Hospitalizations, Nursing Home Admissions, and Deaths Attributable to Diabetes

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1 Epidemiology/Health Services/Psychosocial Research O R I G I N A L A R T I C L E Hospitalizations, Nursing Home Admissions, and Deaths Attributable to Diabetes LOUISE B. RUSSELL, PHD 1,2 ELMIRA VALIYEVA, PHD 1,2,3,4 SHEILA H. ROMAN, MD 5 6 LEONARD M. POGACH, MD DONG-CHURL SUH, PHD 3,4 MONIKA M. SAFFORD, MD 7 OBJECTIVE To estimate all-cause hospitalizations, nursing home admissions, and deaths attributable to using a new methodology based on longitudinal data for a representative sample of older U.S. adults. RESEARCH DESIGN AND METHODS A simulation model, based on data from the National Health and Nutrition Examination Survey (NHANES) I Epidemiologic Followup Study, was used to represent the natural history of and control for a variety of baseline risk factors. The model was applied to 6,265 NHANES III adults aged years. The prevalence of risk factors in NHANES III, fielded in , better represents today s adults. RESULTS For all NHANES III adults aged years, a diagnosis of accounted for 8.6 of hospitalizations, 12.3 of nursing home admissions, and 10.3 of deaths in For people with, alone was responsible for 43.4 of hospitalizations, 52.1 of nursing home admissions, and 47 of deaths. Adjusting for related cardiovascular conditions, which may provide more accurate estimates of attributable risks for people with, increased these estimates to 51.4, 57.1, and 56.8, respectively. CONCLUSIONS Risks of institutionalization and death attributable to are large. Efforts to translate recent trials of primary prevention into practice and continued efforts to prevent complications of could have a substantial impact on hospitalizations, nursing home admissions, and deaths and their societal costs. The prevalence of, 8.7 in 2002 (1), is rising. Although longer survival due to better treatment has contributed to higher prevalence, the sharpest increase has occurred among people aged years, indicating that incidence is also rising. Growing numbers Diabetes Care 28: , 2005 From the 1 Institute for Health, Rutgers University, New Brunswick, New Jersey; the 2 Department of Economics, Rutgers University, New Brunswick, New Jersey; the 3 Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey; the 4 School of Public Health, University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey; the 5 Department of Medicine, Johns Hopkins University, Baltimore, Maryland; 6 New Jersey Veterans Administration Health Care System, East Orange, New Jersey; and the 7 Deep South Center on Effectiveness at the Birmingham VA Medical Center, University of Alabama at Birmingham, Birmingham, Alabama. Address correspondence and reprint requests to Louise B. Russell, PhD, Institute for Health, Rutgers University, 30 College Ave., New Brunswick, NJ lrussell@rci.rutgers.edu. Received for publication 22 December 2004 and accepted in revised form 4 April Abbreviations: ADA, American Diabetes Association; NHANES, National Health and Nutrition Examination Survey; NHDS, National Hospital Discharge Survey; NHEFS, NHANES I Epidemiologic Followup Study. A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances by the American Diabetes Association. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. of people are thus at risk of complications, particularly cardiovascular disease (2), and the disability and death that accompany them. Recent trials (3 9) have shown that complications can be prevented or delayed by new interventions for hyperglycemia, hypertension, and hyperlipidemia. Other trials in high-risk populations have suggested that lifestyle interventions may delay or prevent onset of type 2 (10 12). As background for prevention and control efforts, this report presents new estimates of all-cause hospitalizations, nursing home admissions, and deaths attributable to. We used a methodology based on the first and third National Health and Nutrition Examination Surveys (NHANES), two of the surveys that provide data for national health goals and clinical guidelines. Epidemiological methodologists note that cohort studies are more reliable than cross-sectional data for establishing the existence and magnitude of etiologic relationships (13 14). We used the NHANES I Epidemiologic Followup Study (NHEFS), a cohort study that tracked outcomes over 2 decades ( ), to develop a simulation model that projects outcomes. We applied the simulation model to adults aged years from NHANES III. Fielded in , NHANES III better represents today s adults (e.g., more obesity, fewer smokers). Our methodology allowed us to make estimates for alone and for together with major cardiovascular risk factors that cluster with. The estimates contribute to a more complete picture of impact on the population as a whole and on people with. RESEARCH DESIGN AND METHODS The NHANES, large national surveys conducted by the National Center for Health Statistics, collect health information for representative samples of noninstitutionalized Americans. The simulation model used here is based on equations that link risk factors measured at baseline in NHANES I to outcomes recorded during NHEFS follow-up to reproduce the natural course of. At four follow-ups ( , 1986, 1987, and 1992), survey participants (or proxies for deceased or incapacitated subjects) were interviewed about all stays in health facilities since baseline or DIABETES CARE, VOLUME 28, NUMBER 7, JULY

2 Attributable risk of Table 1 Characteristics of NHANES III adults aged years, by age and sex Aged years Aged years Weighted means (see RESEARCH DESIGN AND METHODS) Men Women Men Women Sample size (n) 1,959 (206) 2,142 (268) 1,084 (160) 1,080 (213) Risk factors Age (years) 53.5 (55.7) 54.1 (56.9) 69.2 (69.2) 69.3 (69.3) Black race () 8.6 (11.5) 10.7 (20.9) 8.0 (11.4) 8.9 (18.5) Current smoker () 30.4 (16.7) 24.6 (23.5) 18.3 (10.6) 13.1 (9.2) Former smoker () 42.9 (60.1) 25.6 (31.7) 56.8 (62.4) 32.3 (40.3) Serum cholesterol (mg/dl) 216 (214) 225 (242) 212 (210) 233 (235) Systolic blood pressure (mmhg) 128 (133) 126 (136) 138 (138) 137 (143) BMI 27.8 kg/m 2 for men, 27.3 for women () 40.3 (72.9) 46.6 (69.4) 43.0 (47.1) 41.9 (69.2) BMI 19 kg/m 2 () 0.9 (0.7) 3.3 (3.8) 1.7 (1.5) 3.7 (0.4) Serum albumin (g/dl) 4.2 (4.1) 4.1 (4.0) 4.1 (4.1) 4.0 (3.9) Diet Fiber (grams daily) 19.2 (18.4) 14.5 (15.1) 18.8 (18.5) 15.7 (15.7) Fish/shellfish (weekly servings) 1.5 (1.3) 1.5 (1.4) 1.5 (1.6) 1.4 (1.5) Fruits/vegetables (weekly servings) 28.0 (28.0) 31.0 (28.8) 31.9 (36.8) 34.8 (31.3) Exercise (regular recreational activity) ()* 57.2 (48.3) 49.8 (32.2) 63.3 (60.1) 56.2 (41.8) Alcohol consumption ne () 39.7 (55.2) 58.5 (80.8) 47.1 (61.7) 70.3 (90.2) 1 3 drinks daily, almost daily () 38.5 (21.4) 22.4 (8.5) 31.6 (19.4) 15.3 (2.5) 4 or more drinks daily, almost daily () 5.2 (1.6) 0.6 (0.7) 6.8 (3.1) 1.3 (0) Any amount, once a week or less often () 16.5 (21.8) 18.4 (10.0) 14.4 (15.8) 13.1 (7.3) Chronic conditions Diabetes () 8.0 (100) 7.9 (100) 11.2 (100) 12.6 (100) Bronchitis/emphysema, chronic cough, asthma, pleurisy () 12.3 (19.8) 16.4 (23.4) 18.0 (15.0) 18.0 (22.6) Heart attack () 7.6 (15.5) 2.8 (11.5) 15.6 (21.2) 7.2 (21.6) Heart failure () 3.4 (7.5) 2.2 (9.6) 7.0 (14.1) 6.3 (21.1) Arthritis or gout () 23.5 (25.6) 33.1 (49.4) 42.5 (45.3) 49.3 (59.2) Fracture of spine, hip, or wrist () 11.5 (13.2) 7.9 (11.5) 12.2 (17.9) 15.4 (10.5) Stroke or polio/paralysis () 2.3 (4.7) 2.0 (6.9) 5.2 (11.6) 5.3 (15.8) Malignant tumor () 2.5 (4.7) 5.8 (3.8) 7.5 (8.5) 9.4 (7.8) Data are overall values (values for people with ). *Omitted category is combinations with only moderate or little activity of either type. Omitted category is nondrinkers. Omitted category is people with no chronic condition. previous follow-up; their reports were matched with institutional records, which were also searched for additional stays. Deaths were confirmed by death certificate. The simulation model is applied to NHANES III adults aged years to estimate the attributable risks of. The simulation model has four components: the cohort dataset, which contains baseline information about NHANES III adults aged years; the mortality submodel, which projects allcause mortality; and the hospitalization and nursing home submodels, which project admissions. Each submodel uses separate regressions, estimated from NHEFS, for men aged years at baseline, women aged years, men aged years, and women aged years; NHEFS excluded people older than 74. The multivariable regressions relate admissions and mortality to all clinical risk factors shown by multiple studies to be statistically significantly related to disease and/or death (Table 1). The effects of in the model are thus its net effects after adjustment for the effects of all other risk factors. Diabetes is a statistically significant determinant of all three outcomes. NHANES I defined as present if an adult reported having received a doctor s diagnosis of (fasting blood glucose was not measured). Type 2, which accounts for of, was not distinguished from type 1. Diagnostic criteria, and the medical management of, changed little from the 1970s to the early 1990s (15); newer antiglycemic agents were not yet available and antihypertensive and anticholesterolemic medications only became widespread in the later 1980s. Thus, equations fitted to NHEFS data reasonably reflect the natural history of and can be applied to NHANES III adults, who were diagnosed by the same criteria. In-sample and outof-sample tests of the model are presented in (16 17). For this report, the model was updated to incorporate data for , the final NHEFS follow-up (18). Cohort dataset The cohort dataset includes all NHANES III respondents aged years who were examined by a physician (n 6,265). Since 84 had complete informa DIABETES CARE, VOLUME 28, NUMBER 7, JULY 2005

3 Russell and Associates *Like all NHANES, NHANES I surveyed noninstitutionalized people capable of traveling to the examination trailers, thus they were healthier than the general noninstitutionalized population. NHANES I women aged years initially had very low mortality; the projection equations reflect this early advantage. Their mortality soon regressed to the mean and to the national men-to-women ratio for this age-group ( 2:1). Relative differences due to are not affected. Year 1 All people Hospital admissions Nursing home admissions Deaths * 0.81* People with Hospital admissions Nursing home admissions Deaths Baseline change Men Women Men Women Total Aged years Aged years Table 2 Attributable risks of for all people and diabetic subjects, per 1,000 people, at year 1 and by age and sex tion and only 13 individuals lacked data for more than five risk factors, all sample subjects were retained; the age-sex mean for each risk factor replaced missing values. Most risk factors, including, were defined identically in NHANES I and III. Simulation submodels The mortality submodel is based on Weibull hazard functions that relate survival to baseline risk factors. The hospital and nursing home submodels use negative binomial regressions to relate annual admissions to the same risk factors. (Regressions available on request.) Estimates of admissions are adjusted for survival using the mortality submodel. Sample weights NHANES III was a stratified cluster sample. Since all individuals in the cohort received medical examinations, the examined sample final weight was used to derive population totals. Estimates We calculated baseline estimates of admissions and deaths by entering observed baseline risk factors for the 6,265 NHANES III adults aged years into the model regressions and running the model. Following the traditional definition of attributable risk as the most we can hope to accomplish in reducing the risk of the disease if we completely eliminate the exposure (13), we estimated outcomes for the scenarios listed below and calculated attributable risks as differences between these scenarios and baseline. All people with a diagnosis of, and thus with a baseline value of one for the dichotomous variable representing, had this value changed to zero. All other risk factors remained at baseline values. In addition to setting at zero, systolic blood pressure, serum cholesterol, and rates of cardiovascular conditions associated with (heart attack, heart failure, and stroke) were reduced in people with so that the means of these risk factors equaled those for people without. Since and other risk factors for heart disease are linked, the true attributable risk of includes higher DIABETES CARE, VOLUME 28, NUMBER 7, JULY

4 Attributable risk of rates of these risk factors in people with. Differences between baseline and scenario 1 represent admissions and deaths attributable to. Differences between baseline and scenario 2 show admissions and deaths attributable to plus related cardiovascular risk factors. RESULTS Table 1 shows risk factor means for all NHANES III adults and people with weighted to represent noninstitutionalized adults in Eight percent of men and women aged years, 11.2 of men aged years, and 12.6 of women aged years had a diagnosis of. People with were more likely to report having had heart attack, heart failure, or stroke. The effects of on hospital admissions, nursing home admissions, and deaths are shown in Table 2 (1st year after baseline) and Table 3 (10th year after baseline) for all people and people with. Percentage reductions from baseline show the attributable risk for each outcome. For all people aged years, accounted for 8.6 of hospital admissions per 1,000 people in the 1st year, 12.3 of nursing home admissions, and 10.3 of all-cause mortality. Percentages were larger for people aged years than for the elderly and larger for women than men. For example, accounted for 8.0 of hospital admissions in middle-aged men and 11.5 in middle-aged women compared with 4.8 and 9.1 for elderly men and women, respectively. The impact on the elderly in numbers of events was larger, however, because their rates of admission and death were higher than those of people aged years. The population impact reflects a much larger impact on the group directly affected: people with. For them, accounts for nearly half of all three outcomes: 43 of hospital admissions per 1,000, 52 of nursing home admissions, and 47 of deaths. Although the percentages are larger for middle-aged than elderly people, they are large in all age-sex groups. Among elderly men with, accounted for 29.4 of hospital admissions; baseline admissions per 1,000 were 280 compared with 197 Table 3 Attributable risks of for all people and diabetic subjects, per 1,000 people, at year 10 and by age and sex Aged years Aged years Men Women Men Women Total change Baseline Year 10 All people Hospital admissions Nursing home admissions Deaths People with Hospital admissions Nursing home admissions Deaths DIABETES CARE, VOLUME 28, NUMBER 7, JULY 2005

5 Russell and Associates Table 4 Attributable risks of and plus cardiovascular conditions at year 1 All people People with Rate change Rate change Hospital admissions per 1,000 people Baseline (1) Diabetes (2) Diabetes 0, cardiovascular conditions equalized* Nursing home admissions per 1,000 people Baseline (1) Diabetes (2) Diabetes 0, cardiovascular conditions equalized* Deaths per 1,000 people Baseline (1) Diabetes (2) Diabetes 0, cardiovascular conditions equalized* *In addition to setting equal to zero for all people with, blood pressure, cholesterol, and prevalence of heart attack, heart failure, and stroke are adjusted so that their means equal those for people without. per 1,000 for scenario 1. Among elderly women with, accounted for 37 of hospital admissions (374 vs. 235 per 1,000). Diabetes also accounted for 56 of nursing home admissions for elderly men and 33 for elderly women and 40 of deaths in both groups. At 10 years, the population risks attributable to were somewhat less, reflecting higher mortality rates among people with, especially the elderly (Table 3). Even with eliminated, Table 2 shows that deaths per 1,000 for diabetic elderly men were 10.8 in year 1, compared with 10 for all elderly men, and 6.8 per 1,000 for diabetic elderly women, compared with 5.2 for all elderly women, because of their higher rates of other serious chronic conditions. Thus, by year 10, diabetic individuals were a smaller share of the cohort. The impact on people with themselves, however, remained large at year 10 (Table 3). Percentage reductions were close to those in year 1 for diabetic people aged years and somewhat lower for people aged years. Fortythree percent of hospital admissions in men aged years and 52 in women aged years were attributable to. Diabetes accounted for 21 and 31 of hospitalizations in elderly men and women, respectively. It accounted for about two-thirds of nursing home admissions in middle-aged people, 51 in elderly men, and 26 in elderly women. Approximately 45 of deaths in diabetic people aged years were attributable to and a third of deaths in those aged years. Table 4 shows the effects of equalizing cardiovascular risk factors related to so that people with had the same means as people without. In addition to eliminating (scenario 1), scenario 2 set the means of systolic blood pressure, total cholesterol, and rates of heart attack, heart failure, and stroke in diabetic people equal to those in people without. Since clusters with these risk factors, scenario 2 may more closely approximate the true attributable risks of. In people with, percentages of hospitalizations and deaths attributable to increased by 8 to 10 percentage points to 51.4 and 56.8, respectively, after cardiovascular conditions were equalized. The attributable percentage of nursing home admissions increased 5 percentage points. CONCLUSIONS We have presented new estimates of the risks of hospitalization, nursing home admission, and all-cause mortality attributable to based on the NHANES I Epidemiologic Follow-up Study and NHANES III. For the population aged years as a whole, diagnosed accounted for 8.6 of hospitalizations in , 12.3 of nursing home admissions, and 10.3 of deaths. The percentages were lower for older people than middle-aged people, but absolute reductions in event rates were similar or greater because of their higher rates. Nursing home admissions, disproportionately an experience of old age, showed the greatest effect of. For people with, the percentages were naturally much larger: 43 of hospital admissions, 52 of nursing home admissions, and 47 of deaths due to. The percentages remained large in the 10th year, and absolute reductions were larger for all age-sex groups and all outcomes than in year 1. The attenuated effects in the overall population at year 10 are explained by higher mortality among diabetic people due to their higher rates of other chronic conditions. Adjusting for factors that cluster with and are part of the metabolic syndrome (higher blood pressure and cholesterol, higher rates of heart attack, heart failure, and stroke) increased the risks attributable to by 8 to 10 percentage points for hospitalizations and deaths but less for nursing home admissions. These adjustments corrected for conditions associated with that were diagnosed at baseline in NHANES III adults. Besides showing substantial health benefits from controlling or preventing, these estimates represent medical resources that would not be used if were prevented. Studies (19 24) show that people with have higher medical costs than nondiabetic people. Most analyzed data for enrollees in a single managed care plan and measured services as a step toward estimating costs. The hospitalization and nursing home estimates that underlie the cost estimates of the ADA are closest to those presented here (25). The ADA used an attributable-risk methodology, for the U.S. population, and defined based on a doctor s diagnosis. The methodology used in this report has several advantages. It used two linked nationally representative datasets rather than multiple datasets as the ADA did to estimate effects of on outcomes. In estimating effects, the methodology controlled not only for age, sex, and race but for numerous patient-level characteristics, including other risk fac- DIABETES CARE, VOLUME 28, NUMBER 7, JULY

6 Attributable risk of tors for cardiovascular disease. The data used for the projection equations were prospective and traced outcomes over 2 decades, providing a stronger basis for correctly measuring causal relationships than cross-sectional data. The estimates presented here agree in magnitude with the ADA estimates, lending strength to their findings and suggesting that itself is driving crosssectional differences in costs. The ADA reported that hospital days attributable to accounted for 9 of all hospital days and nursing home days for 15 of all nursing home days, similar to the 8.6 and 12.3 of admissions estimated here for alone and the 10.2 and 13.6 estimated after adjustment for related cardiovascular conditions. Overall, the ADA reported that diabetic people had more than double the hospital inpatient and nursing home costs of nondiabetic people, which implies that accounts for 50 of the costs of diabetic people in these settings. Our estimates are for admissions, not costs, but are in general agreement. In particular, our estimates for plus related cardiovascular conditions are of hospitalizations and 57.1 of nursing home admissions. Our estimates have potential limitations. First, since our study measures the impact of all, without differentiating between types 1 and 2, it overstates the impact of eliminating type 2; however, the overstatement is modest since type 2 accounts for of all. Second, current treatment is more effective than treatment during the 1970s and 1980s; hence, our baseline estimates may overstate the current impact of. Finally, national trends in hospital admissions during NHEFS follow-up suggested that our hospitalization projections might be high. Hospitalization rates measured by the National Hospital Discharge Survey (NHDS) rose from 1970 to the early 1980s then fell until the early 1990s; they continued to fall through 2000 for people aged years but rose again for people aged 65 years (26). Rates for NHANES III adults should be lower than NHDS rates because they were drawn from the noninstitutionalized population; since they were able to travel to an examination site, they may have been even healthier than the general noninstitutionalized population. To check that our projected admission rates were reasonable, we compared year 1 projections with NHDS rates for the same age-sex groups. As they should be, our projections were uniformly lower, of the NHDS rates. Our results support the extensive work documenting adverse effects by estimating its impact on hospitalizations, nursing home admissions, and mortality in a representative sample of older U.S. adults. They suggest that efforts to generalize the Diabetes Prevention Program, for example through the Small Steps, Big Rewards campaign sponsored by the National Institutes of Health and the Centers for Disease Control and Prevention, could have a substantial impact on -related outcomes. If could be prevented completely, we estimate that rates of hospitalization, nursing home admission, and death would fall by about half for people spared, and the beneficial effects would be sustained over 10 years. Our study offers another insight into the value of efforts to prevent and control. Acknowledgments The work reported in this paper was supported in part by grants from the Agency for Healthcare Research and Quality. The data on which this work is based were collected by the National Center for Health Statistics (NCHS) and were made available, in the form of public use tapes, by NCHS and the Inter-university Consortium for Political and Social Research (ICPSR). 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7 Russell and Associates 17. Russell LB, Teutsch SM, Kumar R, Dey A, Milan E: Preventable smoking and exercise-related hospital admissions: a model based on the NHEFS. Am J Prev Med 20: 26 34, Russell LB, Valiyeva E, Carson JL: Effects of prehypertension on admissions and deaths: a simulation. Arch Intern Med 164: , Brandle M, Zhou H, Smith BR, Marriott D, Burke R, Tabaei BP, Brown MB, Herman WH: The direct medical cost of type 2. Diabetes Care 26: , Selby JV, Ray GT, Zhang D, Colby CJ: Excess costs of medical care for patients with in a managed care population. Diabetes Care 20: , Brown JB, Nichols GA, Glauber HS, Bakst AW: Type 2 : incremental medical care costs during the first 8 years after diagnosis. Diabetes Care 22: , O Brien JA, Shomphe LA, Kavanagh PL, Raggio G, Caro JJ: Direct medical costs of complications resulting from type 2 in the US. Diabetes Care 21: , Brown JB, Pedula KL, Bakst AW: The progressive cost of complications in type 2 mellitus. Arch Intern Med 159: , Ramsey SD, Newton K, Blough D, McCulloch DK, Sandhu N, Wagner EH: Patient-level estimates of the cost of complications in in a managed care population. Pharmacoecon 16: , American Diabetes Association: Economic costs of in the U.S. in Diabetes Care 26: , Hall MJ, Owings MF: 2000 National Hospital Discharge Survey. In Advance Data From Vital and Health Statistics, no Hyattsville, MD, National Center for Health Statistics, 2002 DIABETES CARE, VOLUME 28, NUMBER 7, JULY