Original Article
POPULATION HEALTH MANAGEMENT Volume 0, Number 0, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/pop.2014.0084
Is Diabetes Color-Blind? Growth of Prevalence of Diagnosed Diabetes in Children Through 2030 Omolola E. Adepoju, PhD, MPH,1 Jane N. Bolin, BSN, JD, PhD,2 Eric A. Booth, MS,3 Hongwei Zhao, ScD,4 Szu-Hsuan Lin, MPH,2 Charles D. Phillips, PhD, MPH,2 and Robert L. Ohsfeldt, PhD 2
Abstract
Diabetes knows no age and affects millions of individuals. Preventing diabetes in children is increasingly becoming a major health policy concern and focus. The objective of this study is to project the number of children, aged 0–17 years, with diagnosed diabetes in the United States through 2030, accounting for changing demography, and diabetes and obesity prevalence rates. The study team combined historic diabetes and obesity prevalence data with US child population estimates and projections. A times-series regression model was used to forecast future diabetes prevalence and to account for the relationship between the forecasted diabetes prevalence and the lagged prevalence of childhood obesity. Overall, the prevalence of diagnosed diabetes is projected to increase 67% from 0.22% in 2010 to 0.36% in 2030. Lagged obesity prevalence in Hispanic boys and non-Hispanic black girls was significantly associated with increasing future diabetes prevalence. The study results showed that a 1% increase in obesity prevalence among Hispanic boys from the previous year was significantly associated with a 0.005% increase in future prevalence of diagnosed diabetes in children (P £ 0.01). Likewise, a unit increase in obesity prevalence among non-Hispanic black girls was associated with a 0.003% increase in future diabetes prevalence (P < 0.05). Obesity rates for other race/ethnicity combinations were not associated with increasing future diabetes prevalence. To mitigate the continued threat posed by diabetes, serious discussions need to focus on the pediatric population, particularly non-Hispanic black girls and Hispanic boys whose obesity trends show the strongest associations with future diabetes prevalence in children. (Population Health Management 2014;xx:xxx–xxx) individuals 18–20 years of age, a group typically not considered children. Most researchers consider diabetes cases in children to be predominantly type 1.6 However, routine surveillance over the past 2 decades has shown increasing frequencies of both type 1 and type 2,7 with greater increases in the incidence of Type 2 among children and adolescents.8–11 Recently, the Centers for Disease Control and Prevention’s (CDC) SEARCH for Diabetes in Youth Study reported that the proportion of type 2 diabetes in older youths ranges from 6% for non-Hispanic white youths to 76% for American Indian youths.12 Among younger children, aged 0–4 years, type 1 accounted for ‡ 80% of diabetes cases.12 The reported increase in diabetes prevalence is concurrent with the rise in the number of children who are obese13–17 or have a family history of the disease.18 Some studies have
Background
T
he trends in diabetes prevalence are burdensome but largely remediable. A pediatrician once reported that younger children are frightened that the ‘‘di’’ in diabetes means that they will die soon.1 Of late, numerous health care providers report increasing numbers of children with type 2 diabetes, a disease usually diagnosed in adults aged 40 years or older.2–3 Among hospital discharges of children and young people aged 0–17 years in 2009, about 74% had diabetes as the first listed diagnosis among those with diabetes as any listed diagnosis.4 Current estimates suggest that less than a quarter of a million persons 20 years of age or younger have type 1 or type 2 diabetes.5 Although this figure has been reported to be an underestimation, it is important to note that it includes
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School of Public Health, Texas A&M Health Science Center, College Station, Texas; Accountable Care/Health Homes Program, United Health Group, Sugar Land, Texas. 2 Department of Health Policy & Management, School of Public Health, Texas A&M Health Science Center, College Station, Texas. 3 Texas A&M University Public Policy Research Institute. 4476 TAMU, College Station, Texas. 4 Department of Epidemiology & Biostatistics, School of Public Health, Texas A&M Health Science Center, College Station, Texas.
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shown a strong relationship between childhood obesity and the development of insulin resistance in early adulthood.19 Other related studies have documented positive associations between maternal gestational diabetes and offspring overweight and obesity.20 Furthermore, children from ethnic minorities, specifically American Indians, Hispanics, and African Americans, have higher obesity rates.11,21 The implications of these trends are extremely concerning, especially with regard to increasing numbers of people having and managing diabetes for most of their lives,22 and the potential impacts on the future workforce. The US Census Bureau reported that the US child population (aged 0–17 years) increased from 64.2 million in 1990 to 74.1 million in 2010, representing a 21% increase in the youth and adolescent population. In terms of racial composition, in 1990, 69% of children were non-Hispanic whites compared to 12% who were of Hispanic origin. By 2010, the proportion of non-Hispanic whites decreased to 53.6% and Hispanic children represented 23.2% of the youth and adolescent population, or 25% of all children. Rates for non-Hispanic blacks (African Americans) have remained relatively stable over time, dropping only 1 percentage point between 1990 and 2010.23 Population estimates of children less than 18 years, coupled with the growing percentage of adults aged 65 years and older, reflect a larger proportion of individuals in stages of economic dependence.24 Children and older adults typically rely on economic resources provided by productive members of the society.25 An aging population coupled with an increasingly less healthy youth and adolescent population portends disaster for any economy, as economic resources will constantly be transferred from the working to the nonworking population. For this reason, projection studies are useful planning tools to analyze future health care trends and provide sound warning to inform the delivery of health care services. Previous studies have projected the number of adults with diagnosed and undiagnosed diabetes in the United States26–29 and other countries of the world.30 Most of the recent US-based studies accounted for changing demography and prevalence rates, particularly the increasing size of the Hispanic foreign-born US populations who are at higher risk for developing diabetes.27–28 One study constructed a series of dynamic models employing systems of difference equations to project the future burden of diabetes among US adults. It concluded that annual diabetes incidence will increase from about 8.4 cases per 1000 population in 2008 to 14.7 cases per 1000 population in 2050.27 Another study employed a Markov model and generated forecasts by age, sex, and race/ethnicity, basing its estimates on diabetes incidence and prevalence in 2000.28 Although these studies are laudable, none of them specifically considered children aged 0–17 years. Furthermore, none of these projections focused on the variability of demographics in the pipeline of children who have an increased likelihood of developing diabetes. This study focuses on children aged 0–17 years in the United States. An infinite distributed lag model is used to assess the contributions of historic child obesity prevalence rates on future diabetes prevalence in children. Building on this model, this study forecasts race-specific prevalence rates to estimate the number of children with diabetes through 2030, incorporating US census projections that
ADEPOJU ET AL.
adjust for birth, death, and net immigration rates. Forecasts of this nature are useful for policy decisions on health care needs, the allocation of resources to meet these needs, and the urgency of preventive and treatment interventions that target populations with the largest expected increases in diabetes prevalence.28 Methods Data
Data for the model are obtained from the following sources: 1. Diabetes prevalence data: Historic diabetes prevalence trends for children were obtained from the National Center for Health Statistics (NCHS) databases.31 National Health Interview Survey (NHIS) sample child data files were downloaded for each year from 1997 through 2011. Although a longer trend was sought, as far back as 1980, the NHIS does not report diabetes in children in the 1980–1996 Core (annual) questionnaires. In each of the 1997–2011 data files, the responding parent/guardian was asked ‘‘Ever told sample child has diabetes?’’ All affirmative responses were determined to be records of children with diabetes. Although there might be drawbacks to this self-reported approach, numerous studies have affirmed the accuracy and validity of self-reports in diagnosed diabetes.32–33 For each year, the prevalence rates of diabetes in children were calculated by applying sampling weights to generate unbiased estimates of age-, sex-, and racespecific prevalences. There are several advantages to using the NHIS. Conducted continuously since 1957, this survey is an ongoing, cross-sectional survey of representative samples of the US civilian noninstitutionalized population.34 The survey uses a complex multistage probability design, oversampling for African Americans and Hispanics. Oversampling of these population subgroups is done to ensure more accurate representation of indicator estimates for these groups.34 Data are collected through personal household interviews conducted by trained federal personnel. Additional details of the survey design and methods are described elsewhere.35–36 Another strength of the NHIS is that it provides information on the health of the US population, including information on the prevalence and incidence of disease, the extent of disability, and the use of health care services.34 Several studies have used the NHIS for epidemiologic and policy analyses of pressing health issues, including chronic conditions, functional limitations, and health care access and utilization.37–38 2. Obesity prevalence data: Historic obesity prevalence trends for children also are obtained from the NCHS databases. Earlier NCHS reports by Fryar and colleagues use the National Health and Nutrition Examination Survey (NHANES) to estimate childhood obesity trends from 1963– 1965 through 2009–2010. They then reported tables in an NCHS publication.39 The present study obtained age-, sex-, and race-specific childhood obesity prevalence estimates from this NCHS report. Like the NHIS, the NHANES is an ongoing, cross-sectional survey of representative samples of the US civilian noninstitutionalized population. The survey uses a complex multistage probability design, oversampling for persons aged 60 and older, households with African Americans, and
DIAGNOSED DIABETES IN CHILDREN THROUGH 2030
persons of Hispanic origin. Data are collected through inperson interviews and physical examinations, the latter being a major strength of the NHANES. Obese children were identified based on their measured heights and weights corresponding to a body mass index equal to or greater than the 95th percentile of children of the same age and sex. 3. Population data: For each year from 2012 to 2030, population projections are available by age, sex, race, and Hispanic origin on the US Census Bureau Web site (www.census.gov/ population/projections/data/national/2012.html). These projections were conducted in 2012 and are based on the 2010 census.23 They factor in an intermediate set of assumptions regarding fertility rates, life expectancies, and net immigration. According to the ‘‘middle series’’ census projections, the US child population is likely to grow from approximately 74 million children in 2010 to more than 80 million in 2030. The middle series represents the most likely population scenario through 2030. The present study used the middle series estimates reported in the age classification 0–4 and 5–17 years. Gender and race projections also were estimated. Historic child population estimates (1980–2011) also are available on the US Census Web site are also included in the regression models. Analysis
The study team combined US population data with aggregated historic diabetes and obesity prevalence. For the analysis, an infinite distributed lag model was used to assess the impact of lagged diabetes and obesity prevalence; that is, diabetes and obesity prevalence rates in year ‘‘t’’ on future diabetes prevalence rates in year ‘‘t + 1.’’ The specification of a lagged relationship reflects the role of obesity as a factor that affects the risk of developing type 2 diabetes over time. Three lagged regressions models were assessed: 1. A base model with lagged diabetes prevalence for all children as the only independent variable: ‘‘diabetes only’’; 2. Base model + percentage distribution of child population by race: ‘‘diabetes + population’’; and 3. Model #2 + gender and race-specific obesity rates: ‘‘diabetes + population + gender and race-specific obesity prevalence.’’ The inclusion of the lagged dependent variable represents a statistical artifact of the prior year’s prevalence rate and helps account for the level of diabetes in those years. Several studies have shown that, when used properly, models with a lagged dependent variable are asymptotically unbiased, and that the bias present in many models with a lagged dependent variable is trivial compared with the alternative issue with residual autocorrelation.40–41 Results from all 3 models are thoroughly compared and analyzed. Projection model
Building on the infinite distributed lag model, the study team ran a projection model to forecast the diabetes burden in children through 2030. Analogous to models used in other studies,26–27, 42–44 the team fit a regression model to account for the relationship between time and prevalence of diabetes in children by race/ethnicity (non-Hispanic whites, nonHispanic blacks, and Hispanics), age (0–4, 5–17), and
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gender. For each regression model, a line of best fit to the moving averages was fit. A moving average is commonly used with time series data to smooth out short-term fluctuations on a series of values over time. Moreover, a graph of moving averages against time may show changes against time that are obscured by cyclical effects. A 3-year moving average was used for this study and calculated as follows: Y1 þ Y2 þ Y3 3 MA(3) ¼ Y2 þ Y33 þ Y4
First moving average: MA(3) ¼ Second moving average:
where Y1 = prevalence in year 1, Y2 = prevalence in year 2, and Y3 = prevalence in year 3. A quadratic equation, representing the line of best fit to the moving averages was used to forecast future diabetes prevalence rates after obtaining the coefficients b0 and b1 from the regression equation. Results
Three regression models showing the impact of lagged diabetes and obesity prevalence rates on future diabetes prevalence are shown in Table 1. In the base model, lagged diabetes prevalence was statistically significantly associated with future diabetes prevalence. A unit increase in lagged diabetes prevalence was associated with a 0.96 increase in future diabetes prevalence. In determining the added impact of population demographics on diabetes prevalence, model #2 (diabetes + population) confirmed that lagged population distribution was not significantly associated with future diabetes prevalence. Lagged diabetes prevalence remained highly significant after controlling for population distribution. Results from model #3 (diabetes + population + gender and race-specific obesity prevalence) showed that lagged obesity prevalence for Hispanic boys and Non-Hispanic black girls were statistically significantly associated with increasing future diabetes prevalence. The regression results showed that a 1% increase in obesity prevalence among Hispanic boys from the previous year was significantly associated with a 0.005% increase in future prevalence of diagnosed diabetes in children (P £ 0.01). Likewise, a unit increase in obesity prevalence among non-Hispanic black girls was associated with a 0.003% increase in future diabetes prevalence (P < 0.05). Obesity rates for other race/ethnicity combinations were not associated with increasing future diabetes prevalence. Table 2 documents the diabetes population projections through 2030 based on the most likely population scenario (middle series). The projections indicate that, overall, the number of children 0–17 years with diagnosed diabetes will increase from almost 173,000 in 2010 to almost 288,000 by 2030. This translates to a 67% increase in the number of children with diagnosed diabetes from 2010. As expected, the larger increase stemmed from children (aged 5–17 years) when compared with younger children 0–4 years of age. Gender projections shown in Figure 1 indicate that male children will bear a slightly larger share of the diabetes burden in 2015, 2020, 2025, and 2030. The higher male proportions are consistent throughout the projection period. In addition, these gender-based estimates show that the number of males affected increases by more than 50,000 children, a 52% increase between 2015 and 2030. Females
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Table 1. Regression Models for the Effect of Child Obesity and Population Distribution by Race/Ethnicity on Future Diabetes Prevalence VARIABLES
Base Model: Diabetes only
Diabetes + Population
0.955*** (0.0428)
0.532*** (0.156) Ref - 0.00578 (0.0156) 0.00569 (0.00374)
L. Diabetes Prevalence (DM) L. % Non-Hispanic white L. % Non-Hispanic black L. % Hispanic L.Obesity Prevalence (Non-Hispanic white boys) L.Obesity Prevalence (Non-Hispanic black boys) L.Obesity Prevalence (Hispanic + boys) L.Obesity Prevalence (Non-Hispanic white girls) L.Obesity Prevalence (Non-Hispanic black girls) L.Obesity Prevalence (Hispanic + girls) L.Obesity Prevelance (6–19yo)
Gender and race-specific obesity + Pop. 0.0735 (0.134) 0.0192 (0.0120) 0.000341 (0.00297) Ref 0.00238 (0.00161) 0.00483*** (0.00159) Ref 0.00272** (0.00126) 0.00264 (0.00144) 0.00811 (0.00445)
Standard errors in parentheses. Lagged model; L indicates lag. + Data available for Mexican Americans only. ***P < 0.01, **P < 0.05
also experience an increase in a similar magnitude as males. The number of female children with diabetes increases by more than 47,000 children, a 50% increase between 2015 and 2030. The projected prevalence increase in diabetes by race/ ethnicity produced concerning trends pointing to significant racial disparities (Fig. 2). Non-Hispanic black children had the highest prevalence rate of approximately 0.74% in 2030, followed by Hispanic children at 0.30%, and nonHispanic white children at 0.24%. The results show that white children have the second highest diabetes rates up until 2023, when prevalence rates for Hispanic children overtake them. Discussion
Although the relationship between obesity and diabetes is well documented in the literature, this study is the first, to the study team’s knowledge, to project trends in diabetes prevalence in children and to assess the impact of lagged childhood obesity on diabetes prevalence rates. The study shows that by 2030, black children will bear more than 3 times the diabetes burden compared to non-Hispanic white
children. In addition, future diabetes prevalence will be strongly associated with obesity prevalence rates among non-Hispanic black girls and Hispanic boys. Diabetes prevalence rates for non-Hispanic white children also increase, but at a decreasing rate, while rates for Hispanics increase relatively steadily. Of particular importance is the strong association between future diabetes prevalence and historic obesity rates in nonHispanic black girls and Hispanic boys. This sets the stage for more precise and targeted obesity intervention programs for these populations. Many interventions target the entire non-Hispanic black or Hispanic population. The analyses from the present study indicate the importance of developing interventions that specifically target Hispanic boys and non-Hispanic black girls. Among black females, however, current obesity interventions have been largely ineffective. Others have reported that black females are less likely to meet recommended levels of physical activity compared to other racial-ethnic gender groups, including black males.45,46,47,48 Compared to other male children, Hispanic boys are reported to have significantly higher child obesity rates, although huge variability exists as the Hispanic descent varies.39 Children of Mexican American descent report the highest rates of child
Table 2. Diabetes Population Projections in Children (aged 0–17 years) Through 2030 Year
Projected diagnosed diabetes prevalence (%)
Age 0–4 diagnosed diabetes population(N)
Age 5–17 diagnosed diabetes population(N)
Total Children with diagnosed diabetes(N)
2010 2015 2020 2025 2030
0.23 0.26 0.28 0.31 0.36
47,021 53,709 62,075 69,078 79,726
125,646 136,414 154,706 175,159 208,151
172,667 190,123 216,781 244,237 287,878
DIAGNOSED DIABETES IN CHILDREN THROUGH 2030
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FIG. 1. Diabetes population projections by gender, 2015–2030. obesity within the Hispanic community.39 The challenge of stemming diabetes incidence lies in devising culturally sensitive interventions and addressing social elements through multidisciplinary policy approaches for child obesity in these populations.
FIG. 2.
Perhaps the most important deduction from the findings of this study is that, based on the significant predictors of future diabetes prevalence, type 2 diabetes likely will overtake type 1 as the predominant type of diabetes in children. Until now, type 1 diabetes has been regarded as the
Historical and projected prevalence of diagnosed diabetes in children, by race/ethnicity. NH, non-Hispanic
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most common type of diabetes in children, and the most common endocrine disease in children.6 Bell and colleagues report that the prevalence of type 1 diabetes among nonHispanic whites was higher than among other races and countries.49 Among African American children, the prevalence of type 2 diabetes was 2 times higher than the prevalence of type 1.50 Based on the association between lagged obesity in minority children and future diabetes prevalence, it is the study team’s impression that by 2030, type 2 will become the predominant type of diabetes among children in the United States. From a policy standpoint, many of these type 2 diabetes cases can be prevented and managed through interventions that specifically target susceptible racial/ethnic groups. Clearly, these projections may not seem as drastic as some other diabetes researchers expect. The 2012 US Census population projections upon which the present study’s projections are based have significantly fewer persons than previous US Census projections in 2008 and 2000, upon which previous adult diabetes projection studies are based.26–29 Despite the projected slower pace of population growth, the study team believes that the 2012 projections more accurately reflect US future populations, making the findings of the present study more reliable. Secondly, this study addresses diagnosed diabetes only; therefore, the study team is unable to capture the proportions of persons who are undiagnosed until they are older than 17 years of age. The CDC reports that approximately 25% of persons with diabetes are unaware that they have the disease because it has not been diagnosed.7 Unless more policy efforts are targeted toward early diagnosis, prevention, and treatment of diabetes in children, a larger proportion of children will have undiagnosed diabetes in the future. Another major limitation of this study stems from the data sources. Others have discussed the limitation of the NHIS in that it excludes the institutionalized population wherein diabetes is overrepresented. In addition, the determination of diabetes prevalence in the NHIS is based on self-reports by parents or guardians of the children interviewed. Informants may be subject to recall bias, which can potentially affect the validity of reports. However, numerous studies have reported the accuracy and validity of self-reports in diagnosed diabetes.32–33 In addition, the NHIS does not distinguish between type 1 diabetes and type 2 diabetes, so the study team is unable to explicitly delineate between both types in the predictions. Despite these limitations, this study provides unambiguous evidence that the number of children with diabetes continues to soar and obesity continues to be strongly associated with diabetes. The implications of this overall increase, for both obesity and diabetes, are extremely concerning, especially with regard to the scarcity of medical care in the face of more people having and managing diabetes for most of their lives. Conflict of interest
Drs. Adepoju, Bolin, Zhao, Phillips, and Ohsfeldt, Mr. Booth, and Mr. Lin declared no conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors received no financial support for the research, authorship, and/or publication of this article.
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Address correspondence to: Dr. Omolola E. Adepoju School of Public Health Texas A&M Health Science Center 1266 TAMU College Station, TX 77843 E-mail: [email protected]
POPULATION HEALTH MANAGEMENT Volume 0, Number 0, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/pop.2014.0084
Is Diabetes Color-Blind? Growth of Prevalence of Diagnosed Diabetes in Children Through 2030 Omolola E. Adepoju, PhD, MPH,1 Jane N. Bolin, BSN, JD, PhD,2 Eric A. Booth, MS,3 Hongwei Zhao, ScD,4 Szu-Hsuan Lin, MPH,2 Charles D. Phillips, PhD, MPH,2 and Robert L. Ohsfeldt, PhD 2
Abstract
Diabetes knows no age and affects millions of individuals. Preventing diabetes in children is increasingly becoming a major health policy concern and focus. The objective of this study is to project the number of children, aged 0–17 years, with diagnosed diabetes in the United States through 2030, accounting for changing demography, and diabetes and obesity prevalence rates. The study team combined historic diabetes and obesity prevalence data with US child population estimates and projections. A times-series regression model was used to forecast future diabetes prevalence and to account for the relationship between the forecasted diabetes prevalence and the lagged prevalence of childhood obesity. Overall, the prevalence of diagnosed diabetes is projected to increase 67% from 0.22% in 2010 to 0.36% in 2030. Lagged obesity prevalence in Hispanic boys and non-Hispanic black girls was significantly associated with increasing future diabetes prevalence. The study results showed that a 1% increase in obesity prevalence among Hispanic boys from the previous year was significantly associated with a 0.005% increase in future prevalence of diagnosed diabetes in children (P £ 0.01). Likewise, a unit increase in obesity prevalence among non-Hispanic black girls was associated with a 0.003% increase in future diabetes prevalence (P < 0.05). Obesity rates for other race/ethnicity combinations were not associated with increasing future diabetes prevalence. To mitigate the continued threat posed by diabetes, serious discussions need to focus on the pediatric population, particularly non-Hispanic black girls and Hispanic boys whose obesity trends show the strongest associations with future diabetes prevalence in children. (Population Health Management 2014;xx:xxx–xxx) individuals 18–20 years of age, a group typically not considered children. Most researchers consider diabetes cases in children to be predominantly type 1.6 However, routine surveillance over the past 2 decades has shown increasing frequencies of both type 1 and type 2,7 with greater increases in the incidence of Type 2 among children and adolescents.8–11 Recently, the Centers for Disease Control and Prevention’s (CDC) SEARCH for Diabetes in Youth Study reported that the proportion of type 2 diabetes in older youths ranges from 6% for non-Hispanic white youths to 76% for American Indian youths.12 Among younger children, aged 0–4 years, type 1 accounted for ‡ 80% of diabetes cases.12 The reported increase in diabetes prevalence is concurrent with the rise in the number of children who are obese13–17 or have a family history of the disease.18 Some studies have
Background
T
he trends in diabetes prevalence are burdensome but largely remediable. A pediatrician once reported that younger children are frightened that the ‘‘di’’ in diabetes means that they will die soon.1 Of late, numerous health care providers report increasing numbers of children with type 2 diabetes, a disease usually diagnosed in adults aged 40 years or older.2–3 Among hospital discharges of children and young people aged 0–17 years in 2009, about 74% had diabetes as the first listed diagnosis among those with diabetes as any listed diagnosis.4 Current estimates suggest that less than a quarter of a million persons 20 years of age or younger have type 1 or type 2 diabetes.5 Although this figure has been reported to be an underestimation, it is important to note that it includes
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School of Public Health, Texas A&M Health Science Center, College Station, Texas; Accountable Care/Health Homes Program, United Health Group, Sugar Land, Texas. 2 Department of Health Policy & Management, School of Public Health, Texas A&M Health Science Center, College Station, Texas. 3 Texas A&M University Public Policy Research Institute. 4476 TAMU, College Station, Texas. 4 Department of Epidemiology & Biostatistics, School of Public Health, Texas A&M Health Science Center, College Station, Texas.
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shown a strong relationship between childhood obesity and the development of insulin resistance in early adulthood.19 Other related studies have documented positive associations between maternal gestational diabetes and offspring overweight and obesity.20 Furthermore, children from ethnic minorities, specifically American Indians, Hispanics, and African Americans, have higher obesity rates.11,21 The implications of these trends are extremely concerning, especially with regard to increasing numbers of people having and managing diabetes for most of their lives,22 and the potential impacts on the future workforce. The US Census Bureau reported that the US child population (aged 0–17 years) increased from 64.2 million in 1990 to 74.1 million in 2010, representing a 21% increase in the youth and adolescent population. In terms of racial composition, in 1990, 69% of children were non-Hispanic whites compared to 12% who were of Hispanic origin. By 2010, the proportion of non-Hispanic whites decreased to 53.6% and Hispanic children represented 23.2% of the youth and adolescent population, or 25% of all children. Rates for non-Hispanic blacks (African Americans) have remained relatively stable over time, dropping only 1 percentage point between 1990 and 2010.23 Population estimates of children less than 18 years, coupled with the growing percentage of adults aged 65 years and older, reflect a larger proportion of individuals in stages of economic dependence.24 Children and older adults typically rely on economic resources provided by productive members of the society.25 An aging population coupled with an increasingly less healthy youth and adolescent population portends disaster for any economy, as economic resources will constantly be transferred from the working to the nonworking population. For this reason, projection studies are useful planning tools to analyze future health care trends and provide sound warning to inform the delivery of health care services. Previous studies have projected the number of adults with diagnosed and undiagnosed diabetes in the United States26–29 and other countries of the world.30 Most of the recent US-based studies accounted for changing demography and prevalence rates, particularly the increasing size of the Hispanic foreign-born US populations who are at higher risk for developing diabetes.27–28 One study constructed a series of dynamic models employing systems of difference equations to project the future burden of diabetes among US adults. It concluded that annual diabetes incidence will increase from about 8.4 cases per 1000 population in 2008 to 14.7 cases per 1000 population in 2050.27 Another study employed a Markov model and generated forecasts by age, sex, and race/ethnicity, basing its estimates on diabetes incidence and prevalence in 2000.28 Although these studies are laudable, none of them specifically considered children aged 0–17 years. Furthermore, none of these projections focused on the variability of demographics in the pipeline of children who have an increased likelihood of developing diabetes. This study focuses on children aged 0–17 years in the United States. An infinite distributed lag model is used to assess the contributions of historic child obesity prevalence rates on future diabetes prevalence in children. Building on this model, this study forecasts race-specific prevalence rates to estimate the number of children with diabetes through 2030, incorporating US census projections that
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adjust for birth, death, and net immigration rates. Forecasts of this nature are useful for policy decisions on health care needs, the allocation of resources to meet these needs, and the urgency of preventive and treatment interventions that target populations with the largest expected increases in diabetes prevalence.28 Methods Data
Data for the model are obtained from the following sources: 1. Diabetes prevalence data: Historic diabetes prevalence trends for children were obtained from the National Center for Health Statistics (NCHS) databases.31 National Health Interview Survey (NHIS) sample child data files were downloaded for each year from 1997 through 2011. Although a longer trend was sought, as far back as 1980, the NHIS does not report diabetes in children in the 1980–1996 Core (annual) questionnaires. In each of the 1997–2011 data files, the responding parent/guardian was asked ‘‘Ever told sample child has diabetes?’’ All affirmative responses were determined to be records of children with diabetes. Although there might be drawbacks to this self-reported approach, numerous studies have affirmed the accuracy and validity of self-reports in diagnosed diabetes.32–33 For each year, the prevalence rates of diabetes in children were calculated by applying sampling weights to generate unbiased estimates of age-, sex-, and racespecific prevalences. There are several advantages to using the NHIS. Conducted continuously since 1957, this survey is an ongoing, cross-sectional survey of representative samples of the US civilian noninstitutionalized population.34 The survey uses a complex multistage probability design, oversampling for African Americans and Hispanics. Oversampling of these population subgroups is done to ensure more accurate representation of indicator estimates for these groups.34 Data are collected through personal household interviews conducted by trained federal personnel. Additional details of the survey design and methods are described elsewhere.35–36 Another strength of the NHIS is that it provides information on the health of the US population, including information on the prevalence and incidence of disease, the extent of disability, and the use of health care services.34 Several studies have used the NHIS for epidemiologic and policy analyses of pressing health issues, including chronic conditions, functional limitations, and health care access and utilization.37–38 2. Obesity prevalence data: Historic obesity prevalence trends for children also are obtained from the NCHS databases. Earlier NCHS reports by Fryar and colleagues use the National Health and Nutrition Examination Survey (NHANES) to estimate childhood obesity trends from 1963– 1965 through 2009–2010. They then reported tables in an NCHS publication.39 The present study obtained age-, sex-, and race-specific childhood obesity prevalence estimates from this NCHS report. Like the NHIS, the NHANES is an ongoing, cross-sectional survey of representative samples of the US civilian noninstitutionalized population. The survey uses a complex multistage probability design, oversampling for persons aged 60 and older, households with African Americans, and
DIAGNOSED DIABETES IN CHILDREN THROUGH 2030
persons of Hispanic origin. Data are collected through inperson interviews and physical examinations, the latter being a major strength of the NHANES. Obese children were identified based on their measured heights and weights corresponding to a body mass index equal to or greater than the 95th percentile of children of the same age and sex. 3. Population data: For each year from 2012 to 2030, population projections are available by age, sex, race, and Hispanic origin on the US Census Bureau Web site (www.census.gov/ population/projections/data/national/2012.html). These projections were conducted in 2012 and are based on the 2010 census.23 They factor in an intermediate set of assumptions regarding fertility rates, life expectancies, and net immigration. According to the ‘‘middle series’’ census projections, the US child population is likely to grow from approximately 74 million children in 2010 to more than 80 million in 2030. The middle series represents the most likely population scenario through 2030. The present study used the middle series estimates reported in the age classification 0–4 and 5–17 years. Gender and race projections also were estimated. Historic child population estimates (1980–2011) also are available on the US Census Web site are also included in the regression models. Analysis
The study team combined US population data with aggregated historic diabetes and obesity prevalence. For the analysis, an infinite distributed lag model was used to assess the impact of lagged diabetes and obesity prevalence; that is, diabetes and obesity prevalence rates in year ‘‘t’’ on future diabetes prevalence rates in year ‘‘t + 1.’’ The specification of a lagged relationship reflects the role of obesity as a factor that affects the risk of developing type 2 diabetes over time. Three lagged regressions models were assessed: 1. A base model with lagged diabetes prevalence for all children as the only independent variable: ‘‘diabetes only’’; 2. Base model + percentage distribution of child population by race: ‘‘diabetes + population’’; and 3. Model #2 + gender and race-specific obesity rates: ‘‘diabetes + population + gender and race-specific obesity prevalence.’’ The inclusion of the lagged dependent variable represents a statistical artifact of the prior year’s prevalence rate and helps account for the level of diabetes in those years. Several studies have shown that, when used properly, models with a lagged dependent variable are asymptotically unbiased, and that the bias present in many models with a lagged dependent variable is trivial compared with the alternative issue with residual autocorrelation.40–41 Results from all 3 models are thoroughly compared and analyzed. Projection model
Building on the infinite distributed lag model, the study team ran a projection model to forecast the diabetes burden in children through 2030. Analogous to models used in other studies,26–27, 42–44 the team fit a regression model to account for the relationship between time and prevalence of diabetes in children by race/ethnicity (non-Hispanic whites, nonHispanic blacks, and Hispanics), age (0–4, 5–17), and
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gender. For each regression model, a line of best fit to the moving averages was fit. A moving average is commonly used with time series data to smooth out short-term fluctuations on a series of values over time. Moreover, a graph of moving averages against time may show changes against time that are obscured by cyclical effects. A 3-year moving average was used for this study and calculated as follows: Y1 þ Y2 þ Y3 3 MA(3) ¼ Y2 þ Y33 þ Y4
First moving average: MA(3) ¼ Second moving average:
where Y1 = prevalence in year 1, Y2 = prevalence in year 2, and Y3 = prevalence in year 3. A quadratic equation, representing the line of best fit to the moving averages was used to forecast future diabetes prevalence rates after obtaining the coefficients b0 and b1 from the regression equation. Results
Three regression models showing the impact of lagged diabetes and obesity prevalence rates on future diabetes prevalence are shown in Table 1. In the base model, lagged diabetes prevalence was statistically significantly associated with future diabetes prevalence. A unit increase in lagged diabetes prevalence was associated with a 0.96 increase in future diabetes prevalence. In determining the added impact of population demographics on diabetes prevalence, model #2 (diabetes + population) confirmed that lagged population distribution was not significantly associated with future diabetes prevalence. Lagged diabetes prevalence remained highly significant after controlling for population distribution. Results from model #3 (diabetes + population + gender and race-specific obesity prevalence) showed that lagged obesity prevalence for Hispanic boys and Non-Hispanic black girls were statistically significantly associated with increasing future diabetes prevalence. The regression results showed that a 1% increase in obesity prevalence among Hispanic boys from the previous year was significantly associated with a 0.005% increase in future prevalence of diagnosed diabetes in children (P £ 0.01). Likewise, a unit increase in obesity prevalence among non-Hispanic black girls was associated with a 0.003% increase in future diabetes prevalence (P < 0.05). Obesity rates for other race/ethnicity combinations were not associated with increasing future diabetes prevalence. Table 2 documents the diabetes population projections through 2030 based on the most likely population scenario (middle series). The projections indicate that, overall, the number of children 0–17 years with diagnosed diabetes will increase from almost 173,000 in 2010 to almost 288,000 by 2030. This translates to a 67% increase in the number of children with diagnosed diabetes from 2010. As expected, the larger increase stemmed from children (aged 5–17 years) when compared with younger children 0–4 years of age. Gender projections shown in Figure 1 indicate that male children will bear a slightly larger share of the diabetes burden in 2015, 2020, 2025, and 2030. The higher male proportions are consistent throughout the projection period. In addition, these gender-based estimates show that the number of males affected increases by more than 50,000 children, a 52% increase between 2015 and 2030. Females
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Table 1. Regression Models for the Effect of Child Obesity and Population Distribution by Race/Ethnicity on Future Diabetes Prevalence VARIABLES
Base Model: Diabetes only
Diabetes + Population
0.955*** (0.0428)
0.532*** (0.156) Ref - 0.00578 (0.0156) 0.00569 (0.00374)
L. Diabetes Prevalence (DM) L. % Non-Hispanic white L. % Non-Hispanic black L. % Hispanic L.Obesity Prevalence (Non-Hispanic white boys) L.Obesity Prevalence (Non-Hispanic black boys) L.Obesity Prevalence (Hispanic + boys) L.Obesity Prevalence (Non-Hispanic white girls) L.Obesity Prevalence (Non-Hispanic black girls) L.Obesity Prevalence (Hispanic + girls) L.Obesity Prevelance (6–19yo)
Gender and race-specific obesity + Pop. 0.0735 (0.134) 0.0192 (0.0120) 0.000341 (0.00297) Ref 0.00238 (0.00161) 0.00483*** (0.00159) Ref 0.00272** (0.00126) 0.00264 (0.00144) 0.00811 (0.00445)
Standard errors in parentheses. Lagged model; L indicates lag. + Data available for Mexican Americans only. ***P < 0.01, **P < 0.05
also experience an increase in a similar magnitude as males. The number of female children with diabetes increases by more than 47,000 children, a 50% increase between 2015 and 2030. The projected prevalence increase in diabetes by race/ ethnicity produced concerning trends pointing to significant racial disparities (Fig. 2). Non-Hispanic black children had the highest prevalence rate of approximately 0.74% in 2030, followed by Hispanic children at 0.30%, and nonHispanic white children at 0.24%. The results show that white children have the second highest diabetes rates up until 2023, when prevalence rates for Hispanic children overtake them. Discussion
Although the relationship between obesity and diabetes is well documented in the literature, this study is the first, to the study team’s knowledge, to project trends in diabetes prevalence in children and to assess the impact of lagged childhood obesity on diabetes prevalence rates. The study shows that by 2030, black children will bear more than 3 times the diabetes burden compared to non-Hispanic white
children. In addition, future diabetes prevalence will be strongly associated with obesity prevalence rates among non-Hispanic black girls and Hispanic boys. Diabetes prevalence rates for non-Hispanic white children also increase, but at a decreasing rate, while rates for Hispanics increase relatively steadily. Of particular importance is the strong association between future diabetes prevalence and historic obesity rates in nonHispanic black girls and Hispanic boys. This sets the stage for more precise and targeted obesity intervention programs for these populations. Many interventions target the entire non-Hispanic black or Hispanic population. The analyses from the present study indicate the importance of developing interventions that specifically target Hispanic boys and non-Hispanic black girls. Among black females, however, current obesity interventions have been largely ineffective. Others have reported that black females are less likely to meet recommended levels of physical activity compared to other racial-ethnic gender groups, including black males.45,46,47,48 Compared to other male children, Hispanic boys are reported to have significantly higher child obesity rates, although huge variability exists as the Hispanic descent varies.39 Children of Mexican American descent report the highest rates of child
Table 2. Diabetes Population Projections in Children (aged 0–17 years) Through 2030 Year
Projected diagnosed diabetes prevalence (%)
Age 0–4 diagnosed diabetes population(N)
Age 5–17 diagnosed diabetes population(N)
Total Children with diagnosed diabetes(N)
2010 2015 2020 2025 2030
0.23 0.26 0.28 0.31 0.36
47,021 53,709 62,075 69,078 79,726
125,646 136,414 154,706 175,159 208,151
172,667 190,123 216,781 244,237 287,878
DIAGNOSED DIABETES IN CHILDREN THROUGH 2030
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FIG. 1. Diabetes population projections by gender, 2015–2030. obesity within the Hispanic community.39 The challenge of stemming diabetes incidence lies in devising culturally sensitive interventions and addressing social elements through multidisciplinary policy approaches for child obesity in these populations.
FIG. 2.
Perhaps the most important deduction from the findings of this study is that, based on the significant predictors of future diabetes prevalence, type 2 diabetes likely will overtake type 1 as the predominant type of diabetes in children. Until now, type 1 diabetes has been regarded as the
Historical and projected prevalence of diagnosed diabetes in children, by race/ethnicity. NH, non-Hispanic
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most common type of diabetes in children, and the most common endocrine disease in children.6 Bell and colleagues report that the prevalence of type 1 diabetes among nonHispanic whites was higher than among other races and countries.49 Among African American children, the prevalence of type 2 diabetes was 2 times higher than the prevalence of type 1.50 Based on the association between lagged obesity in minority children and future diabetes prevalence, it is the study team’s impression that by 2030, type 2 will become the predominant type of diabetes among children in the United States. From a policy standpoint, many of these type 2 diabetes cases can be prevented and managed through interventions that specifically target susceptible racial/ethnic groups. Clearly, these projections may not seem as drastic as some other diabetes researchers expect. The 2012 US Census population projections upon which the present study’s projections are based have significantly fewer persons than previous US Census projections in 2008 and 2000, upon which previous adult diabetes projection studies are based.26–29 Despite the projected slower pace of population growth, the study team believes that the 2012 projections more accurately reflect US future populations, making the findings of the present study more reliable. Secondly, this study addresses diagnosed diabetes only; therefore, the study team is unable to capture the proportions of persons who are undiagnosed until they are older than 17 years of age. The CDC reports that approximately 25% of persons with diabetes are unaware that they have the disease because it has not been diagnosed.7 Unless more policy efforts are targeted toward early diagnosis, prevention, and treatment of diabetes in children, a larger proportion of children will have undiagnosed diabetes in the future. Another major limitation of this study stems from the data sources. Others have discussed the limitation of the NHIS in that it excludes the institutionalized population wherein diabetes is overrepresented. In addition, the determination of diabetes prevalence in the NHIS is based on self-reports by parents or guardians of the children interviewed. Informants may be subject to recall bias, which can potentially affect the validity of reports. However, numerous studies have reported the accuracy and validity of self-reports in diagnosed diabetes.32–33 In addition, the NHIS does not distinguish between type 1 diabetes and type 2 diabetes, so the study team is unable to explicitly delineate between both types in the predictions. Despite these limitations, this study provides unambiguous evidence that the number of children with diabetes continues to soar and obesity continues to be strongly associated with diabetes. The implications of this overall increase, for both obesity and diabetes, are extremely concerning, especially with regard to the scarcity of medical care in the face of more people having and managing diabetes for most of their lives. Conflict of interest
Drs. Adepoju, Bolin, Zhao, Phillips, and Ohsfeldt, Mr. Booth, and Mr. Lin declared no conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors received no financial support for the research, authorship, and/or publication of this article.
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Address correspondence to: Dr. Omolola E. Adepoju School of Public Health Texas A&M Health Science Center 1266 TAMU College Station, TX 77843 E-mail: [email protected]
