls (Bath & Morgan, 1999), hip fracture (Dargent-Molina et al., 1996), inflammation (Cesari et al., 2004), and mortality (Cooper et al., 2010). Interviewers marked a 2.5-meter course over an obstacle-free surface, and the respondent was asked to walk the course twice at their usual walking speed. We add these two walk times together to generate a continuous 5-meter walk time. As field procedures differed somewhat between countries in the cross-national comparison (MHAS used a 2-meter course), we generated a continuous variable of gait speed (total walk distance/walk time) to standardize this measure. Gait speeds below 0.2 m/second or above 2 m/second were treated as out-of-range and set to missing. Respondents who were unable to walk were not tested (N = 144).

Hand Grip Strength

Hand grip strength predicts physical functioning and disability (Rantanen et al., 1999), health-related quality of life (Sayer et al., 2006), inflammation (Cesari et al., 2004) and mortality (Cooper et al., 2010). The test assesses muscle strength and presence of joint conditions affecting the hand (e.g., arthritis) (Crimmins et al., 2008). A Smedley digital hand dynamometer was used to test grip strength, with the test repeated twice for each hand. For this analysis, we use the average of the two grip strength measures on the self-reported dominant hand (or the average of the two highest measures on either hand for ambidextrous individuals) as our measure of dominant hand grip strength. Grip strength measures above 75 kg were treated as out-of-range and set to missing. Respondents with a hand injury or self-reported severe arthritis were not tested (N = 215).

HIV

HIV status was assessed from a blood spot using the Vironostika Uniform 11 (Biomeriuex, France) screening assay.

Cardiovascular Disease

Heart disease was assessed through a series of questions about physician’s diagnosis of angina, heart failure, and previous heart attacks, from which we generate a binary variable of heart disease (1 = any reported heart disease, 0 = no reported heart disease). Similarly, respondents were asked about previous physician diagnosed strokes, with stroke coded as 1 = reported stroke and 0 = no reported stroke.

Demographic Indicators

Educational attainment was grouped into four categories: no formal schooling, some primary education (1–7 completed years), some secondary education (8–11 completed years), and completed secondary education or more (12+ years). The HAALSI household asset index was calculated following DHS methodology (Rutstein & Johnson, 2004) that combines information on household durable goods and infrastructures through principal component analysis, we utilize quantiles of household asset index as a measure of SES. Other demographic indicators included: age and 5-year age groups (40–85+), sex (men and women), marital status (never married, married/cohabiting, divorced/separated, widowed), total household size, and respondent’s employment status (currently employed and not currently employed).

Data Analysis

All analyses were conducted using STATA version 14.1.

Summary Statistics for the HAALSI Population

We first describe the socio-demographic characteristics of the HAALSI population by 10-year age groups. We then investigate the age patterns of physical functioning outcomes by sex based on marginal means from linear (grip strength, gait speed, and self-reported health) or logistic (1+ ADL) regressions of the respective physical health indicator on indicator variables for each 5-year age grouping.

Associations Between Self-Reports and Objective Measures

Analyses of the associations between self-reported and objective measures of physical health are based on correlations, with a supplemental analysis directly comparing self-reported ADL walking difficulty with gait speed.

Associations Between Physical/Functional Limitations and Socio-demographic and Health Characteristics

In the SSA rural context studied here, most often family is the primary resource for helping older adults with functional limitations maintain critical functions (Aboderin, 2011), and thus we expect that the social and economic conditions of the household (such as socioeconomic circumstances, education, and family/household composition) may play an significant role in an individual’s experience with disability. We estimate associations between physical functioning (ordinary least squares [OLS] for grip strength, gait speed, and self-reported health, logit for ADL limitation) and socio-demographic and health characteristics using multivariate regression.

Cross-National Comparisons of Age Patterns in Physical Functioning

Cross-national comparisons are conducted through fully interacted models (Gordon, 2015), regressing the respective physical health indicator (OLS for grip strength, gait speed, and self-reported health, logit for ADL limitation) on 5-year age group dummies, sex, and sample dummies (HAALSI, HRS, MHAS, and CHARLS), and including interactions between age group and sample dummies, age groups and gender, gender and sample dummies, and age group, gender, and sample dummies. Due to the large number of interaction terms in the model, the regression coefficients are difficult to directly interpret. A figure presenting the average marginal effects for each physical health indicator by gender and country over age groups is included in the main text, with a detailed table of regression estimates included in Supplementary Materials. Individual-level sample weights are used for the HRS, MHAS, and CHARLS samples (including weights for subsamples selected for gait speed and grip strength collection); the HAALSI sample is self-weighting by age and gender and thus requires no additional weighting.

Results

Sample Characteristics

Table 1 (columns 1–5) reports summary statistics by 10-year age group, from the HAALSI sample aged 40–49 to those aged 80+. All respondents are 40 years old or older, with about 46% aged 40–59 and 54% aged 60+. Schooling attainment is low overall, though it declines substantially with increasing age—fewer than half of the sample over age 60 had received any formal education. Nearly 80% of respondents lived with 2 or more other people, though with increasing age, household sized declined, and over 30% of respondents over age 80 were living either alone or with only one other person. Employment is fairly low at all ages, but declines very rapidly with age. About 23% of the sample is HIV+, and of those who tested positive, about half (53%) report currently being on antiretroviral therapy. HIV exhibits a strong age gradient—almost 34% of the sample aged 40–59 are HIV+, whereas only 14% over the age of 60 are HIV+, possibly reflecting high levels of mortality among HIV+ individuals for these cohorts in the past. Rates of physician diagnosed heart disease and stroke are low at only 3%–4%, though they do increase slightly with age. Supplementary Table 3 presents these summary statistics by HIV status. HIV+ individuals tend to be younger, less educated, live in less wealthy households, and are slightly less likely to be married than HIV− individuals.

Table 1.

Summary Statistics for the HAALSI Sample by Age

(1) (2) (3) (4) (5) (6)
Age group
40–49 50–59 60–69 70–79 80+ Total
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
Age 44.1 (2.8) 54.1 (2.9) 64.1 (2.8) 74.0 (2.7) 85.4 (4.5) 61.7 (13.0)
Gender
 Men 46% 44% 49% 51% 39% 46%
 Women 54% 56% 51% 49% 61% 54%
Education level
 No formal education 19% 34% 50% 65% 80% 46%
 Primary (1–7 years) 29% 40% 41% 29% 18% 34%
 Some secondary (8–11 years) 25% 15% 7% 5% 1% 11%
 Secondary or more (12+ years) 26% 11% 3% 1% 0% 9%
Marital status
 Never married 16% 6% 3% 2% 1% 6%
 Divorced/separated 16% 16% 13% 9% 6% 13%
 Widowed 12% 22% 30% 44% 62% 30%
 Married/cohabitating 56% 56% 54% 46% 30% 51%
Household composition
 Living alone 9% 10% 10% 12% 14% 11%
 Living with 1 other person 9% 9% 10% 13% 17% 11%
 Living in 3–6 person household 57% 47% 46% 47% 44% 48%
 Living in 7+ person household 26% 34% 34% 28% 25% 31%
Employment status
 Employed 35% 27% 7% 2% 1% 16%
 Not currently employed 65% 73% 93% 98% 99% 84%
Household asset index
 First (lowest) quantile 20% 21% 19% 21% 25% 21%
 Second quantile 20% 19% 18% 21% 24% 20%
 Third quantile 20% 18% 19% 21% 20% 20%
 Fourth quantile 19% 20% 22% 19% 18% 20%
 Fifth (highest) quantile 21% 22% 22% 18% 12% 20%
HIV+ 38% 31% 20% 11% 3% 23%
Diagnosed heart disease 2% 3% 3% 4% 4% 3%
Diagnosed stroke 1% 3% 3% 4% 4% 3%
Number of observations 918 1,410 1,304 878 548 5,058
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Note. HAALSI = Health and Aging in Africa: a Longitudinal Study of an INDEPTH Community in South Africa.

Age Patterns of Physical Health in the HAALSI Sample

Figure 1 presents the age patterns of ADL limitation, grip strength, 5-meter walk time, and self-rated health. The proportion of the sample with ADL limitations increases markedly with rising age—after remaining fairly steady at just under 0.1 from age 40–60, the proportion rises rapidly from ages 65–85, reaching just under 0.3 for men at age 85 and just under 0.4 for women at age 85 (panel A). Overall, the patterns of ADL limitation by age are very similar between men and women. Levels of self-rated health were again similar between men and women with a steady decline with increasing age, though women reported somewhat worse health after age 50 (panel B). Men were slightly faster than women at the 5-meter walk at all ages, though gait speed decreases substantially with rising age (panel C). Grip strength declines with age, from about 37 kg at age 40 for men (27 kg for women) to about 23 kg for men aged 80 (16 kg for women) (panel D). Supplementary Table 4 presents means of our physical health measures for the HIV+, HIV−, and total population by 10-year age groups, we see no substantial or significant differences by HIV serostatus within these age groups, though the HIV+ population is quite small above age 70.

Figure 1.

Figure 1.

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Age patterns of activities of daily living (ADL) limitation, self-rated health, gait speed, and grip strength for men and women in the Health and Aging in Africa: a Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) sample. Notes: Marginal means (with 95% confidence intervals) for men and women obtained by regressing the respective physical health indicator on 5-year age groups and gender.

Associations Between Physical Health Indicators

Table 2 presents the means and standard deviations of the indicators of physical function (column 1), as well as the correlations across the measures of function and self-reported health, disaggregated by gender. As expected, ADL limitation is correlated negatively with grip strength and self-rated health, and negatively with gait speed. Self-rated health is positively correlated with gait speed, and self-rated health positively correlates with grip strength. The magnitude of these correlations are similar between men and women, though they are slightly weaker overall for women compared with men.

Table 2.

Summary Statistics for Physical Health Indicators by Gender, HAALSI Sample

(1) (2) (3) (4)
Correlation across measures
Mean (SD) 1+ ADL Grip Str. Gait Speed
Women
 1+ ADL limitation 0.10 (0.29) — — —
 Grip strength 21.62 (7.42) −0.16 — —
 Gait speed (m/s) 0.66 (0.27) −0.13 0.15 —
 Self-rated health 3.62 (1.06) −0.31 0.21 0.16
Men
 ADL limitation 0.10 (0.30) — — —
 Grip strength 29.60 (9.72) −0.15 — —
 Gait speed (m/s) 0.70 (0.28) −0.17 0.16 —
 Self-rated health 3.73 (1.03) −0.27 0.26 0.16
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Note. ADL = activities of daily living; HAALSI = Health and Aging in Africa: a Longitudinal Study of an INDEPTH Community in South Africa. Self-rated health ranges from 1 (Very bad) to 5 (Very good). ADL limitation classified as reporting difficulty on one or more of the following activities: eating, bathing, toileting, getting in/out of bed, walking.

In order to understand the relationship between self-reported and performance-based function, we examined the associations between reporting an ADL walking difficulty and gait speed. In essence, this comparison provides a useful benchmark for how well our objective measure of walking ability (gait speed) compares to a self-rated measure of walking ability. Supplementary Figure 1 shows boxplots (Tukey, 1997) of gait speed by self-reported ADL walking difficulty, over 10-year age groups. Overall, individuals reporting an ADL walking difficulty have slightly slower gait speed, though there is substantial overlap in the distributions of walking speed within these groups.

Associations Between Physical Functioning and Socio-demographic and Health Characteristics

Table 3 presents the results of multivariate regressions of each physical health measure on socio-demographic characteristics for the HAALSI sample. Column 1 includes basic demographic characteristics (gender, age, education, marital status) and measures of HIV and chronic conditions (stroke and heart disease). Column 2 adds household composition, employment status, and a measure of household assets. As expected, physical function declines substantially with age in each of our four measures—risk of ADL limitation increases, grip strength declines, gait speed decreases, and self-rated health declines. Controlling for socio-demographic and health characteristics, men reported slightly better self-rated health and had a 0.032 meters per second faster gait speed and a 7.8 kg stronger grip strength than women. However, controlling for these characteristics, men had a 30% higher odds of reporting ADL limitation than women. Increased schooling attainment is associated with higher grip strength, faster walking speed, and improved self-reported health, but is not significantly associated with ADL limitation. Never-married individuals have worse performance based physical functioning and worse self-rated health compared to married counterparts, and widowed individuals had a slower gait speed, worse self-rated health, and had a 40% higher odds of reporting an ADL limitation than currently married individuals.

Table 3.

Regression-Based Associations Between Physical Functioning and Socio-demographic and Health Characteristics, HAALSI Sample

(1) (2) (1) (2) (1) (2) (1) (2)
1+ ADL (Logit) SR Health (OLS) Gait speed (m/s) (OLS) Grip strength (kg) (OLS)
Age (continuous) 0.050** [0.040,0.060] 0.047** [0.037,0.057] −0.018** [−0.021, −0.016] −0.017** [−0.020, −0.014] −0.0035** [−0.004, −0.003] −0.0031** [−0.004, −0.002] −0.24** [−0.27, −0.22] −0.23** [−0.26, −0.21]
Gender (Women = ref)
 Men 0.20*** [−0.030,0.44] 0.26* [0.0063,0.51] 0.053*** [−0.0063,0.11] 0.057*** [−0.0049,0.12] 0.032** [0.015,0.049] 0.029** [0.012,0.047] 7.82** [7.31,8.34] 7.77** [7.23,8.31]
Education level (none = ref)
 Primary (1–7 years) −0.16 [−0.40,0.075] −0.065 [−0.31,0.18] 0.057*** [−0.0084,0.12] 0.034 [−0.034,0.10] 0.012 [−0.0058,0.030] 0.018*** [−0.000039,0.037] 0.73** [0.23,1.23] 0.63* [0.11,1.15]
 Some secondary (8–11 years) −0.26 [−0.70,0.18] −0.063 [−0.51,0.39] 0.15** [0.057,0.24] 0.090*** [−0.0070,0.19] 0.018 [−0.0093,0.046] 0.028* [0.000046,0.057] 1.94** [1.08,2.81] 1.71** [0.81,2.60]
 Secondary or more (12+ years) −0.21 [−0.75,0.34] 0.25 [−0.33,0.83] 0.33** [0.23,0.42] 0.18** [0.071,0.28] 0.019 [−0.0100,0.048] 0.026 [−0.0070,0.059] 2.96** [1.95,3.97] 2.23** [1.15,3.31]
Marital status (currently married = ref)
 Never married 0.44*** [−0.063,0.95] 0.42 [−0.11,0.95] −0.22** [−0.35, −0.092] −0.17* [−0.30, −0.035] −0.049** [−0.081, −0.017] −0.054** [−0.088, −0.020] −2.47** [−3.76, −1.19] −1.91** [−3.21, −0.60]
 Divorced/ separated 0.30*** [−0.043,0.64] 0.31 [−0.072,0.69] −0.13** [−0.22, −0.047] −0.091*** [−0.18,0.0033] 0.01 [−0.016,0.036] 0.0092 [−0.019,0.038] −0.73*** [−1.51,0.047] −0.26 [−1.08,0.56]
 Widowed 0.35** [0.085,0.62] 0.35* [0.066,0.64] −0.14** [−0.21, −0.065] −0.12** [−0.20, −0.046] −0.026* [−0.047, −0.0059] −0.025* [−0.046, −0.0037] −0.54*** [−1.10,0.018] −0.32 [−0.91,0.27]
HIV (HIV− = ref)
 HIV*** 0.087 [−0.21,0.38] 0.076 [−0.22,0.37] 0.034 [−0.035,0.10] 0.035 [−0.034,0.10] 0.027* [0.0054,0.048] 0.027* [0.0060,0.048] 0.28 [−0.31,0.86] 0.3 [−0.28,0.89]
Heart diseasea (no diagnosed heart disease = ref)
 Heart disease 0.077 [−0.45,0.61] 0.024 [−0.52,0.57] −0.18* [−0.35, −0.016] −0.17* [−0.34, −0.0043] −0.071** [−0.12, −0.024] −0.068** [−0.11, −0.022] −0.056 [−1.50,1.39] 0.013 [−1.45,1.47]
Strokeb (no diagnosed stroke = ref)
 Stroke 2.03** [1.67,2.38] 2.08** [1.72,2.44] −0.53** [−0.72, −0.34] −0.52** [−0.72, −0.33] −0.091** [−0.14, −0.038] −0.091** [−0.14, −0.041] −2.87** [−4.21, −1.52] −3.03** [−4.37, −1.69]
Household composition (3–6 person household = ref)
 Living alone −0.46* [−0.85, −0.071] −0.026 [−0.13,0.079] −0.006 [−0.036,0.024] 0.19 [−0.71,1.09]
 Living with 1 other person 0.067 [−0.25,0.38] −0.082*** [−0.18,0.013] −0.015 [−0.040,0.011] −0.70*** [−1.41,0.019]
 Living in 7+ person household −0.093 [−0.35,0.16] −0.066* [−0.13, −0.0015] 0.019* [0.00068,0.037] 0.33 [−0.19,0.85]
Employment status (unemployed = ref)
 Employed −0.74** [−1.24, −0.24] 0.21** [0.13,0.28] 0.027* [0.0036,0.050] 2.01** [1.29,2.73]
Household asset index quantile (first = ref)
 Second quantile 0.18 [−0.13,0.49] 0.081*** [−0.010,0.17] −0.011 [−0.039,0.016] −0.16 [−0.88,0.56]
 Third quantile −0.16 [−0.49,0.17] 0.097* [0.0034,0.19] −0.058** [−0.083, −0.033] 0.88* [0.12,1.64]
 Fourth quantile −0.23 [−0.59,0.12] 0.13* [0.029,0.22] −0.031* [−0.059, −0.0041] 0.63*** [−0.11,1.37]
 Fifth quantile −0.66** [−1.06, −0.27] 0.17** [0.077,0.27] −0.035* [−0.063, −0.0068] 1.22** [0.40,2.03]
 Number of observations 5,038 4,936 5,036 4,934 4,664 4,581 4,628 4,540
Open in a new tab

Notes. ADL = activities of daily living; HAALSI = Health and Aging in Africa: a Longitudinal Study of an INDEPTH Community in South Africa; OLS = ordinary least squares. Self-rated health ranges from 1 (Very bad) to 5 (Very good). ADL limitation classified as reporting difficulty on one or more of the following activities: eating, bathing, toileting, getting in/out of bed, walking.

aHeart disease is coded as 1 if respondent reported being previously diagnosed with heart failure, prior heart attack, and 0 otherwise.

bCoded as 1 if respondent reported having a previous diagnosis of stroke, 0 otherwise.

*p < .05; **p < .01; ***p < .10.

When we turn to health conditions and functioning, we find modest associations. HIV status was not associated with ADL limitation, grip strength, or self-rated health, though HIV+ individuals had a 0.027 faster meters per second gait speed on average compared to HIV− individuals. Doctor diagnosed heart disease was associated with a slower gait speed and worse self-rated health. Doctor diagnosis of stroke was very strongly associated with all physical functioning outcomes, with individuals reporting stroke diagnosis having a 3 kg weaker grip strength, a 0.091 meters per second slower walking speed, lower self-reported health, and an eightfold higher odds of ADL limitation.

Column 2 incorporates variables on household size, employment, and household assets into these models. Household size is not systematically associated with physical performance, but individuals living alone had a 35% lower odds of reporting an ADL limitation than their counterparts living in 3–7 person households. Employed individuals had markedly better physical functioning on all four measures than those not currently employed. Higher household asset quintiles are associated with better grip strength, faster gait speed, and better self-reported health. The association with ADL limitation was somewhat weaker—individuals in the highest asset quantile had a lower likelihood of being ADL limited than individuals in the lowest quantile, but no other differences were large or significant.

Overall, these findings suggest that physical performance is fairly closely tied with socioeconomic conditions. However, these performance differences do not appear to translate into improved self-reported functioning in daily life. We observe no differences in reported ADL limitation by level of education, and only the difference between the lowest and highest household asset index quintiles was significant. Unsurprisingly, physician diagnosed stroke was strongly associated with worse reported physical functioning and physical performance.

Cross-National Comparisons of Physical Health

We turn now to compare our results with those in other HRS sister studies. Our aim here is to add to the knowledge base on the comparability of population-level physical functioning across these four HRS sister studies, and ultimately to better understand the uniqueness or generalizability of patterns of functioning across LMIC populations. In Figure 2, we plot the age patterns of our physical health outcomes from HAALSI, the 2012 wave of HRS (United States), the 2012 wave of MHAS (Mexico), and the 2013 wave of CHARLS (China). We restrict our analyses to individuals aged 50+ to directly compare across these surveys. These graphs show the estimated age pattern for each physical health outcome, with a full table of regression coefficients included in Supplementary Table 5. Panel A compares the proportion of men and women aged 50+ with 1+ ADL limitations in each country by age. We see that the HAALSI cohort stands out in two ways—the level of self-reported ADL limitation is somewhat lower than in the other contexts (particularly in ages 50–70), and there are only minimal gender differences in the proportion ADL limited by age. In contrast, men in the HRS, MHAS, and CHARLS samples report markedly lower rates of ADL limitation than women (Supplementary Table 5), a finding consistent with the large majority of previous studies of ADL prevalence by gender (Crimmins, Kim, & Solé-Auró, 2011).

Figure 2.

Figure 2.

Open in a new tab

Age patterns of activities of daily living (ADL) limitation (A), self-rated health (B), gait speed (C), and grip strength (D) for men and women in the Health and Aging in Africa: a Longitudinal Study of an INDEPTH Community in South Africa (HAALSI), Health and Retirement Study (HRS), Mexican Health and Aging Study (MHAS), and China Health and Retirement Longitudinal Survey (CHARLS) samples. Notes: Marginal means (with 95% confidence intervals) obtained by regressing the respective physical health indicator on 5-year age groups and gender, fully interacted with sample country dummies.

Panel B of Figure 2 displays the age patterns of self-reported health in the four samples. Self-rated health is quite high in the HAALSI cohort compared to the other contexts, with individuals more likely to report that they are in good or very good health than in the other three countries (though the differences in response categories between HAALSI and the other studies limits a firm interpretation of these differences). Women in MHAS, CHARLS, and HAALSI report significantly worse overall self-reported health than men, but there is no significant difference between males and females in the HRS sample.

In panel C, we compare the walking speed of men and women in these four contexts. HRS and CHARLS collected timed walks only from the older population (65+ in HRS, 60+ in CHARLS), so these analyses are restricted to ages 65+ for comparability. Physical functioning (grip strength and walk time) tests were conducted on subsamples in HRS and MHAS, resulting in a somewhat decreased sample size for these analyses (see Supplementary Table 5, columns 3 and 4). Walking speed is slowest in HAALSI and CHARLS, and somewhat faster in HRS and MHAS.

Panel D of Figure 2 compares dominant hand grip strength between the four samples. Levels of grip strength for men and women are highest in the HRS, followed by CHARLS. We see that grip strength levels in HAALSI are comparable to those from MHAS, particularly for women. Gender differences in grip strength are significantly lower for older adults in the HAALSI sample than in the United States, Mexico or China (see Supplementary Table 5, column 4).

Discussion

In this article, we present baseline data on physical performance and self-reported measures of health and functioning from the HAALSI cohort, describe the prevalence of self-reported and objective measures of physical functioning and their associations with socio-demographic characteristics, and place the physical ability of the HAALSI cohort in the context of the HRS and other HRS sister studies in LMICs. The objective of these analyses is to provide a comprehensive evidence-base of the functional ability of the older adult population in rural South Africa, and to use standardized and validated measures to provide a benchmark for understanding how older adults in rural South Africa compare to older adults enrolled in other major global studies.

As the first HRS sister study undertaken in Africa, HAALSI adds vital information on population aging and health in the region. The HAALSI cohort, a community-based sample drawn from the Agincourt HDSS, represents a rural, low-income population, with very low levels of schooling, low levels of employment, and an HIV+ prevalence of over 20%. The sample response rate was quite high at 86%, and we had very little item-level missingness on our key outcome measures—under 3% for self-reported measures of health and ADL items, and under 6% for performance-based assessments of gait speed and hand grip strength (with the majority of missingness from physical inability to complete the assessment, not refusal).

Older adults in HAALSI report fairly good self-reported health and low rates of ADL limitation, though both performance-based and self-reported physical health decline substantially with age. Women and men had overall very similar age patterns of ADL limitation and self-rated health, but women had slightly slower gait speed and substantially lower grip strength than men. Controlling for socio-demographic and health characteristics, men had a 30% higher odds of being ADL limited—a finding at odds with almost all published research on gender differences in disability among older individuals (Crimmins et al., 2011).

Measured physical performance appears to be closely tied with socioeconomic conditions—individuals in wealthier households and those with more education had substantially faster gait speeds and stronger grip strengths than their less-educated and poorer counterparts, a finding in keeping with prior research on SES gradients in higher-income contexts (Haas, Krueger, & Rohlfsen, 2012; Hurst et al., 2013). However, these performance differences do not appear to translate into improved functioning in daily life—we observe no differences in reported ADL limitation by level of education (in contrast to many other studies in middle- and high-income contexts (Latham, 2014; Saenz & Wong, 2016; Sulander et al., 2006), and only the difference between the lowest and highest household asset index quintiles was significant. Individuals living alone are significantly less likely to report an ADL limitation—an unsurprising finding, as individuals with an ADL limitation are likely unable to live independently.

Our cross-national analyses found substantial differences in physical health across the HAALSI, HRS, MHAS, and CHARLS samples. Though older adults in the HAALSI sample reported good self-rated health and low levels of ADL limitation, they had comparatively low physical performance, both in comparison to the HRS, MHAS, and CHARLS samples studied here and to other published findings (Busch et al., 2015; Capistrant et al., 2014; Cooper et al., 2011; Kowal et al., 2012). Older individuals develop disability for a number of reasons—such as harsh environments, psychological stress, and chronic disease processes. As HAALSI data are currently cross-sectional, we are limited in our ability to conduct a comprehensive, empirical analysis of the reasons for the differences in physical and functional ability. Though the ADL scale and general self-rated health have been proven to be broadly comparable in previous cross-national studies (Chan, Kasper, Brandt, & Pezzin, 2012), there may be unidentified socio-structural reasons leading to our observed cross-national differences. Continuing waves of HAALSI data will be key for understanding differences in the complex processes of disability in this population, as well as how these processes differ across low-, middle-, and high-income contexts.

Our findings are not without limitations. As our data are currently cross-sectional, we cannot look into causal relationships between individual characteristics and physical functioning, or measure trajectories of physical functioning with age. We are also not currently able to standardize our self-rated measures for international comparison—that is, since HAALSI is currently cross-sectional, we have no way to compare how predictive our self-rated measures are of subsequent outcomes (such as mortality, in the case of (Su, Wen, & Markides, 2013)) across contexts. In addition, the wording of responses to the self-rated health measure differed somewhat across studies, limiting direct comparability. Though physical performance-based assessments are general thought to be more “objective” than self-reported measures of well-being and functional ability, differences in norms, resources, and cultural constraints may affect performance measurements across international contexts. The meaning of disability (in self-reported measures) and even the relative importance of physical performance (due to differential levels of financial resources and access to adaptive technologies) may differ between the contexts under study. The HAALSI sample is a community cohort based in the Agincourt HDSS, not a national-level sample of South Africa—that is, although it represents the older population in the surrounding rural regions, it is not a national-level sample of South Africa. Thus, our cross-national comparisons only inform how the surrounding rural regions of South Africa compare to other older adults in the United States and other LMICs, not how these countries compare to South Africa as a whole.

Future work is needed to understand the individual and household-level correlates of different health trajectories in this aging population. There are likely to be substantial, but as of yet unexplored, cohort differences in this population. Though HIV+ prevalence in the HAALSI sample is currently high at over 20%, there is substantial cohort variation in this prevalence. Rates of HIV+ prevalence in ages 40–59 substantially higher than in the older population, likely due in large part to increased survivorship through the use of antiretroviral therapy. However, this treatment was not available to the older cohorts in the sample during their earlier life, meaning that many of the HIV+ members of the older cohorts are “missing”—that is, they died before reaching older ages. The older portion of the HAALSI sample represent the survivors of one of the most widespread HIV epidemics in the world. We expect that these divergent life-courses may lead to important variation in trajectories of health as this population ages. Future longitudinal analysis, based on additional waves of HAALSI data, will be key for understanding the complex and dynamic relationships between age, physical health, and individual characteristics in this population.

Supplementary Material

Supplementary data is available at The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences online.

Funding

This work was supported by the National Institute on Aging from NIH (P01 AG041710). The HAALSI study is nested within the Agincourt Health and Socio-demographic Surveillance System run by the MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, University of the Witwatersrand, South Africa, and supported by the Wellcome Trust, UK (058893/Z/99/A; 069683/Z/02/Z; 085477/Z/08/Z; 085477/B/08/Z), and the Medical Research Council and University of the Witwatersrand, South Africa. The HAALSI study has been conducted through a collaboration between the Harvard Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, the MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, University of the Witwatersrand, South Africa, and the INDEPTH Network in Accra, Ghana.

Conflict of Interest

The authors declare no conflict of interest.

Supplementary Material

Supplemental Materials
Click here for additional data file. (64.1KB, docx)

Acknowledgments

The authors would like to thank the Agincourt community for their continued participation in the AHDSS and HAALSI studies. C. F. Payne planned the study, conducted the literature search, implemented all statistical analyses, and wrote the manuscript. F. X. Gómez-Olivé oversaw field operations and assisted with the writing and critical interpretation of the analysis. K. Kahn and L. Berkman helped plan the study, including instrumentation, and revise the manuscript. All authors saw and approved the final version.

References

  1. Aboderin I. (2011). Intergenerational Support and Old Age in Africa. Piscataway, NJ: Transaction Publishers. [Google Scholar]
  2. Aboderin I. (2016). Coming into its own? Developments and challenges for research on aging in Africa. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. doi:https://doi.org/10.1093/geronb/gbw017 [DOI] [PubMed] [Google Scholar]
  3. Aboderin I. A. G., & Beard J. R (2015). Older people’s health in sub-Saharan Africa. The Lancet, 385, e9–e11. doi:https://doi.org/10.1016/S0140-6736(14)61602-0 [DOI] [PubMed] [Google Scholar]
  4. African Union (2007). Africa Health Strategy: 2007-2015 (No. CAMH/MIN/5(III)). Johannesburg. Retrieved from http://www.who.int/workforcealliance/knowledge/resources/africahealthstrategy/en/ [Google Scholar]
  5. Balfour J. L., Kaplan G. A. (2002). Neighborhood environment and loss of physical function in older adults: Evidence from the Alameda County Study. American Journal of Epidemiology, 155, 507–515. doi:https://doi.org/10.1093/aje/155.6.507 [DOI] [PubMed] [Google Scholar]
  6. Bath P. A., Morgan K. (1999). Differential risk factor profiles for indoor and outdoor falls in older people living at home in Nottingham, UK. European Journal of Epidemiology, 15, 65–73. [DOI] [PubMed] [Google Scholar]
  7. Busch T. de A. Duarte Y. A. Pires Nunes D. Lebrão M. L. Satya Naslavsky M. dos Santos Rodrigues A., & Amaro E (2015). Factors associated with lower gait speed among the elderly living in a developing country: A cross-sectional population-based study. BMC Geriatrics, 15, 35. doi:https://doi.org/10.1186/s12877-015-0031-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Capistrant B. D., Glymour M. M., Berkman L. F. (2014). Assessing mobility difficulties for cross-national comparisons: Results from the World Health Organization Study on Global AGEing and Adult Health. Journal of the American Geriatrics Society, 62, 329–335. doi:10.1111/jgs.12633 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cesari M. Penninx B. W. Pahor M. Lauretani F. Corsi A. M. Rhys Williams G.…Ferrucci L (2004). Inflammatory markers and physical performance in older persons: The InCHIANTI study. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 59, 242–248. [DOI] [PubMed] [Google Scholar]
  10. Chan K. S., Kasper J. D., Brandt J., Pezzin L. E. (2012). Measurement equivalence in ADL and IADL difficulty across international surveys of aging: Findings from the HRS, SHARE, and ELSA. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 67, 121–132. doi:10.1093/geronb/gbr133 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. CHARLS China Health and Retirement Longitudinal Survey (2013). CHARLS China Health and Retirement Longitudinal Survey, Data Files and Documentation 2013 Retrieved from http://charls.pku.edu.cn
  12. Cooper R. Hardy R. Aihie Sayer A. Ben-Shlomo Y. Birnie K. Cooper C.…Kuh D; HALCyon Study Team (2011). Age and gender differences in physical capability levels from mid-life onwards: The harmonisation and meta-analysis of data from eight UK cohort studies. Plos One, 6, e27899. doi:10.1371/journal.pone.0027899 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cooper R., Kuh D., Hardy R; Mortality Review Group; FALCon and HALCyon Study Teams (2010). Objectively measured physical capability levels and mortality: Systematic review and meta-analysis. BMJ (Clinical research ed.), 341, c4467. doi:https://doi.org/10.1136/bmj.c4467 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Crimmins E. M. Guyer H. Langa K. Ofstedal M. B. Wallace R. B., & Weir D. R (2008). Documentation of physical measures, anthropometrics and blood pressure in the health and retirement study. Ann Arbor, MI: University of Michigan Survey Research Center. [Google Scholar]
  15. Crimmins E. M., Kim J. K., Solé-Auró A. (2011). Gender differences in health: Results from SHARE, ELSA and HRS. European Journal of Public Health, 21, 81–91. doi:10.1093/eurpub/ckq022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Dargent-Molina P. Favier F. Grandjean H. Baudoin C. Schott A. M. Hausherr E.…Bréart G (1996). Fall-related factors and risk of hip fracture: The EPIDOS prospective study. Lancet (London, England), 348, 145–149. doi:https://doi.org/10.1016/S0140-6736(96)01440-7 [DOI] [PubMed] [Google Scholar]
  17. Goldman N., Glei D. A., Rosero-Bixby L., Chiou S. T., Weinstein M. (2014). Self-reported versus performance-based measures of physical function: Prognostic value for survival. Demographic Research, 30, 227–252. doi:10.4054/DemRes.2013.30.7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gómez-Olivé F. X., Thorogood M., Bocquier P., Mee P., Kahn K., Berkman L., Tollman S. (2014). Social conditions and disability related to the mortality of older people in rural South Africa. International Journal of Epidemiology, 43, 1531–1541. doi:10.1093/ije/dyu093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gómez-Olivé F. X., Thorogood M., Clark B., Kahn K., Tollman S. (2013). Self-reported health and health care use in an ageing population in the Agincourt sub-district of rural South Africa. Global Health Action, 6, 19305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gordon R. A. (2015). Regression analysis for the social sciences. New York, NY: Routledge. [Google Scholar]
  21. Guralnik J. M., Land K. C., Blazer D., Fillenbaum G. G., Branch L. G. (1993). Educational status and active life expectancy among older blacks and whites. The New England Journal of Medicine, 329, 110–116. doi:10.1056/NEJM199307083290208 [DOI] [PubMed] [Google Scholar]
  22. Guralnik J. M. Simonsick E. M. Ferrucci L. Glynn R. J. Berkman L. F. Blazer D. G.…Wallace R. B (1994). A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. Journal of Gerontology, 49, M85–M94. [DOI] [PubMed] [Google Scholar]
  23. Haas S. A., Krueger P. M., Rohlfsen L. (2012). Race/ethnic and nativity disparities in later life physical performance: The role of health and socioeconomic status over the life course. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 67, 238–248. doi:10.1093/geronb/gbr155 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Health and Retirement Study (n.d). Health and Retirement Study, 2012 public use dataset. Ann Arbor, MI: Produced and distributed by the University of Michigan with funding from the National Institute on Aging (grant number NIA U01AG009740). [Google Scholar]
  25. Hurst L., Stafford M., Cooper R., Hardy R., Richards M., Kuh D. (2013). Lifetime socioeconomic inequalities in physical and cognitive aging. American Journal of Public Health, 103, 1641–1648. doi:10.2105/AJPH.2013.301240 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Jette A. M. (1994). How measurement techniques influence estimates of disability in older populations. Social Science & Medicine (1982), 38, 937–942. [DOI] [PubMed] [Google Scholar]
  27. Jylhä M., Guralnik J. M., Balfour J., Fried L. P. (2001). Walking difficulty, walking speed, and age as predictors of self-rated health: The women’s health and aging study. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 56, M609–M617. [DOI] [PubMed] [Google Scholar]
  28. Kahn K. Collinson M. A. Gómez-Olivé F. X. Mokoena O. Twine R. Mee P.…Tollman S. M (2012). Profile: Agincourt health and socio-demographic surveillance system. International Journal of Epidemiology, 41, 988–1001. doi:10.1093/ije/dys115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Karlamangla A. S., Singer B. H., McEwen B. S., Rowe J. W., Seeman T. E. (2002). Allostatic load as a predictor of functional decline. MacArthur studies of successful aging. Journal of Clinical Epidemiology, 55, 696–710. doi:https://doi.org/10.1016/S0895-4356(02)00399-2 [DOI] [PubMed] [Google Scholar]
  30. Katz S., Ford A. B., Moskowitz R. W., Jackson B. A., Jaffe M. W. (1963). Studies of illness in the aged. The index of adl: A standardized measure of biological and psychosocial function. JAMA, 185, 914–919. [DOI] [PubMed] [Google Scholar]
  31. Kowal P. Biritwum R. Haldia K. Isingo R. Kumogola Y. Mathur A.,…Chatterji S (2012). WHO Study on global AGEing and adult health (SAGE): Summary results of a pilot in three countries. (SAGE publications: Working papers No. 2). Geneva, Switzerland: World Health Organization. Retrieved from http://www.who.int/healthinfo/sage/SAGEWorkingPaper2_Pilot_summary_31Oct12.pdf?ua=1 [Google Scholar]
  32. Krieger N. (2001). Theories for social epidemiology in the 21st century: An ecosocial perspective. International Journal of Epidemiology, 30, 668–677. doi:https://doi.org/10.1093/ije/30.4.668 [DOI] [PubMed] [Google Scholar]
  33. Latham K. (2014). Racial and educational disparities in mobility limitation among older women: What is the role of modifiable risk factors? The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 69, 772–783. doi:10.1093/geronb/gbu028 [DOI] [PubMed] [Google Scholar]
  34. Leong D. P. Teo K. K. Rangarajan S. Lopez-Jaramillo P. Avezum A. Orlandini A.,…Yusuf S (2015). Prognostic value of grip strength: Findings from the Prospective Urban Rural Epidemiology (PURE) study. The Lancet, 386, 266–273. doi:https://doi.org/10.1016/S0140-6736(14)62000-6 [DOI] [PubMed] [Google Scholar]
  35. MHAS Mexican Health and Aging Study, 2012 (n.d). Data Files and Documentation: Mexican Health and Aging Study Retrieved from www.mhasweb.org
  36. Minicuci N., Noale M., Pluijm S. M. F., Zunzunegui M. V., Blumstein T., Deeg D. J. H,…;Group, for the C. working. (2004). Disability-free life expectancy: A cross-national comparison of six longitudinal studies on aging. The CLESA project. European Journal of Ageing, 1, 37–44. doi:https://doi.org/10.1007/s10433-004-0002-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Nagi S. Z. (1976). An epidemiology of disability among adults in the United States. The Milbank Memorial Fund Quarterly. Health and Society, 54, 439–467. [PubMed] [Google Scholar]
  38. Payne C. F. (2015). Aging in the Americas: Disability-free life expectancy among adults aged 65 and older in the United States, Costa Rica, Mexico, and Puerto Rico. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. doi:https://doi.org/10.1093/geronb/gbv076 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Payne C. F., Mkandawire J., Kohler H. P. (2013). Disability transitions and health expectancies among adults 45 years and older in Malawi: A cohort-based model. PLoS medicine, 10, e1001435. doi:10.1371/journal.pmed.1001435 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Rantanen T., Guralnik J. M., Foley D., Masaki K., Leveille S., Curb J. D., White L. (1999). Midlife hand grip strength as a predictor of old age disability. JAMA, 281, 558–560. [DOI] [PubMed] [Google Scholar]
  41. Reuben D. B. Seeman T. E. Keeler E. Hayes R. P. Bowman L. Sewall A.…Guralnik J. M (2004). Refining the categorization of physical functional status: The added value of combining self-reported and performance-based measures. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 59, M1056–M1061. doi:https://doi.org/10.1093/gerona/59.10.M1056 [DOI] [PubMed] [Google Scholar]
  42. Rutstein S. O., & Johnson K (2004). The DHS Wealth Index (DHS Comparative Reports No. 6). Calverton, MD: ORC Macro. [Google Scholar]
  43. Saenz J. L., Wong R. (2016). Educational gradients and pathways of disability onset among older Mexicans. Research on Aging, 38, 299–321. doi:10.1177/0164027515620243 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sayer A. A., Syddall H. E., Martin H. J., Dennison E. M., Roberts H. C., Cooper C. (2006). Is grip strength associated with health-related quality of life? Findings from the Hertfordshire Cohort Study. Age and Ageing, 35, 409–415. doi:10.1093/ageing/afl024 [DOI] [PubMed] [Google Scholar]
  45. Schneidert M., Hurst R., Miller J., Ustün B. (2003). The role of environment in the International Classification of Functioning, Disability and Health (ICF). Disability and Rehabilitation, 25, 588–595. doi:10.1080/0963828031000137090 [DOI] [PubMed] [Google Scholar]
  46. Schoeni R. F., Freedman V. A., Martin L. G. (2008). Why is late-life disability declining? The Milbank Quarterly, 86, 47–89. doi:10.1111/j.1468-0009.2007.00513.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Sonnega A., Faul J. D., Ofstedal M. B., Langa K. M., Phillips J. W., Weir D. R. (2014). Cohort profile: The Health and Retirement Study (HRS). International Journal of Epidemiology, 43, 576–585. doi:10.1093/ije/dyu067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. South African Ministry of Health (2013). Strategic Plan for the Prevention and Control of Non-Communicable Diseases 2013–17 (No. RP06/2013) Retrieved from http://www.health-e.org.za/wp-content/uploads/2013/09/NCDs-STRAT-PLAN-CONTENT-8-april-proof.pdf
  49. Stuck A. E., Walthert J. M., Nikolaus T., Büla C. J., Hohmann C., Beck J. C. (1999). Risk factors for functional status decline in community-living elderly people: A systematic literature review. Social Science & Medicine (1982), 48, 445–469. [DOI] [PubMed] [Google Scholar]
  50. Su D., Wen M., Markides K. S. (2013). Is self-rated health comparable between non-Hispanic whites and Hispanics? Evidence from the health and retirement study. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 68, 622–632. doi:10.1093/geronb/gbt037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Sulander T., Martelin T., Sainio P., Rahkonen O., Nissinen A., Uutela A. (2006). Trends and educational disparities in functional capacity among people aged 65-84 years. International Journal of Epidemiology, 35, 1255–1261. doi:10.1093/ije/dyl183 [DOI] [PubMed] [Google Scholar]
  52. Tollman S. M., Kahn K., Sartorius B., Collinson M. A., Clark S. J., Garenne M. L. (2008). Implications of mortality transition for primary health care in rural South Africa: A population-based surveillance study. Lancet (London, England), 372, 893–901. doi:10.1016/S0140-6736(08)61399-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Tukey J. W. (1997). Exploratory data analysis. Reading, MA: Addison-Wesley. [Google Scholar]
  54. US Census Bureau (2016). US Census International Data Base (2016) Retrieved from http://www.census.gov/data/developers/data-sets/international-database.html
  55. Verbrugge L. M., Jette A. M. (1994). The disablement process. Social Science & Medicine (1982), 38, 1–14. [DOI] [PubMed] [Google Scholar]
  56. Visser M., Goodpaster B. H., Kritchevsky S. B., Newman A. B., Nevitt M., Rubin S. M.,…, Harris T. B. (2005). Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 60, 324–333. doi:https://doi.org/10.1093/gerona/60.3.324 [DOI] [PubMed] [Google Scholar]
  57. Wong R. Michaels-Obregon A., & Palloni A (2015). Cohort Profile: The Mexican Health and Aging Study (MHAS). International Journal of Epidemiology. doi:https://doi.org/10.1093/ije/dyu263 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. World Health Organization (2001). International Classification of Functioning, Disability and Health: ICF. Geneva, Switzerland: Author. [Google Scholar]
  59. Xavier Gómez-Olivé F. Thorogood M. Clark B. D. Kahn K., & Tollman S. M (2010). Assessing health and well-being among older people in rural South Africa. Global Health Action, 3, 23–35. doi:https://doi.org/10.3402/gha.v3i0.2126 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Zhao Y., Hu Y., Smith J. P., Strauss J., Yang G. (2014). Cohort profile: The China Health and Retirement Longitudinal Study (CHARLS). International Journal of Epidemiology, 43, 61–68. doi:10.1093/ije/dys203 [DOI] [PMC free article] [PubMed] [Google Scholar]

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