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Kerstin Gerst-Emerson, Rebeca Wong, Alejandra Michaels-Obregon, Alberto Palloni, Cross-National Differences in Disability Among Elders: Transitions in Disability in Mexico and the United States, The Journals of Gerontology: Series B, Volume 70, Issue 5, September 2015, Pages 759–768, https://doi.org/10.1093/geronb/gbu185
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Abstract
Little is known about how exposure to a combination of infectious and chronic conditions throughout the lifecourse could impact disability in old age. This paper compares 2 cohorts of adults who have aged under very different country contexts by contrasting disability transitions among elders in Mexico with elders in the United States.
Data comes from the Mexican Health and Aging Study (MHAS) and the U.S. Health and Retirement Study (HRS). Estimated probabilities of 2-year transitions among disability states and mortality are presented for adults aged 50 and older.
The levels of disability prevalence and 2 year transitions are consistent with a higher rate of disability for the United States compared to Mexico. In 2-year transitions, the U.S. sample was more likely to transition to a disabled state or increase the number of disabilities than the Mexican counterparts, while Mexicans are more likely to move out of disability or reduce the number of disabilities reported.
The findings suggest that the current rate of disability in old age is lower for a less developed country compared with a developed society. We discuss implications, possible explanations, and likely future scenarios.
Cross-country disparities in health outcomes, such as life expectancy and morbidity, are well documented, and researchers increasingly turn to the country context as explanations for such population differences (Clark & Smith, 2011). Although findings consistently show the importance of economic and political context on individual health outcomes, national differences in demographic and epidemiologic contexts may also be essential forces in shaping health trajectories.
A particularly interesting comparison is that of Latin American countries compared with the United States. Over the 20th century, demographic changes in the Latin America region tracked a familiar pattern of high mortality and high fertility, followed by declining mortality and then a decline in fertility (Brea, 2003). The consequence of rapid mortality and fertility declines in the region is a relatively fast pace of population aging compared to the speed experienced by developed countries that aged before (Palloni, Pinto-Aguirre, & Pelaez, 2002). The case of Mexico illustrates this rapid pace. The proportion of the population aged 60 and older is expected to grow steadily, from 6% in 2000, to 15% in 2027 (CONAPO, 2005). This 27-year pace is relatively fast. By comparison, it will have taken the United States 70 years to close this gap and reach similar percentage (in 2013); it took Japan about 40 years (from 1947 to 1985).
An important feature of this population aging is that it is “premature,” given the low level of economic development and institutional infrastructure to support this process (Palloni et al., 2002; Wong & Palloni, 2009). Developed countries that aged before, such as the United States or Japan, enjoyed high standards of living at the time that their population aging started.
An additional remarkable feature of the population aging in Latin America is the mixed epidemiological regime experienced by elders. The prevalence of chronic conditions is rising, while infectious diseases continue to prevail in certain groups (Samper-Ternent, Michaels-Obregon, Wong, & Palloni, 2012). The current cohort of older adults in Latin America were therefore more likely to be exposed to a combination of chronic and infectious diseases during their lifetime than the current cohort in more developed countries. Such differences in exposure can have implications on mortality and later life health outcomes (Doblhammer, 2004).
However, relatively little is known about how exposure to such a mixed epidemiological regime throughout the lifecourse could impact disability and mortality in old age (Grimard, Laszlo, & Lim, 2012; Palloni & Souza, 2013; Palloni & Thomas, 2013; Ruiz-Pantoja & Ham-Chande, 2007). It is likely that the assault of chronic conditions in old age results in different consequences depending on the regime experienced in earlier adulthood. One way to assess these consequences is to focus on the extent of physical limitations or disability, and the progression to more severe disability or death in populations that have aged under the two vastly different regimes (one developed and one less-developed country).
The goal of this article therefore is to compare two cohorts of adults who have aged under very different country contexts. One possible outcome is that disability in old age will be higher in Latin America compared with countries that are at a more advanced stage of development because they have experienced a combination of infectious and chronic diseases throughout their lifecourse. This would leave the survivors of such a combined regime more vulnerable in older age. If this is true, compared to populations in developed countries, we should observe populations in less developed countries to have higher disability rates, and moving faster toward disabled states or death over time. Conversely, it is possible that physical disability in old age is lower in Latin America compared with a more developed country because they are a highly selected group of elders, having survived high mortality rates in early life. This suggests that those that survive into adulthood are the most resilient. If this is true, we should see lower disability rates among elders in Latin American compared to those in a more developed country and Latin American elders would have a slower move toward disabled states or death over time. We use two countries as case studies: (a) Mexico, which fits the pattern of rapid population aging under premature conditions, and cohorts of elderly experiencing a mixed epidemiological regime. (b) U.S. non-Hispanic white population, which fits the pattern of developed-country population aging and can be used as benchmark or comparison group.
Data and Methods
We used panel survey data from two waves of the United States Health and Retirement Study (HRS) and the Mexican Health and Aging Study (MHAS). These two studies are highly comparable and ideal for cross-national comparisons. The HRS is a longitudinal study of Americans aged 50 and older (HRS, 2008). We used the dataset prepared by the RAND Corporation (RAND, 2011). With sampling procedures and survey designs modelled after the HRS, the MHAS serves as the companion study to the HRS. The MHAS is a three-wave prospective panel study of community-dwelling individuals born prior to 1951 (MHAS, 2004; Wong, Pelaez, Palloni, & Markides, 2006).
In order to maximize comparability, we used data from 2000 and 2002 of the HRS and data from 2001 and 2003 of the MHAS. The MHAS analytical sample was narrowed to respondents age 52 years and older in 2001 (Time 1) and persons 54 years and over in 2003 (Time 2) in order to exclude any new partners that were added to the sample that were younger than the selection criteria. To mirror this selection criteria, the HRS sample included persons aged 51 and older at Wave 2000, and any younger spouses added to the sample were removed. The HRS analytical sample was further narrowed to non-Hispanic whites that were born in the United States to create a straightforward comparison group.
Both surveys provide individual-level sampling weights to adjust for attrition across waves and result in nationally representative samples for the United States and Mexican populations. The HRS weights are a product of the baseline sample weight and a nonresponse adjustment, based on sample post-stratification to the Current Population Survey or American Community Survey (Ofstedal, Weir, Chen, & Wagner, 2011). The MHAS weights are based on the birth cohort and place of residence by urban/rural areas and geographic area, and adjusted for nonresponse to ensure a total population for each domain of interest of the survey, given from the projection generated by INEGI (MHAS, 2004, 2014; Wong, Michaels-Obregon, & Palloni, 2014).
It is important to note the cross-national differences in long-term care options. The HRS provides data for persons that become institutionalized. However, the MHAS does not have this information, since most long-term care is provided by family members in Mexico. We handled this by excluding persons in nursing homes in the United States from the analyses, creating two community dwelling populations. Because those in nursing homes are also most likely to be the most disabled, this may bias our measure of disability. This is especially the case in the United States where nursing homes are more common. Although it is not a major concern because of the small proportion of both the United States and Mexico population living in a nursing home, the results are likely an undercount in both countries, with higher degree of under-count in the United States.
We adopt the functional dependence definition to capture the concept of disability. This is traditionally measured at the population level through surveys with self-reports on the ability to conduct without help the basic activities of daily living (ADL; Katz, Ford, Moskowitz, Jackson, & Jaffe, 1963). We focus on five basic ADLs: bathing, toileting, transferring into and out of bed, walking, and eating. Both surveys asked respondents for each of these five components: “Because of a health problem, do you have any difficulty…” with the response options of Yes, No, Can’t do, or Don’t do.
The recoding methods were modeled after RAND recoding of HRS data (St.Clair et al., 2009), an approach which has been previously used to examine the difference between Mexico and the United States (Hayward, Wong, Chiu, & Gonzales, 2010). For each ADL a respondent was coded disabled if they answered “Yes” or “Can’t do”. If the respondent answered “No,” disability was coded as nondisabled. If they answered “Don’t do” then it was coded as missing. If they answered “Don’t do” and received help performing the activity, they were coded as disabled. If the respondent answered “Don’t do” and did not get help, they were coded as missing. We collapsed the number of disabilities into three categories: no-ADLs, one ADL, and 2+ ADLs.
Although most variables were consistent across the surveys, several were measured differently, including education. For the HRS we created a three-category variable of highest education: less than highschool (reference), highschool, and more than highschool. This coding was not appropriate for the MHAS because of the lower average education in Mexico for this cohort. We therefore created a four-category variable: zero years of education (reference), 1–5 years, 6 years, and 7 years or more. Six years of education (or complete elementary school) was considered as a separate category because it is a meaningful grouping with relevant social and economic research applications. Other studies have previously used this same convention (Wong, Espinoza, & Palloni, 2007).
We examined health conditions that are likely to influence the disability outcome. Acute conditions were measured by whether the respondent had a stroke or a heart attack in the 2 years prior to baseline. Both MHAS and HRS measured stroke by asking whether a doctor or medical personnel had told them they had a stroke. A follow-up question provided information about the date of the most recent stroke; we considered only the acute events that occurred in the 2 years prior to baseline. This same method was used to determine heart attack or myocardial infarction for the MHAS. The HRS, however, asked respondents specifically if in the last 2 years they had a heart attack or myocardial infarction (without specifying a doctor diagnosis) and thus only this question was used for the HRS. Chronic conditions were measured for both samples using questions that asked respondents whether a doctor had ever diagnosed them with diabetes, arthritis, or cancer.
Place of residence was also measured differently. The HRS measure was based on the 10 category variable, which was collapsed into urban/suburban (population 250,000 or greater) and ex-urban (<250,000). The MHAS used a locality size measure of four categories. We considered a community with 100,000 people or more the cut-off point for urban; the other three categories were coded rural.
Finally, the health insurance variable differed across surveys. HRS respondents were considered covered if they reported at least one of the following: a government health insurance program, an employer-based program, or some other health insurance plan. MHAS respondents were coded as insured if they reported at least one of the following: Mexican Institute of Social Security (IMSS), Institute of Social Security and Services Workers (ISSSTE), Social Security of Mexican Oil Workers (PEMEX), Armed forces Social Security, any other private medical or other health insurance.
Methods
To create an overview of disability and transitions in disability, we produced detailed descriptives of disability status across the two time periods for each country. We use age standardized rates because the age distribution across countries differs and disability is positively associated with age. Second, we calculated bivariate transition tables for the outcome at Time 2 for the United States and Mexico by disability status at Time 1 and age.
We used a multinomial logistic model to determine transition rates across disability outcomes at Time 2 (no-ADLs, one ADL, 2+ ADLs, death and lost to follow-up/nursing home). Covariates at Time 1 included in the model were: age, sex, marital status, education, wealth, urban/rural residence, insurance coverage, and health conditions. We also control for body mass index (BMI) since research shows it is connected to disability (e.g., Backholer, Wong, Freak-Poli, Walls, & Peeters, 2012; Gerst, Michaels-Obregon, & Wong, 2012). We included ADLs at Time 1 to estimate transition rates between the two time periods. The estimators of this model can be interpreted as a relative risk ratio compared to a given category (Hilbe, 2009). To facilitate the interpretation of results, we estimated the probabilities of each outcome at Time 2, controlling for ADLs at Time 1.
Finally, we constructed an index to summarize disability in both countries. Using the probabilities obtained from the multinomial logit model, we calculated the number of years that a person at certain age will spend active, moderately disabled (one ADL limitation), and severely disabled (2+ ADL limitations). We used age and gender specific life expectancies with the life tables provided by the World Health Organization (2009) to establish the number of years a person is expected to live for each age group. For each age group, we multiplied the probabilities of disability states by the expected years lived and obtain a measure that approximates the number of years in each state: no disability, with 1 disability and 2+ disabilities. By adding these estimates across the age groups we obtained the overall index for each country, capturing the disability progression net of mortality. Thus the index takes into account differential survival selectivity at older ages.
Results
Descriptives
Descriptive statistics show that disability rates are higher for the United States compared with Mexico at Time 1 (Box 1a). In the United States, the percent (age standardized using U.S. population as reference) with at least one ADL limitation at Time 1 was higher than for Mexico (11.5% and 10.6%, respectively). However, the prevalence rates of disability are higher for Mexico compared with the United States for each of the ADL.
. | United States (%) . | Mexico (%) . |
---|---|---|
At least one ADL | 11.5 | 10.6* |
Walking | 5.2 | 7.5* |
Bathing | 5.0 | 5.3* |
Eating | 2.1 | 3.0* |
Transferring in/out of bed | 4.6 | 7.2* |
Toileting | 4.2 | 5.2* |
. | United States (%) . | Mexico (%) . |
---|---|---|
At least one ADL | 11.5 | 10.6* |
Walking | 5.2 | 7.5* |
Bathing | 5.0 | 5.3* |
Eating | 2.1 | 3.0* |
Transferring in/out of bed | 4.6 | 7.2* |
Toileting | 4.2 | 5.2* |
Notes. Age standardized using the weighted average of the two countries as the standard; population 51 years and older in the United States and 52 years and older in Mexico; weighted statistics using community-dwelling population only.
*Significant differences between countries at p = .01.
. | United States (%) . | Mexico (%) . |
---|---|---|
At least one ADL | 11.5 | 10.6* |
Walking | 5.2 | 7.5* |
Bathing | 5.0 | 5.3* |
Eating | 2.1 | 3.0* |
Transferring in/out of bed | 4.6 | 7.2* |
Toileting | 4.2 | 5.2* |
. | United States (%) . | Mexico (%) . |
---|---|---|
At least one ADL | 11.5 | 10.6* |
Walking | 5.2 | 7.5* |
Bathing | 5.0 | 5.3* |
Eating | 2.1 | 3.0* |
Transferring in/out of bed | 4.6 | 7.2* |
Toileting | 4.2 | 5.2* |
Notes. Age standardized using the weighted average of the two countries as the standard; population 51 years and older in the United States and 52 years and older in Mexico; weighted statistics using community-dwelling population only.
*Significant differences between countries at p = .01.
This suggests that there is more overlap in ADL limitations among persons in Mexico than in the United States and Box 1b confirms this. Persons in Mexico have higher prevalence of three or more ADL limitations. Although about 6.2% of adults in the United States had one limitation, only 3.2% of persons in Mexico had one limitation. Although only 0.6% of adults in the United States reported having limitations in all five ADLs, more than three times as many (1.9%) of adults in Mexico reported five ADL limitations.
. | United States (%) . | Mexico (%) . |
---|---|---|
At least one ADL | 11.5 | 10.6* |
None | 88.5 | 89.4* |
One | 6.2 | 3.2* |
Two | 2.7 | 2.4* |
Three | 1.3 | 1.7* |
Four | 0.7 | 1.4* |
Five | 0.6 | 1.9* |
. | United States (%) . | Mexico (%) . |
---|---|---|
At least one ADL | 11.5 | 10.6* |
None | 88.5 | 89.4* |
One | 6.2 | 3.2* |
Two | 2.7 | 2.4* |
Three | 1.3 | 1.7* |
Four | 0.7 | 1.4* |
Five | 0.6 | 1.9* |
Notes. Age standardized using the weighted average of the two countries as the standard; population 51 years and older in the United States and 52 years and older in Mexico; weighted statistics using community-dwelling population only.
*Significant differences between countries at p = .01.
. | United States (%) . | Mexico (%) . |
---|---|---|
At least one ADL | 11.5 | 10.6* |
None | 88.5 | 89.4* |
One | 6.2 | 3.2* |
Two | 2.7 | 2.4* |
Three | 1.3 | 1.7* |
Four | 0.7 | 1.4* |
Five | 0.6 | 1.9* |
. | United States (%) . | Mexico (%) . |
---|---|---|
At least one ADL | 11.5 | 10.6* |
None | 88.5 | 89.4* |
One | 6.2 | 3.2* |
Two | 2.7 | 2.4* |
Three | 1.3 | 1.7* |
Four | 0.7 | 1.4* |
Five | 0.6 | 1.9* |
Notes. Age standardized using the weighted average of the two countries as the standard; population 51 years and older in the United States and 52 years and older in Mexico; weighted statistics using community-dwelling population only.
*Significant differences between countries at p = .01.
Table 1 provides detailed descriptives for those having at least one ADL limitation versus nondisabled across the two time periods for both countries. All differences across the outcome were significant at the 0.05 level within each country. The unadjusted descriptive statistics are similar for the two countries. However, some variables differ across the two countries, including health insurance, health conditions, and BMI categories.
Time 1 . | HRS . | . | MHAS . | . | . | . | |||
---|---|---|---|---|---|---|---|---|---|
Time 1 . | Time 2 . | . | Time 1 . | Time 2 . | |||||
. | Disabled . | Nondisabled . | Disabled . | Nondisabled . | . | Disabled . | Nondisabled . | Disabled . | Nondisabled . |
Age | Age | ||||||||
Mean (SD) | 71.81 (11.55) | 65.72 (9.74) | 72.03 (11.35) | 65.69 (9.75) | Mean (SD) | 72.20 (12.07) | 63.32 (8.84) | 71.62 (11.51) | 62.95 (8.61) |
Age categories | Age categories | ||||||||
51–59 | 7.72% | 92.28% | 7.22% | 92.78% | 52–59 | 4.59% | 95.41% | 4.47% | 95.53% |
60–69 | 8.46% | 91.54% | 8.43% | 91.57% | 60–69 | 6.41% | 93.59% | 7.13% | 92.87% |
70–79 | 13.75% | 86.25% | 14.77% | 85.23% | 70–79 | 13.31% | 86.69% | 13.96% | 86.04% |
80+ | 28.79% | 71.21% | 28.30% | 71.70% | 80+ | 35.00% | 65.00% | 36.46% | 63.54% |
Sex | Sex | ||||||||
Male | 8.87% | 91.13% | 8.89% | 91.11% | Male | 7.95% | 92.05% | 7.64% | 92.36% |
Female | 14.77% | 85.23% | 14.79% | 85.21% | Female | 10.57% | 89.43% | 11.03% | 88.97% |
Marital status | Marital status | ||||||||
Married, union | 9.11% | 90.89% | 8.83% | 91.17% | Married, union | 6.75% | 93.25% | 6.99% | 93.01% |
Single, divorced, separated | 13.16% | 86.84% | 13.32% | 86.68% | Single, divorced, separated | 10.50% | 89.50% | 13.26% | 86.74% |
Widowed | 21.75% | 78.25% | 22.88% | 77.12% | Widowed | 15.26% | 84.74% | 13.76% | 86.24% |
Education | Education | ||||||||
< 12 years | 20.77% | 79.23% | 20.12% | 79.88% | 0 years | 14.64% | 85.36% | 13.45% | 86.55% |
12 years | 12.04% | 87.96% | 11.55% | 88.45% | 1–5 years | 7.83% | 92.17% | 8.83% | 91.17% |
> 12 years | 8.19% | 91.81% | 8.93% | 91.07% | 6 years | 6.66% | 93.34% | 6.23% | 93.77% |
7+ years | 4.26% | 95.74% | 5.66% | 94.34% | |||||
Assets | Assets | ||||||||
Low | 20.40% | 79.60% | 17.98% | 82.02% | Low | 10.60% | 89.40% | 11.79% | 88.21% |
Medium | 10.78% | 89.22% | 11.76% | 88.24% | Medium | 9.26% | 90.74% | 8.24% | 91.76% |
High | 8.29% | 91.71% | 8.94% | 91.06% | High | 7.95% | 92.05% | 8.03% | 91.97% |
Location | Location | ||||||||
Urban | 11.53% | 88.50% | 11.35% | 88.65% | Urban | 8.32% | 91.68% | 7.66% | 92.34% |
Rural | 13.20% | 86.80% | 13.66% | 86.34% | Rural | 10.17% | 89.83% | 10.88% | 89.12% |
Health insurance | Health insurance | ||||||||
Uninsured | 9.68% | 90.32% | 10.04% | 89.96% | Uninsured | 10.41% | 89.59% | 10.12% | 89.88% |
Insured | 12.17% | 87.83% | 12.18% | 87.82% | Insured | 8.41% | 91.59% | 8.90% | 91.90% |
Health conditions | Health conditions | ||||||||
Chronic (count) | 1.22 (0.69) | 0.75 (0.71) | 1.16 (0.69) | 0.75 (0.71) | Chronic (count) | 0.69 (0.67) | 0.36 (0.56) | 0.58 (0.63) | 0.37 (0.56) |
Diabetes | 23.41% | 11.18% | 21.77% | 11.40% | Diabetes | 25.43% | 14.53% | 23.00% | 14.02% |
Cancer | 17.21% | 11.92% | 16.06% | 12.08% | Cancer | 4.17% | 1.80% | 2.16% | 1.88% |
Arthritis | 81.19% | 51.46% | 78.46% | 51.82% | Arthritis | 38.63% | 19.72% | 32.41% | 20.79% |
Acute (count) | 0.11 (0.32) | 0.03 (0.18) | 0.06 (0.25) | 0.03 (0.19) | Acute (count) | 0.07 (0.26) | 0.01 (0.11) | 0.03 (0.18) | 0.01 (0.11) |
Heart attack | 5.14% | 1.90% | 3.32% | 2.15% | Heart attack | 3.35% | 0.72% | 0.56% | 0.86% |
Stroke | 5.42% | 0.96% | 3.02% | 1.29% | Stroke | 3.27% | 0.43% | 2.67% | 0.38% |
BMI | BMI | ||||||||
Mean (SD) | 2.86 (0.89) | 2.83 (0.78) | 2.89 (0.87) | 2.83 (0.78) | Mean (SD) | 2.67 (0.89) | 2.82 (0.79) | 2.67 (0.84) | 2.83 (0.79) |
BMI categories | BMI categories | ||||||||
Underweight | 4.37% | 1.48% | 3.14% | 1.65% | Underweight | 8.24% | 2.35% | 4.69% | 2.49% |
Normal Weight | 35.04% | 35.82% | 34.01% | 35.96% | Normal Weight | 36.53% | 35.05% | 42.50% | 33.96% |
Overweight | 31.25% | 40.66% | 33.44% | 40.37% | Overweight | 34.95% | 40.76% | 33.59% | 41.51% |
Obese | 29.34% | 22.03% | 29.40% | 22.02% | Obese | 20.29% | 21.82% | 19.22% | 22.01% |
Total sample | 1,672 | 11,732 | 1,671 | 11,733 | Total sample | 1,170 | 10,667 | 1,087 | 9,655 |
12.09% | 87.01% | 12.11% | 87.89% | 9.33% | 90.67% | 9.45% | 90.55% |
Time 1 . | HRS . | . | MHAS . | . | . | . | |||
---|---|---|---|---|---|---|---|---|---|
Time 1 . | Time 2 . | . | Time 1 . | Time 2 . | |||||
. | Disabled . | Nondisabled . | Disabled . | Nondisabled . | . | Disabled . | Nondisabled . | Disabled . | Nondisabled . |
Age | Age | ||||||||
Mean (SD) | 71.81 (11.55) | 65.72 (9.74) | 72.03 (11.35) | 65.69 (9.75) | Mean (SD) | 72.20 (12.07) | 63.32 (8.84) | 71.62 (11.51) | 62.95 (8.61) |
Age categories | Age categories | ||||||||
51–59 | 7.72% | 92.28% | 7.22% | 92.78% | 52–59 | 4.59% | 95.41% | 4.47% | 95.53% |
60–69 | 8.46% | 91.54% | 8.43% | 91.57% | 60–69 | 6.41% | 93.59% | 7.13% | 92.87% |
70–79 | 13.75% | 86.25% | 14.77% | 85.23% | 70–79 | 13.31% | 86.69% | 13.96% | 86.04% |
80+ | 28.79% | 71.21% | 28.30% | 71.70% | 80+ | 35.00% | 65.00% | 36.46% | 63.54% |
Sex | Sex | ||||||||
Male | 8.87% | 91.13% | 8.89% | 91.11% | Male | 7.95% | 92.05% | 7.64% | 92.36% |
Female | 14.77% | 85.23% | 14.79% | 85.21% | Female | 10.57% | 89.43% | 11.03% | 88.97% |
Marital status | Marital status | ||||||||
Married, union | 9.11% | 90.89% | 8.83% | 91.17% | Married, union | 6.75% | 93.25% | 6.99% | 93.01% |
Single, divorced, separated | 13.16% | 86.84% | 13.32% | 86.68% | Single, divorced, separated | 10.50% | 89.50% | 13.26% | 86.74% |
Widowed | 21.75% | 78.25% | 22.88% | 77.12% | Widowed | 15.26% | 84.74% | 13.76% | 86.24% |
Education | Education | ||||||||
< 12 years | 20.77% | 79.23% | 20.12% | 79.88% | 0 years | 14.64% | 85.36% | 13.45% | 86.55% |
12 years | 12.04% | 87.96% | 11.55% | 88.45% | 1–5 years | 7.83% | 92.17% | 8.83% | 91.17% |
> 12 years | 8.19% | 91.81% | 8.93% | 91.07% | 6 years | 6.66% | 93.34% | 6.23% | 93.77% |
7+ years | 4.26% | 95.74% | 5.66% | 94.34% | |||||
Assets | Assets | ||||||||
Low | 20.40% | 79.60% | 17.98% | 82.02% | Low | 10.60% | 89.40% | 11.79% | 88.21% |
Medium | 10.78% | 89.22% | 11.76% | 88.24% | Medium | 9.26% | 90.74% | 8.24% | 91.76% |
High | 8.29% | 91.71% | 8.94% | 91.06% | High | 7.95% | 92.05% | 8.03% | 91.97% |
Location | Location | ||||||||
Urban | 11.53% | 88.50% | 11.35% | 88.65% | Urban | 8.32% | 91.68% | 7.66% | 92.34% |
Rural | 13.20% | 86.80% | 13.66% | 86.34% | Rural | 10.17% | 89.83% | 10.88% | 89.12% |
Health insurance | Health insurance | ||||||||
Uninsured | 9.68% | 90.32% | 10.04% | 89.96% | Uninsured | 10.41% | 89.59% | 10.12% | 89.88% |
Insured | 12.17% | 87.83% | 12.18% | 87.82% | Insured | 8.41% | 91.59% | 8.90% | 91.90% |
Health conditions | Health conditions | ||||||||
Chronic (count) | 1.22 (0.69) | 0.75 (0.71) | 1.16 (0.69) | 0.75 (0.71) | Chronic (count) | 0.69 (0.67) | 0.36 (0.56) | 0.58 (0.63) | 0.37 (0.56) |
Diabetes | 23.41% | 11.18% | 21.77% | 11.40% | Diabetes | 25.43% | 14.53% | 23.00% | 14.02% |
Cancer | 17.21% | 11.92% | 16.06% | 12.08% | Cancer | 4.17% | 1.80% | 2.16% | 1.88% |
Arthritis | 81.19% | 51.46% | 78.46% | 51.82% | Arthritis | 38.63% | 19.72% | 32.41% | 20.79% |
Acute (count) | 0.11 (0.32) | 0.03 (0.18) | 0.06 (0.25) | 0.03 (0.19) | Acute (count) | 0.07 (0.26) | 0.01 (0.11) | 0.03 (0.18) | 0.01 (0.11) |
Heart attack | 5.14% | 1.90% | 3.32% | 2.15% | Heart attack | 3.35% | 0.72% | 0.56% | 0.86% |
Stroke | 5.42% | 0.96% | 3.02% | 1.29% | Stroke | 3.27% | 0.43% | 2.67% | 0.38% |
BMI | BMI | ||||||||
Mean (SD) | 2.86 (0.89) | 2.83 (0.78) | 2.89 (0.87) | 2.83 (0.78) | Mean (SD) | 2.67 (0.89) | 2.82 (0.79) | 2.67 (0.84) | 2.83 (0.79) |
BMI categories | BMI categories | ||||||||
Underweight | 4.37% | 1.48% | 3.14% | 1.65% | Underweight | 8.24% | 2.35% | 4.69% | 2.49% |
Normal Weight | 35.04% | 35.82% | 34.01% | 35.96% | Normal Weight | 36.53% | 35.05% | 42.50% | 33.96% |
Overweight | 31.25% | 40.66% | 33.44% | 40.37% | Overweight | 34.95% | 40.76% | 33.59% | 41.51% |
Obese | 29.34% | 22.03% | 29.40% | 22.02% | Obese | 20.29% | 21.82% | 19.22% | 22.01% |
Total sample | 1,672 | 11,732 | 1,671 | 11,733 | Total sample | 1,170 | 10,667 | 1,087 | 9,655 |
12.09% | 87.01% | 12.11% | 87.89% | 9.33% | 90.67% | 9.45% | 90.55% |
Notes. Percentages, mean, and standard deviations are weighted statistics; All differences across disability groups (disabled vs nondisabled) were statistically significant within each country at the .05 level; Sample sizes may vary due to missing values; data may not add to 100% due to rounding; HRS included persons age 51 and older at Time 1; MHAS included 52 years and older at Time 1; HRS and MHAS data includes only community-dwelling populations at Time 1; HRS data for Time 2 includes persons who became institutionalized between Time 1 and 2.
Time 1 . | HRS . | . | MHAS . | . | . | . | |||
---|---|---|---|---|---|---|---|---|---|
Time 1 . | Time 2 . | . | Time 1 . | Time 2 . | |||||
. | Disabled . | Nondisabled . | Disabled . | Nondisabled . | . | Disabled . | Nondisabled . | Disabled . | Nondisabled . |
Age | Age | ||||||||
Mean (SD) | 71.81 (11.55) | 65.72 (9.74) | 72.03 (11.35) | 65.69 (9.75) | Mean (SD) | 72.20 (12.07) | 63.32 (8.84) | 71.62 (11.51) | 62.95 (8.61) |
Age categories | Age categories | ||||||||
51–59 | 7.72% | 92.28% | 7.22% | 92.78% | 52–59 | 4.59% | 95.41% | 4.47% | 95.53% |
60–69 | 8.46% | 91.54% | 8.43% | 91.57% | 60–69 | 6.41% | 93.59% | 7.13% | 92.87% |
70–79 | 13.75% | 86.25% | 14.77% | 85.23% | 70–79 | 13.31% | 86.69% | 13.96% | 86.04% |
80+ | 28.79% | 71.21% | 28.30% | 71.70% | 80+ | 35.00% | 65.00% | 36.46% | 63.54% |
Sex | Sex | ||||||||
Male | 8.87% | 91.13% | 8.89% | 91.11% | Male | 7.95% | 92.05% | 7.64% | 92.36% |
Female | 14.77% | 85.23% | 14.79% | 85.21% | Female | 10.57% | 89.43% | 11.03% | 88.97% |
Marital status | Marital status | ||||||||
Married, union | 9.11% | 90.89% | 8.83% | 91.17% | Married, union | 6.75% | 93.25% | 6.99% | 93.01% |
Single, divorced, separated | 13.16% | 86.84% | 13.32% | 86.68% | Single, divorced, separated | 10.50% | 89.50% | 13.26% | 86.74% |
Widowed | 21.75% | 78.25% | 22.88% | 77.12% | Widowed | 15.26% | 84.74% | 13.76% | 86.24% |
Education | Education | ||||||||
< 12 years | 20.77% | 79.23% | 20.12% | 79.88% | 0 years | 14.64% | 85.36% | 13.45% | 86.55% |
12 years | 12.04% | 87.96% | 11.55% | 88.45% | 1–5 years | 7.83% | 92.17% | 8.83% | 91.17% |
> 12 years | 8.19% | 91.81% | 8.93% | 91.07% | 6 years | 6.66% | 93.34% | 6.23% | 93.77% |
7+ years | 4.26% | 95.74% | 5.66% | 94.34% | |||||
Assets | Assets | ||||||||
Low | 20.40% | 79.60% | 17.98% | 82.02% | Low | 10.60% | 89.40% | 11.79% | 88.21% |
Medium | 10.78% | 89.22% | 11.76% | 88.24% | Medium | 9.26% | 90.74% | 8.24% | 91.76% |
High | 8.29% | 91.71% | 8.94% | 91.06% | High | 7.95% | 92.05% | 8.03% | 91.97% |
Location | Location | ||||||||
Urban | 11.53% | 88.50% | 11.35% | 88.65% | Urban | 8.32% | 91.68% | 7.66% | 92.34% |
Rural | 13.20% | 86.80% | 13.66% | 86.34% | Rural | 10.17% | 89.83% | 10.88% | 89.12% |
Health insurance | Health insurance | ||||||||
Uninsured | 9.68% | 90.32% | 10.04% | 89.96% | Uninsured | 10.41% | 89.59% | 10.12% | 89.88% |
Insured | 12.17% | 87.83% | 12.18% | 87.82% | Insured | 8.41% | 91.59% | 8.90% | 91.90% |
Health conditions | Health conditions | ||||||||
Chronic (count) | 1.22 (0.69) | 0.75 (0.71) | 1.16 (0.69) | 0.75 (0.71) | Chronic (count) | 0.69 (0.67) | 0.36 (0.56) | 0.58 (0.63) | 0.37 (0.56) |
Diabetes | 23.41% | 11.18% | 21.77% | 11.40% | Diabetes | 25.43% | 14.53% | 23.00% | 14.02% |
Cancer | 17.21% | 11.92% | 16.06% | 12.08% | Cancer | 4.17% | 1.80% | 2.16% | 1.88% |
Arthritis | 81.19% | 51.46% | 78.46% | 51.82% | Arthritis | 38.63% | 19.72% | 32.41% | 20.79% |
Acute (count) | 0.11 (0.32) | 0.03 (0.18) | 0.06 (0.25) | 0.03 (0.19) | Acute (count) | 0.07 (0.26) | 0.01 (0.11) | 0.03 (0.18) | 0.01 (0.11) |
Heart attack | 5.14% | 1.90% | 3.32% | 2.15% | Heart attack | 3.35% | 0.72% | 0.56% | 0.86% |
Stroke | 5.42% | 0.96% | 3.02% | 1.29% | Stroke | 3.27% | 0.43% | 2.67% | 0.38% |
BMI | BMI | ||||||||
Mean (SD) | 2.86 (0.89) | 2.83 (0.78) | 2.89 (0.87) | 2.83 (0.78) | Mean (SD) | 2.67 (0.89) | 2.82 (0.79) | 2.67 (0.84) | 2.83 (0.79) |
BMI categories | BMI categories | ||||||||
Underweight | 4.37% | 1.48% | 3.14% | 1.65% | Underweight | 8.24% | 2.35% | 4.69% | 2.49% |
Normal Weight | 35.04% | 35.82% | 34.01% | 35.96% | Normal Weight | 36.53% | 35.05% | 42.50% | 33.96% |
Overweight | 31.25% | 40.66% | 33.44% | 40.37% | Overweight | 34.95% | 40.76% | 33.59% | 41.51% |
Obese | 29.34% | 22.03% | 29.40% | 22.02% | Obese | 20.29% | 21.82% | 19.22% | 22.01% |
Total sample | 1,672 | 11,732 | 1,671 | 11,733 | Total sample | 1,170 | 10,667 | 1,087 | 9,655 |
12.09% | 87.01% | 12.11% | 87.89% | 9.33% | 90.67% | 9.45% | 90.55% |
Time 1 . | HRS . | . | MHAS . | . | . | . | |||
---|---|---|---|---|---|---|---|---|---|
Time 1 . | Time 2 . | . | Time 1 . | Time 2 . | |||||
. | Disabled . | Nondisabled . | Disabled . | Nondisabled . | . | Disabled . | Nondisabled . | Disabled . | Nondisabled . |
Age | Age | ||||||||
Mean (SD) | 71.81 (11.55) | 65.72 (9.74) | 72.03 (11.35) | 65.69 (9.75) | Mean (SD) | 72.20 (12.07) | 63.32 (8.84) | 71.62 (11.51) | 62.95 (8.61) |
Age categories | Age categories | ||||||||
51–59 | 7.72% | 92.28% | 7.22% | 92.78% | 52–59 | 4.59% | 95.41% | 4.47% | 95.53% |
60–69 | 8.46% | 91.54% | 8.43% | 91.57% | 60–69 | 6.41% | 93.59% | 7.13% | 92.87% |
70–79 | 13.75% | 86.25% | 14.77% | 85.23% | 70–79 | 13.31% | 86.69% | 13.96% | 86.04% |
80+ | 28.79% | 71.21% | 28.30% | 71.70% | 80+ | 35.00% | 65.00% | 36.46% | 63.54% |
Sex | Sex | ||||||||
Male | 8.87% | 91.13% | 8.89% | 91.11% | Male | 7.95% | 92.05% | 7.64% | 92.36% |
Female | 14.77% | 85.23% | 14.79% | 85.21% | Female | 10.57% | 89.43% | 11.03% | 88.97% |
Marital status | Marital status | ||||||||
Married, union | 9.11% | 90.89% | 8.83% | 91.17% | Married, union | 6.75% | 93.25% | 6.99% | 93.01% |
Single, divorced, separated | 13.16% | 86.84% | 13.32% | 86.68% | Single, divorced, separated | 10.50% | 89.50% | 13.26% | 86.74% |
Widowed | 21.75% | 78.25% | 22.88% | 77.12% | Widowed | 15.26% | 84.74% | 13.76% | 86.24% |
Education | Education | ||||||||
< 12 years | 20.77% | 79.23% | 20.12% | 79.88% | 0 years | 14.64% | 85.36% | 13.45% | 86.55% |
12 years | 12.04% | 87.96% | 11.55% | 88.45% | 1–5 years | 7.83% | 92.17% | 8.83% | 91.17% |
> 12 years | 8.19% | 91.81% | 8.93% | 91.07% | 6 years | 6.66% | 93.34% | 6.23% | 93.77% |
7+ years | 4.26% | 95.74% | 5.66% | 94.34% | |||||
Assets | Assets | ||||||||
Low | 20.40% | 79.60% | 17.98% | 82.02% | Low | 10.60% | 89.40% | 11.79% | 88.21% |
Medium | 10.78% | 89.22% | 11.76% | 88.24% | Medium | 9.26% | 90.74% | 8.24% | 91.76% |
High | 8.29% | 91.71% | 8.94% | 91.06% | High | 7.95% | 92.05% | 8.03% | 91.97% |
Location | Location | ||||||||
Urban | 11.53% | 88.50% | 11.35% | 88.65% | Urban | 8.32% | 91.68% | 7.66% | 92.34% |
Rural | 13.20% | 86.80% | 13.66% | 86.34% | Rural | 10.17% | 89.83% | 10.88% | 89.12% |
Health insurance | Health insurance | ||||||||
Uninsured | 9.68% | 90.32% | 10.04% | 89.96% | Uninsured | 10.41% | 89.59% | 10.12% | 89.88% |
Insured | 12.17% | 87.83% | 12.18% | 87.82% | Insured | 8.41% | 91.59% | 8.90% | 91.90% |
Health conditions | Health conditions | ||||||||
Chronic (count) | 1.22 (0.69) | 0.75 (0.71) | 1.16 (0.69) | 0.75 (0.71) | Chronic (count) | 0.69 (0.67) | 0.36 (0.56) | 0.58 (0.63) | 0.37 (0.56) |
Diabetes | 23.41% | 11.18% | 21.77% | 11.40% | Diabetes | 25.43% | 14.53% | 23.00% | 14.02% |
Cancer | 17.21% | 11.92% | 16.06% | 12.08% | Cancer | 4.17% | 1.80% | 2.16% | 1.88% |
Arthritis | 81.19% | 51.46% | 78.46% | 51.82% | Arthritis | 38.63% | 19.72% | 32.41% | 20.79% |
Acute (count) | 0.11 (0.32) | 0.03 (0.18) | 0.06 (0.25) | 0.03 (0.19) | Acute (count) | 0.07 (0.26) | 0.01 (0.11) | 0.03 (0.18) | 0.01 (0.11) |
Heart attack | 5.14% | 1.90% | 3.32% | 2.15% | Heart attack | 3.35% | 0.72% | 0.56% | 0.86% |
Stroke | 5.42% | 0.96% | 3.02% | 1.29% | Stroke | 3.27% | 0.43% | 2.67% | 0.38% |
BMI | BMI | ||||||||
Mean (SD) | 2.86 (0.89) | 2.83 (0.78) | 2.89 (0.87) | 2.83 (0.78) | Mean (SD) | 2.67 (0.89) | 2.82 (0.79) | 2.67 (0.84) | 2.83 (0.79) |
BMI categories | BMI categories | ||||||||
Underweight | 4.37% | 1.48% | 3.14% | 1.65% | Underweight | 8.24% | 2.35% | 4.69% | 2.49% |
Normal Weight | 35.04% | 35.82% | 34.01% | 35.96% | Normal Weight | 36.53% | 35.05% | 42.50% | 33.96% |
Overweight | 31.25% | 40.66% | 33.44% | 40.37% | Overweight | 34.95% | 40.76% | 33.59% | 41.51% |
Obese | 29.34% | 22.03% | 29.40% | 22.02% | Obese | 20.29% | 21.82% | 19.22% | 22.01% |
Total sample | 1,672 | 11,732 | 1,671 | 11,733 | Total sample | 1,170 | 10,667 | 1,087 | 9,655 |
12.09% | 87.01% | 12.11% | 87.89% | 9.33% | 90.67% | 9.45% | 90.55% |
Notes. Percentages, mean, and standard deviations are weighted statistics; All differences across disability groups (disabled vs nondisabled) were statistically significant within each country at the .05 level; Sample sizes may vary due to missing values; data may not add to 100% due to rounding; HRS included persons age 51 and older at Time 1; MHAS included 52 years and older at Time 1; HRS and MHAS data includes only community-dwelling populations at Time 1; HRS data for Time 2 includes persons who became institutionalized between Time 1 and 2.
One potential mechanism to explain differences in disability across the countries is health. In the United States, respondents with a disability reported more chronic conditions. Prevalence rates were higher in the United States compared with Mexico for all conditions except for diabetes, where rates were higher for Mexican respondents. While mean BMI did not differ greatly, Mexico had a greater proportion of elders in the underweight category, regardless of disability status. More respondents in the United States were in the obese category compared with Mexico, especially for those respondents that reported disability. Additionally, the prevalence of disability differs across the two countries by whether or not respondents had health insurance. In the U.S. persons with health insurance were more likely to be disabled than persons who were not insured, whereas the opposite is true in Mexico. This is to be expected because in the U.S. persons aged 65 and older are more likely to be insured under Medicare, and this table does not control for age.
It is worth noting that the prevalence of disability is lower in Mexico than in the United States for younger cohorts. However, this pattern reverses for people aged 80+, where disability is more prevalent in Mexico. Additionally, the gender gap in disability rates seems greater in the United States than in Mexico, even though in both countries women have a higher prevalence of disability.
Disability Transitions
Table 2 presents bivariate results for the outcome at Time 2 for the United States and Mexico, by disability outcomes at Time 1. Results show that among those with no ADL limitations at Time 1, a slightly higher proportion transitioned to one ADL at Time 2 in the United States compared with Mexico (4.3% vs 2.7%). A small and similar proportion of persons went from no ADL limitations at Time 1 to several ADL limitations at Time 2 in Mexico and the United States (2.9% vs 1.9%).
Time 1 . | HRS . | Time 1 . | MHAS . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
. | No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF/NH . | p Value . | . | No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF . | p Value . |
Disability status | Disability status | ||||||||||||
None (%) | 84.4 | 4.3 | 1.9 | 4.1 | 5.3 | 0.000 | None | 86.4 | 2.7 | 2.9 | 2.8 | 5.2 | 0.000 |
One ADL (%) | 41.9 | 23.6 | 15.8 | 12.6 | 6.1 | One ADL | 62.5 | 10.3 | 13.9 | 11.2 | 2.0 | ||
2+ ADLs (%) | 13.6 | 14.9 | 40.1 | 21.7 | 9.7 | 2+ ADLs | 29.5 | 8.4 | 32.0 | 25.3 | 4.8 | ||
By disability status and age | By disability status and age | ||||||||||||
None | 0.000 | None | 0.000 | ||||||||||
51–59 (%) | 90.3 | 2.9 | 1.0 | 1.2 | 4.6 | 51–59 | 90.0 | 1.4 | 1.9 | 1.0 | 5.7 | ||
60–69 (%) | 87.9 | 3.1 | 1.3 | 2.9 | 4.7 | 60–69 | 86.2 | 2.7 | 2.4 | 3.3 | 5.5 | ||
70–79 (%) | 80.1 | 5.6 | 2.5 | 5.9 | 5.9 | 70–79 | 84.2 | 5.0 | 3.3 | 3.9 | 3.6 | ||
80+ (%) | 64.2 | 9.9 | 5.4 | 12.6 | 7.9 | 80+ | 69.2 | 6.0 | 12.0 | 9.0 | 3.8 | ||
One ADL | 0.000 | One ADL | 0.000 | ||||||||||
51–59 (%) | 56.5 | 24.4 | 10.2 | 4.9 | 4.0 | 51–59 | 84.9 | 8.7 | 3.2 | 1.6 | 1.7 | ||
60–69 (%) | 50.9 | 24.5 | 16.7 | 6.1 | 1.8 | 60–69 | 76.5 | 4.9 | 14.0 | 3.5 | 1.0 | ||
70–79 (%) | 38.7 | 26.4 | 18.2 | 12.1 | 4.6 | 70–79 | 37.6 | 14.8 | 18.0 | 25.3 | 4.3 | ||
80+ (%) | 27.3 | 18.9 | 16.4 | 24.4 | 13.1 | 80+ | 44.0 | 15.5 | 24.1 | 16.1 | 0.3 | ||
2+ ADLs | 0.000 | 2+ ADLs | 0.000 | ||||||||||
51–59 (%) | 26.6 | 13.7 | 48.4 | 5.2 | 6.0 | 51–59 | 53.4 | 9.1 | 23.4 | 6.7 | 7.3 | ||
60–69 (%) | 13.8 | 22.9 | 39.2 | 18.4 | 5.8 | 60–69 | 44.8 | 15.0 | 20.5 | 16.5 | 3.3 | ||
70–79 (%) | 13.8 | 16.6 | 38.9 | 23.0 | 7.7 | 70–79 | 27.8 | 10.8 | 34.4 | 23.0 | 4.0 | ||
80+ (%) | 5.1 | 9.6 | 36.1 | 33.3 | 16.0 | 80+ | 12.7 | 3.3 | 39.9 | 38.9 | 5.2 | ||
Total sample | 10,287 | 846 | 679 | 818 | 774 | Total sample | 9,655 | 472 | 615 | 511 | 618 | ||
77.7% | 6.2% | 4.9% | 5.6% | 5.6% | 82.0% | 3.5% | 5.0% | 4.5% | 5.0% |
Time 1 . | HRS . | Time 1 . | MHAS . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
. | No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF/NH . | p Value . | . | No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF . | p Value . |
Disability status | Disability status | ||||||||||||
None (%) | 84.4 | 4.3 | 1.9 | 4.1 | 5.3 | 0.000 | None | 86.4 | 2.7 | 2.9 | 2.8 | 5.2 | 0.000 |
One ADL (%) | 41.9 | 23.6 | 15.8 | 12.6 | 6.1 | One ADL | 62.5 | 10.3 | 13.9 | 11.2 | 2.0 | ||
2+ ADLs (%) | 13.6 | 14.9 | 40.1 | 21.7 | 9.7 | 2+ ADLs | 29.5 | 8.4 | 32.0 | 25.3 | 4.8 | ||
By disability status and age | By disability status and age | ||||||||||||
None | 0.000 | None | 0.000 | ||||||||||
51–59 (%) | 90.3 | 2.9 | 1.0 | 1.2 | 4.6 | 51–59 | 90.0 | 1.4 | 1.9 | 1.0 | 5.7 | ||
60–69 (%) | 87.9 | 3.1 | 1.3 | 2.9 | 4.7 | 60–69 | 86.2 | 2.7 | 2.4 | 3.3 | 5.5 | ||
70–79 (%) | 80.1 | 5.6 | 2.5 | 5.9 | 5.9 | 70–79 | 84.2 | 5.0 | 3.3 | 3.9 | 3.6 | ||
80+ (%) | 64.2 | 9.9 | 5.4 | 12.6 | 7.9 | 80+ | 69.2 | 6.0 | 12.0 | 9.0 | 3.8 | ||
One ADL | 0.000 | One ADL | 0.000 | ||||||||||
51–59 (%) | 56.5 | 24.4 | 10.2 | 4.9 | 4.0 | 51–59 | 84.9 | 8.7 | 3.2 | 1.6 | 1.7 | ||
60–69 (%) | 50.9 | 24.5 | 16.7 | 6.1 | 1.8 | 60–69 | 76.5 | 4.9 | 14.0 | 3.5 | 1.0 | ||
70–79 (%) | 38.7 | 26.4 | 18.2 | 12.1 | 4.6 | 70–79 | 37.6 | 14.8 | 18.0 | 25.3 | 4.3 | ||
80+ (%) | 27.3 | 18.9 | 16.4 | 24.4 | 13.1 | 80+ | 44.0 | 15.5 | 24.1 | 16.1 | 0.3 | ||
2+ ADLs | 0.000 | 2+ ADLs | 0.000 | ||||||||||
51–59 (%) | 26.6 | 13.7 | 48.4 | 5.2 | 6.0 | 51–59 | 53.4 | 9.1 | 23.4 | 6.7 | 7.3 | ||
60–69 (%) | 13.8 | 22.9 | 39.2 | 18.4 | 5.8 | 60–69 | 44.8 | 15.0 | 20.5 | 16.5 | 3.3 | ||
70–79 (%) | 13.8 | 16.6 | 38.9 | 23.0 | 7.7 | 70–79 | 27.8 | 10.8 | 34.4 | 23.0 | 4.0 | ||
80+ (%) | 5.1 | 9.6 | 36.1 | 33.3 | 16.0 | 80+ | 12.7 | 3.3 | 39.9 | 38.9 | 5.2 | ||
Total sample | 10,287 | 846 | 679 | 818 | 774 | Total sample | 9,655 | 472 | 615 | 511 | 618 | ||
77.7% | 6.2% | 4.9% | 5.6% | 5.6% | 82.0% | 3.5% | 5.0% | 4.5% | 5.0% |
Notes. LTF/NH = loss to follow-up or nursing home. Percentages, mean, and standard deviations are weighted statistics; Sample sizes may vary due to missing values; data may not add to 100% due to rounding; HRS included persons age 51 and older at Time 1; MHAS included 52 years and older at Time 1; HRS and MHAS data includes only community-dwelling population at Time 1; HRS data at Time 2 includes persons who became institutionalized between Time 1 and 2.
p-Values indicate significance of the difference across Time-2 outcome categories using chi-squared tests within each country.
Time 1 . | HRS . | Time 1 . | MHAS . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
. | No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF/NH . | p Value . | . | No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF . | p Value . |
Disability status | Disability status | ||||||||||||
None (%) | 84.4 | 4.3 | 1.9 | 4.1 | 5.3 | 0.000 | None | 86.4 | 2.7 | 2.9 | 2.8 | 5.2 | 0.000 |
One ADL (%) | 41.9 | 23.6 | 15.8 | 12.6 | 6.1 | One ADL | 62.5 | 10.3 | 13.9 | 11.2 | 2.0 | ||
2+ ADLs (%) | 13.6 | 14.9 | 40.1 | 21.7 | 9.7 | 2+ ADLs | 29.5 | 8.4 | 32.0 | 25.3 | 4.8 | ||
By disability status and age | By disability status and age | ||||||||||||
None | 0.000 | None | 0.000 | ||||||||||
51–59 (%) | 90.3 | 2.9 | 1.0 | 1.2 | 4.6 | 51–59 | 90.0 | 1.4 | 1.9 | 1.0 | 5.7 | ||
60–69 (%) | 87.9 | 3.1 | 1.3 | 2.9 | 4.7 | 60–69 | 86.2 | 2.7 | 2.4 | 3.3 | 5.5 | ||
70–79 (%) | 80.1 | 5.6 | 2.5 | 5.9 | 5.9 | 70–79 | 84.2 | 5.0 | 3.3 | 3.9 | 3.6 | ||
80+ (%) | 64.2 | 9.9 | 5.4 | 12.6 | 7.9 | 80+ | 69.2 | 6.0 | 12.0 | 9.0 | 3.8 | ||
One ADL | 0.000 | One ADL | 0.000 | ||||||||||
51–59 (%) | 56.5 | 24.4 | 10.2 | 4.9 | 4.0 | 51–59 | 84.9 | 8.7 | 3.2 | 1.6 | 1.7 | ||
60–69 (%) | 50.9 | 24.5 | 16.7 | 6.1 | 1.8 | 60–69 | 76.5 | 4.9 | 14.0 | 3.5 | 1.0 | ||
70–79 (%) | 38.7 | 26.4 | 18.2 | 12.1 | 4.6 | 70–79 | 37.6 | 14.8 | 18.0 | 25.3 | 4.3 | ||
80+ (%) | 27.3 | 18.9 | 16.4 | 24.4 | 13.1 | 80+ | 44.0 | 15.5 | 24.1 | 16.1 | 0.3 | ||
2+ ADLs | 0.000 | 2+ ADLs | 0.000 | ||||||||||
51–59 (%) | 26.6 | 13.7 | 48.4 | 5.2 | 6.0 | 51–59 | 53.4 | 9.1 | 23.4 | 6.7 | 7.3 | ||
60–69 (%) | 13.8 | 22.9 | 39.2 | 18.4 | 5.8 | 60–69 | 44.8 | 15.0 | 20.5 | 16.5 | 3.3 | ||
70–79 (%) | 13.8 | 16.6 | 38.9 | 23.0 | 7.7 | 70–79 | 27.8 | 10.8 | 34.4 | 23.0 | 4.0 | ||
80+ (%) | 5.1 | 9.6 | 36.1 | 33.3 | 16.0 | 80+ | 12.7 | 3.3 | 39.9 | 38.9 | 5.2 | ||
Total sample | 10,287 | 846 | 679 | 818 | 774 | Total sample | 9,655 | 472 | 615 | 511 | 618 | ||
77.7% | 6.2% | 4.9% | 5.6% | 5.6% | 82.0% | 3.5% | 5.0% | 4.5% | 5.0% |
Time 1 . | HRS . | Time 1 . | MHAS . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
. | No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF/NH . | p Value . | . | No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF . | p Value . |
Disability status | Disability status | ||||||||||||
None (%) | 84.4 | 4.3 | 1.9 | 4.1 | 5.3 | 0.000 | None | 86.4 | 2.7 | 2.9 | 2.8 | 5.2 | 0.000 |
One ADL (%) | 41.9 | 23.6 | 15.8 | 12.6 | 6.1 | One ADL | 62.5 | 10.3 | 13.9 | 11.2 | 2.0 | ||
2+ ADLs (%) | 13.6 | 14.9 | 40.1 | 21.7 | 9.7 | 2+ ADLs | 29.5 | 8.4 | 32.0 | 25.3 | 4.8 | ||
By disability status and age | By disability status and age | ||||||||||||
None | 0.000 | None | 0.000 | ||||||||||
51–59 (%) | 90.3 | 2.9 | 1.0 | 1.2 | 4.6 | 51–59 | 90.0 | 1.4 | 1.9 | 1.0 | 5.7 | ||
60–69 (%) | 87.9 | 3.1 | 1.3 | 2.9 | 4.7 | 60–69 | 86.2 | 2.7 | 2.4 | 3.3 | 5.5 | ||
70–79 (%) | 80.1 | 5.6 | 2.5 | 5.9 | 5.9 | 70–79 | 84.2 | 5.0 | 3.3 | 3.9 | 3.6 | ||
80+ (%) | 64.2 | 9.9 | 5.4 | 12.6 | 7.9 | 80+ | 69.2 | 6.0 | 12.0 | 9.0 | 3.8 | ||
One ADL | 0.000 | One ADL | 0.000 | ||||||||||
51–59 (%) | 56.5 | 24.4 | 10.2 | 4.9 | 4.0 | 51–59 | 84.9 | 8.7 | 3.2 | 1.6 | 1.7 | ||
60–69 (%) | 50.9 | 24.5 | 16.7 | 6.1 | 1.8 | 60–69 | 76.5 | 4.9 | 14.0 | 3.5 | 1.0 | ||
70–79 (%) | 38.7 | 26.4 | 18.2 | 12.1 | 4.6 | 70–79 | 37.6 | 14.8 | 18.0 | 25.3 | 4.3 | ||
80+ (%) | 27.3 | 18.9 | 16.4 | 24.4 | 13.1 | 80+ | 44.0 | 15.5 | 24.1 | 16.1 | 0.3 | ||
2+ ADLs | 0.000 | 2+ ADLs | 0.000 | ||||||||||
51–59 (%) | 26.6 | 13.7 | 48.4 | 5.2 | 6.0 | 51–59 | 53.4 | 9.1 | 23.4 | 6.7 | 7.3 | ||
60–69 (%) | 13.8 | 22.9 | 39.2 | 18.4 | 5.8 | 60–69 | 44.8 | 15.0 | 20.5 | 16.5 | 3.3 | ||
70–79 (%) | 13.8 | 16.6 | 38.9 | 23.0 | 7.7 | 70–79 | 27.8 | 10.8 | 34.4 | 23.0 | 4.0 | ||
80+ (%) | 5.1 | 9.6 | 36.1 | 33.3 | 16.0 | 80+ | 12.7 | 3.3 | 39.9 | 38.9 | 5.2 | ||
Total sample | 10,287 | 846 | 679 | 818 | 774 | Total sample | 9,655 | 472 | 615 | 511 | 618 | ||
77.7% | 6.2% | 4.9% | 5.6% | 5.6% | 82.0% | 3.5% | 5.0% | 4.5% | 5.0% |
Notes. LTF/NH = loss to follow-up or nursing home. Percentages, mean, and standard deviations are weighted statistics; Sample sizes may vary due to missing values; data may not add to 100% due to rounding; HRS included persons age 51 and older at Time 1; MHAS included 52 years and older at Time 1; HRS and MHAS data includes only community-dwelling population at Time 1; HRS data at Time 2 includes persons who became institutionalized between Time 1 and 2.
p-Values indicate significance of the difference across Time-2 outcome categories using chi-squared tests within each country.
Recovery from disability appears higher in Mexico. Among persons with one ADL limitation at Time 1, less than half (41.9%) reported no ADL limitations at Time 2 in the United States, whereas this was 62.5% in Mexico. Individuals with one ADL limitation at Time 1 were more likely to remain with one ADL limitation at Time 2 in the United States than in Mexico (23.6% and 10.3%, respectively). Overall, the proportion of people reporting no-ADL limitations decreased with age in both countries.
In the United States, a lower proportion of persons that had several ADL limitations at Time 1 went to no ADLs compared to Mexico. However, a higher proportion went from several ADLs at Time 1 to one ADL at Time 2 in the United States than in Mexico. Additionally, a higher proportion (40.1%) remained in the 2+ ADL limitation category in the United States compared with Mexico (32%).
The transitions to mortality are somewhat more difficult to examine because of the relatively few cases that died in the panels in 2 years. However, the percent that went from no ADL limitation at Time 1 to death at follow-up was slightly higher for the United States than for Mexico (4.1% vs 2.8%). This was also true for those with one ADL at Time 1 where 12.6% died over the 2 years in the United States versus 11.2% in Mexico. A slightly higher proportion of persons with 2+ ADLs died in Mexico (25%) compared with the United States (22%). In addition, as explained earlier, a higher proportion moved to nursing homes in the United States than in Mexico by Time 2. About 6% of cases with one ADL and 10% of cases with 2+ ADL at Time 1 moved to the category of lost to follow-up or nursing homes for the United States. The comparable percentages for Mexico are 2% and 4.8%, respectively.
Estimated Transition Probabilities
Tables 3 and 4 presents the predicted probabilities of transitioning between Time 1 and Time 2 for the United States (Table 3) and for Mexico (Table 4). The predicted probabilities confirm the findings of the bivariate analyses and show that the transitions differ for the United States and Mexico, even after controlling for relevant variables.
Time 1 . | HRS . | |||||
---|---|---|---|---|---|---|
. | Time 2 . | |||||
No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF/NH . | Total . | |
None | 0.858 | 0.041 | 0.018 | 0.031 | 0.052 | 1.000 |
1 disability | 0.530 | 0.196 | 0.123 | 0.084 | 0.070 | 1.000 |
≥2 disabilities | 0.196 | 0.150 | 0.357 | 0.167 | 0.129 | 1.000 |
By gender | ||||||
Female | ||||||
None | 0.860 | 0.044 | 0.019 | 0.024 | 0.053 | 1.000 |
1 disability | 0.526 | 0.208 | 0.131 | 0.064 | 0.071 | 1.000 |
≥2 disabilities | 0.197 | 0.160 | 0.383 | 0.128 | 0.132 | 1.000 |
Male | ||||||
None | 0.851 | 0.038 | 0.017 | 0.044 | 0.051 | 1.000 |
1 disability | 0.521 | 0.180 | 0.113 | 0.119 | 0.068 | 1.000 |
≥2 disabilities | 0.190 | 0.135 | 0.320 | 0.231 | 0.124 | 1.000 |
Time 1 . | HRS . | |||||
---|---|---|---|---|---|---|
. | Time 2 . | |||||
No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF/NH . | Total . | |
None | 0.858 | 0.041 | 0.018 | 0.031 | 0.052 | 1.000 |
1 disability | 0.530 | 0.196 | 0.123 | 0.084 | 0.070 | 1.000 |
≥2 disabilities | 0.196 | 0.150 | 0.357 | 0.167 | 0.129 | 1.000 |
By gender | ||||||
Female | ||||||
None | 0.860 | 0.044 | 0.019 | 0.024 | 0.053 | 1.000 |
1 disability | 0.526 | 0.208 | 0.131 | 0.064 | 0.071 | 1.000 |
≥2 disabilities | 0.197 | 0.160 | 0.383 | 0.128 | 0.132 | 1.000 |
Male | ||||||
None | 0.851 | 0.038 | 0.017 | 0.044 | 0.051 | 1.000 |
1 disability | 0.521 | 0.180 | 0.113 | 0.119 | 0.068 | 1.000 |
≥2 disabilities | 0.190 | 0.135 | 0.320 | 0.231 | 0.124 | 1.000 |
Note. LTF/NH = Lost to follow up or moved to nursing homes by Time-2.
Time 1 . | HRS . | |||||
---|---|---|---|---|---|---|
. | Time 2 . | |||||
No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF/NH . | Total . | |
None | 0.858 | 0.041 | 0.018 | 0.031 | 0.052 | 1.000 |
1 disability | 0.530 | 0.196 | 0.123 | 0.084 | 0.070 | 1.000 |
≥2 disabilities | 0.196 | 0.150 | 0.357 | 0.167 | 0.129 | 1.000 |
By gender | ||||||
Female | ||||||
None | 0.860 | 0.044 | 0.019 | 0.024 | 0.053 | 1.000 |
1 disability | 0.526 | 0.208 | 0.131 | 0.064 | 0.071 | 1.000 |
≥2 disabilities | 0.197 | 0.160 | 0.383 | 0.128 | 0.132 | 1.000 |
Male | ||||||
None | 0.851 | 0.038 | 0.017 | 0.044 | 0.051 | 1.000 |
1 disability | 0.521 | 0.180 | 0.113 | 0.119 | 0.068 | 1.000 |
≥2 disabilities | 0.190 | 0.135 | 0.320 | 0.231 | 0.124 | 1.000 |
Time 1 . | HRS . | |||||
---|---|---|---|---|---|---|
. | Time 2 . | |||||
No ADLs . | One ADL . | 2+ ADLs . | Death . | LTF/NH . | Total . | |
None | 0.858 | 0.041 | 0.018 | 0.031 | 0.052 | 1.000 |
1 disability | 0.530 | 0.196 | 0.123 | 0.084 | 0.070 | 1.000 |
≥2 disabilities | 0.196 | 0.150 | 0.357 | 0.167 | 0.129 | 1.000 |
By gender | ||||||
Female | ||||||
None | 0.860 | 0.044 | 0.019 | 0.024 | 0.053 | 1.000 |
1 disability | 0.526 | 0.208 | 0.131 | 0.064 | 0.071 | 1.000 |
≥2 disabilities | 0.197 | 0.160 | 0.383 | 0.128 | 0.132 | 1.000 |
Male | ||||||
None | 0.851 | 0.038 | 0.017 | 0.044 | 0.051 | 1.000 |
1 disability | 0.521 | 0.180 | 0.113 | 0.119 | 0.068 | 1.000 |
≥2 disabilities | 0.190 | 0.135 | 0.320 | 0.231 | 0.124 | 1.000 |
Note. LTF/NH = Lost to follow up or moved to nursing homes by Time-2.
Time 1 | MHAS | |||||
Time 2 | ||||||
No ADLs | One ADL | 2+ ADLs | Death | LTF/NH | Total | |
None | 0.861 | 0.029 | 0.027 | 0.027 | 0.056 | 1.000 |
1 disability | 0.627 | 0.143 | 0.134 | 0.065 | 0.031 | 1.000 |
≥2 disabilities | 0.334 | 0.107 | 0.315 | 0.198 | 0.046 | 1.000 |
By gender | ||||||
Female | ||||||
None | 0.883 | 0.031 | 0.022 | 0.015 | 0.049 | 1.000 |
1 disability | 0.732 | 0.126 | 0.079 | 0.028 | 0.035 | 1.000 |
≥2 disabilities | 0.533 | 0.108 | 0.199 | 0.087 | 0.074 | 1.000 |
Male | ||||||
None | 0.884 | 0.024 | 0.020 | 0.025 | 0.047 | 1.000 |
1 disability | 0.746 | 0.099 | 0.074 | 0.046 | 0.035 | 1.000 |
≥2 disabilities | 0.527 | 0.083 | 0.181 | 0.139 | 0.071 | 1.000 |
Time 1 | MHAS | |||||
Time 2 | ||||||
No ADLs | One ADL | 2+ ADLs | Death | LTF/NH | Total | |
None | 0.861 | 0.029 | 0.027 | 0.027 | 0.056 | 1.000 |
1 disability | 0.627 | 0.143 | 0.134 | 0.065 | 0.031 | 1.000 |
≥2 disabilities | 0.334 | 0.107 | 0.315 | 0.198 | 0.046 | 1.000 |
By gender | ||||||
Female | ||||||
None | 0.883 | 0.031 | 0.022 | 0.015 | 0.049 | 1.000 |
1 disability | 0.732 | 0.126 | 0.079 | 0.028 | 0.035 | 1.000 |
≥2 disabilities | 0.533 | 0.108 | 0.199 | 0.087 | 0.074 | 1.000 |
Male | ||||||
None | 0.884 | 0.024 | 0.020 | 0.025 | 0.047 | 1.000 |
1 disability | 0.746 | 0.099 | 0.074 | 0.046 | 0.035 | 1.000 |
≥2 disabilities | 0.527 | 0.083 | 0.181 | 0.139 | 0.071 | 1.000 |
Note. LTF/NH = Lost to follow up or moved to nursing homes by Time-2.
Time 1 | MHAS | |||||
Time 2 | ||||||
No ADLs | One ADL | 2+ ADLs | Death | LTF/NH | Total | |
None | 0.861 | 0.029 | 0.027 | 0.027 | 0.056 | 1.000 |
1 disability | 0.627 | 0.143 | 0.134 | 0.065 | 0.031 | 1.000 |
≥2 disabilities | 0.334 | 0.107 | 0.315 | 0.198 | 0.046 | 1.000 |
By gender | ||||||
Female | ||||||
None | 0.883 | 0.031 | 0.022 | 0.015 | 0.049 | 1.000 |
1 disability | 0.732 | 0.126 | 0.079 | 0.028 | 0.035 | 1.000 |
≥2 disabilities | 0.533 | 0.108 | 0.199 | 0.087 | 0.074 | 1.000 |
Male | ||||||
None | 0.884 | 0.024 | 0.020 | 0.025 | 0.047 | 1.000 |
1 disability | 0.746 | 0.099 | 0.074 | 0.046 | 0.035 | 1.000 |
≥2 disabilities | 0.527 | 0.083 | 0.181 | 0.139 | 0.071 | 1.000 |
Time 1 | MHAS | |||||
Time 2 | ||||||
No ADLs | One ADL | 2+ ADLs | Death | LTF/NH | Total | |
None | 0.861 | 0.029 | 0.027 | 0.027 | 0.056 | 1.000 |
1 disability | 0.627 | 0.143 | 0.134 | 0.065 | 0.031 | 1.000 |
≥2 disabilities | 0.334 | 0.107 | 0.315 | 0.198 | 0.046 | 1.000 |
By gender | ||||||
Female | ||||||
None | 0.883 | 0.031 | 0.022 | 0.015 | 0.049 | 1.000 |
1 disability | 0.732 | 0.126 | 0.079 | 0.028 | 0.035 | 1.000 |
≥2 disabilities | 0.533 | 0.108 | 0.199 | 0.087 | 0.074 | 1.000 |
Male | ||||||
None | 0.884 | 0.024 | 0.020 | 0.025 | 0.047 | 1.000 |
1 disability | 0.746 | 0.099 | 0.074 | 0.046 | 0.035 | 1.000 |
≥2 disabilities | 0.527 | 0.083 | 0.181 | 0.139 | 0.071 | 1.000 |
Note. LTF/NH = Lost to follow up or moved to nursing homes by Time-2.
The probability of beginning with no ADL limitation and remaining without any limitation was similar across both countries for both men and women. However, recovery from one or several ADL limitations at Time 1 to no ADL limitations at Time 2 was more likely in Mexico than the United States. For persons that did not have a disability at Time 1, the probability of transitioning to one ADL limitation or death was higher among persons in the United States than in Mexico. Similarly, for persons starting with one disability or with several disabilities at Time 1, the probability of moving to one ADL at Time 2 was higher for person in the United States than in Mexico. Conversely, the probability of recovering (moving from one or several disabilities to none) was lower for persons in the United States than in Mexico. However, the story differs somewhat for persons moving into 2+ disabilities at Time 2. Persons in the United States that had no ADL at Time 1 had lower probability of transitioning to several disabilities at Time 2 than persons in Mexico.
Additionally, persons in the United States that had no ADL limitations or just one limitation at Time 1 were more likely to die within 2 years than those in the same two categories in Mexico. The differences in probability are most striking for persons that reported one disability at Time 1. In Mexico the probability of death at follow-up is 0.043, whereas it is nearly double (0.091) in the United States.
The transition comparisons were similar by sex. However, in both countries the probability of having 2+ ADLs at Time 2 for persons with 2+ ADLs at Time 1 was higher for women than men. On the other hand, in both countries, the probability of death at follow-up for those who had several ADL limitations at Time 1 was higher for men compared to women.
Index of Disability
We used the estimated probabilities to construct an index that summarizes the pattern of disability in the two countries. The index uses the transition probabilities in Tables 3 and 4, weighed by the proportion of the samples that are in each disability category at Time 1, to obtain the proportions in each category at Time 2. These proportions take into account not only the prevalence of disability at a given time, but also the movement across states.
Table 5 presents the resulting index by age group, for the total and by gender. The percent disabled at young ages (50–59) is higher for the United States (7.6%) compared with Mexico (1.3%), whereas the percent disabled at old age (80+), is higher for Mexico (27.3%) than the United States (20.5%). Examining disability by number of limitations, the U.S. propensities are higher than Mexico for one disability (moderately disabled). However for 2+ (severely disabled), the probabilities are much higher for Mexico.
. | % Active . | % (MD + SD) . | % MD . | % SD . | Life expectancya . | Active (years) . | Moderately disabled (years) . | Severely disabled (years) . | LTF/NH (years) . |
---|---|---|---|---|---|---|---|---|---|
United States | |||||||||
By age | |||||||||
50–59 | 88.5 | 7.6 | 4.2 | 3.3 | 27.9 | 7.2 | 0.3 | 0.3 | 0.3 |
60–69 | 84.5 | 10.1 | 5.6 | 4.5 | 19.8 | 5.8 | 0.4 | 0.3 | 0.4 |
70–79 | 79.1 | 13.3 | 7.4 | 6.0 | 12.9 | 6.3 | 0.6 | 0.5 | 0.6 |
80+ | 67.1 | 20.5 | 11.1 | 9.4 | 4.9 | 3.3 | 0.5 | 0.5 | 0.6 |
22.6 | 1.9 | 1.5 | 1.9 | ||||||
Overall index at age 50 | 81.0% | 6.7% | 5.4% | 6.9% | |||||
By gender and age | |||||||||
Females | |||||||||
50–59 | 88.1 | 2.2 | 4.4 | 3.5 | 29.7 | 7.4 | 0.4 | 0.3 | 0.3 |
60–69 | 84.0 | 3.6 | 5.9 | 4.6 | 21.3 | 6.2 | 0.4 | 0.3 | 0.4 |
70–79 | 78.6 | 8.7 | 7.7 | 6.2 | 13.9 | 6.8 | 0.7 | 0.5 | 0.7 |
80+ | 66.3 | 21.4 | 11.6 | 9.8 | 5.2 | 3.5 | 0.6 | 0.5 | 0.6 |
23.9 | 2.1 | 1.7 | 2.0 | ||||||
Overall index at age 50 | 80.5% | 7.0% | 5.7% | 6.9% | |||||
Males | |||||||||
50–59 | 88.9 | 2.2 | 4.0 | 3.2 | 25.9 | 7.0 | 0.3 | 0.2 | 0.3 |
60–69 | 85.0 | 3.4 | 5.3 | 4.3 | 18.0 | 5.5 | 0.3 | 0.3 | 0.4 |
70–79 | 79.8 | 7.8 | 7.0 | 5.7 | 11.5 | 5.7 | 0.5 | 0.4 | 0.5 |
80+ | 68.2 | 19.5 | 10.5 | 9.0 | 4.4 | 3.0 | 0.5 | 0.4 | 0.5 |
21.2 | 1.6 | 1.3 | 1.7 | ||||||
Overall index at age 50 | 81.9% | 6.2% | 5.1% | 6.8% | |||||
Mexico | |||||||||
By age | |||||||||
50–59 | 89.7 | 1.3 | 2.6 | 2.1 | 27.2 | 7.0 | 0.2 | 0.2 | 0.4 |
60–69 | 87.4 | 2.6 | 3.7 | 4.0 | 19.4 | 5.8 | 0.2 | 0.3 | 0.3 |
70–79 | 82.9 | 7.7 | 5.5 | 7.4 | 12.8 | 6.3 | 0.4 | 0.6 | 0.3 |
80+ | 69.8 | 27.3 | 9.1 | 18.2 | 5.2 | 3.6 | 0.5 | 0.9 | 0.2 |
22.7 | 1.3 | 1.9 | 1.2 | ||||||
Overall index at age 50 | 83.4% | 4.9% | 7.1% | 4.5% | |||||
By gender and age | |||||||||
Females | |||||||||
50–59 | 89.2 | 5.1 | 2.9 | 2.2 | 28.6 | 7.2 | 0.2 | 0.2 | 0.5 |
60–69 | 86.7 | 8.3 | 4.2 | 4.1 | 20.5 | 6.0 | 0.3 | 0.3 | 0.3 |
70–79 | 82.0 | 13.8 | 6.2 | 7.6 | 13.6 | 6.7 | 0.5 | 0.6 | 0.3 |
80+ | 68.5 | 28.6 | 10.1 | 18.5 | 5.4 | 3.7 | 0.5 | 1.0 | 0.2 |
23.6 | 1.6 | 2.1 | 1.3 | ||||||
Overall index at age 50 | 82.6% | 5.5% | 7.3% | 4.6% | |||||
Males | |||||||||
50–59 | 90.1 | 4.3 | 2.3 | 2.1 | 25.7 | 6.7 | 0.2 | 0.2 | 0.4 |
60–69 | 87.9 | 7.2 | 3.3 | 3.8 | 18.3 | 5.5 | 0.2 | 0.2 | 0.3 |
70–79 | 83.7 | 12.2 | 4.9 | 7.3 | 12.0 | 5.8 | 0.3 | 0.5 | 0.3 |
80+ | 71.1 | 25.9 | 8.1 | 17.8 | 5.0 | 3.6 | 0.4 | 0.9 | 0.1 |
21.6 | 1.1 | 1.8 | 1.2 | ||||||
Overall index at age 50 | 84.1% | 4.4% | 7.0% | 4.5% |
. | % Active . | % (MD + SD) . | % MD . | % SD . | Life expectancya . | Active (years) . | Moderately disabled (years) . | Severely disabled (years) . | LTF/NH (years) . |
---|---|---|---|---|---|---|---|---|---|
United States | |||||||||
By age | |||||||||
50–59 | 88.5 | 7.6 | 4.2 | 3.3 | 27.9 | 7.2 | 0.3 | 0.3 | 0.3 |
60–69 | 84.5 | 10.1 | 5.6 | 4.5 | 19.8 | 5.8 | 0.4 | 0.3 | 0.4 |
70–79 | 79.1 | 13.3 | 7.4 | 6.0 | 12.9 | 6.3 | 0.6 | 0.5 | 0.6 |
80+ | 67.1 | 20.5 | 11.1 | 9.4 | 4.9 | 3.3 | 0.5 | 0.5 | 0.6 |
22.6 | 1.9 | 1.5 | 1.9 | ||||||
Overall index at age 50 | 81.0% | 6.7% | 5.4% | 6.9% | |||||
By gender and age | |||||||||
Females | |||||||||
50–59 | 88.1 | 2.2 | 4.4 | 3.5 | 29.7 | 7.4 | 0.4 | 0.3 | 0.3 |
60–69 | 84.0 | 3.6 | 5.9 | 4.6 | 21.3 | 6.2 | 0.4 | 0.3 | 0.4 |
70–79 | 78.6 | 8.7 | 7.7 | 6.2 | 13.9 | 6.8 | 0.7 | 0.5 | 0.7 |
80+ | 66.3 | 21.4 | 11.6 | 9.8 | 5.2 | 3.5 | 0.6 | 0.5 | 0.6 |
23.9 | 2.1 | 1.7 | 2.0 | ||||||
Overall index at age 50 | 80.5% | 7.0% | 5.7% | 6.9% | |||||
Males | |||||||||
50–59 | 88.9 | 2.2 | 4.0 | 3.2 | 25.9 | 7.0 | 0.3 | 0.2 | 0.3 |
60–69 | 85.0 | 3.4 | 5.3 | 4.3 | 18.0 | 5.5 | 0.3 | 0.3 | 0.4 |
70–79 | 79.8 | 7.8 | 7.0 | 5.7 | 11.5 | 5.7 | 0.5 | 0.4 | 0.5 |
80+ | 68.2 | 19.5 | 10.5 | 9.0 | 4.4 | 3.0 | 0.5 | 0.4 | 0.5 |
21.2 | 1.6 | 1.3 | 1.7 | ||||||
Overall index at age 50 | 81.9% | 6.2% | 5.1% | 6.8% | |||||
Mexico | |||||||||
By age | |||||||||
50–59 | 89.7 | 1.3 | 2.6 | 2.1 | 27.2 | 7.0 | 0.2 | 0.2 | 0.4 |
60–69 | 87.4 | 2.6 | 3.7 | 4.0 | 19.4 | 5.8 | 0.2 | 0.3 | 0.3 |
70–79 | 82.9 | 7.7 | 5.5 | 7.4 | 12.8 | 6.3 | 0.4 | 0.6 | 0.3 |
80+ | 69.8 | 27.3 | 9.1 | 18.2 | 5.2 | 3.6 | 0.5 | 0.9 | 0.2 |
22.7 | 1.3 | 1.9 | 1.2 | ||||||
Overall index at age 50 | 83.4% | 4.9% | 7.1% | 4.5% | |||||
By gender and age | |||||||||
Females | |||||||||
50–59 | 89.2 | 5.1 | 2.9 | 2.2 | 28.6 | 7.2 | 0.2 | 0.2 | 0.5 |
60–69 | 86.7 | 8.3 | 4.2 | 4.1 | 20.5 | 6.0 | 0.3 | 0.3 | 0.3 |
70–79 | 82.0 | 13.8 | 6.2 | 7.6 | 13.6 | 6.7 | 0.5 | 0.6 | 0.3 |
80+ | 68.5 | 28.6 | 10.1 | 18.5 | 5.4 | 3.7 | 0.5 | 1.0 | 0.2 |
23.6 | 1.6 | 2.1 | 1.3 | ||||||
Overall index at age 50 | 82.6% | 5.5% | 7.3% | 4.6% | |||||
Males | |||||||||
50–59 | 90.1 | 4.3 | 2.3 | 2.1 | 25.7 | 6.7 | 0.2 | 0.2 | 0.4 |
60–69 | 87.9 | 7.2 | 3.3 | 3.8 | 18.3 | 5.5 | 0.2 | 0.2 | 0.3 |
70–79 | 83.7 | 12.2 | 4.9 | 7.3 | 12.0 | 5.8 | 0.3 | 0.5 | 0.3 |
80+ | 71.1 | 25.9 | 8.1 | 17.8 | 5.0 | 3.6 | 0.4 | 0.9 | 0.1 |
21.6 | 1.1 | 1.8 | 1.2 | ||||||
Overall index at age 50 | 84.1% | 4.4% | 7.0% | 4.5% |
Note. Moderately disabled (MD) = 1 ADL limitation, severely disabled (SD) = 2+ ADL limitations.
aWith life expectancy (LE) using age and gender specific life tables.
Source: World Health Organization. Global Health Observatory, 2000.
. | % Active . | % (MD + SD) . | % MD . | % SD . | Life expectancya . | Active (years) . | Moderately disabled (years) . | Severely disabled (years) . | LTF/NH (years) . |
---|---|---|---|---|---|---|---|---|---|
United States | |||||||||
By age | |||||||||
50–59 | 88.5 | 7.6 | 4.2 | 3.3 | 27.9 | 7.2 | 0.3 | 0.3 | 0.3 |
60–69 | 84.5 | 10.1 | 5.6 | 4.5 | 19.8 | 5.8 | 0.4 | 0.3 | 0.4 |
70–79 | 79.1 | 13.3 | 7.4 | 6.0 | 12.9 | 6.3 | 0.6 | 0.5 | 0.6 |
80+ | 67.1 | 20.5 | 11.1 | 9.4 | 4.9 | 3.3 | 0.5 | 0.5 | 0.6 |
22.6 | 1.9 | 1.5 | 1.9 | ||||||
Overall index at age 50 | 81.0% | 6.7% | 5.4% | 6.9% | |||||
By gender and age | |||||||||
Females | |||||||||
50–59 | 88.1 | 2.2 | 4.4 | 3.5 | 29.7 | 7.4 | 0.4 | 0.3 | 0.3 |
60–69 | 84.0 | 3.6 | 5.9 | 4.6 | 21.3 | 6.2 | 0.4 | 0.3 | 0.4 |
70–79 | 78.6 | 8.7 | 7.7 | 6.2 | 13.9 | 6.8 | 0.7 | 0.5 | 0.7 |
80+ | 66.3 | 21.4 | 11.6 | 9.8 | 5.2 | 3.5 | 0.6 | 0.5 | 0.6 |
23.9 | 2.1 | 1.7 | 2.0 | ||||||
Overall index at age 50 | 80.5% | 7.0% | 5.7% | 6.9% | |||||
Males | |||||||||
50–59 | 88.9 | 2.2 | 4.0 | 3.2 | 25.9 | 7.0 | 0.3 | 0.2 | 0.3 |
60–69 | 85.0 | 3.4 | 5.3 | 4.3 | 18.0 | 5.5 | 0.3 | 0.3 | 0.4 |
70–79 | 79.8 | 7.8 | 7.0 | 5.7 | 11.5 | 5.7 | 0.5 | 0.4 | 0.5 |
80+ | 68.2 | 19.5 | 10.5 | 9.0 | 4.4 | 3.0 | 0.5 | 0.4 | 0.5 |
21.2 | 1.6 | 1.3 | 1.7 | ||||||
Overall index at age 50 | 81.9% | 6.2% | 5.1% | 6.8% | |||||
Mexico | |||||||||
By age | |||||||||
50–59 | 89.7 | 1.3 | 2.6 | 2.1 | 27.2 | 7.0 | 0.2 | 0.2 | 0.4 |
60–69 | 87.4 | 2.6 | 3.7 | 4.0 | 19.4 | 5.8 | 0.2 | 0.3 | 0.3 |
70–79 | 82.9 | 7.7 | 5.5 | 7.4 | 12.8 | 6.3 | 0.4 | 0.6 | 0.3 |
80+ | 69.8 | 27.3 | 9.1 | 18.2 | 5.2 | 3.6 | 0.5 | 0.9 | 0.2 |
22.7 | 1.3 | 1.9 | 1.2 | ||||||
Overall index at age 50 | 83.4% | 4.9% | 7.1% | 4.5% | |||||
By gender and age | |||||||||
Females | |||||||||
50–59 | 89.2 | 5.1 | 2.9 | 2.2 | 28.6 | 7.2 | 0.2 | 0.2 | 0.5 |
60–69 | 86.7 | 8.3 | 4.2 | 4.1 | 20.5 | 6.0 | 0.3 | 0.3 | 0.3 |
70–79 | 82.0 | 13.8 | 6.2 | 7.6 | 13.6 | 6.7 | 0.5 | 0.6 | 0.3 |
80+ | 68.5 | 28.6 | 10.1 | 18.5 | 5.4 | 3.7 | 0.5 | 1.0 | 0.2 |
23.6 | 1.6 | 2.1 | 1.3 | ||||||
Overall index at age 50 | 82.6% | 5.5% | 7.3% | 4.6% | |||||
Males | |||||||||
50–59 | 90.1 | 4.3 | 2.3 | 2.1 | 25.7 | 6.7 | 0.2 | 0.2 | 0.4 |
60–69 | 87.9 | 7.2 | 3.3 | 3.8 | 18.3 | 5.5 | 0.2 | 0.2 | 0.3 |
70–79 | 83.7 | 12.2 | 4.9 | 7.3 | 12.0 | 5.8 | 0.3 | 0.5 | 0.3 |
80+ | 71.1 | 25.9 | 8.1 | 17.8 | 5.0 | 3.6 | 0.4 | 0.9 | 0.1 |
21.6 | 1.1 | 1.8 | 1.2 | ||||||
Overall index at age 50 | 84.1% | 4.4% | 7.0% | 4.5% |
. | % Active . | % (MD + SD) . | % MD . | % SD . | Life expectancya . | Active (years) . | Moderately disabled (years) . | Severely disabled (years) . | LTF/NH (years) . |
---|---|---|---|---|---|---|---|---|---|
United States | |||||||||
By age | |||||||||
50–59 | 88.5 | 7.6 | 4.2 | 3.3 | 27.9 | 7.2 | 0.3 | 0.3 | 0.3 |
60–69 | 84.5 | 10.1 | 5.6 | 4.5 | 19.8 | 5.8 | 0.4 | 0.3 | 0.4 |
70–79 | 79.1 | 13.3 | 7.4 | 6.0 | 12.9 | 6.3 | 0.6 | 0.5 | 0.6 |
80+ | 67.1 | 20.5 | 11.1 | 9.4 | 4.9 | 3.3 | 0.5 | 0.5 | 0.6 |
22.6 | 1.9 | 1.5 | 1.9 | ||||||
Overall index at age 50 | 81.0% | 6.7% | 5.4% | 6.9% | |||||
By gender and age | |||||||||
Females | |||||||||
50–59 | 88.1 | 2.2 | 4.4 | 3.5 | 29.7 | 7.4 | 0.4 | 0.3 | 0.3 |
60–69 | 84.0 | 3.6 | 5.9 | 4.6 | 21.3 | 6.2 | 0.4 | 0.3 | 0.4 |
70–79 | 78.6 | 8.7 | 7.7 | 6.2 | 13.9 | 6.8 | 0.7 | 0.5 | 0.7 |
80+ | 66.3 | 21.4 | 11.6 | 9.8 | 5.2 | 3.5 | 0.6 | 0.5 | 0.6 |
23.9 | 2.1 | 1.7 | 2.0 | ||||||
Overall index at age 50 | 80.5% | 7.0% | 5.7% | 6.9% | |||||
Males | |||||||||
50–59 | 88.9 | 2.2 | 4.0 | 3.2 | 25.9 | 7.0 | 0.3 | 0.2 | 0.3 |
60–69 | 85.0 | 3.4 | 5.3 | 4.3 | 18.0 | 5.5 | 0.3 | 0.3 | 0.4 |
70–79 | 79.8 | 7.8 | 7.0 | 5.7 | 11.5 | 5.7 | 0.5 | 0.4 | 0.5 |
80+ | 68.2 | 19.5 | 10.5 | 9.0 | 4.4 | 3.0 | 0.5 | 0.4 | 0.5 |
21.2 | 1.6 | 1.3 | 1.7 | ||||||
Overall index at age 50 | 81.9% | 6.2% | 5.1% | 6.8% | |||||
Mexico | |||||||||
By age | |||||||||
50–59 | 89.7 | 1.3 | 2.6 | 2.1 | 27.2 | 7.0 | 0.2 | 0.2 | 0.4 |
60–69 | 87.4 | 2.6 | 3.7 | 4.0 | 19.4 | 5.8 | 0.2 | 0.3 | 0.3 |
70–79 | 82.9 | 7.7 | 5.5 | 7.4 | 12.8 | 6.3 | 0.4 | 0.6 | 0.3 |
80+ | 69.8 | 27.3 | 9.1 | 18.2 | 5.2 | 3.6 | 0.5 | 0.9 | 0.2 |
22.7 | 1.3 | 1.9 | 1.2 | ||||||
Overall index at age 50 | 83.4% | 4.9% | 7.1% | 4.5% | |||||
By gender and age | |||||||||
Females | |||||||||
50–59 | 89.2 | 5.1 | 2.9 | 2.2 | 28.6 | 7.2 | 0.2 | 0.2 | 0.5 |
60–69 | 86.7 | 8.3 | 4.2 | 4.1 | 20.5 | 6.0 | 0.3 | 0.3 | 0.3 |
70–79 | 82.0 | 13.8 | 6.2 | 7.6 | 13.6 | 6.7 | 0.5 | 0.6 | 0.3 |
80+ | 68.5 | 28.6 | 10.1 | 18.5 | 5.4 | 3.7 | 0.5 | 1.0 | 0.2 |
23.6 | 1.6 | 2.1 | 1.3 | ||||||
Overall index at age 50 | 82.6% | 5.5% | 7.3% | 4.6% | |||||
Males | |||||||||
50–59 | 90.1 | 4.3 | 2.3 | 2.1 | 25.7 | 6.7 | 0.2 | 0.2 | 0.4 |
60–69 | 87.9 | 7.2 | 3.3 | 3.8 | 18.3 | 5.5 | 0.2 | 0.2 | 0.3 |
70–79 | 83.7 | 12.2 | 4.9 | 7.3 | 12.0 | 5.8 | 0.3 | 0.5 | 0.3 |
80+ | 71.1 | 25.9 | 8.1 | 17.8 | 5.0 | 3.6 | 0.4 | 0.9 | 0.1 |
21.6 | 1.1 | 1.8 | 1.2 | ||||||
Overall index at age 50 | 84.1% | 4.4% | 7.0% | 4.5% |
Note. Moderately disabled (MD) = 1 ADL limitation, severely disabled (SD) = 2+ ADL limitations.
aWith life expectancy (LE) using age and gender specific life tables.
Source: World Health Organization. Global Health Observatory, 2000.
Because disability rates and projections may be driven by the greater proportion of women in later life that have at least one ADL disability, the index was also broken down by sex. The table shows that women are more likely to be disabled than men for all age groups in both countries. This holds true for both moderate and severe disability categories.
The overall index capturing the rates of disability is also given in Table 5. The index can be interpreted as follows: for the total U.S. population 50 and older, out of the years expected to live at age 50 (27.9), 81% are expected to be free of disability, 6.7% with one disability, and 5.4% with several disabilities. The comparable figures for Mexico are: out of 27.2 years, 83.4% would be free of disability, 4.9% with one, and 7.1% with several disabilities. Thus, persons in Mexico have a higher proportion of their life expectancy (at age 50) that is estimated to be free of disability compared with the United States. This takes into account mortality differences across the two countries.
Of the years expected to live at age 50 for U.S. women, 80.5% are expected to be disability free years, compared with 81.3% among Mexican women. Among men, the expected share of disability free years at age 50 are 81.3% in the United States, compared with 82.6% in Mexico. There is not a large gender gap in either country with the loss to follow up/nursing home shares, between 6.8% (men) and 6.9% (women) for the United States and 4.5% and 4.6%, respectively for Mexico.
Discussion and Conclusions
We set out to compare disability rates and 2-year transitions across disability states for two countries at vastly different stages of the epidemiologic and demographic transitions as well as economic development. The levels of disability prevalence and the 2-year transitions are consistent with higher rates of disability for the United States than for Mexico, at least in terms of disability measured by limitations with ADL. Although such higher disability rates may be driven by the greater proportion of women with ADL limitations, our finding holds even after controlling for sex. In 2-year transitions, among older adults aged 50 or older, the U.S. population is more likely to transition to a disabled state or increase the number of disabilities than the Mexican counterparts, while Mexicans are more likely to move out of disability or reduce the number of disabilities reported. Although we are cautious to generalize our findings regarding mortality transitions because of a low number of deaths in Mexico, the transition to mortality is also higher for the United States compared to Mexico.
The two studies that we used for our analysis are highly comparable in study protocols and contents, and our analyses were carefully harmonized across the two databases, thus we rule out differences in the surveys, measures, or methods as possible explanations for these results. Rather, we speculate that these results could be explained by several other factors.
First, it is possible that the current cohorts of older adults in Mexico are already a highly selected group in terms of survival compared to the population we use as a benchmark in the United States. Therefore, it may be that Mexicans who made it into our sample of persons aged 50 and older are actually the sturdiest members of their cohorts, because they survived a regime of very high child and infant mortality. On the other hand, the sample of elders in the United States had less challenging early childhood conditions to overcome and are therefore less selected.
Such differential mortality selection is supported by data that shows infant and childhood mortality levels were much higher in Mexico than in the United States. For the youngest of our cohorts (those born in 1950), the mortality rate was more than three times as high in Mexico than in the United States—98.2 deaths per 1,000 births in Mexico versus 29.2 deaths per 1,000 births in the United States for the year 1950 (INEGI, 2009; US Census, 2003). While the earliest comparable data available for infant mortality among our oldest cohort (those born between 1900 and 1930) is only for 1930, the numbers still show a higher infant mortality rate for Mexico compared with the United States. The infant mortality rate (per 1,000 live births) was 131.6 in Mexico and 64.6 for the United States (INEGI, 2009; US Census, 2003). Because infant and childhood mortality levels were so much higher in Mexico, we can say that only the “fittest” survived to a larger extent in Mexico than in the United States. The gap between the two countries’ infant mortality rates differ by birth cohort however, suggesting that selective mortality is a cohort process. Therefore, future research should examine cohort differences in more detail between and within countries over time.
Mortality selection is not limited to younger ages. To test whether there is a cohort difference explained by mortality selection at the older ages between Mexico and the United States, we ran an additional analysis. We removed those who died by Time 2 from both samples and compared the new disability prevalence rates to the original results. If the removal of those who died were to show a more similar prevalence in disability rate across the two countries, this would suggest a considerable mortality selection. However, the analyses (using weighted data) consistently showed a difference between countries in our prevalence tables, for each disability item and count, when removing those who died (results available upon request). Finding very little difference in disability prevalence even after removing from the baseline those who died suggests that the prevalence difference in disability across countries found in our results is not entirely explained by mortality selection at older ages. In addition, the overall index of disability takes into account differential mortality across countries after age 50. Using life expectancy data for each country, the index shows that out of the years expected to live, Mexicans adults have a higher proportion that will be free of disability.
Second, the current stage of the epidemiological and lifestyle transitions in both countries are quite different, such that U.S. older adults have been exposed longer in their life cycle to chronic co-morbidities and other behaviors and risk factors associated with disability, such as smoking, obesity, and a sedentary lifestyle than comparable populations in Mexico, and this may translate into higher disability rates for current older population in the United States.
Third, it is possible that Mexican older adults tend to under-report functional limitations compared to their U.S. counterparts. Researchers have previously discussed the validity of self-reported measures in particular when performing cross-country analyses and when comparing different cultures (Dowd & Todd, 2011; Finch, Hummer, Reindl, & Vega, 2002; Kandula, Lauderdale, & Baker, 2007). Self-reports of physical conditions may be biased because of cultural as well as linguistic differences. Latino cultures are distinct from the culture of the United States and their beliefs and traditions may affect the way they perceive health (Kandula et al., 2007). Other studies have found potential artifactual components of self-reported measures such as disability and global health (Finch et al., 2002). This “cultural bias” could affect the way that Mexicans perceive health as well as their attitudes and health behaviors, which could result in misreports or underreports of disability. However, because we analyze the individuals’ transitions and not only levels of disability, we partially take into account this possibility.
Fourth, it is possible that we find this difference in disability prevalence across the two countries because while the United States has higher rates of institutionalization than Mexico and we took this into account, the state of disability at the time of institutionalization may be on average quite different between the two countries. If persons entering institutions in the United States are a lot more functional than the population entering institutions in Mexico, then the community dwelling populations we observe may be disproportionately skewed toward the disabled states in the United States compared with Mexico. We have no data to take into account the level of disability of institutionalized populations in both countries, however.
The challenges lie immediately ahead if the development path followed by Mexico mirrors the one followed by the United States. For example, tobacco smoking, dietary patterns associated with higher consumption of processed foods, and sedentary lifestyle associated with urban living, have all been established as risk factors for chronic degenerative conditions associated with physical disability in the United States. Our findings suggest that if Mexico follows the path of the United States, the challenge will be to minimize or avoid these and other negative consequences of modernization and urbanization and their related changes in lifestyle.
Funding
This work was supported by the National Institute on Aging/National Institutes of Health (AG018016); and the infrastructure support from the Sealy Center on Aging at UTMB.
Acknowledgments
K. Gerst-Emerson and A. Michaels-Obregon helped plan the study, conducted all statistical analyses and helped write the manuscript. R. Wong planned the study, supervised the data analysis and helped write the manuscript. A. Palloni guided conceptualization, data analysis, and contributed to revising the manuscript.
References
Rand (
Author notes
Decision Editor: Merril Silverstein, PhD