Publications
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Ali MK, Bullard KM, Saydah S, et al., 2018, Cardiovascular and renal burdens of prediabetes in the USA: analysis of data from serial cross-sectional surveys, 1988-2014, LANCET DIABETES & ENDOCRINOLOGY, Vol: 6, Pages: 392-403, ISSN: 2213-8587
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- Citations: 119
Siegel KR, Bullard KM, Imperatore G, et al., 2018, Prevalence of Major Behavioral Risk Factors for Type 2 Diabetes, DIABETES CARE, Vol: 41, Pages: 1032-1039, ISSN: 0149-5992
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- Citations: 21
Burrows NR, Li Y, Geiss LS, et al., 2018, Declining Rates of Hospitalization for Selected Cardiovascular Disease Conditions Among Adults Aged >= 35 Years With Diagnosed Diabetes, US, 1998-2014. Diabetes Care 2018;41:293-302 RESPONSE, Diabetes Care, Vol: 41, Pages: E59-E59, ISSN: 0149-5992
Benoit SR, Zhang Y, Geiss LS, et al., 2018, Trends in Diabetic Ketoacidosis Hospitalizations and In-Hospital Mortality - United States, 2000-2014, MMWR : Morbidity & Mortality Weekly Report, Vol: 67, Pages: 362-365, ISSN: 0149-2195
Mercado CI, Gregg E, Gillespie C, et al., 2018, Trends in lipid profiles and descriptive characteristics of US adults with and without diabetes and cholesterol-lowering medication use-National Health and Nutrition Examination Survey, 2003-2012, United States, PLoS ONE, Vol: 13, ISSN: 1932-6203
BackgroundWith a cholesterol-lowering focus for diabetic adults and in the age of polypharmacy, it is important to understand how lipid profile levels differ among those with and without diabetes.ObjectiveInvestigate the means, differences, and trends in lipid profile measures [TC, total cholesterol; LDL-c, low-density lipoprotein; HDL-c, high-density lipoprotein; and TG, triglycerides] among US adults by diabetes status and cholesterol-lowering medication.MethodsPopulation number and proportion of adults aged ≥21 years with diabetes and taking cholesterol-lowering medication were estimated using data on 10,384 participants from NHANES 2003–2012. Age-standardized means, trends, and differences in lipid profile measures were estimated by diabetes status and cholesterol medication use. For trends and differences, linear regression analysis were used adjusted for age, gender, and race/ethnicity.ResultsAmong diabetic adults, 52% were taking cholesterol-lowering medication compared to the 14% taking cholesterol-lowering medication without diabetes. Although diabetic adults had significantly lower TC and LDL-c levels than non-diabetic adults [% difference (95% confidence interval): TC = -5.2% (-6.8 –-3.5), LDL-c = -8.0% (-10.4 –-5.5)], the percent difference was greater among adults taking cholesterol medication [TC = -8.0% (-10.3 –-5.7); LDL-c = -13.7% (-17.1 –-10.2)] than adults not taking cholesterol medication [TC = -3.5% (-5.2 –-1.6); LDL-c = -4.3% (-7.1 –-1.5)] (interaction p-value: TC = <0.001; LDL-c = <0.001). From 2003–2012, mean TC and HDL-c significantly decreased among diabetic adults taking cholesterol medication [% difference per survey cycle (p-value for linear trend): TC = -2.3% (0.003) and HDL-c = -2.3% (0.033)]. Mean TC, HDL-c, and LDL-c levels did not significantly change from 2003 to 2012 in non-diabetic adults taking cholesterol medication or for adults not taking cholesterol medications.Conclus
Pasquel FJ, Gregg EW, Ali MK, 2018, The Evolving Epidemiology of Atherosclerotic Cardiovascular Disease in People with Diabetes, Endocrinology and Metabolism Clinics of North America, Vol: 47, Pages: 1-32, ISSN: 0889-8529
Duru OK, Mangione CM, Rodriguez HP, et al., 2018, Introductory Overview of the Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) Network: Examining the Impact of US Health Policies and Practices to Prevent Diabetes and Its Complications, Current Diabetes Reports, Vol: 18, ISSN: 1534-4827
Saydah SH, Gregg EW, Kahn HS, et al., 2018, Mortality associated with less intense risk-factor control among adults with diabetes in the United States, PRIMARY CARE DIABETES, Vol: 12, Pages: 3-12, ISSN: 1751-9918
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- Citations: 3
Burrows NR, Li Y, Gregg EW, et al., 2018, Declining Rates of Hospitalization for Selected Cardiovascular Disease Conditions Among Adults Aged ≥35 Years With Diagnosed Diabetes, US, 1998-2014, Publisher: AMER DIABETES ASSOC, Pages: 293-302, ISSN: 0149-5992
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- Citations: 45
Geiss LS, Bullard KM, Brinks R, et al., 2018, Trends in type 2 diabetes detection among adults in the USA, 1999-2014, BMJ Open Diabetes Research and Care, Vol: 6, ISSN: 2052-4897
Objective To examine recent trends in type 2 diabetes detection among adults in the USA.Research design and methods We used data from the 1999–2014 National Health and Nutrition Examination Surveys on non-pregnant adults (aged ≥18 years) not reporting a diagnosis of diabetes (n=16 644 participants, averaging about 2000 for each 2-year cycle). We defined undiagnosed diabetes as a fasting plasma glucose ≥126 mg/dL or a hemoglobin A1c ≥6.5% (48 mmol/mol). We measured case detection as the probability of finding undiagnosed type 2 diabetes among the population without diagnosed diabetes. Linear regression models were used to examine trends overall and by sociodemographic characteristics (ie, age, gender, race/ethnicity, education, poverty-income ratio (PIR)).Results Age-standardized probability of finding undiagnosed type 2 diabetes was 3.0% (95% CI 2.1% to 4.2%) during 1999–2000 and 2.8% (2.2%–3.6%) during 2013–2014 (P for trend=0.52). Probability increased among Mexican-Americans (P for trend=0.01) but decreased among adults aged 65 years or older (P for trend=0.04), non-Hispanic (NH) white (P for trend=0.02), and adults in the highest PIR tertile (P for trend=0.047). For all other sociodemographic groups, no significant trends were detected.Conclusions We found little evidence of increased detection of undiagnosed type 2 diabetes among adults in the USA during the past 15 years. Although improvements were seen among NH white, older, and wealthy adults, these improvements were not large. As the scope of primary prevention efforts increases, case detection may improve.
Burrows NR, Hora I, Geiss LS, et al., 2017, Incidence of End-Stage Renal Disease Attributed to Diabetes Among Persons with Diagnosed Diabetes - United States and Puerto Rico, 2000-2014, MMWR : Morbidity & Mortality Weekly Report, Vol: 66, Pages: 1165-1170, ISSN: 0149-2195
Johnson KC, Bray GA, Cheskin LJ, et al., 2017, The Effect of Intentional Weight Loss on Fracture Risk in Persons With Diabetes: Results From the Look AHEAD Randomized Clinical Trial, JOURNAL OF BONE AND MINERAL RESEARCH, Vol: 32, Pages: 2278-2287, ISSN: 0884-0431
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- Citations: 47
Alva ML, Hoerger TJ, Zhang P, et al., 2017, Identifying risk for type 2 diabetes in different age cohorts: does one size fit all?, BMJ Open Diabetes Res Care, Vol: 5, Pages: 1-7, ISSN: 2052-4897
Objective: To estimate age-specific risk equations for type 2 diabetes onset in young, middle-aged, and older US adults, and to compare the performance of simple equations based on readily available demographic information alone, against enhanced equations that require both demographic and clinical information (fasting plasma glucose, high-density lipoprotein, and triglyceride levels). Research design and methods: We estimated the probability of developing diabetes by age group using data from the Coronary Artery Risk Development in Young Adults (for ages 18-40 years), Atherosclerosis Risk in Communities (for ages 45-64 years), and the Cardiovascular Health Study (for ages 65 years and older). Simple and enhanced equations were estimated using logistic regression models, and performance was compared by age group. Thresholds based on these risk equations were evaluated using split-sample bootstraps and calibrating the constant of one age cohort to others. Results: Simple risk equations had an area under the receiver-operating curve (AUROC) of 0.72, 0.79, 0.75, and 0.69 for age groups 18-30, 28-40, 45-64, and 65 and older, respectively. The corresponding AUROCs for enhanced equations were 0.75, 0.85, 0.85, and 0.81. Risk equations based on younger populations, when applied to older cohorts, underpredict diabetes incidence and risk. Conversely, risk equations based on older populations overpredict the likelihood of diabetes in younger cohorts. Conclusions: In general, risk equations are more successful in middle-aged adults than in young and old populations. The results demonstrate the importance of applying age-specific risk equations to identify target populations for intervention. While the predictive capacity of equations that include biomarkers is better than of those based solely on self-reported variables, biomarkers are more important in older populations than in younger ones.
Ely EK, Gruss SM, Luman ET, et al., 2017, A national effort to prevent type 2 diabetes: Participant-level evaluation of CDC's national diabetes prevention program, Diabetes Care, Vol: 40, Pages: 1331-1341, ISSN: 0149-5992
OBJECTIVE To assess participant-level results from the first 4 years of implementation of the National Diabetes Prevention Program (National DPP), a national effort to prevent type 2 diabetes in those at risk through structured lifestyle change programs.RESEARCH DESIGN AND METHODS Descriptive analysis was performed on data from 14,747 adults enrolled in year-long type 2 diabetes prevention programs during the period February 2012 through January 2016. Data on attendance, weight, and physical activity minutes were summarized and predictors of weight loss were examined using a mixed linear model. All analyses were performed using SAS 9.3.RESULTS Participants attended a median of 14 sessions over an average of 172 days in the program (median 134 days). Overall, 35.5% achieved the 5% weight loss goal (average weight loss 4.2%, median 3.1%). Participants reported a weekly average of 152 min of physical activity (median 128 min), with 41.8% meeting the physical activity goal of 150 min per week. For every additional session attended and every 30 min of activity reported, participants lost 0.3% of body weight (P < 0.0001).CONCLUSIONS During the first 4 years, the National DPP has achieved widespread implementation of the lifestyle change program to prevent type 2 diabetes, with promising early results. Greater duration and intensity of session attendance resulted in a higher percent of body weight loss overall and for subgroups. Focusing on retention may reduce disparities and improve overall program results. Further program expansion and investigation is needed to continue lowering the burden of type 2 diabetes nationally.
Gregg EW, 2017, The Changing Tides of the Type 2 Diabetes Epidemic-Smooth Sailing or Troubled Waters Ahead? Kelly West Award Lecture 2016, Diabetes Care, Vol: 40, Pages: 1289-1297, ISSN: 0149-5992
Gregg EW, Wing R, 2017, Looking again at the Look AHEAD study, LANCET DIABETES & ENDOCRINOLOGY, Vol: 5, Pages: 763-764, ISSN: 2213-8587
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- Citations: 4
Perreault L, Frch K, Gregg EW, 2017, Can Cardiovascular Epidemiology and Clinical Trials Close the Risk Management Gap Between Diabetes and Prediabetes?, Current Diabetes Reports, Vol: 17, ISSN: 1534-4827
Albu JB, Sohler N, Li R, et al., 2017, An interrupted time series analysis to determine the effect of an electronic health record-based intervention on appropriate screening for type 2 diabetes in urban primary care clinics in New York city, Diabetes Care, Vol: 40, Pages: 1058-1064, ISSN: 0149-5992
OBJECTIVETo determine the impact of a health system–wide primary care diabetes management system, which included targeted guidelines for type 2 diabetes (T2DM) andprediabetes (dysglycemia) screening, on detection of previously undiagnosed dysglycemia cases.RESEARCH DESIGN AND METHODSIntervention included electronic health record (EHR)–based decision support andstandardized providers and staff training for using the American Diabetes Associationguidelines for dysglycemia screening. Using EHR data, we identified 40,456 adultswithout T2DM or recent screening with a face-to-face visit (March 2011–December2013) in five urban clinics. Interrupted time series analyses examined the impact ofthe intervention on trends in three outcomes: 1) monthly proportion of eligiblepatients receiving dysglycemia testing, 2) two negative comparison conditions (dysglycemia testing among ineligible patients and cholesterol screening), and 3) yield ofundiagnosed dysglycemia among those tested.RESULTSBaseline monthly proportion of eligible patients receiving testing was 7.4–10.4%.After the intervention, screening doubled (mean increase + 11.0% [95% CI 9.0, 13.0],proportion range 18.6–25.3%). The proportion of ineligible patients tested also increased (+5.0% [95% CI 3.0, 8.0]) with no concurrent change in cholesterol testing(+0% [95% CI 20.02, 0.05]). About 59% of test results in eligible patients showeddysglycemia both before and after the intervention.CONCLUSIONSImplementation of a policy for systematic dysglycemia screening including formaltraining and EHR templates in urban academic primary care clinics resulted in adoubling of appropriate testing and the number of patients who could be targetedfor treatment to prevent or delay T2DM.
Kalyani RR, Ji N, Carnethon M, et al., 2017, Diabetes, depressive symptoms, and functional disability in African Americans: the Jackson Heart Study, JOURNAL OF DIABETES AND ITS COMPLICATIONS, Vol: 31, Pages: 1259-1265, ISSN: 1056-8727
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- Citations: 11
Gregg EW, Shaw JE, 2017, Global Health Effects of Overweight and Obesity, New England Journal of Medicine, Vol: 377, Pages: 80-81, ISSN: 0028-4793
Shrestha SS, Zhang P, Thompson TJ, et al., 2017, Medical expenditures associated with diabetes among youth with medicaid coverage, Medical Care, Vol: 55, Pages: 646-653, ISSN: 1537-1948
Background: Information on diabetes-related excess medical expenditures for youth is important to understand the magnitude of financial burden and to plan the health care resources needed for managing diabetes. However, diabetes-related excess medical expenditures for youth covered by Medicaid program have not been investigated recently.Objective: To estimate excess diabetes-related medical expenditures among youth aged below 20 years enrolled in Medicaid programs in the United States.Methods: We analyzed data from 2008 to 2012 MarketScan multistate Medicaid database for 6502 youths with diagnosed diabetes and 6502 propensity score matched youths without diabetes, enrolled in fee-for-service payment plans. We stratified analysis by Medicaid eligibility criteria (poverty or disability). We used 2-part regression models to estimate diabetes-related excess medical expenditures, adjusted for age, sex, race/ethnicity, year of claims, depression status, asthma status, and interaction terms.Results: For poverty-based Medicaid enrollees, estimated annual diabetes-related total medical expenditure was $9046 per person [$3681 (no diabetes) vs. $12,727 (diabetes); P<0001], of which 41.7%, 34.0%, and 24.3% were accounted for by prescription drugs, outpatient, and inpatient care, respectively. For disability-based Medicaid enrollees, the estimated annual diabetes-related total medical expenditure was $9944 per person ($14,149 vs. $24,093; P<0001), of which 41.5% was accounted for by prescription drugs, 31.3% by inpatient, and 27.3% by outpatient care.Conclusions: The per capita annual diabetes-related medical expenditures in youth covered by publicly financed Medicaid programs are substantial, which is larger among those with disabilities than without disabilities. Identifying cost-effective ways of managing diabetes in this vulnerable segment of the youth population is needed.
Peters A, Pi-Sunyer X, Pownall H, et al., 2017, Effects of a long-term lifestyle modification programme on peripheral neuropathy in overweight or obese adults with type 2 diabetes: the Look AHEAD study, Diabetologia, Vol: 60, Pages: 980-988, ISSN: 0012-186X
Aims/hypothesisThe aim of this study was to evaluate the effects on diabetic peripheral neuropathy (DPN) of a long-term intensive lifestyle intervention (ILI) programme designed to achieve and maintain weight loss.MethodsBeginning in 2001, a total of 5145 overweight or obese people with type 2 diabetes, aged 45–76 years, participating in the multicentre Look AHEAD (Action for Health in Diabetes) study were randomised to ILI (n = 2570) or to a diabetes support and education (DSE) control group (n = 2575) using a web-based management system at the study coordinating centre at Wake Forest School of Medicine (Winston-Salem, NC, USA). Randomisation was stratified by clinical centre and was not revealed to the clinical staff responsible for obtaining data on study outcomes. Because of the nature of the study, patients and the local centre interventionists were not blinded to the study group assignments. In addition, the coordinating centre staff members responsible for data management and statistical analyses were not blinded to the study group assignments. The interventions were terminated in September 2012, 9–11 years after randomisation, but both groups continued to be followed for both primary and secondary outcomes. Neuropathy evaluations included the Michigan Neuropathy Screening Instrument (MNSI) questionnaire completed at baseline in 5145 participants (ILI n = 2570, DSE n = 2575) and repeated annually thereafter and the MNSI physical examination and light touch sensation testing conducted in 3775 participants (ILI n = 1905, DSE n = 1870) 1–2.3 years after discontinuation of the intervention.ResultsAt baseline, the MNSI questionnaire scores were 1.9 ± 0.04 and 1.8 ± 0.04 in the ILI and DSE groups, respectively (difference not statistically significant). After 1 year, when weight loss was maximal in the ILI group (8.6 ± 6.9%) compared with DSE (0.7 ± 4.8%), the respective MNSI scores were 1.7 ± 0.04 and 2.0 &plusm
Zhang P, Gregg E, 2017, Global economic burden of diabetes and its implications, LANCET DIABETES & ENDOCRINOLOGY, Vol: 5, Pages: 404-405, ISSN: 2213-8587
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- Citations: 49
Zhang X, Devlin HM, Smith B, et al., 2017, Effect of lifestyle interventions on cardiovascular risk factors among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis, PLoS ONE, Vol: 12, ISSN: 1932-6203
Structured lifestyle interventions can reduce diabetes incidence and cardiovascular disease (CVD) risk among persons with impaired glucose tolerance (IGT), but it is unclear whether they should be implemented among persons without IGT. We conducted a systematic review and meta-analyses to assess the effectiveness of lifestyle interventions on CVD risk among adults without IGT or diabetes. We systematically searched MEDLINE, EMBASE, CINAHL, Web of Science, the Cochrane Library, and PsychInfo databases, from inception to May 4, 2016. We selected randomized controlled trials of lifestyle interventions, involving physical activity (PA), dietary (D), or combined strategies (PA+D) with follow-up duration ≥12 months. We excluded all studies that included individuals with IGT, confirmed by 2-hours oral glucose tolerance test (75g), but included all other studies recruiting populations with different glycemic levels. We stratified studies by baseline glycemic levels: (1) low-range group with mean fasting plasma glucose (FPG) <5.5mmol/L or glycated hemoglobin (A1C) <5.5%, and (2) high-range group with FPG ≥5.5mmol/L or A1C ≥5.5%, and synthesized data using random-effects models. Primary outcomes in this review included systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Totally 79 studies met inclusion criteria. Compared to usual care (UC), lifestyle interventions achieved significant improvements in SBP (-2.16mmHg[95%CI, -2.93, -1.39]), DBP (-1.83mmHg[-2.34, -1.31]), TC (-0.10mmol/L[-0.15, -0.05]), LDL-C (-0.09mmol/L[-0.13, -0.04]), HDL-C (0.03mmol/L[0.01, 0.04]), and TG (-0.08mmol/L[-0.14, -0.03]). Similar effects were observed among both low-and high-range study groups except for TC and TG. Similar effects also appeared in SBP and DBP categories regardless of follow-up duration. PA+D interventions had larger im
Brinks R, Hoyer A, Rolka DB, et al., 2017, Comparison of surveillance-based metrics for the assessment and monitoring of disease detection: simulation study about type 2 diabetes, BMC Medical Research Methodology, Vol: 17, ISSN: 1471-2288
BackgroundScreening and detection of cases are a common public health priority for treatable chronic conditions with long subclinical periods. However, the validity of commonly-used metrics from surveillance systems for rates of detection (or case-finding) have not been evaluated.MethodsUsing data from a Danish diabetes register and a recently developed illness-death model of chronic diseases with subclinical conditions, we simulate two scenarios of different performance of case-finding. We report different epidemiological indices to assess case-finding in both scenarios and compare the validity of the results.ResultsThe commonly used ratio of detected cases over total cases may lead to misleading conclusions. Instead, the ratio of undetected cases over persons without a diagnosis is a more valid index to distinguish the quality of case-finding. However, incidence-based measures are preferable to prevalence based indicators.ConclusionPrevalence-based indices for assessing case-finding should be interpreted with caution. If possible, incidence-based indices should be preferred.
Geiss LS, Kirtland K, Lin J, et al., 2017, Changes in diagnosed diabetes, obesity, and physical inactivity prevalence in US counties, 2004-2012, PLoS ONE, Vol: 12, ISSN: 1932-6203
Recent studies suggest that prevalence of diagnosed diabetes in the United States reached aplateau or slowed around 2008, and that this change coincided with obesity plateaus andincreases in physical activity. However, national estimates can obscure important variations ingeographic subgroups. We examine whether a slowing or leveling off in diagnosed diabetes,obesity, and leisure time physical inactivity prevalence is also evident across the 3143 counties of the United States. We used publicly available county estimates of the age-adjustedprevalence of diagnosed diabetes, obesity, and leisure-time physical inactivity, which weregenerated by the Centers for Disease Control and Prevention (CDC). Using a Bayesian multilevel regression that included random effects by county and year and applied cubic splines tosmooth these estimates over time, we estimated the average annual percentage point change(APPC) from 2004 to 2008 and from 2008 to 2012 for diabetes, obesity, and physical inactivityprevalence in each county. Compared to 2004–2008, the median APPCs for diabetes, obesity, and physical inactivity were lower in 2008–2012 (diabetes APPC difference = 0.16, 95%CI 0.14, 0.18; obesity APPC difference = 0.65, 95%CI 0.59, 0.70; physical inactivity APPC difference = 0.43, 95%CI 0.37, 0.48). APPCs and APPC differences between time periods varied among counties and U.S. regions. Despite improvements, levels of these risk factorsremained high with most counties merely slowing rather than reversing, which suggests thatall counties would likely benefit from reductions in these risk factors. The diversity of trajectories in the prevalence of these risk factors across counties underscores the continued need toidentify high risk areas and populations for preventive interventions. Awareness of how thesefactors are changing might assist local policy makers in targeting and tracking the impact ofefforts to reduce diabetes, obesity and physical inactivity.
Benoit SR, Gregg EW, Jonnalagadda S, et al., 2017, Association of Diabetes and Tuberculosis Disease among US-Bound Adult Refugees, 2009-2014, Emerging Infectious Diseases, Vol: 23, Pages: 543-545, ISSN: 1080-6040
Diabetes is associated with an increased risk for active tuberculosis (TB) disease. We conducted a case–control study and found a significant association between diabetes and TB disease among US-bound refugees. These findings underscore the value of collaborative management of both diseases.
Zhang X, Imperatore G, Thomas W, et al., 2017, Effect of lifestyle interventions on glucose regulation among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis, DIABETES RESEARCH AND CLINICAL PRACTICE, Vol: 123, Pages: 149-164, ISSN: 0168-8227
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- Citations: 48
Cheng YJ, Gregg EW, Rolka DB, et al., 2016, Using multi-year national survey cohorts for period estimates: an application of weighted discrete Poisson regression for assessing annual national mortality in US adults with and without diabetes, 2000-2006, Population Health Metrics, Vol: 14, ISSN: 1478-7954
BackgroundMonitoring national mortality among persons with a disease is important to guide and evaluate progress in disease control and prevention. However, a method to estimate nationally representative annual mortality among persons with and without diabetes in the United States does not currently exist. The aim of this study is to demonstrate use of weighted discrete Poisson regression on national survey mortality follow-up data to estimate annual mortality rates among adults with diabetes.MethodsTo estimate mortality among US adults with diabetes, we applied a weighted discrete time-to-event Poisson regression approach with post-stratification adjustment to national survey data. Adult participants aged 18 or older with and without diabetes in the National Health Interview Survey 1997–2004 were followed up through 2006 for mortality status. We estimated mortality among all US adults, and by self-reported diabetes status at baseline. The time-varying covariates used were age and calendar year. Mortality among all US adults was validated using direct estimates from the National Vital Statistics System (NVSS).ResultsUsing our approach, annual all-cause mortality among all US adults ranged from 8.8 deaths per 1,000 person-years (95% confidence interval [CI]: 8.0, 9.6) in year 2000 to 7.9 (95% CI: 7.6, 8.3) in year 2006. By comparison, the NVSS estimates ranged from 8.6 to 7.9 (correlation = 0.94). All-cause mortality among persons with diabetes decreased from 35.7 (95% CI: 28.4, 42.9) in 2000 to 31.8 (95% CI: 28.5, 35.1) in 2006. After adjusting for age, sex, and race/ethnicity, persons with diabetes had 2.1 (95% CI: 2.01, 2.26) times the risk of death of those without diabetes.ConclusionPeriod-specific national mortality can be estimated for people with and without a chronic condition using national surveys with mortality follow-up and a discrete time-to-event Poisson regression approach with post-stratification adjustment.
Earnshaw SR, Richter A, Sorensen SW, et al., 2016, Optimal allocation of resources across four interventions for type 2 diabetes, MEDICAL DECISION MAKING, Vol: 22, Pages: S80-S91, ISSN: 0272-989X
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- Citations: 32
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