Context Although diabetes mellitus is one of the most prevalent and costly chronic
diseases in the United States, no estimates have been published of individuals'
average lifetime risk of developing diabetes.
Objective To estimate age-, sex-, and race/ethnicity-specific lifetime risk of
diabetes in the cohort born in 2000 in the United States.
Design, Setting, and Participants Data from the National Health Interview Survey (1984-2000) were used
to estimate age-, sex-, and race/ethnicity-specific prevalence and incidence
in 2000. US Census Bureau data and data from a previous study of diabetes
as a cause of death were used to estimate age-, sex-, and race/ethnicity-specific
mortality rates for diabetic and nondiabetic populations.
Main Outcome Measures Residual (remaining) lifetime risk of diabetes (from birth to 80 years
in 1-year intervals), duration with diabetes, and life-years and quality-adjusted
life-years lost from diabetes.
Results The estimated lifetime risk of developing diabetes for individuals born
in 2000 is 32.8% for males and 38.5% for females. Females have higher residual
lifetime risks at all ages. The highest estimated lifetime risk for diabetes
is among Hispanics (males, 45.4% and females, 52.5%). Individuals diagnosed
as having diabetes have large reductions in life expectancy. For example,
we estimate that if an individual is diagnosed at age 40 years, men will lose
11.6 life-years and 18.6 quality-adjusted life-years and women will lose 14.3
life-years and 22.0 quality-adjusted life-years.
Conclusions For individuals born in the United States in 2000, the lifetime probability
of being diagnosed with diabetes mellitus is substantial. Primary prevention
of diabetes and its complications are important public health priorities.
Diabetes is a serious and costly disease.1,2 The
prevalence of diagnosed diabetes among US adults has increased by 40% in 10
years from 4.9% in 1990 to 6.9% in 1999.3,4 It
is estimated that the number of individuals in the United States with diagnosed
diabetes will increase by 165% between 2000 and 2050, with the fastest increases
occurring in older and minority subpopulations.5
While the prevalence of diabetes can provide information about the burden
of disease in the community, prevalence rates do not capture individuals'
risks of developing diabetes during a defined period. Prevalence rates contain
no information about the impact of a disease on length and quality of life.
Although mortality rates and disease incidence are also useful for assessing
the impact of a disease at the community level, they say little about how
they affect individuals. Lifetime risk, as well as estimates of length and
quality of life with disease, are informative and easily understood measures
of the effect of disease in individuals.6,7
Although estimates of lifetime risk are available for several chronic
conditions (hypertension, breast cancer, dementia, fractures, and coronary
heart disease) and have been used effectively in public education campaigns,8-12 the
lifetime risk of diabetes has not been previously reported.
We used data from the National Health Interview Surveys (NHIS; 1984-2000)
to estimate prevalence and incidence of diabetes in 2000 specific to age (birth
through ≥100 years), sex, and race/ethnicity (non-Hispanic white, non-Hispanic
black, Hispanic, other). Data from the US Census Bureau and from a previous
study of diabetes as a cause of death were used to estimate mortality rates
specific to age, sex, and race/ethnicity for the individuals with and without
diabetes. These estimates were then entered into a Markov model to estimate
residual (remaining) lifetime risk of diabetes specific to sex and race/ethnicity
from birth to 80 years for the US population born in 2000. We also estimated
age at diagnosis, duration with diabetes, and life-years lost from diabetes
as well as quality-adjusted life-years (QALYs) lost.
Diabetes prevalence and incidence rates, as well as mortality rates,
were based on estimates for 2000. We calculated prevalence and incidence rates
from the nationally representative NHIS.13-16 Prevalence
was assessed from the answer to the question "Have you ever been told by a
doctor or health professional (other than during pregnancy, if female) that
you have diabetes or sugar diabetes?" Incidence was assessed by cross-tabulating
age at the time of the survey and the question "How old were you when a doctor
first told you that you had diabetes or sugar diabetes?"
The NHIS is an ongoing continuous nationwide cross-sectional survey
of the health status and behaviors of the US noninstitutionalized population
conducted by the National Center for Health Statistics and by the US Bureau
of Census. The NHIS uses a multistage, probability sampling strategy to select
households and individuals each year; in 2000, there were approximately 45 000
households and 120 000 individuals selected. The overall response rate
varies annually, but is approximately 90%. We jointly modeled NHIS data for
1984-2000 to improve the precision of the estimates for 2000.
There were 14 325 prevalent cases of diagnosed diabetes among the
356 787 respondents in the NHIS for 1984-2000. We used logistic regression
to estimate diabetes prevalence as a function of age (birth through ≥100
years in 1-year intervals), race/ethnicity (non-Hispanic white, non-Hispanic
black, Hispanic, other), and sex, and used indicator variables to represent
each calendar year. We used the Bayesian information criterion17 to
select the best fitting model. The Bayesian information criterion selects
a model from a collection of possibly non-nested models by maximizing the
likelihood but with a penalty for larger dimensional models. The final model
included a cubic spline for age (knots at 20, 40, 60, 80 years), race/ethnicity,
sex, calendar year, age by race/ethnicity interaction, and race/ethnicity
by sex interaction. Cubic splines18 are a flexible
class of curves that can be used to model nonlinear responses in regression
models. Our model appeared to fit the observed data well. The Hosmer-Lemeshow19 goodness-of-fit test for the final model yielded
a χ28 of 8.99 (P = .34)
and we calculated R2 to be 0.70.20
There were 1349 incident cases of diagnosed diabetes among the 343 856
nondiabetic NHIS respondents for 1984-2000. The estimated incidence standardized
to the 2000 US population ranged from 3.0/1000 during 1984-1990 to 4.2/1000
during 1997-2000. To improve precision, we used logistic regression to model
incidence as a function of age, race/ethnicity, sex, and calendar year. The
final model included a cubic spline for age (knots at 25, 50, 75 years), 4
racial/ethnic groups (non-Hispanic white, non-Hispanic black, Hispanic, other),
sex, an age by sex interaction, and indicator variables for 3 calendar year
groups (1984-1990, 1991-1996, 1997-2000). We used this calendar year grouping
because the NHIS changed diabetes incidence reporting in 1997. This required
that 1997-2000 be treated differently from previous years. The Hosmer-Lemeshow
goodness-of-fit test for this final model yielded a χ28 of 10.85 (P = .21) and we calculated R2 to be 0.24.
We used estimates of the US mortality rate by age, race/ethnicity, and
sex for 2000 that are provided in the US Census Bureau's projected components
of change in the US resident population.21 Because
our Markov models require separate mortality rates for individuals diagnosed
as having diabetes and those without the disease, we applied mortality relative
risks (RRs) for diabetes to the mortality rates corresponding with age, sex,
and race/ethnicity for persons without diabetes. In a recent study of death
certificate data, Tierney et al22 developed
sex-specific estimates of the RR for death attributable to diabetes for adults
(aged ≥18 years) in 4 age categories. They found that the RR for death
from diabetes was highest in the youngest age group and declined progressively
in older age groups. The age- and sex-specific estimates of RR used herein
are also consistent with recent estimates from a National Health and Nutrition
Examination Survey (NHANES) II mortality study23 and
other previously published estimates.24
Markov chain models are frequently used to simulate the progression
of individuals through mutually exclusive disease states. Transitions between
states in a Markov model take place at discrete intervals, such as 1 month
or 1 year, and the number of individuals who move from one state of the model
to another during each cycle is determined by transition probabilities. For
each race/ethnicity-sex combination, we estimated the age-specific 1-year
probability of (1) remaining nondiabetic, (2) becoming diabetic, and (3) dying
without diabetes. We estimated the probability of (1) remaining diabetic (for
this analysis we assumed that once diagnosed, diabetes was not reversible)
and (2) dying with diabetes for individuals who have developed diabetes.
Using these probabilities in a Markov chain model,25 we
estimated the (1) "residual" or remaining lifetime risk for diabetes among
persons not diabetic at a specific "baseline" age, (2) average length of time
or duration that a person is expected to live after diabetes diagnosis (assumes
diabetes is not reversible), (3) life-years lost, which is the diabetes-related
reduction in remaining life expectancy after a specific age at diagnosis,
(4) QALYs lost, which we calculated by weighting each year with diabetes by
0.75 of a year without diabetes,26 and (5)
distributions of age at diagnosis by sex and race/ethnicity.
The Markov chain model used (available from the corresponding author
on request) here can be considered an extension of the lifetable technique,
a commonly used statistical method for demographic projections and clinical
trial analysis. It begins with age-specific transition rates for a given period
and then assumes that this schedule of rates is in operation for the lifetime
of a hypothetical birth cohort. This cohort is "aged" year by year to produce
residual lifetime risks at birth and at each age thereafter. Hence the residual
lifetime risks for diabetes are those that would be realized if the age-specific
transition rates do not change.
We estimated 8 sets of parameters and the associated Markov chains corresponding
to the 8 race/ethnicity-sex combinations: non-Hispanic white male and female,
non-Hispanic black male and female, Hispanic male and female, and males and
females of other races/ethnicities. We calculated all race/ethnicity estimates
by weighting the race/ethnicity-specific values by the proportions of nondiabetic
individuals in the 2000 US population. We calculated total population estimates
by weighting the race/ethnicity-specific values by the proportions of newly
incident cases in the 2000 US population.
We conducted sensitivity analyses for several of our assumptions. In
a probabilistic sensitivity analysis,27 we
simultaneously varied prevalence and incidence rates and RRs of mortality
from diabetes. This approach generates confidence intervals for residual lifetime
risks from distributions reflecting the uncertainty in the parameter estimates.
Because contemporary population-based data on the RR for death among
children and adolescents (aged <18 years) with diabetes are not available,
we assumed that their RR was the same as the RR for 18- to 44-year-olds with
diabetes; 6.5 for males and 8.8 for females. In addition, we performed a sensitivity
analysis by setting the RR for males and females (aged ≤18 years) equal
to 1.0. This implies that death rates were not elevated for adolescents with
diabetes. The resulting lifetime risks were almost identical, differing only
in the fifth decimal place.
For the base-case analysis of QALYs lost, we weighted each year with
diabetes by 0.75 of a year without diabetes.26 A
study, using the self-administered Quality of Well-Being Index28 instead
of the Euroqol26 found that the health utility
associated with diabetes may be as low as 0.65. However, individuals may adapt
to diabetes with time and may not perceive their quality of life as poor as
these data suggest. In a sensitivity analysis, we varied the weighting factor
by 0.05 units in calculating QALYs lost from 0.65 to 0.90. For example, for
a man diagnosed as having diabetes at age 40 years, the estimate of QALYs
lost was 21.4 years for a weighting factor of 0.65; 20.0 for 0.70; 18.6 for
0.75; 17.2 for 0.80; 15.8 for 0.85; and 14.4 for 0.90. For a woman diagnosed
at the same age, the respective QALYs lost were 25.1, 23.5, 22.0, 20.5, 18.9,
and 17.4 years.
We also performed 2 procedures to check the accuracy of the model. First,
we compared life expectancies specific to sex and race/ethnicity from the
Markov model with US Census Bureau life expectancies for 2000.21 In
all cases, the life expectancies from the Markov model were within 1 year
of the US Census Bureau life expectancies. Second, we compared our estimates
of the prevalence of diabetes, assuming the Markov model was at steady state,
with previously published projections for 2050.5,29 The
Markov model produced a prevalence estimate of 8.9% vs previous projections
of 7.2% by Boyle et al5 and 9.7% by Honeycutt
et al.29 Boyle et al5 used
different data and a different modeling strategy from our analysis while Honeycutt
et al29 used NHIS data and Markov chains.
Table 1 and Table 2 list the residual lifetime risks for diabetes for males
and females by baseline age and race/ethnicity. For individuals born in 2000,
the lifetime risk for diabetes was higher for females than males (38.5% vs
32.8%). The residual lifetime risk for diabetes remained higher among females
than males at all ages, declining to 22.4% for females and 18.9% for males
at age 60 years, and to 6.9% and 5.2%, respectively, at age 80 years. The
lifetime risk for diabetes was higher among minority groups at birth and at
all ages. Among males, the lifetime risk at birth ranged from 45.4% for Hispanics
to 26.7% for non-Hispanic whites. Among females, the lifetime risk ranged
from 52.5% for Hispanics to 31.2% for non-Hispanic whites. The residual risk
for diabetes remained high for minority groups even at older ages, ranging
from 31.7% for Hispanic men to 16.6% for non-Hispanic white men at age 60
years and from 36.2% for Hispanic women to 19.5% for non-Hispanic white women.
In the cohort of individuals born in 2000, we estimated that 0.88% of
males and 1.11% of females will develop diabetes by age 20 years; by age 40
years, 4.05% and 7.19%; by age 60 years, 18.09% and 20.38%; and by age 80
years, 30.77% and 35.08%, respectively. The estimated proportion of individuals
who will develop diabetes before various ages is higher among minority groups
Life-Years and QALYs Lost
Among children diagnosed as having diabetes at age 10 years, we project
that on average boys will lose 18.7 life-years and 31.0 QALYs (Table 3) and girls will lose 19.0 life-years and 32.8 QALYs (Table 4). We project loss of life-years
and QALYs to be higher in minority groups and highest for non-Hispanic blacks.
Black males diagnosed as having diabetes at age 10 years lose 22.2 life-years
and 32.6 QALYs; black females diagnosed at the same age lose 23.1 life-years
and 35.3 QALYs. The projected loss of life-years and QALYs is substantial
even among individuals diagnosed as having diabetes at older ages. If diagnosed
as having diabetes at 60 years, men are projected to lose 7.3 life-years and
11.1 QALYs; women, 9.5 years and 13.8 QALYs, respectively.
Age at Diagnosis of Diabetes
In 2000, the mean age at diagnosis of diabetes was 55.7 years for non-Hispanic
black males and females; 57.9 years for Hispanic males and 57.4 years for
Hispanic females; 58.1 years for non-Hispanic white males and 57.9 for non-Hispanic
white females; and 58.8 years for males and females of other races/ethnicities.
The distribution of age at diagnosis displayed a sigmoid shape: relatively
few cases occurred among those aged 20 years or younger and the numbers increased
rapidly from age 40 years before plateauing among those aged 80 years or older.
Among eventual diabetes patients, about 3% of cases were diagnosed by age
20 years; about 13% of male and 19% of female cases, respectively, by age
40 years; about 55% by age 60 years; and about 90% by age 80 years.
For individuals born in the United States in 2000, we estimate the lifetime
risk of diagnosed diabetes mellitus to be roughly 1 in 3 for males and 2 in
5 for females. The estimated lifetime risk is even higher among minority populations,
with Hispanic females having roughly 1 in 2 risk at birth and 1 in 3 residual
risk at age 60 years. The lifetime risk of diabetes is comparable with or
higher than that for many diseases and conditions that are perceived as common.6-12 For
example, the lifetime risk of diabetes is considerably higher than the widely
publicized 1 in 8 risk for breast cancer among US women.9 At
age 40 years, the residual lifetime risk of diabetes is roughly 1 in 3 for
men and women, and is nearly as high as that for coronary heart disease (1
in 2 for men and 1 in 3 for women).12 At age
50 years, the residual lifetime risk of diabetes for women is a little less
than 3 in 10, which is close to the residual risk for hip fracture (about
1 in 3).11 The residual lifetime risk of diabetes
remains high even at older ages. For example, at age 70 years the residual
lifetime risk of diabetes for men is about 1 in 10, the same as dementia.10
These estimates of lifetime risk for diabetes must be carefully interpreted.
The lifetime risk estimates are for an average person in the population. The
estimates, thus, incorporate the effects of diabetes risk factors on an average
person. The level of diabetes risk factors, especially obesity, lifestyle,
and socioeconomic factors, may raise or lower the lifetime risks away from
the average for an individual. Our estimates of the lifetime risk for diabetes
are likely to be lower than the true risk for a number of reasons. First,
we only used data on diagnosed diabetes. A third or more of individuals may
have diabetes but the disease has not been diagnosed.30 Therefore,
our estimates only apply to the risk of diagnosed diabetes. However, there
are no data on the effect of undiagnosed diabetes on mortality. Thus, it was
not feasible to include rates of undiagnosed diabetes in our estimates. Second,
our data on diagnosed diabetes was based on self-report, but a report31 indicates that the accuracy of self-reporting for
diabetes is reasonably high in population surveys. Third, we modeled for constant
diabetes incidence rates even though obesity incidence is increasing rapidly
in the United States.32 Thus, the incidence
of diabetes is likely to increase and the results of several studies suggest
that this increase may already be occurring,33-35 especially
among younger people.36 A fourth factor limiting
the accuracy of our projections is the projected increase in life expectancy
in the United States, particularly for ethnic minority groups at greatest
risk for diabetes.37 Longer life expectancies
will also increase the average lifetime risk for diabetes in the total US
population. Our estimates, however, are based on diabetes incidence and mortality
rates specific to age, sex, and race/ethnicity.
The data used for our estimates did not differentiate between type 1
and type 2 diabetes. However, the major form of diabetes in the population
is type 2 diabetes, which accounts for up to 95% of diabetes cases in the
United States.38 Among children, however, type
1 diabetes poses a greater risk, although this may change in the future as
the rate of type 2 diabetes in children and adolescents increases.36 Although the accuracy of our estimates depends on
the accuracy of the RRs for death from diabetes that we used, we believe that
the age-specific RR estimates we used closely reflect those of the population
of people with diabetes in the United States, and the age- and sex-specific
estimates of RR we used herein are consistent with recent estimates from an
NHANES II mortality study and with those from previous studies.23,24 However,
if the true RR is different than our estimate, then the lifetime risks of
diabetes we report may be affected. If the true age-specific RRs of death
from diabetes are higher than the values we used, life-years lost due to diagnosed
diabetes will be higher than our estimates, and the duration of diabetes will
be lower, but the precise impact on lifetime risk of diabetes is not clear.
Our estimation of life-years lost do not imply causality to diabetes per se,
but rather take into account all the aspects of morbidity (eg, obesity, cardiovascular
disease) an average person with diabetes may experience. We also estimated
QALYs lost due to diabetes to describe in a composite manner the combined
impact of life-years lost and quality of life lost due to diabetes. Published
data on quality of life lost due to diabetes are scant, and it was not possible
to estimate from the available data the utility associated with diabetes after
accounting for other comorbidities. Our estimates of QALYs lost due to diabetes
are far less precise than those of life-years lost.
Our estimates of lifetime risk for diabetes are from a carefully constructed
dynamic model that uses nationally representative data, including incidence
and mortality rates specific to age, sex, and race/ethnicity. The use of a
dynamic model to estimate lifetime risk is an extension of the well-established
tradition of projecting life expectancy with life tables. Indeed, life expectancies
estimated from our Markov model were within 1 year of the US Census Bureau
life expectancies. Also, our model's steady-state prevalence is close to previous
projections of prevalence in 2050. Our estimate of life-years lost when diabetes
was diagnosed at age 60 years is also quite similar to that estimated directly
from the NHANES I for individuals aged 55 to 64 years,39 and
fairly close to a recently published report from England.40
Lifetime risk estimates have been published for several diseases and
there has been no previous estimates for diabetes. To our knowledge, all previous
lifetime risk estimates for these other diseases and conditions have been
based on epidemiological cohort studies of disease incidence. Cohort studies
are subject to several biases, including volunteer bias for healthy participants.
In addition, cohort studies are rarely nationally representative in terms
of demographics, disease risk, or mortality. Temporal trends within a cohort
may also confound the estimation of lifetime risks. Therefore, we believe
that our method of estimation of lifetime risk allows more accurate inference
to the general population than methods based on the experience of individuals
followed up in cohort studies.
The population burden of diabetes complications is large in terms of
mortality, morbidity, and loss of quality of life.2,40 We
have quantified this burden in a way that is easily communicated to both policy
makers and individuals. For example, we project that, on average, a US male
diagnosed as having diabetes at age 40 years will lose almost 12 life-years
and 19 QALYs compared with a person of the same age without diabetes. A US
female diagnosed as having diabetes at age 40 years will lose about 14 life-years
and 22 QALYs. Estimates of lifetime risk and life-years and QALYs lost will
also be useful tools for communicating the risk of diabetes and its affect
on health to the general public, to individuals at high risk for developing
diabetes, to clinicians, to policy makers, and to the media. These estimates
can also serve as baseline data to monitor secular trends in the burden of
diabetes. Implementation of available treatments to prevent diabetes complications
is suboptimal.41 Furthermore, results of recent
clinical trials show promise that diabetes itself may be prevented or at least
delayed with lifestyle interventions that produce modest weight loss or with
the use of drugs.42-44 Our
estimates of lifetime risk of diabetes and life-years and QALYs lost due to
diabetes further support concerted action to prevent diabetes and its complications.
American Diabetes Association. Economic consequences of diabetes mellitus in the US in 1997. Diabetes Care.1998;21:296-309.
Narayan KM, Gregg EW, Fagot-Campagna A, Engelgau MM, Vinicor F. Diabetes: a common, growing, serious, costly, and potentially preventable
public health problem. Diabetes Res Clin Pract.2000;50:S77-S84.
Mokdad AH, Ford ES, Bowman BA.
et al. Diabetes trends in the US: 1990-1998. Diabetes Care.2000;23:1278-1283.
Mokdad AH, Ford ES, Bowman BA.
et al. The continuing increase in diabetes in the US. Diabetes Care.2001;24:412.
Boyle JP, Honeycutt AA, Narayan KM.
et al. Projection of diabetes burden through 2050: impact of changing demography
and disease prevalence in the US. Diabetes Care.2001;24:1936-1940.
Phillips K, Glendon G, Knight JA. Putting the risk of breast cancer in perspective. N Engl J Med.1999;340:141-144.
Sasieni PD, Adams J. Standardized lifetime risk. Am J Epidemiol.1999;149:869-875.
Vasan RS, Beiser A, Seshadri S.
et al. Residual lifetime risk for developing hypertension in middle-aged women
and men: the Framingham Heart Study. JAMA.2002;287:1003-1011.
Feuer EJ, Wung L, Boring CC, Flanders WD, Timmel MJ, Tong T. The lifetime risk of developing breast cancer. J Natl Cancer Inst.1993;85:892-897.
Seshadri S, Wolf PA, Beiser A.
et al. Lifetime risk of dementia and Alzheimer's disease: the impact of mortality
on risk estimates in the Framingham Study. Neurology.1997;49:1498-1504.
Cummings SR, Black DM, Rubin SM. Lifetime risks of hip, Colles', or vertebral fracture and coronary
heart disease among white postmenopausal women. Arch Intern Med.1989;149:2445-2448.
Lloyd-Jones DM, Larson MG, Beiser A, Levy D. Lifetime risk of developing coronary heart disease. Lancet.1999;353:89-92.
National Center for Health Statistics. 1990 National Health Interview Survey [database
on CD-ROM]. CD-ROM Series 10, No. 4. SETS Version 1.21. Washington, DC: US Government Printing Office;1993.
Botman SL, Moore TF, Moriarity CL, Parsons VL. Design and estimation for the National Health Interview Survey, 1995-2004. In: Vital Health Statistics. Hyattsville,
Md: National Center for Health Statistics; 2000:2.
Division of Health Interview Statistics, National Center for Health
Statistics. 2000 National Health Interview Survey (NHIS) public use data release:
NHIS survey description. Available at: http://www.cdc.gov/nchs/nhis.htm. Accessibility
verified September 11, 2003.
Schwarz G. Estimating the dimension of a model. Ann Stat.1978;6:461-464.Google Scholar
Rothman KL, Greenland S. Modern Epidemiology. 2nd ed. Philadelphia, Pa: Lippincott-Raven; 1998.
Hosmer DW, Lemeshow S. Goodness of fit tests for the multiple logistic regression model. Comm Stat Part A Theory Methods.1980;9:1043-1069.Google Scholar
Nagelkerke NJD. A note on a general definition of the coefficient of determination. Biometrika.1991;78:691-692.Google Scholar
Bureau of the Census. Component Assumptions of the Resident Population
by Age, Sex, Race/Ethnicity, and Hispanic Origin: Lowest, Middle, and Highest
Series, 1999 to 2100. Washington DC: Population Projections Program, Population Division,
US Census Bureau; 2000.
Tierney EF, Geiss LS, Engelgau MM.
et al. Population-based estimates of mortality associated with diabetes: use
of a death certificate check box in North Dakota. Am J Public Health.2001;91:84-92.
Saydah SH, Eberhardt MS, Loria CM, Brancati FL. Age and burden of death attributable to diabetes in the United States. Am J Epidemiol.2002;156:714-719.
Geiss LS, Herman WH, Smith PJ. Mortality in non-insulin-dependent diabetes. In: Harris MI, Cowie CC, Stern MP, Boyko EJ, Reiber GE, Bennet PH,
eds. Diabetes in America. 2nd ed. Washington, DC:
US Government Printing Office; 1995:233-257.
Hoel PG, Port SC, Stone CJ. Introduction to Stochastic Processes. Boston, Mass: Houghton Mifflin Co; 1972.
Clarke P, Gray A, Holman R. Estimating utility values for health state of type 2 diabetic patients
using the EQ-5d (UKPDS 62). Med Decis Making.2002;22:340-349.
Briggs AH. Handling uncertainty in cost-effectiveness models. Pharmacoeconomics.2000;17:479-500.
Coffey JT, Brandle M, Zhou H.
et al. Valuing health-related quality of life in diabetes mellitus. Diabetes Care.2002;25:2238-2248.
Honeycutt AA, Boyle JP, Broglio KR, Thompson TJ, Geiss LS, Narayan KM. A dynamic Markov model for forecasting diabetes prevalence in the United
States through 2050. Health Care Manag Sci.2003;6:155-164.Google Scholar
Engelgau MM, Narayan KM, Herman WH. Screening for type 2 diabetes. Diabetes Care.2000;23:1563-1580.
O'Connor PJ, Rush WA, Pronk NP, Cherney LM. Identifying diabetes mellitus or heart disease among health maintenance
organization members: sensitivity, specificity, predictive value, and cost
of survey and database methods. Am J Manage Care.1998;4:335-342.Google Scholar
Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000. JAMA.2002;288:1723-1727.
Narayan KM, Nelson R, Hanson RL, Pettitt DJ, Knowler WC. Epidemiology of diabetes mellitus and diabetes complications in American
Indians. In: Ekoe J, Williams R, Zimmet P, eds. The Epidemiology
of Diabetes Mellitus: An International Perspective. Sussex, England:
John Wiley & Sons; 2002.
Leibson CL, O'Brien PC, Atkinsin E, Palumbo PJ, Melton III LJ. Relative contributions of incidence and survival to increasing prevalence
of adult-onset diabetes mellitus: a population-based study. Am J Epidemiol.1997;146:12-22.
Burke JP, Williams K, Gaskill SP, Hazuda HP, Haffner SM, Stern MP. Rapid rise in the incidence of type 2 diabetes from 1987 to 1997: results
from the San Antonio Heart Study. Arch Intern Med.1999;159:1450-1456.
Fagot-Campagna A, Pettitt DJ, Engelgau MM.
et al. Type 2 diabetes among North American children and adolescents: an epidemiological
review and a public health perspective. J Pediatr.2000;136:664-672.
Harris MI. Summary. In: Diabetes in America. 2nd ed. Washington,
DC: Government Printing Office; 1995:1-14. NIH publication 95-1468.
Gu K, Cowie CC, Harris MI. Mortality in adults with and without diabetes in a national cohort
of the US population, 1971-1993. Diabetes Care.1998;21:1138-1145.
Jagger C, Goyder E, Clarke M, Brouard N, Arthur A. Active life expectancy in people with and without diabetes. J Public Health Med.2003;25:42-46.
Narayan KM, Gregg EW, Engelgau MM.
et al. Translation research for chronic disease: the case of diabetes. Diabetes Care.2000;23:1794-1798.
Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention
or metformin. N Engl J Med.2002;346:393-403.
Chiasson JL, Josse RG, Gomis R, Hanefeld M, Karasik A, Laasko M. Acarbose can prevent the progression of impaired glucose tolerance
to type 2 diabetes mellitus: results of a randomized clinical trial: the STOP-NIDDM
American iabetes Association and National Institutes of Diabetes, Digestive
and Kidney Diseases. The prevention or delay of type 2 diabetes. Diabetes Care.2002;25:1-8.