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diabetes research and clinical practice 107 (2015) 37–45
Contents available at ScienceDirect
Diabetes Research
and Clinical Practice
journ al h ome pa ge : www m/lo cate/diabres
Active life expectancy of Americans with diabetes:
Risks of heart disease, obesity, and inactivity
Sarah B. Laditka b,*, James N. Laditka a,1
Department of Public Health Sciences, and Associate Professor of Public Policy, University of North Carolina at
Charlotte, 9201 University City Boulevard, Charlotte, NC 28223, United States of America
Department of Public Health Sciences, and Associate Professor of Public Policy, University of North Carolina at
Charlotte, 9201 University City Boulevard, Charlotte, NC 28223, United States of America
article info
Article history:
Aims: Few researchers have studied whether diabetes itself is responsible for high rates of
Received 29 April 2014
disability or mortality, or if factors associated with diabetes contribute importantly. We
Received in revised form
estimated associations of diabetes, heart disease, obesity, and physical inactivity with life
23 September 2014
expectancy (LE), the proportion of life with disability (DLE), and disability in the last year of life.
Accepted 17 October 2014
Methods: Data were from the Panel Study of Income Dynamics (1999-2011 and 1986, African
Available online 23 October 2014
American and white women and men ages 55+, n = 1,980, 17,352 person-years). Activities of
daily living defined disability. Multinomial logistic Markov models estimated disability
transition probabilities adjusted for age, sex, race/ethnicity, education, and the health
African Americans
factors. Microsimulation measured outcomes.
Results: White women and men exemplify results. LE was, for women: 3.5 years less with
diabetes than without (95% confidence interval, 3.1–4.0), 11.1 less (10.3–12.0) adding heart
disease, 21.9 less with all factors (15.3–28.5), all p < 0.001. Corresponding results for men: 1.7 Mortality years (0.9–2.3, not significant), 8.2 (6.8–9.5) and 18.1 (15.6–20.6), both p < 0.001. DLE was, for Physical activity women: 23.5% (21.7–25.4) with no risk factors, 27.1% (25.7–28.6) with diabetes alone, 34.6% (33.1–36.1) adding heart disease, 52.9% (38.9–66.8) with all factors, all p < 0.001; for men: 13.2% (11.7–14.6), 16.3% (14.8-17.8, p < 0.01); and 22.1% (20.5–23.7), 36.4% (25.0–47.8), both p < .001. Among people with diabetes, those with other conditions were much less likely to have no disability in the final year of life. Conclusions: Much of the disability and mortality with diabetes was due to heart disease, obesity, and inactivity, risks that can be modified by health behaviors and medical care. # 2014 Elsevier Ireland Ltd. All rights reserved. 1. Introduction About 26 million people in the United States have diabetes, a common cause of disability and death [1]. Lifetime risk of having diabetes is over 33% for men and 39% for women [2]. Diabetes often causes difficulty with activities of daily living (ADLs) such as walking and dressing [3,4]. Many people with diabetes also have heart disease, or are obese or physically inactive. Each of those health factors reduces life expectancy and increases the risk of disability. Those factors may confound the association of diabetes with disability and life * Corresponding author. Tel.: +1 704 687 5390; fax: +1 704 687 1644. E-mail addresses: [email protected] (S.B. Laditka), [email protected] (J.N. Laditka). 1 Tel.: +1 704 687 8742; fax: +1 704 687 1644. 0168-8227/# 2014 Elsevier Ireland Ltd. All rights reserved. 38 diabetes research and clinical practice 107 (2015) 37–45 expectancy, yet no study has examined that association after controlling for them. This study adds to knowledge about diabetes by estimating the association of diabetes with disability and life expectancy with and without heart disease, obesity, and inactivity. Heart disease is one of the most prevalent diseases in the United States [5–7]. Diabetes causes vascular damage that substantially increases the risk of developing heart disease, and doubles the risk of dying from it [6–9]. It is nonetheless useful to consider the health risks of diabetes that are independent of heart disease because people can control diabetes and reduce heart disease risk by avoiding smoking, maintaining healthy weight, eating a heart healthy diet, controlling blood pressure and low-density lipoprotein cholesterol, and following the treatments recommended by their physicians [6–9]. One study has examined associations of both diabetes and heart disease with life expectancy [9]. The researchers found that although diabetes increased heart disease risk, the substantial reduction in life expectancy associated with diabetes was not significantly different for those with and without heart disease [9]. However, that study represented a limited population in a small area, measured diabetes and heart disease only every 12 years, and did not consider effects of disability on mortality [9]. The growing prevalence of overweight and obesity is contributing to a dramatic increase in type 2 diabetes [10– 13]. Obesity is also linked with a greater risk of disability [3,4,14]. Results of research associating obesity with mortality among people with diabetes are not consistent. There is evidence suggesting an ‘‘obesity paradox,’’ higher mortality with normal weight than with overweight or obesity for people with diabetes [15], although a recent study did not support this theory [16]. Some studies have found greater mortality risk only with severe obesity [17,18]. Other researchers have found that being physically inactive, which is common among people who are obese, increases mortality with diabetes [11,12,19]. No related research has measured activity and obesity with adequate time before measuring disability to limit the possibility that disability caused the inactivity or obesity, rather than being caused by them. We address that substantial research gap. Measuring active life expectancy is a useful way to understand effects of diabetes on health. This central measure of population health estimates the proportions of remaining life from a given age with and without disability, as well as life expectancy [20,21]. Relevant studies have found that diabetes reduces life expectancy and increases disability [22–26]. However, given substantial evidence that heart disease, obesity, and inactivity are associated with both diabetes and active life expectancy, we addressed two hypotheses. The first was that people with diabetes and one or more of those health factors would have a larger proportion of older life with disability and shorter life expectancy than people with diabetes alone. Thus, we hypothesized that the impact of diabetes on disability and life expectancy would be less than previously estimated because researchers have not controlled for those factors. Our second hypothesis was that these associations would last to the end of life, resulting in a markedly smaller percentage of those with diabetes and one or more of the other health conditions having no month of disability in the last year of life, compared with those with diabetes without the other factors. Researchers call this outcome successful aging to the end of life [27]. 2. Subjects, materials and methods 2.1. Data source and study sample We used data from the Panel Study of Income Dynamics (PSID) [28]. We followed a nationally representative sample ages 55 and over who identified themselves as African American or non-Hispanic white (hereafter white) from 1999 through 2011 (n = 1,980). We excluded races/ethnicities with samples too small for analysis (n = 78). 2.2. Dependent variables We identified disability from participants’ reports of having ‘‘any difficulty. . .because of a health or physical problem,’’ doing any ADL by themselves and without special equipment: bathing, eating, dressing, getting into or out of a bed or chair, walking, getting around outside, and getting to and using the toilet. Death was another measured outcome. The dependent variable indicated one of several transitions: remaining nondisabled, becoming disabled, recovering from disability, remaining disabled, dying when non-disabled, or dying when disabled. Each transition was defined by a participant’s responses about each of the ADLs in a pair of successive survey waves, or in one wave followed by death. With responses in up to seven survey waves plus death, each individual could have up to seven measured transitions. The PSID identified death dates using the National Death Index, compiled by the National Center for Health Statistics from state vital records. The analytic data represented 9039 transitions occurring through 17,352 person-years. 2.3. Measuring diabetes and heart disease In all seven waves interviewers asked, ‘‘Has a doctor ever told you that you have or had [diabetes/heart disease]?’’ Interviewers were instructed: ‘‘Do not accept self-diagnosed or diagnosed by a person who is not a doctor or other health professional.’’ We updated the data with each new diagnosis, but assumed that diagnosed individuals did not recover from the disease. We examined the consistency of disease reports across survey waves. 2.4. Measuring obesity We calculated body mass index (BMI, kg/m2) using height and weight reported by participants in 1986. We used 1986 data for BMI both to examine effects of earlier-life weight status and to limit the likelihood that disability measured by the outcome variable contributed to the person’s BMI. We defined obesity as BMI 35.0, using a World Health Organization threshold. This definition identified individuals whose BMIs were most likely to affect health, disability, and mortality, excluding those with lower levels of obesity diabetes research and clinical practice 107 (2015) 37–45 that may not be associated with poor health outcomes in older populations [17,18]. We examined the sensitivity of the results to defining obesity as BMI 30.0. 2.5. Measuring physical inactivity In 1986 the PSID asked, ‘‘Do you get any regular exercise, such as doing hard physical work, or walking a mile or more without stopping, or playing an active sport?’’ We considered those responding negatively to be inactive. Disability reported in 1999 would rarely cause inactivity in 1986, although permanent disability might do so for a small number of participants. For the few participants (n = 39) who did not provide BMI or activity measures in 1986, we used equivalent self-reports from 1999. We examined correlations among the 1986 obesity and activity measures with reports from that year of having only fair or poor health, and with a measure of disability defined by health conditions that ‘‘keep you from doing some types of work’’ (‘‘somewhat,’’ ‘‘a lot,’’ or completely). 2.6. Controlling for education We controlled for six education levels [29]: less than 8 years, completion of grade 8, completion of grades 9 through 12 without a high school diploma, high school graduation, some education after high school, and at least a 4-year college degree. 2.7. The model associating diabetes and related health factors with disability and death Our regression model represented increasing risks of disability and death with age, and allowed for an accelerating increase, including controls for: age, age-squared, age85plus, and (age85plus age), where age85plus indicated that age level (yes/no). The latter two terms defined a spline function that allowed a shift in disability and death risks beginning at age 85 [30]. The model included sex, race/ethnicity (African American or white), education, and the health factors. To provide specific probabilities for each population, the model included all 2-way interactions of sex and race/ethnicity with the health factors, and all 3-way interactions of those variables. Likelihood ratio tests indicated the interactions were significant ( p < 0.001). 2.8. Discrete-time Markov chains
The regression model applied maximum likelihood methods
to the measured interval of each disability transition to
identify Markov chains in the observed data, estimating
disability and death transition probabilities for each month
of life conditional on all measures in the previous month.
Researchers who study active life expectancy often use this
approach [22,24–26,30–37].
Variance-adjusted standard errors
Repeated measures for individuals in longitudinal studies
produce underestimated standard errors [30,34,38]. To account for repeated measures, we re-estimated the model with
a subject-specific random effect [30,34]. There is no accepted
method for adding random effects to microsimulations [39].
We therefore used the estimates from the standard regression model described in sections 2.7 and 2.8 for the
microsimulations, to which we turn in Section 2.10. However,
for covariates with larger variance in the random effect
model, we adjusted the corresponding standard errors of the
regression results to reflect that greater variance. This
procedure was analogous to standard error adjustments for
survey design effects [30,34]. It did not alter the point
estimates from the microsimulations, but did provide
considerably more conservative standard errors for those
results. Of the 80 covariates, 77 were statistically significant
(p < 0.001). 2.10. Microsimulation Microsimulation helps researchers understand complex phenomena that cannot be studied with more common statistical methods. Microsimulation used the transition probabilities to create large populations in which each individual had a complete record of monthly disability measures from age 55 until death. The expected age at death was the average age at death in the microsimulated population. We provide summary results for that measure, the proportion of remaining life with disability, and successful aging. We also exemplify the life course successful aging results with a detailed figure for one population, for which we used the Wilcoxon-Mann-Whitney test to compare the distributions. We held education constant at the high school level in the microsimulations, the most common educational attainment of the sampled population. Details of the methods are published [20,26,30,33–37]. 2.11. Estimating variation in the microsimulation results We used bootstrapping to estimate variation in the microsimulation results, accounting for parameter uncertainty (represented by 95% confidence intervals for the estimated parameters) and the Monte Carlo variation that affects most simulation research [30,34,35]. Bootstrapping repeated the microsimulation for each population 500 times. For each repetition we made a random selection for each parameter from its variance-adjusted 95% confidence interval (CI). The final CIs we report are the 2.5th and 97.5th percentiles of the 500 results [30,34,35]. We conducted the analyses using SAS IML (Cary, North Carolina). The Institutional Review Board (IRB) at the University of North Carolina at Charlotte determined that this research, which used de-identified secondary data, did not require IRB review. 3. Results 3.1. Sample characteristics The mean age in the analytic sample was 78.3 (weighted for national representativeness, 78.5). Women were 60.5% (59.1%). The PSID over-sampled African Americans, who were 20.1% (9.0%). The percentages with diabetes, heart disease, obesity, and physical inactivity, respectively, were: 24.2% (22.5%), 40 diabetes research and clinical practice 107 (2015) 37–45 21.5% (22.4%), 7.8% (6.2%), and 8.8% (8.2%) (results not shown in tables). There was little evidence of correlation in the 1986 measures between inactivity and either obesity (r = 0.03, p = 0.07), fair/poor health (r = 0.03, p = 0.12), or work disability (r = 0.01, p = 0.39). Obesity was weakly correlated with fair/ poor health (r = 0.04, p < 0.05) and work disability (r = 0.04, p < 0.05). Of participants with diabetes, 16% reported in a later survey response that they did not have diabetes. These participants confirmed the diabetes diagnosis with an average of 3.4 survey responses, and 54% had responded that they had the disease for more than two years. Of those reporting heart disease, 21% reported in a later survey that they did not have heart disease. They confirmed the diagnosis with an average of 3.5 diagnosis reports, and 76% said that they had the disease for more than two years. 3.2. Patterns of active life expectancy Table 1 shows the average age at death from age 55, and the percentage of remaining life with disability, together with the CIs. The estimate for people without diabetes is for those without heart disease or earlier-life obesity or inactivity. Compared with people without diabetes, those with diabetes but none of the other risk factors had shorter lives and a greater proportion of remaining life with disability. For white women, compared to those without diabetes, the average age at death for those with diabetes but none of the other factors was 3.5 years less (CI 3.1–4.0, p < 0.001, difference in years not shown in table). In the comparable result for disability, white women without diabetes were disabled 23.5% of remaining life (CI 21.7–25.4), those with diabetes 27.1% (CI 25.7–28.6, p < 0.01). Among African American women the analogous life Table 1 – Diabetes, associated factors, life expectancy and percentage of remaining life disableda White Women No Diabetes Diabetes Diabetes, Heart Disease Diabetes, Obese Diabetes, Inactive Diabetes, Heart Disease, Obese Diabetes, Heart Disease, Inactive Diabetes, Obese, Inactive Diabetes, Heart Disease, Obese, Inactive African American women No Diabetes Diabetes Diabetes, Heart Disease Diabetes, Obese Diabetes, Inactive Diabetes, Heart Disease, Obese Diabetes, Heart Disease, Inactive Diabetes, Obese, Inactive Diabetes, Heart Disease, Obese, Inactive White Men No Diabetes Diabetes Diabetes, Heart Disease Diabetes, Obese Diabetes, Inactive Diabetes, Heart Disease, Obese Diabetes, Heart Disease, Inactive Diabetes, Obese, Inactive Diabetes, Heart Disease, Obese, Inactive African American Men No Diabetes Diabetes Diabetes, Heart Disease Diabetes, Obese Diabetes, Inactive Diabetes, Heart Disease, Obese Diabetes, Heart Disease, Inactive Diabetes, Obese, Inactive Diabetes, Heart Disease, Obese, Inactive a Life Expectancy (Average Age at Death) Percent of Remaining Life Disabled Mean (95% CI) Mean (95% CI) 88.6 85.1 77.5 84.1 69.4 76.6 65.8 70.9 66.7 (86.7–90.5) (83.6–86.5) (76.4–78.5) (82.7–85.4) (63.2–75.7) (75.4–77.8) (60.3–71.2) (61.3–80.6) (58.2–75.2) 23.5 27.1 34.6 42.1 28.4 50.8 34.6 45.9 52.9 (21.7–25.4) (25.7–28.6) (33.1–36.1) (39.0–45.1) (21.7–35.1) (47.9–53.6) (25.5–43.6) (33.6–58.1) (38.9–66.8) 85.2 82.7 75.7 83.6 66.8 76.4 64.4 70.3 67.1 (82.5–87.9) (80.8–84.6) (74.3–77.1) (82.2–84.9) (61.1–72.5) (75.3–77.5) (59.3–69.6) (61.0–79.7) (58.4–75.8) 22.5 27.0 38.3 43.6 27.3 53.6 34.8 46.3 54.5 (20.3–24.7) (25.2–28.8) (36.5–40.1) (40.3–46.9) (21.7–33.0) (50.3–56.9) (26.3–43.2) (35.5–57.2) (41.8–67.2) 83.2 81.5 75.0 82.4 64.7 75.1 62.7 68.1 65.1 (79.9–86.4) (79.0–84.1) (73.1–76.9) (80.5–84.2) (60.8–68.6) (73.5–76.6) (59.5–66.0) (61.8–74.3) (59.3–70.8) 13.2 16.3 22.1 29.2 15.0 37.4 19.7 29.4 36.4 (11.7–14.6) (14.8–17.8) (20.5–23.7) (26.5–32.0) (11.6–18.5) (34.2–40.6) (14.1–25.2) (20.7–38.2) (25.0–47.8) 77.1 76.7 71.4 79.9 61.7 73.4 60.6 65.5 63.5 (73.2–81.1) (73.7–79.8) (69.2–73.6) (77.8–82.1) (58.8–64.6) (72.0–74.8) (57.8–63.4) (59.7–71.3) (58.1–68.9) 13.5 17.4 25.1 31.6 ... 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