SUPPLEMENTARY DATA. Appendix

Transcription

1 Appendix This appendix supplements the Study Data and Methods section of the article The Total Economic Burden of Elevated Blood Glucose Levels in 2012: Diagnosed and Undiagnosed Diabetes, Gestational Diabetes, and Pre-diabetes. Also, additional tables containing state-level disease prevalence and economic burden are provided. Previously published studies document the data and methods used to estimate the national economic burden of diagnosed diabetes mellitus (DDM) 1-3, undiagnosed diabetes (UDM) 4, prediabetes (PDM) 5, and gestational diabetes (GDM). 6 The approach used to provide state-level estimates of diagnosed diabetes burden also has been documented. 1 This appendix provides a summary of these methods and an update of the data sources used to produce 2012 estimates of economic burden, as well as information on the data and methods used to calculate statelevel estimates of disease prevalence and economic burden. Summary of Methods and Data Sources Table A-1 summarizes the data sources used and indicates whether they were used in the DDM, UDM, PDM, and GDM analyses. Table A-2 lists criteria used to determine national and state-level prevalence of disease, criteria to identify disease status for purposes of analyzing health care use patterns, and information on the populations analyzed for the health care use analysis. Table A-3 summarizes study dimensions and defines categories used in the analysis.

2 Supplementary Table 1. Summary of Data Sources Analyzed Source Description Use DDM UDM PDM GDM National Health Interview Survey (NHIS) National Health and Nutrition Examination Survey(NHANES) 2010 Nationwide Inpatient Sample (NIS) 2012 American Community Survey (ACS) Behavioral Risk Factor Surveillance System (BRFSS) 2004 National Nursing Home Survey (NNHS) Stratified random sample of non-institutionalized households surveyed annually by the Centers for Disease Control and Prevention(CDC). Nationally representative sample of the noninstitutionalized population that collects data on demographic, socioeconomic, clinical, nutritional, and behavioral factors; by CDC. National sample with inpatient records representing 7.8 million discharges (weighted estimate) from 1,051 non- Federal short-stay hospitals across 45 states; by the Agency for Healthcare Research and Quality (AHRQ). Representative sample of the population in each state containing data on demographics, household income level, medical insurance status and type of residence; by U.S. Census Bureau. Representative sample of ~1 million noninstitutionalized population; contains selfreported diabetes status and risk factors for diabetes (body weight, smoker, and diagnosed with various medical conditions); by CDC. Nationally representative sample of ~14,000 patients in nursing homes; contains ICD-9 diagnosis codes which identify diagnosed diabetes; by CDC. Calculate national diabetes prevalence rates by age group, sex, and race/ethnicity Calculate work-days absent, inability to work because of permanent disability, and rates of insulin and oral agent use for people with DDM Calculate national UDM and PDM prevalence rates by age group, sex, and race/ethnicity Calculate national inpatient days and hospital costs by patient demographic and complication category Calculate the proportion of deliveries where the mother has GDM, by mother s age group and state Used to construct state-level population database Used to construct state-level population database Estimate DDM prevalence by state Risk factors used to extrapolate UDM and PDM prevalence by state Calculate national DDM prevalence for population in the nursing home by age group, sex, and race/ethnicity Calculate total resident days in nursing facilities for DDM analysis Used to construct state-level population database Source Description Use D D M U D M P D G D M

3 Source Description Use DDM UDM PDM GDM Optum Deidentified Normative Health Information database (dnhi) Contains medical claims, membership, provider, and ancillary data for over 101 million currently insured individuals from UnitedHealth Group and other insurers. This longitudinally-linked database consists of deidentified individual-level data. Also contains laboratory results for a subset of members. We identified ~4.9 million adults continuously enrolled in the dnhi database between January 1, 2010 and December 31, dnhi contains medical records for about 422,200 adults with diagnosed diabetes age Estimate Poisson regression with medical claims to quantify the impact of: o GDM on health care use for the mother during the 9 months preceding and 12 months following birth o GDM on health care use for the newborn during the 12 months following birth o DDM status on health care use o PDM status on health care use o UDM status on health care use (using adults within two years of first diagnosis of diabetes mellitus as a proxy for the UDM population) 16,900 women who gave birth in 2011, as well as records for the newborns 40,300 adults first diagnosed with diabetes in ,000 adults with a Hemoglobin A1c, FPG or OGTT results in 2010 and 2011 Poisson regression results used to provide rate ratios for health care use by disease status, age, sex, medical condition, and care setting (hospital inpatient days, emergency department visits, and ambulatory visits) 2010 Medicare Standard Analytical Files (SAFs) Medical Expenditure Panel Survey (MEPS) 4 million adults with no history of diabetes between 2010 and 2012 Annual medical claims for ~2 million people age 65 and older (~361,000 with a diagnosis of diabetes) Stratified random subset of the NHIS households surveyed; contains demographic, socio-economic and medical event-level data. Includes cost and utilization data for prescribed medicine, inpatient, emergency, outpatient, office-based provider, home health, supplies, and other Estimate age/sex specific relative rate ratios for each medical condition for hospital inpatient days, emergency department visits, and ambulatory visits (physician office and hospital outpatient combined) for population age 65 and older with diagnosed diabetes Calculate average medical cost per health care event (e.g., per visit), annual utilization (e.g., home health care, podiatric care, medical supplies), or per prescription

4 Source Description Use DDM UDM PDM GDM National Ambulatory Medical Care Survey (NAMCS) National Hospital Ambulatory Medical Care Survey (NHAMCS) 2007 National Home and Hospice Care Survey (NHHCS) services; produced by AHRQ. National sample of visits to non-federally employed office-based physicians who are primarily engaged in direct patient care; by CDC. National sample of visits to emergency and outpatient departments of general and short-stay hospitals; by CDC. Survey of home and hospice care agencies; by CDC. Calculate national physician office visits and drugs prescribed during those visits by patient demographic and complication category Calculate national emergency and outpatient visits and drugs prescribed during those visits by patient demographic and complication category Calculate diabetes-associated national hospice visits by patient demographic

5 Supplementary Table 2. Summary of Methods and Data and their Use in the Cost of Diabetes Analyses Steps DDM UDM PD GDM CDC estimate is based on analysis of NHIS; we use published National prevalence estimates State prevalence estimates Clinical definition for identification for health care use analysis estimate 1 based on NHIS (noninstitutionaliz ed population) supplemented with estimated prevalence among population in nursing home 7 From the constructed state population database which contains BRFSS self-reported data on diagnosed diabetes status and estimated prevalence among patients in nursing homes ICD-9-CM code 250.xx CDC estimate is based on FPG and A1c in NHANES and negative response to question of having been previously told by a health professional that one has diabetes CDC estimate is based on single FPG and A1c test in NHANES Estimated using prediction equations estimated from MEPS and applied to the constructed state population database At least one visit diagnosed as diabetes (250.xx) in an inpatient, emergency, hospice, or skilled nursing facility setting or At least two visits diagnosed as diabetes from an ambulatory setting or At least one claim of anti-hyperglycemic therapy or FPG>125mg/dL or OGTT>199mg/dL or A1c>6.4% 8 ICD-9-CM code or or FPG mg/dl or OGTT mg/dl or A1c % 8 ICD-9-CM code at time of delivery using 2010 NIS to estimate incidence rates by mother s age; applied to estimated total births in 2012 ICD-9-CM code at time of delivery Overall health care use and cost pertaining to diabetes and its comorbidities 2010 NIS NAMCS NHAMCS 2007 NHHCS 2004 NNHS MEPS 2010 NIS NAMCS NHAMCS MEPS

6 Steps DDM UDM PD GDM by delivery setting Diabetesinduced increase in health care by major comorbidity by health care delivery setting a 2011 Optum dnhi (over 23 million covered lives) 2011 Medicare Standard Analytical Files (5% sample of Medicare enrollees, n=2,580,254) Commercially insured population continuously enrolled from January 2010 through December 2012 (n=4,870,413). Health care use in for patients first diagnosed with diabetes in 2012 is used as proxy to estimate health care use patterns for the UDM population. Health care use in 2012 (or 2011) for patients identified with PDM in 2011 (or 2010). Adjusted rate ratios from multivariate Poisson regression are used Women with deliveries (n=16,902) and their children born in 2011 and their health care use for 9 months preceding and 12 months following delivery/birth. Abbreviations: DDM Diagnosed Diabetes Mellitus; UDM Undiagnosed Diabetes Mellitus; PDM Pre-diabetes; GDM Gestational Diabetes Mellitus; A1c hemoglobin A1c test; FPG fasting plasma glucose test; OGTT oral glucose tolerance test; MEPS Medical Expenditure Panel Survey; dnhi Optum dnhi database; NAMCS National Ambulatory Medical Care Survey; NHAMCS National Hospital Ambulatory Medical Care Survey; NHANES National Health and Nutrition Examination Survey; NHHCS National Home and Hospice Care Survey; NHIS National Health Interview Survey; NIS Nationwide Inpatient Sample; NNHS National Nursing Home Survey; ACS American Community Survey; BRFSS Behavioral Risk Factor Surveillance System. Notes: a We analyzed differences in per capita use of inpatient days, emergency visits, and ambulatory visits (by age group and sex) for categories of complications linked to diabetes and GDM.

7 Supplementary Table 3. Study Dimensions Dimensions DDM UDM PDM GDM Mother Newborn Age groups 0-17, 18-34, age at delivery: 45-54, 55-59, 18-34, 45-54, 55-59, <20, 20-24, 25-29, 60-64, 65-69, 60-64, 65-69, , 35-39, up to 12 months Sex Male/female Female Male/female Non-Hispanic (NH) white, NH black, NH Race/ethnicity other, Hispanic Complication categories Cost time period Medical cost components Productivity loss Neurological symptoms, peripheral vascular disease, cardiovascular disease, renal complications, endocrine complications, ophthalmic complications, hypertension, other complications, all other care (general medical conditions) Annual Cesarean delivery, polyhydramnios, urinary tract infection, amniotic cavity infection, preeclampsia and eclampsia, other hypertension complicating pregnancy, other pregnancy-related events, and all other events with pregnancy codes shown in secondary diagnosis fields 9 months preceding birth to 12 months following birth Intrauterine hypoxia and birth asphyxia, macrosomia, endocrine and metabolic disturbances specific to the fetus and newborn, birth trauma due to long gestation and high birth weight, fetus or newborn affected by other complications of labor and delivery, respiratory distress syndrome, jaundice, congenital anomalies, other neonatal events, and all other events with neonatal codes shown in secondary diagnosis fields 12 months following birth Hospital inpatient, emergency, and ambulatory care; prescription medications Podiatry; hospice; nursing home; home health; insulin and anti-diabetic agents; diabetic supplies; and other equipment and supplies Mortality, disability, absenteeism, and presenteeism Absenteeism and presenteeism Not modeled Not modeled

8 Construction of the State Population Database To estimate prevalence of DDM and prevalence of risk factors for UDM and PDM by state, we created a health profile for a representative sample of each state s population by combining information from the 2012 ACS (n=2,375,715), 2011 and 2012 files of the BRFSS (n=982,154), and 2004 NNHS (n=14,017). The ACS is a representative sample of the population in each state containing data on demographics, household income level, medical insurance status and type of residence. The BRFSS is a representative sample of the non-institutionalized population that contains self-reported diabetes status and risk factors for diabetes (body weight, smoker, and diagnosed with various medical conditions). The NNHS is a nationally representative sample of the nursing home population and contains ICD-9 diagnosis codes with an indication of diabetes (ICD-9=250). Additional information on the database construction is documented elsewhere. 10 For each non-institutionalized person in the ACS, we randomly matched them to someone in the BRFSS from the same state, age group (18 groups), sex, race/ethnicity (non-hispanic white, non- Hispanic black, non-hispanic other, and Hispanic), insured status, and household income level (8 levels). Each ACS person residing in a nursing home was matched to someone in the NNHS from the same age group (15 groups), sex, and race/ethnicity. Estimating diabetes and pre-diabetes prevalence National prevalence estimates for DDM, UDM, and PDM come from CDC, with the DDM prevalence numbers adjusted to reflect higher prevalence rates among an institutionalized population. 1;7;9 We estimated national prevalence rates for DDM by demographic (age, sex, race/ethnicity) and insurance type (commercially insured, publically insured, and uninsured). We used the National Health Interview Survey (NHIS) to estimate the prevalence rate for diagnosed diabetes based on responses to the question of whether the respondents have ever been told by a health professional that they had diabetes (excluding GDM). We used predictive equations developed using NHANES data to estimate UDM and PDM prevalence by state using health risk factors in the constructed ACS-BRFSS-NNHS population database (see discussion in following section). Development and Validation of a Predictive Model for Undiagnosed Diabetes and Pre-diabetes Using NHANES, we estimated a predictive model for undiagnosed diabetes and pre-diabetes using polytomous logistic regression. 11 This regression approach allowed the dependent variable to have three values: normal glucose levels, PDM, and UDM. Explanatory variables reflect risk factors for diabetes common to both NHANES and the

9 constructed population database sex; age group (18-34, 35-44, 45-54, 55-64, 65-74, and 75+ years); race/ethnicity (white, black, other race, Hispanic); previous diagnoses or history of asthma, arthritis, heart attack, stroke, cancer, hypertension, high cholesterol, and cardiovascular disease; current smoker; body weight (normal, overweight, obese); insured; Medicaid recipient; and NHANES survey year. Arthritis and asthma are not recognized risk factors for diabetes, but patients with these conditions might have earlier detection of diabetes if treated with corticosteroids (which have known hyperglycemic effects). To validate the predictive model we divided the NHANES sample into two groups with 9,528 observations each. We used the first group to estimate a predictive model for pre-diabetes and undiagnosed diabetes similar to the model summarized in Appendix Table 2. We applied this predictive model to the second group to compare the sum of predicted probabilities of pre-diabetes and undiagnosed diabetes with actual clinical indication from lab test results (A1c, FPG, or OGTT). The model reliably predicted total cases of pre-diabetes and undiagnosed diabetes by age group. We then used the full model to estimate individual probabilities of undiagnosed diabetes and pre-diabetes for individuals in the constructed population database used to develop state-level estimates of diabetes burden. Table A-4 contains summary statistics for the NHANES sample analyzed. The odds ratios (Table A-5) from the regression indicate that recognized risk factors for diabetes are also predictors of UDM and PDM.

10 Supplementary Table 4. NHANES Descriptive Statistics (% or $) Explanatory Variables Normal Glucose Levels (n=11,520) Pre-Diabetes (n=6,480) Undiagnosed Diabetes (n=1,056) Male (%) Age category (%) Age Age Age Age Age Age Race and ethnicity (%) Non-Hispanic white Non-Hispanic black Non-Hispanic other Hispanic Has been diagnosed with (%) Asthma Arthritis History of heart attack History of stroke History of cancer/malignancy High blood pressure High cholesterol Cardiovascular disease Current smoker (%) Body weight status (%) Normal Overweight Obese Median annual family income $40 $34 $30 ($1,000s) Has medical insurance (%) Insured through Medicaid (%) NHANES survey wave (%) Years Years Years Years Source: Analysis of National Health and Nutrition Examination Survey.

11 Supplementary Table 5. Odds Ratios from Polytomous Logistic Regression Variable Odds Ratios Male 1.44* Age (comparison) Age * Age * Age * Age * Age * Non-Hispanic white (comparison) Non-Hispanic black 1.61* Non-Hispanic other 1.97* Hispanic 1.61* Has been diagnosed with Asthma 1.05 Arthritis 1.02 History of heart attack 1.00 History of stroke 1.06 History of cancer/malignancy 1.03 High blood pressure 1.36* High cholesterol 1.18* Cardiovascular disease 1.18* Current smoker 1.17* Body weight status Normal (comparison) 1.60* Overweight 1.60* Obese 2.96* Annual family income ($1,000) 0.998* Has medical insurance 0.81* Insured through Medicaid 1.17 NHANES survey wave Years (comparison) Years * Years * Years * Sample (# in category/total #) 7,536/19,056 Percent Concordant 76.2 * Odds ratio statistically different from 1.0 at the 0.05 level. We analyzed the 2010 Nationwide Inpatient Sample (NIS) to estimate the proportion of deliveries with GDM identified by diagnosis code (648.8x) at time of delivery, by mother s age. We applied GDM prevalence rates to Census Bureau s estimates of births in 2012 to estimate national prevalence of diabetes and GDM in 2012.

12 Estimating patterns of health care use by diabetes type and stage We analyzed the 2011 medical claims in the dnhi database and the 2010 Medicare SAF to quantify average differences in health care use for people with diagnosed diabetes compared to a population with no history of diabetes. dnhi contains a complete set of medical claims for over 23 million commercially insured beneficiaries in The Medicare SAF contains annual medical claims for just under 2 million people age 65 and older. We analyzed differences in per capita use of inpatient days, emergency visits, and ambulatory visits (by age group and sex) for each of seven broad categories of complications linked to diabetes neurological symptoms, peripheral vascular disease, cardiovascular disease, renal complications, endocrine complications, ophthalmic complications, hypertension, other complications as well as an all other category. We use primary diagnosis code to determine the complication group. By definition, people with undiagnosed diabetes cannot be identified in medical claims databases. Consequently, to model the health care use patterns for this population we identified a proxy i.e., people within two years of first diagnosis with diabetes. The dnhi contains records on approximately 5 million beneficiaries who were continuously enrolled between January 1, 2010 and December 31, 2012 including 40,300 adults with a diagnosis of diabetes in 2012 but no history of diabetes in 2010 or This group of adults is considered to be the proxy population for undiagnosed diabetes. We use multivariate Poisson regression to compare its health care use (inpatient days, emergency visits, ambulatory visits) in 2010 and 2011 to patients with no history of diabetes between 2010 and Control variables include patient age and sex, insurance type, year of utilization, the presence of health conditions not associated with diabetes, and Census region. We estimated Poisson regressions with the dnhi claims data to estimate differences in annual health care use in 2011 and 2012 by the patient s PDM status in 2010 and 2011, respectively. Health care use for people with PDM was compared to health care use for people with no claims for the following lab tests A1c, OGTT, and FPG. Control variables include age-group, sex, year of utilization, Census region, insurance type and the presence of other health complications not associated with diabetes including, pregnancy, cancer, HIV/AIDS, organ transplantation. For further validation of our model, we compare health care utilization of the PDM population to utilization by those with no available lab results in the database, no PDM based on diagnosis and lab results, and with confirmed diabetes based on diagnosis, drug use and lab results. As suggested by literature, we use the population with no indication of diabetes and no claims for A1c, OGTT, and FPG exams as the reference group. 5 Consequently, differences in health care use patterns will likely be biased towards

13 zero because the comparison group contains people with undiagnosed diabetes (during 2010 and 2011) and PD. For the GDM analysis, we estimated Poisson regressions to quantify the impact of GDM on health care use for the 16,900 women who gave birth in 2011 (as well as health care use for the newborns). 6 The period covered by the GDM analysis is health care use for the mother during the 9 months preceding and 12 months following birth, and for the newborn is health care use during the 12 months following birth. Control variables in the regression included presence of GDM (1=present, 0=absent); mother s age group at time of delivery (for the maternity analysis only); sex (for the newborn analysis only); Census Region; presence of HIV/AIDS, organ transplantation, cancer, and other pregnancy related high risk conditions (for the maternity analysis only); presence of pre-term or post-term conditions (for the newborn analysis only); and presence of maternal or perinatal complications. Table A-6 shows rate-ratios which compare utilization of those with and without disease by setting and condition for the age group 45 to 64.

14 Supplementary Table 6. Utilization Rate Ratios: Illustration for Population Age Service setting Utilization rate ratios Ambulatory visits DDM UDM PD Neurological symptoms Peripheral vascular disease Cardiovascular disease Hypertension Renal complications Endocrine complications Ophthalmic complications Other diabetes complications All other medical conditions Emergency visits Neurological symptoms Peripheral vascular disease Cardiovascular disease Hypertension Renal complications Endocrine complications Ophthalmic complications Other diabetes complications All other medical conditions Hospital inpatient days Neurological symptoms Peripheral vascular disease Cardiovascular disease Hypertension Renal complications Endocrine complications Ophthalmic complications Other diabetes complications All other medical conditions Note: Rate ratio of 1.0 means that people with diabetes or PDM have the same rate of health care use as the comparison population. Comparison group is people with no diagnosis of diabetes, people with no history of diabetes, and people with no indication of PD. The rate ratios with a value of 1.0 include estimates from Poisson regression that were not statistically different from 1.0, but were set to 1.0 for the cost analysis. The diagnosis codes used to define the complication groups are documented elsewhere. 1

15 Medical Conditions Analyzed For diabetes and pre-diabetes, the conditions were neurological symptoms, peripheral vascular disease, cardiovascular disease, renal complications, endocrine complications, hypertension, ophthalmic complications, other diabetes complications, all other medical conditions. For gestational diabetes, mothers health care use was analyzed for cesarean delivery, polyhydramnios, urinary tract infection, amniotic cavity infection, preeclampsia and eclampsia, other hypertension complicating pregnancy, and all other pregnancyrelated events. Newborns health care use was analyzed for intrauterine hypoxia and birth asphyxia, macrosomia, endocrine and metabolic disturbances, birth trauma, fetus or newborn affected by other complications of labor and delivery, respiratory distress syndrome, jaundice, congenital anomalies, and all other neonatal events. The ICD-9 diagnosis codes to identify these conditions are noted elsewhere. 5-7 Etiological fractions (ϵ) provide estimates of the proportion of national health care use attributed to each type or stage of diabetes for three major health care services: hospital inpatient days, emergency visits, and other ambulatory visits. These fractions reflect the increase in health care use above levels expected in the absence of diabetes. The etiological fractions are calculated by age group, sex, delivery setting, and medical condition (c). For presentation, subscripts for age group, sex, and delivery setting are omitted. The fractions combine diabetes prevalence rates (Prev) with rate ratios (RR) that reflect average, annual medical encounters ( visits ) for people with diabetes (or PDM) divided by the same measure for people with no history of diabetes (or PDM). Diagnosed diabetes The etiological fraction for costs associated with all cases of diagnosed diabetes (DDM) is described by the following formula: (, ) DDM,c = (, ) with rate ratios RR, DDM,c =, The comparison group to people with diagnosed diabetes is everyone without a history of diabetes, which includes people with UDM, PDM, and normal glucose levels (NGL). Undiagnosed diabetes Poisson regression with annual medical claims produced rate ratios that reflect the ratio of average health care use for the UDM proxy population compared to people with no history of diabetes. Because

16 people with UDM cannot be identified in medical claims, we analyzed health care use patterns using a proxy population that consists of people within two years of first diagnosis with diabetes. For people first diagnosed with diabetes in 2012, their health care use in 2010 and 2011 is compared to health care use for a population with no history of diabetes. The etiological fractions for UDM use the following equations: (, ) UDM,c = (1, ) (, ) Where and RR UDM,c = I DDM,c =,,, (, (, ) Pre-diabetes The Poisson regressions we implemented using medical claims data produce rate ratios (RR) reflecting the ratio of annual ambulatory visits ( visits ) for people with pre-diabetes compared with people with assumed NGL. The etiological fractions for PDM use the following equations: PDM,c = (1 ) Where I DDM = I UDM = 1 (, ) ( ) (, ) (,, (, ) (, (, ) And RR, PDM,c =., Gestational diabetes The etiological fractions for GDM reflect the proportion of pregnancy, delivery, and newborn costs attributed to GDM: where GDM, c Pr evgdm ( RRGDM, c 1) Pr ev ( RR 1) 1 RR GDM GDM Visits Visits GDM, c GDM NoGDM

17 Separate fractions are calculated for women and newborns by complication category, demographic (mothers age, newborns sex), and health care service (hospital inpatient days, emergency visits, and other ambulatory visits).

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