People with diabetes can suffer from diverse complications that seriously erode quality of life. Diabetes, costing the United States more than $174 billion per year in 2007, is expected to take an increasingly large financial toll in subsequent years. Accurate projections of diabetes burden are essential to policymakers planning for future health care needs and costs.

Using data on prediabetes and diabetes prevalence in the United States, forecasted incidence, and current US Census projections of mortality and migration, the authors constructed a series of dynamic models employing systems of difference equations to project the future burden of diabetes among US adults. A three-state model partitions the US population into no diabetes, undiagnosed diabetes, and diagnosed diabetes. A four-state model divides the state of "no diabetes" into high-risk (prediabetes) and low-risk (normal glucose) states. A five-state model incorporates an intervention designed to prevent or delay diabetes in adults at high risk.

The authors project that annual diagnosed diabetes incidence (new cases) will increase from about 8 cases per 1,000 in 2008 to about 15 in 2050. Assuming low incidence and relatively high diabetes mortality, total diabetes prevalence (diagnosed and undiagnosed cases) is projected to increase from 14% in 2010 to 21% of the US adult population by 2050. However, if recent increases in diabetes incidence continue and diabetes mortality is relatively low, prevalence will increase to 33% by 2050. A middle-ground scenario projects a prevalence of 25% to 28% by 2050. Intervention can reduce, but not eliminate, increases in diabetes prevalence.

These projected increases are largely attributable to the aging of the US population, increasing numbers of members of higher-risk minority groups in the population, and people with diabetes living longer. Effective strategies will need to be undertaken to moderate the impact of these factors on national diabetes burden. Our analysis suggests that widespread implementation of reasonably effective preventive interventions focused on high-risk subgroups of the population can considerably reduce, but not eliminate, future increases in diabetes prevalence.

People with diabetes often develop diverse microvascular, macrovascular, and neuropathic complications that seriously erode quality of life. The high prevalence, high incidence, chronicity, and long-term implications for health and health care costs make diabetes a major concern for the United States and much of the developed and developing world [

Several future projections of the prevalence, incidence, and total number of diabetes cases for the US and other countries have been carried out [

To overcome these limitations and provide contemporary, realistic estimates of the growth of the national diabetes burden, we constructed a system of dynamic equations that incorporate initial prevalence (percentage of population with diabetes, both diagnosed and undiagnosed), incidence (percentage of population with newly diagnosed diabetes), migration, mortality, and prevalence of prediabetes. These equations model the future burden of diabetes on US adults through 2050. We also consider the effect of a hypothetical, large-scale preventive intervention.

The data sources for this study include the US Census Bureau [

Let the annual incidence rate of diagnosed diabetes and its estimated standard error be denoted by (_{t}, s_{t}_{t }Incidence projections are denoted as _{t }for

Diffuse normal prior distributions were used for _{0 }and _{1}. Four different specifications for _{t }were considered: unstructured, first order autoregressive (AR(1)), first order moving average (MA(1)), and first order autoregressive-moving average (ARMA(1,1)). Graphical displays of the posterior distributions of the residuals, _{t }- _{t}, and the residual sum of squares were used to compare models. The AR(1) and ARMA(1,1) were indistinguishable and clearly fit better than the unstructured and MA(1) models. We used the AR(1) model because it contains fewer parameters than the ARMA(1,1) model. The AR(1) model's residuals did not exhibit serial correlation and were all less than 0.0005 in absolute value, indicating good model fit. Posterior distributions of the projected incidences of diagnosed diabetes are simulated as part of the model fitting process. Modeling was done using WinBUGS software [

We constructed a series of dynamic models that consisted of systems of difference equations in time that are similar to models described elsewhere [

Key assumptions for the models follow. First, people cannot move from diabetes to nondiabetes; this assumption is reasonable because remission is extremely rare. Second, the relative risks of death for the two diabetes states versus the no diabetes state are constant over time. The number of ways that relative risk might vary over time is infinite. In the absence of data about which of these patterns of varying relative risk to choose, we chose the simplest one: no time variation. Third, the transition rates to diagnosed or undiagnosed diabetes for nondiabetics are constant multiples of the transition rate to diagnosed diabetes for undiagnosed diabetics. This assumption implies the proportion of diagnosed diabetics among all new diabetics in any given year is constant over time.

A detailed description of these models, including all assumptions, references for key parameter estimates, and algebraic derivations, are presented in Appendix 1 and Appendix 2. The programs for implementing the models were written in GAUSS [

Figure ^{th }percentile of the posterior distribution; ^{th }percentile of the posterior distribution. Historical incidence rates range from 3.3 cases per 1,000 in 1980 to 7.8 cases per 1,000 in 2007. The middle incidence scenario increases steadily over the projection horizon, from 8.4 cases per 1,000 in 2008 to 14.7 cases per 1,000 in 2050. The low incidence scenario remains relatively flat, with an average incidence of 8.4 cases per 1,000, while the high incidence scenario projects extreme increases in incidence from 9.2 to 22.9 cases per 1,000 for the years 2008 through 2050.

We denote the relative risk of death for individuals with undiagnosed diabetes versus those without diabetes as _{1 }and the relative risk of death for individuals with diagnosed diabetes versus those without diabetes as _{2}. Published results [_{1 }= 1.77 and _{2 }= 2.11, and we refer to this set of values as _{1 }= 1.00 and _{2 }= 4.08, consistent with projections from Narayan et al [

Table

Projections from the Three-State Model of Numbers of People in Millions with No Diabetes, Undiagnosed Diabetes, and Diagnosed Diabetes for Selected Years

Year | Relative Risk r_{1} | Relative Risk _{2} | No Diabetes (Low, Middle) | Undiagnosed Diabetes (Low, Middle) | Diagnosed Diabetes (Low, Middle) | Total US Adult Population |
---|---|---|---|---|---|---|

2010 | 1.77 | 2.11 | (191.4, 191.2) | (12.0, 11.5) | (20.3, 21.0) | 223.7 |

1.00 | 4.08 | (192.1, 191.9) | (12.1, 11.6) | (19.5, 20.2) | ||

2015 | 1.77 | 2.11 | (196.1, 194.6) | (13.1, 12.2) | (26.6, 29.1) | 235.9 |

1.00 | 4.08 | (198.1, 196.6) | (13.3, 12.4) | (24.4, 26.8) | ||

2020 | 1.77 | 2.11 | (200.7, 196.9) | (13.9, 12.7) | (32.9, 37.9) | 247.5 |

1.00 | 4.08 | (204.0, 200.3) | (14.3, 13.0) | (29.2, 34.1) | ||

2025 | 1.77 | 2.11 | (205.4, 198.5) | (14.4, 13.0) | (38.7, 47.0) | 258.5 |

1.00 | 4.08 | (210.2, 203.6) | (14.9, 13.5) | (33.4, 41.4) | ||

2030 | 1.77 | 2.11 | (209.5, 199.3) | (14.7, 13.1) | (43.7, 55.5) | 267.9 |

1.00 | 4.08 | (216.0, 206.2) | (15.4, 13.7) | (36.5, 48.0) | ||

2035 | 1.77 | 2.11 | (213.9, 200.1) | (15.0, 13.2) | (48.1, 63.6) | 276.9 |

1.00 | 4.08 | (222.1, 208.9) | (15.8, 14.0) | (39.1, 54.1) | ||

2040 | 1.77 | 2.11 | (218.3, 201.0) | (15.2, 13.3) | (52.0, 71.2) | 285.5 |

1.00 | 4.08 | (228.2, 211.6) | (16.2, 14.2) | (41.1, 59.7) | ||

2045 | 1.77 | 2.11 | (223.6, 202.7) | (15.5, 13.4) | (55.6, 78.6) | 292.9 |

1.00 | 4.08 | (235.1, 215.0) | (16.6, 14.4) | (43.1, 65.4) | ||

2050 | 1.77 | 2.11 | (230.6, 206.0) | (16.0, 13.7) | (59.7, 86.6) | 306.3 |

1.00 | 4.08 | (243.5, 219.7) | (17.2, 14.8) | (45.6, 71.8) |

Note: There are four scenarios included (1) low incidence projections and r_{1 }= 1.77, r_{2 }= 2.11, (2) low incidence projections and r_{1 }= 1.00, r_{2 }= 4.08, (3) middle incidence projections and r_{1 }= 1.77, r_{2 }= 2.11, (4) middle incidence projections and r_{1 }= 1.00, r_{2 }= 4.08. Entries in the last column are the Census projections of the total US adult population.

_{1 }= 1.77, r_{2 }= 2.11; low incidence projections and r_{1 }= 1.00, r_{2 }= 4.08; middle incidence projections and r_{1 }= 1.77, r_{2 }= 2.11; middle incidence projections and r_{1 }= 1.00, r_{2 }= 4.08

Table

Projections for Selected Years of Incident Cases in Thousands from the Adult Population with No Diabetes from the No-Intervention Model (Three-State Model) and the Preventive Intervention Model (Five-State Model)

Year | Relative | Relative | No-Intervention | Intervention | Difference (Low, Middle) |
---|---|---|---|---|---|

Risk r_{1} | Risk r_{2} | Incident Cases (Low, Middle) | Incident Cases (Low, Middle) | ||

2010 | 1.77 | 2.11 | (2018.4, 2145.7) | (1681.6, 1787.9) | (336.8, 357.8) |

1.00 | 4.08 | (2021.1, 2148.4) | (1683.8, 1790.1) | (337.3, 358.3) | |

2015 | 1.77 | 2.11 | (2095.4, 2468.1) | (1773.2, 2093.2) | (322.2, 374.9) |

1.00 | 4.08 | (2106.8, 2481.9) | (1782.7, 2104.9) | (324.1, 377.0) | |

2020 | 1.77 | 2.11 | (2143.3, 2721.9) | (1833.3, 2341.0) | (310.0, 380.9) |

1.00 | 4.08 | (2164.9, 2752.2) | (1851.7, 2366.9) | (313.2, 385.3) | |

2025 | 1.77 | 2.11 | (2176.1, 2933.5) | (1875.8, 2551.9) | (300.3, 381.6) |

1.00 | 4.08 | (2208.9, 2984.9) | (1904.1, 2596.6) | (304.8, 388.3) | |

2030 | 1.77 | 2.11 | (2230.0, 3098.4) | (1933.6, 2721.5) | (296.4, 376.9) |

1.00 | 4.08 | (2276.0, 3175.1) | (1973.5, 2789.1) | (302.5, 386.0) | |

2035 | 1.77 | 2.11 | (2300.8, 3225.0) | (2004.4, 2855.9) | (296.4, 369.1) |

1.00 | 4.08 | (2361.4, 3329.7) | (2057.3, 2949.2) | (304.1, 380.5) | |

2040 | 1.77 | 2.11 | (2334.3, 3323.2) | (2041.4, 2963.2) | (292.9, 360.0) |

1.00 | 4.08 | (2408.8, 3456.9) | (2107.0, 3083.6) | (301.8, 373.3) | |

2045 | 1.77 | 2.11 | (2341.5, 3401.4) | (2054.0, 3050.5) | (287.5, 350.9) |

1.00 | 4.08 | (2428.3, 3562.9) | (2130.6, 3197.3) | (297.7, 365.6) | |

2050 | 1.77 | 2.11 | (2403.8, 3490.9) | (2113.8, 3146.1) | (290.0, 344.8) |

1.00 | 4.08 | (2502.6, 3677.2) | (2201.7, 3316.8) | (302.9, 360.4) |

Note: There are four scenarios included (1) low incidence projections and r_{1 }= 1.77, r_{2 }= 2.11, (2) low incidence projections and r_{1 }= 1.00, r_{2 }= 4.08, (3) middle incidence projections and r_{1 }= 1.77, r_{2 }= 2.11, (4) middle incidence projections and r_{1 }= 1.00, r_{2 }= 4.08.

Our estimates of diabetes prevalence paint a sobering picture of the future growth of diabetes. Under an assumption of low incidence and relatively high diabetes mortality, total prevalence is projected to increase to 21% of the US adult population by 2050. On the other hand, if recent increases in diabetes incidence continue (middle incidence projections) and diabetes mortality ratios are relatively low, diabetes prevalence will increase to 33% by 2050. The middle-ground (low incidence with low mortality or middle incidence with high mortality) scenarios project a prevalence of 25% to 28% by 2050. In each of the scenarios, the increases are, in part, attributable to demographic changes. The population of the United States is aging, and older adults are more likely to develop diabetes than younger adults. The size of minority populations in the United States also is growing, and some minorities are at greater risk of developing diabetes than non-Hispanic whites. Finally, mortality among people with diabetes is declining. The result is that people with diabetes live longer and contribute to prevalence for longer periods of time.

Two previous diabetes forecasts have linearly extrapolated historical prevalence trends. In 2004, Wild et al [

Our models, which include the ability to evaluate preventive interventions, suggest that the future prevalence of diagnosed diabetes could be significantly worse than previously suggested. A large increase in diabetes prevalence could be driven by multiple factors, including increasing incidence, better detection, and in-migration. Our updated model includes a higher level of incidence based on the CDC National Diabetes Surveillance System and projects lower future mortality rates than were used in previous models based on US Census data. In addition, our model assumes that the mortality rate of the diabetic population will decline at least as much as that of the nondiabetic population (i.e., the mortality rate ratio associated with diabetes will be constant). Recent comparison of US cohorts suggests that this assumption is reasonable [

The projected loss in quality of life and the projected costs of providing health care could be significant. Increased efforts in primary prevention of diabetes can help to decrease loss in quality of life and the future cost of providing care for people with diabetes. Indeed, such efforts are essential if we hope to moderate or slow the growth of diabetes prevalence. However, as Table

Our five-state model made the assumption that a hypothetical intervention would reach 100% of those with IFG and would reduce the annual incidence of diabetes in this group by 25%. Future efforts to refine our modeling approach will focus on more realistic specification of intervention scenarios applied to a variety of population subgroups at high risk of developing diabetes. Had we split the population at high risk into intervention and nonintervention subsets, we would have obtained estimates between the no intervention and intervention cases in Table

Our model is subject to several limitations. Cases of diabetes in people younger than age 18 or older than age 79 years were not considered. Although diabetes in the young is rare, and a relatively small portion of the US population is aged 80 years or older, these numbers might not be negligible. Our model made many reasonable but untestable assumptions. For example, we assumed that the relative risks of death for those with detected or undetected diabetes, compared to those without diabetes, are constant over time. We assumed that the observed increase in diabetes incidence fits a logistic growth curve. Given the logistic model, we could have chosen either a more or less precise prior for

We performed a sensitivity analysis that assumed 98% prior probability for

We anticipate that the modeling methods described here could be used by other countries, especially those with reliable census estimates, to estimate future diabetes burden, as well as the potential effects of interventions to reduce disease burden. Country-specific data elements could be easily substituted for the data elements we used to develop a model that fit US population dynamics. Further, a modified form of this model might be applicable to other chronic, near-irreversible, and sometimes undiagnosed conditions such as heart disease.

We project that, over the next 40 years, the prevalence of total diabetes (diagnosed and undiagnosed) in the United States will increase from its current level of about 1 in 10 adults to between 1 in 5 and 1 in 3 adults in 2050. The health care costs of a person with diagnosed diabetes are approximately 2.3 times that of a person without [

The authors declare that they have no competing interests.

JPB developed and programmed the multistate dynamic models, participated in study design and coordination, and helped draft the manuscript. TJT developed and programmed the incidence projection model, participated in study design and coordination, and helped draft the manuscript. EWG participated in study design and coordination and helped draft the manuscript. LEB participated in study design and coordination and helped draft the manuscript. DFW conceived of the study, participated in study design and coordination, and helped draft the manuscript. All authors contributed to critical revision of the draft manuscript, and all authors read and approved the final manuscript.

The US adult population is modeled at 1-year intervals starting at year

_{1}_{2}_{1 }and _{2 }are relative risks.

_{x}(

_{z}(

_{y}(

_{x}(

_{z}(

_{y}(

The relations _{x}(_{z}(_{y}(_{x}(_{z}(_{y}(_{1}(_{2}(

Consider the following transition matrix:

Note that this matrix displays the distribution of the beginning year stocks (rows) to the ending year stocks (the columns), and thus, the transition rates in each row must be nonnegative and add to unity for each year _{1 }= 1.77 and _{2 }= 2.11. We also set _{1 }= 1.00 and _{2 }= 4.08 in a sensitivity analysis, consistent with projections from Narayan et al [_{1 }and ξ_{2 }could be obtained. This assumption implies the proportion of diagnosed diabetics among all new diabetics in any given year

with initial conditions

Consistency with census projections of the number of US adults

This is guaranteed if the following two equations are satisfied:

But the second equation is equivalent to

(with a little algebra). The first is a consequence of

where the values .398 and .129 come from [

with

Now, given a projection

and

Noting that, in general,

then

where the value .0106 is derived in Appendix 2. Solving this equation for x_{1 }yields

Thus, _{1}, _{2 }are determined.

Finally, the distributions of births and net migration across the three subpopulations for each year are determined by _{x}(t) _{z}(t) _{y}(t)

and

The first set of equations reflects our baseline assumption that all incoming births are nondiabetic. The second set of equations simply distributes the net migration for year

The four-state model expands the three-state model by splitting

To this end, consider the transition matrix

Note that the death rates are equal to those in the three-state model when

Also, transition rates to diabetes are the same as in the three-state model if the following relation holds:

One additional assumption is made to determine

The two equations above yield the expressions

The constant _{1 }+ ξ_{2})

For the risk strata IFG, this gives

and

To complete the model, the α's must be chosen. The constraints are

Let

Then there exists

For the high-risk population with IFG, we set _{1}(29) = .89 from [

The actual computation of the four-state model can be implemented through the system of difference equations

with initial conditions

Given the outputs from the two previous baseline models, consider the following transition matrix reflecting intervention on the high-risk population. Note that

where rows are labeled as columns with times

State variables are relabeled because we expect the intervention model to deviate from the previous models. The initial conditions are the same as in the four-state model, with

There are no published nationally representative estimates for annual incidence of total diabetes (diagnosed or undiagnosed) for the US adult nondiabetic population or for subgroups defined by glycemic level. The one US estimate of diabetes incidence in 2007 for adults aged 18-79 (0.78%) applies only to diagnosed diabetes [

Diabetes incidence in the entire population is a function of diabetes incidence in four mutually exclusive glycemic subgroups and their prevalence in the nondiabetic population. These subgroups are 1)

Gerstein et al [

Santaguida et al [

The annualized diabetes incidence for the NG subgroup from the four studies was 0.19%, 0.25%, 0.38%, and 0.64%. We used the median annual incidence of these four studies, 0.32%, to estimate diabetes incidence in the NG subgroup in the United States. Engberg et al [

We multiplied the 0.32% annual diabetes incidence in the NG subgroup by the relative risks from Gerstein et al [

We multiplied the annual diabetes incidence of each independent glycemic subgroup (NG, IIGT, IIFG, and CIFGT) by its prevalence in the US nondiabetic population and summed the result, yielding an estimate of annual diabetes incidence of 1.06%. This finding is similar to that reported by recent population-based cohort studies. Bonora et al [

The findings and conclusions in this report are those of the authors and do not necessarily represent the official positions of the Centers for Disease Control and Prevention.