No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Risk of Negative Health Outcomes and High Costs for People With Diabetes and Unmet Psychological Needs in the United States
Abstract
Measuring the population-level relationship between compromised mental health and diabetes care remains an important goal for clinicians and health care decision-makers. We evaluated the impact of self-reported unmet psychological need on health care resource utilization and total health care expenditure in people with type 2 diabetes. Patients who reported unmet psychological needs were more likely than those who did not to incur a higher annual medical expenditure, have greater resource utilization, and have a higher risk of all-cause mortality.
Background:. Australians with cirrhosis have significant practical and psychosocial needs. This longitudinal study examined the association between supportive care needs and health service use and costs, and patient outcomes from June 2017 to December 2018.
Methods:. The Supportive Needs Assessment tool for Cirrhosis (SNAC), quality of life (Chronic Liver Disease Questionnaire and Short Form 36), and distress (distress thermometer) were self-reported through an interview at recruitment (n=433). Clinical data were obtained from medical records and through linkage, and health service use and costs through linkage. Patients were grouped as by needs status. Rates of hospital admissions (per person days at risk) and costs were assessed by needs status [incidence rate ratios (IRR), Poisson regression]. Multivariable linear regression was used to assess the differences in SNAC scores by quality of life and distress. Multivariable models included Child-Pugh class, age, sex, recruitment hospital, living arrangements, place of residence, comorbidity burden, and primary liver disease etiology.
Results:. In adjusted analyses, compared with patients with low/no needs, patients with unmet needs had more cirrhosis-related admissions (adjusted IRR=2.11, 95% CI=1.48–3.13; p
Diabetes is a major public health problem worldwide. Depression is a serious mental condition that decreases mental and physical functioning and reduces the quality of life. Several lines of evidence suggest a bidirectional relationship between diabetes and depression: diabetes patients are twice as likely to experience depression than nondiabetic individuals. In contrast, depression increases the risk of diabetes and interferes with its daily self-management. Diabetes patients with depression have poor glycemic control, reduced quality of life, and an increased risk of diabetes complications, consequently having an increased mortality rate. Conflicting evidence exists on the potential role of factors that may account for or modulate the relationship between diabetes and depression. Therefore, this review aims to highlight the most notable body of literature that dissects the various facets of the bidirectional relationship between diabetes and depression. A focused discussion of the proposed mechanisms underlying this relationship is also provided. We systematically reviewed the relevant literature in the PubMed database, using the keywords "Diabetes AND Depression". After exclusion of duplicate and irrelevant material, literature eligible for inclusion in this review was based on meta-analysis studies, clinical trials with large sample sizes (n≥1,000), randomized clinical trials, and comprehensive national and cross-country clinical studies. The evidence we present in this review supports the pressing need for long, outcome-oriented, randomized clinical trials to determine whether the identification and treatment of patients with these comorbid conditions will improve their medical outcomes and quality of life.
Objective:
This study updates previous estimates of the economic burden of diagnosed diabetes and quantifies the increased health resource use and lost productivity associated with diabetes in 2017.
Research design and methods:
We use a prevalence-based approach that combines the demographics of the U.S. population in 2017 with diabetes prevalence, epidemiological data, health care cost, and economic data into a Cost of Diabetes Model. Health resource use and associated medical costs are analyzed by age, sex, race/ethnicity, insurance coverage, medical condition, and health service category. Data sources include national surveys, Medicare standard analytical files, and one of the largest claims databases for the commercially insured population in the U.S.
Results:
The total estimated cost of diagnosed diabetes in 2017 is $327 billion, including $237 billion in direct medical costs and $90 billion in reduced productivity. For the cost categories analyzed, care for people with diagnosed diabetes accounts for 1 in 4 health care dollars in the U.S., and more than half of that expenditure is directly attributable to diabetes. People with diagnosed diabetes incur average medical expenditures of ∼$16,750 per year, of which ∼$9,600 is attributed to diabetes. People with diagnosed diabetes, on average, have medical expenditures ∼2.3 times higher than what expenditures would be in the absence of diabetes. Indirect costs include increased absenteeism ($3.3 billion) and reduced productivity while at work ($26.9 billion) for the employed population, reduced productivity for those not in the labor force ($2.3 billion), inability to work because of disease-related disability ($37.5 billion), and lost productivity due to 277,000 premature deaths attributed to diabetes ($19.9 billion).
Conclusions:
After adjusting for inflation, economic costs of diabetes increased by 26% from 2012 to 2017 due to the increased prevalence of diabetes and the increased cost per person with diabetes. The growth in diabetes prevalence and medical costs is primarily among the population aged 65 years and older, contributing to a growing economic cost to the Medicare program. The estimates in this article highlight the substantial financial burden that diabetes imposes on society, in addition to intangible costs from pain and suffering, resources from care provided by nonpaid caregivers, and costs associated with undiagnosed diabetes.
The presence of major depressive disorder (MDD) in people with diabetes may be up to three times more common than in the general population. People with diabetes and major depressive disorder have worse health outcomes and higher mortality rates. Diabetes distress refers to an emotional state where people experience feelings such as stress, guilt, or denial that arise from living with diabetes and the burden of self-management. Diabetes distress has also been linked to worse health outcomes. There are multiple treatment options for MDD including pharmacotherapy and cognitive behavioral approaches. Providers treating patients with diabetes must be aware of the frequent comorbidity of diabetes, diabetes distress, and depression and manage patients using a multidisciplinary team approach. This article discusses the epidemiology, pathophysiology, and bi-directional relationship of diabetes and depression and provides a practical, patient-centered approach to diagnosis and management.
Introduction:
The Disease Management Association of America identifies diabetes as one of the chronic conditions with the greatest potential for management. TRICARE Management Activity, which administers health care benefits for US military service personnel, retirees, and their dependents, created a disease management program for beneficiaries with diabetes. The objective of this study was to determine whether participation intensity and prior indication of uncontrolled diabetes were associated with health care use and costs for participants enrolled in TRICARE's diabetes management program.
Methods:
This ongoing, opt-out study used a quasi-experimental approach to assess program impact for beneficiaries (n = 37,370) aged 18 to 64 living in the United States. Inclusion criteria were any diabetes-related emergency department visits or hospitalizations, more than 10 diabetes-related ambulatory visits, or more than twenty 30-day prescriptions for diabetes drugs in the previous year. Beginning in June 2007, all participants received educational mailings. Participants who agreed to receive a baseline telephone assessment and telephone counseling once per month in addition to educational mailings were considered active, and those who did not complete at least the baseline telephone assessment were considered passive. We categorized the diabetes status of each participant as "uncontrolled" or "controlled" on the basis of medical claims containing diagnosis codes for uncontrolled diabetes in the year preceding program eligibility. We compared observed outcomes to outcomes predicted in the absence of diabetes management. Prediction equations were based on regression analysis of medical claims for a historical control group (n = 23,818) that in October 2004 met the eligibility criteria for TRICARE's program implemented June 2007. We conducted regression analysis comparing historical control group patient outcomes after October 2004 with these baseline characteristics.
Results:
Per-person total annual medical savings for program participants, calculated as the difference between observed and predicted outcomes, averaged $783. Active participants had larger reductions in inpatient days and emergency department visits, larger increases in ambulatory visits, and larger increases in receiving retinal examinations, hemoglobin A1c tests, and urine microalbumin tests compared with passive participants. Participants with prior indication of uncontrolled diabetes had higher per-person total annual medical savings, larger reduction in inpatient days, and larger increases in ambulatory visits than did participants with controlled diabetes.
Conclusion:
Greater intensity of participation in TRICARE's diabetes management program was associated with lower medical costs and improved receipt of recommended testing. That patients who were categorized as having uncontrolled diabetes realized greater program benefits suggests diabetes management programs should consider indication of uncontrolled diabetes in their program candidate identification criteria.
Diabetes is a complex chronic disease that requires active involvement of patients in its management. Diabetes self-management education and training (DSMT), "the ongoing process of facilitating the knowledge, skill, and ability necessary for prediabetes and diabetes self-care," is an important component of integrated diabetes care. It is an intervention in which patients learn about diabetes and how to implement the self-management that is imperative to control the disease. The curriculum of DSMT often includes the diabetes disease process and treatment options; healthy lifestyle; blood glucose monitoring; preventing, detecting and treating diabetes complications; and developing personalized strategies for decision making. The American Diabetes Association recommends providing DSMT to those with newly diagnosed diabetes, because data suggest that when diabetes is first diagnosed is the time when patients are most receptive to such engagement. However, little is known about the proportion of persons with newly diagnosed diabetes participating in DSMT. CDC analyzed data from the Marketscan Commercial Claims and Encounters database (Truven Health Analytics) for the period 2009-2012 to estimate the claim-based proportion of privately insured adults (aged 18-64 years) with newly diagnosed diabetes who participated in DSMT during the first year after diagnosis. During 2011-2012, an estimated 6.8% of privately insured, newly diagnosed adults participated in DSMT during the first year after diagnosis of diabetes. These data suggest that there is a large gap between the recommended guideline and current practice, and that there is both an opportunity and a need to enhance rates of DSMT participation among persons newly diagnosed with diabetes.
Propensity score matching is a method to reduce bias in non-randomized and observational studies. Propensity score matching is mainly applied to two treatment groups rather than multiple treatment groups, because some key issues affecting its application to multiple treatment groups remain unsolved, such as the matching distance, the assessment of balance in baseline variables, and the choice of optimal caliper width. The primary objective of this study was to compare propensity score matching methods using different calipers and to choose the optimal caliper width for use with three treatment groups. The authors used caliper widths from 0.1 to 0.8 of the pooled standard deviation of the logit of the propensity score, in increments of 0.1. The balance in baseline variables was assessed by standardized difference. The matching ratio, relative bias, and mean squared error (MSE) of the estimate between groups in different propensity score-matched samples were also reported. The results of Monte Carlo simulations indicate that matching using a caliper width of 0.2 of the pooled standard deviation of the logit of the propensity score affords superior performance in the estimation of treatment effects. This study provides practical solutions for the application of propensity score matching of three treatment groups.
To project the number of people with diagnosed diabetes in the U.S. through 2050, accounting for changing demography and diabetes prevalence rates.
We combined age-, sex-, and race-specific diagnosed diabetes prevalence rates-predicted from 1980-1998 trends in prevalence data from the National Health Interview Survey-with Bureau of Census population demographic projections. Sensitivity analyses were performed by varying both prevalence rate and population projections.
The number of Americans with diagnosed diabetes is projected to increase 165%, from 11 million in 2000 (prevalence of 4.0%) to 29 million in 2050 (prevalence of 7.2%). The largest percent increase in diagnosed diabetes will be among those aged > or =75 years (+271% in women and +437% in men). The fastest growing ethnic group with diagnosed diabetes is expected to be black males (+363% from 2000-2050), with black females (+217%), white males (+148%), and white females (+107%) following. Of the projected 18 million increase in the number of cases of diabetes in 2050, 37% are due to changes in demographic composition, 27% are due to population growth, and 36% are due to increasing prevalence rates.
If recent trends in diabetes prevalence rates continue linearly over the next 50 years, future changes in the size and demographic characteristics of the U.S. population will lead to dramatic increases in the number of Americans with diagnosed diabetes.
MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.
Interface of diabetes and psychiatry has fascinated both endocrinologists and mental health professionals for years. Diabetes and psychiatric disorders share a bidirectional association -- both influencing each other in multiple ways. The current article addresses different aspects of this interface. The interaction of diabetes and psychiatric disorders has been discussed with regard to aetio-pathogenesis, clinical presentation, and management. In spite of a multifaceted interaction between the two the issue remains largely unstudied in India.
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile-quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative.
Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions—most importantly for the empirical estimating functions—from which various types of sandwich estimators can be computed.
Individuals with schizophrenia are at increased risk for poor health outcomes and mortality. This may be due to inadequate self-management of co-occurring conditions, such as type 2 diabetes. We compared adherence to oral hypoglycemic medications for diabetes patients with vs without comorbid schizophrenia. Using Veterans Affairs (VA) health system administrative data, we identified all patients with both schizophrenia and type 2 diabetes and with at least one oral hypoglycemic prescription fill in fiscal year 2002 (N = 11 454) and a comparison group of patients with diabetes who were not diagnosed with schizophrenia (N = 10 560). Nonadherence was operationalized as having a medication possession ratio indicating receipt of less than 80% of needed hypoglycemic medications. Poor adherence was less prevalent among diabetes patients with (43%) than without schizophrenia (52%, P < .001). In multivariable analyses, having schizophrenia was associated with a 25% lower likelihood of poor adherence compared with not having schizophrenia (adjusted odds ratio: 0.75, 95% confidence interval: 0.70-0.80). Poorer adherence was associated with black race, homelessness, depression, substance use disorder, and medical comorbidity. Having more outpatient visits, a higher proportion of prescriptions delivered by mail, lower prescription copayments, and more complex medication regimens were each associated with increased adherence. Among veterans with diabetes receiving ongoing VA care, overall hypoglycemic medication adherence was low, but individuals with comorbid schizophrenia were more likely to be adherent to these medications. Future studies should investigate whether factors such as comanagement of a chronic psychiatric illness or regular contact with mental health providers bestow benefits for diabetes self-management in persons with schizophrenia.
To describe a new measure of psychosocial adjustment specific to diabetes, the Problem Areas in Diabetes Survey (PAID), and to present initial information on its reliability and validity.
Before their routine clinic appointments, 451 female patients with type I and type II diabetes, all of whom required insulin, completed a self-report survey. Included in the survey was the PAID, a 20-item questionnaire in which each item represents a unique area of diabetes-related psychosocial distress. Each item is rated on a six-point Likert scale, reflecting the degree to which the item is perceived as currently problematic. A total scale score, hypothesized to reflect the overall level of diabetes-related emotional distress, is computed by summing the total item responses. To examine the concurrent validity of the PAID, the survey also included a series of standardized questionnaires assessing psychosocial functioning (general emotional distress, fear of hypoglycemia, and disordered eating), attitudes toward diabetes, and self-care behaviors. All subjects were assessed for HbA1, within 30 days of survey completion and again approximately 1-2 years later. Finally, long-term diabetic complications were determined through chart review.
Internal reliability of the PAID was high, with good item-to-total correlations. Approximately 60% of the subject sample reported at least one serious diabetes-related concern. As expected, the PAID was positively associated with relevant psychosocial measures of distress, including general emotional distress, disordered eating, and fear of hypoglycemia, short- and long-term diabetic complications, and HbA1, and negatively associated with reported self-care behaviors. The PAID accounted for approximately 9% of the variance in HbA1. Diabetes-related emotional distress, as measured by the PAID, was found to be a unique contributor to adherence to self-care behaviors after adjustment for age, diabetes duration, and general emotional distress. In addition, the PAID was associated with HbA1 even after adjustment for age, diabetes duration, general emotional distress, and adherence to self-care behaviors.
These findings suggest that the PAID, a brief, easy-to-administer instrument, may be valuable in assessing psychosocial adjustment to diabetes. In addition to high internal reliability, the consistent pattern of correlational findings indicates that the PAID is tapping into relevant aspects of emotional distress and that its particular feature, the measurement of diabetes-related emotional distress, is uniquely associated with diabetes-relevant outcomes. These data are also consistent with the hypothesis that diabetes-related emotional distress, separate from general emotional distress, is an independent and major contributor to poor adherence. Given that the study was limited to female patients using insulin, further examination of the clinical usefulness of the PAID will need to focus on more heterogeneous samples.
To evaluate the efficacy of self-management education on GHb in adults with type 2 diabetes.
We searched for English language trials in Medline (1980-1999), Cinahl (1982-1999), and the Educational Resources Information Center database (ERIC) (1980-1999), and we manually searched review articles, journals with highest topic relevance, and reference lists of included articles. Studies were included if they were randomized controlled trials that were published in the English language, tested the effect of self-management education on adults with type 2 diabetes, and reported extractable data on the effect of treatment on GHb. A total of 31 studies of 463 initially identified articles met selection criteria. We computed net change in GHb, stratified by follow-up interval, tested for trial heterogeneity, and calculated pooled effects sizes using random effects models. We examined the effect of baseline GHb, follow-up interval, and intervention characteristics on GHb.
On average, the intervention decreased GHb by 0.76% (95% CI 0.34-1.18) more than the control group at immediate follow-up; by 0.26% (0.21% increase - 0.73% decrease) at 1-3 months of follow-up; and by 0.26% (0.05-0.48) at > or = 4 months of follow-up. GHb decreased more with additional contact time between participant and educator; a decrease of 1% was noted for every additional 23.6 h (13.3-105.4) of contact.
Self-management education improves GHb levels at immediate follow-up, and increased contact time increases the effect. The benefit declines 1-3 months after the intervention ceases, however, suggesting that learned behaviors change over time. Further research is needed to develop interventions effective in maintaining long-term glycemic control.
The purpose of this study was to describe the development of the Diabetes Distress Scale (DDS), a new instrument for the assessment of diabetes-related emotional distress, based on four independent patient samples.
In consultation with patients and professionals from multiple disciplines, a preliminary scale of 28 items was developed, based a priori on four distress-related domains: emotional burden subscale, physician-related distress subscale, regimen-related distress subscale, and diabetes-related interpersonal distress. The new instrument was included in a larger battery of questionnaires used in diabetes studies at four diverse sites: waiting room at a primary care clinic (n = 200), waiting room at a diabetes specialty clinic (n = 179), a diabetes management study program (n = 167), and an ongoing diabetes management program (n = 158).
Exploratory factor analyses revealed four factors consistent across sites (involving 17 of the 28 items) that matched the critical content domains identified earlier. The correlation between the 28-item and 17-item scales was very high (r = 0.99). The mean correlation between the 17-item total score (DDS) and the four subscales was high (r = 0.82), but the pattern of interscale correlations suggested that the subscales, although not totally independent, tapped into relatively different areas of diabetes-related distress. Internal reliability of the DDS and the four subscales was adequate (alpha > 0.87), and validity coefficients yielded significant linkages with the Center for Epidemiological Studies Depression Scale, meal planning, exercise, and total cholesterol. Insulin users evidenced the highest mean DDS total scores, whereas diet-controlled subjects displayed the lowest scores (P < 0.001).
The DDS has a consistent, generalizable factor structure and good internal reliability and validity across four different clinical sites. The new instrument may serve as a valuable measure of diabetes-related emotional distress for use in research and clinical practice.
We assessed whether patients with comorbid minor and major depression and type 2 diabetes had a higher mortality rate over a 3-year period compared with patients with diabetes alone.
In a large health maintenance organization (HMO), 4,154 patients with type 2 diabetes were surveyed and followed for up to 3 years. Patients initially filled out a written questionnaire, and HMO-automated diagnostic, laboratory, and pharmacy data and Washington State mortality data were collected to assess diabetes complications and deaths. Cox proportional hazards regression models were used to calculate adjusted hazard ratios of death for each group compared with the reference group.
There were 275 (8.3%) deaths in 3,303 patients without depression compared with 48 (13.6%) deaths in 354 patients with minor depression and 59 (11.9%) deaths among 497 patients with major depression. A proportional hazards model with adjustment for age, sex, race/ethnicity, and educational attainment found that compared with the nondepressed group, minor depression was associated with a 1.67-fold increase in mortality (P = 0.003), and major depression was associated with a 2.30-fold increase (P < 0.0001). In a second model that controlled for multiple potential mediators, both minor and major depression remained significant predictors of mortality.
Among patients with diabetes, both minor and major depression are strongly associated with increased mortality. Further research will be necessary to disentangle causal relationships among depression, behavioral risk factors (adherence to medical regimens), diabetes complications, and mortality.
I present software for analysing complex survey samples in R. The sampling scheme can be explicitly described or represented by replication weights. Variance estimation uses either replication or linearisation.
The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc20-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
Major depression is a common and treatable mental disorder characterized
by changes in mood, and cognitive and physical symptoms over a 2-week
period (1). It is associated with high societal costs (2) and greater functional
impairment than many other chronic diseases, including diabetes and arthritis
(3). Depression rates differ by age, sex, income, and health behaviors (4).
This report provides the most recent national estimates of depression among
adults. Prevalence of depression is based on scores from the Patient Health
Questionnaire (PHQ-9), a symptom-screening questionnaire that allows for
criteria-based diagnoses of depressive disorders (5). Estimates for non-Hispanic
Asian persons are presented for the first time.
Objective:
Integrating primary care services into specialty mental health clinics has been proposed as a method for improving health care utilization for medical conditions by adults with serious mental illness. This paper examines the impact of a mental health based primary care program on emergency department (ED) visits and hospitalizations.
Method:
The program was implemented in seven New York City outpatient mental health clinics in two waves. Medicaid claims were used to identify patients treated in intervention clinics and a control group of patients treated in otherwise similar clinics in New York City. Impacts of the program were estimated using propensity score adjusted difference-in-differences models on a longitudinally followed cohort.
Results:
Hospital stays for medical conditions increased significantly in intervention clinics relative to control clinics in both waves (ORs = 1.21 (Wave 1) and 1.33 (Wave 2)). ED visits for behavioral health conditions decreased significantly relative to controls in Wave 1 (OR = 0.89), but not in Wave 2. No other significant differences in utilization trends between the intervention and control clinics were found.
Conclusion:
Introducing primary care services into mental health clinics may increase utilization of inpatient services, perhaps due to newly identified unmet medical need in this population.
Objective:
Patients with type 2 diabetes (T2DM) often have multiple comorbidities which may impact the selection of antihyperglycemic therapies. The purpose of this study was to quantify the prevalence and co-prevalence of common comorbidities.
Research design and methods:
A retrospective study was conducted using the Quintiles Electronic Medical Record database. Adult patients with T2DM who had ≥1 encounter from July 2014 to June 2015 (index period) with ≥1 year medical history available were included. The index date was defined as the most recent encounter date during the 1 year index period.
Main outcome measures:
Comorbid conditions were assessed using all data available prior to and including the index date. Patient characteristics, laboratory measures, and comorbidities were summarized via descriptive analyses, overall and by subgroups of age (<65, 65-74, 75+ years) and gender.
Results:
Of the 1,389,016 eligible patients, 53% were female and the median age was 65 years. 97.5% of patients had at least one comorbid condition in addition to T2DM and 88.5% had at least two. The comorbidity burden tended to increase in older age groups and was higher in men than women. The most common conditions in patients with T2DM included hypertension (HTN) in 82.1%; overweight/obesity in 78.2%; hyperlipidemia in 77.2%; chronic kidney disease (CKD) in 24.1%; and cardiovascular disease (CVD) in 21.6%. The highest co-prevalence was demonstrated for the combination of HTN and hyperlipidemia (67.5%), followed by overweight/obesity and HTN (66.0%), overweight/obesity and hyperlipidemia (62.5%), HTN and CKD (22.4%), hyperlipidemia and CKD (21.1%), HTN and CVD (20.2%), hyperlipidemia and CVD (20.1%), overweight/obesity and CKD (19.1%) and overweight/obesity and CVD (17.0%).
Limitations:
Limitations include the potential for misclassification/underreporting due to the use of diagnostic codes, drug codes, or laboratory measures for identification of medical conditions.
Conclusions:
The vast majority of patients with T2DM have multiple comorbidities. To ensure a comprehensive approach to patient management, the presence of multimorbidity should be considered in the context of clinical decision making.
Objective
To investigate differences in healthcare cost trends over 8 years in adults with diabetes and one of four categories of comorbid depression: no depression, unrecognized depression, asymptomatic depression, or symptomatic depression.
Research Design and Methods
Data from the 2004–2011 Medical Expenditure Panel Survey (MEPS) was used to create nationally representative estimates. The dependent variable was total healthcare expenditures for the calendar year, including office-based, hospital outpatient, emergency room, inpatient hospital, prescription, dental, and home health care expenditures. The 2004–2011 direct medical costs were adjusted to a common 2014 dollar value. The primary independent variable was four mutually exclusive depression categories created from ICD-9-CM codes and the PHQ-2 depression screening tool. Healthcare expenditures were estimated using a two-part model and were adjusted for age, sex, race, marital status, education, health insurance, metropolitan statistical area status, region, income level, and comorbidities.
Results
Based on a national sample of adults with diabetes (unweighted sample of 15,548, weighted sample of 17,465,579), 10.2 % had unrecognized depression, 13.6 % had asymptomatic depression, and 8.9 % had symptomatic depression. In the pooled sample, after adjusting for covariates, the incremental cost of unrecognized depression was $2872 (95 % CI 1660–4084), asymptomatic depression increased by $3347 (95 % CI 2568–4386), and symptomatic depression increased by $5170 (CI 95 % 3610–6731) compared to patients with no depression.
Conclusions
Adjusted analyses showed that expenditures were $2000–3000 higher for unrecognized and asymptomatic depression than no depression, and $5000 higher for symptomatic depression. Higher medical expenditures persisted over time, with only symptomatic depression showing a sustained decrease over time.
Objective:
This study used the Medical Expenditures Panel Survey (MEPS) to estimate the cost of diabetes, depression, and comorbid diabetes and depression over 8 years.
Methods:
An 8-year pooled dataset was created using the household and medical provider components of MEPS. Medical expenditures were adjusted to a common 2014 dollar value. Analyses used responses of 147,095 individuals ≥18 years of age for the years 2004-2011. The dependent variable in this study was total healthcare expenditure and the primary independent variables were diabetes and depression status. A two-part (probit/GLM) model was used to estimate the annual medical spending and marginal effects were calculated for incremental cost.
Results:
In the pooled sample, after adjusting for socio-demographic factors, comorbidities and time trend covariates, the incremental cost of depression only was $2654 (95% CI 2343-2966), diabetes was $2692 (95% CI 2338-3046), and both was $6037 (CI 95% 5243-6830) when compared to patients with none. Based on the unadjusted mean, annual average aggregate cost of depression only was estimated at $238.3 billion, diabetes only $150.1 billion and depression and diabetes together was $77.6 billion.
Conclusion:
Costs at both the individual and aggregate level are significant, with comorbid diagnoses resulting in higher incremental costs than the sum of the costs for each diagnosis alone. In addition, while the cost of depression increased over time, the cost of diabetes decreased over time, much due to decreased inpatient costs. This study highlights the tremendous cost savings possible through more aggressive screening, diagnosis, and treatment of depression.
Background:
Despite growing recognition that psychosocial care is an essential component of comprehensive cancer care, evidence suggests many patients with cancer do not receive needed psychosocial care.
Methods:
Four areas were identified as potentially increasing the number of patients with cancer who receive needed psychosocial care: (1) formulating care standards, (2) issuing clinical practice guidelines, (3) developing and using measurable indicators of quality of care, and (4) demonstrating projects designed to improve the delivery of care.
Results:
Standards for psychosocial care are identified, including a standard issued in 2015 by an accrediting organization. Three clinical practice guidelines for provisioning psychosocial care are also identified and reviewed. Methods for monitoring the quality of psychosocial care are characterized and the impact of monitoring changes in quality are evaluated in relation to existing evidence. Examples are provided of 2 large-scale efforts designed to improve the delivery of psychosocial care in community settings.
Conclusions:
Although considerable progress has been made in integrating psychosocial care into routine cancer care, work must still be done. Additional progress will be fostered by continued efforts to promote adherence to clinical practice guidelines and care standards for psychosocial care and by the development and dissemination of models that demonstrate how practices can implement these guidelines and standards.
Medicare began reimbursing for outpatient diabetes self-management training (DSMT) in 2000; however, little is known about program utilization. Individuals diagnosed with diabetes in 2010 were identified from a 20% random selection of the Medicare fee-for-service population (N = 110,064). Medicare administrative and claims files were used to determine DSMT utilization. Multivariate logistic regression analyses evaluated the association of demographic, health status, and provider availability factors with DSMT utilization. Approximately 5% of Medicare beneficiaries with newly diagnosed diabetes used DSMT services. The adjusted odds of any utilization were lower among men compared with women, older individuals compared with younger, non-Whites compared with Whites, people dually eligible for Medicare and Medicaid compared with nondual eligibles, and patients with comorbidities compared with individuals without those conditions. Additionally, the adjusted odds of utilizing DSMT increased as the availability of providers who offered DSMT services increased and varied by Census region. Utilization of DSMT among Medicare beneficiaries with newly diagnosed diabetes is low. There appear to be marked disparities in access to DSMT by demographic and health status factors and availability of DSMT providers. In light of the increasing prevalence of diabetes, future research should identify barriers to DSMT access, describe DSMT providers, and explore the impact of DSMT services. With preventive services being increasingly covered by insurers, the low utilization of DSMT, a preventive service benefit that has existed for almost 15 years, highlights the challenges that may be encountered to achieve widespread dissemination and uptake of the new services.
© 2015 Society for Public Health Education.
Individuals living with type 1 or type 2 diabetes are at increased risk for depression, anxiety, and eating disorder diagnoses. Mental health comorbidities of diabetes compromise adherence to treatment and thus increase the risk for serious short- and long-term complications, which can result in blindness, amputations, stroke, cognitive decline, decreased quality of life, and premature death. When mental health comorbidities of diabetes are not diagnosed and treated, the financial cost to society and health care systems is substantial, as are the morbidity and health consequences for patients. In this Viewpoint, we summarize the prevalence and consequences of mental health problems for patients with type 1 or type 2 diabetes and suggest strategies for identifying and treating patients with diabetes and mental health comorbidities.
OBJECTIVE: To provide a tutorial for using propensity score methods with complex survey data.
DATA SOURCES: Simulated data and the 2008 Medical Expenditure Panel Survey.
STUDY DESIGN: Using simulation, we compared the following methods for estimating the treatment effect: a naïve estimate (ignoring both survey weights and propensity scores), survey weighting, propensity score methods (nearest neighbor matching, weighting, and subclassification), and propensity score methods in combination with survey weighting. Methods are compared in terms of bias and 95 percent confidence interval coverage. In Example 2, we used these methods to estimate the effect on health care spending of having a generalist versus a specialist as a usual source of care.
PRINCIPAL FINDINGS: In general, combining a propensity score method and survey weighting is necessary to achieve unbiased treatment effect estimates that are generalizable to the original survey target population.
CONCLUSIONS: Propensity score methods are an essential tool for addressing confounding in observational studies. Ignoring survey weights may lead to results that are not generalizable to the survey target population. This paper clarifies the appropriate inferences for different propensity score methods and suggests guidelines for selecting an appropriate propensity score method based on a researcher's goal.
The Affordable Care Act (ACA) includes provisions to shift the U.S. health care system to address achieving wellness rather than just treating illness. In this Open Forum, the Prevention Committee of the Group for the Advancement of Psychiatry describes opportunities created by the ACA for improving prevention of mental illnesses and promotion of mental health. These include improved coverage of preventive services, models to integrate primary and behavioral health care, and establishment of the National Prevention, Health Promotion, and Public Health Council, which has developed a National Prevention Strategy. The authors describe the important role that psychiatrists can play in advancing prevention of mental illnesses, in particular by working to incorporate prevention strategies in integrated care initiatives and by collaborating with primary care providers to screen for risk factors and promote mental and emotional well-being.
The widely-used Kessler K6 non-specific distress scale screens for severe mental illness defined as a K6 score ≥ 13, estimated to afflict about 6% of US adults. The K6, as currently used, fails to capture individuals struggling with more moderate mental distress that nonetheless warrants mental health intervention. The current study determined a cutoff criterion on the K6 scale indicative of moderate mental distress based on mental health treatment need and assessed the validity of this criterion by comparing participants with identified moderate and severe mental distress on relevant clinical, impairment, and risk behavior measures. Data were analyzed from 50,880 adult participants in the 2007 California Health Interview Survey. Receiver operating characteristic curve analysis identified K6 ≥ 5 as the optimal lower threshold cut-point indicative of moderate mental distress. Based on the K6, 8.6% of California adults had serious mental distress and another 27.9% had moderate mental distress. Correlates of moderate and serious mental distress were similar. Respondents with moderate mental distress had rates of mental health care utilization, impairment, substance use and other risks lower than respondents with serious mental distress and greater than respondents with none/low mental distress. The findings support expanded use and analysis of the K6 scale in quantifying and examining correlates of mental distress at a moderate, yet still clinically relevant, level.
To estimate the prevalence of serious psychological distress (SPD) according to diabetes status and to assess the association of diabetes-related risks and conditions with SPD among U.S. adults.
We analyzed data from the Behavioral Risk Factor Surveillance System, 2007. SPD was determined by a score of > or = 13 on the Kessler-6 scale. We used log-binomial regression analysis to estimate prevalence ratios (PRs) and 95 % confidence intervals (CIs).
We estimated the prevalence of SPD to be 7.6 % and 3.6 % among U.S. adults with and without diagnosed diabetes (unadjusted PR: 2.09; 95 % CI: 1.87, 2.34). The association of diagnosed diabetes with SPD was attenuated after adjustments for potential confounding effects of cardiovascular risk factors and cardiovascular comorbid conditions (adjusted PR, 1.12; 95 % CI: 0.99, 1.27). Significant correlates of SPD among persons with diagnosed diabetes were young age, low education levels, low household income, obesity, current smoking, no leisure-time physical activity, presence of one or more micro- or macro-vascular complications, and disability.
The crude prevalence of SPD among adults with diagnosed diabetes was twice as high as that among those without diabetes. The increased prevalence of SPD may be accounted for by the excessive rates of cardiovascular risks and comorbid conditions among people with diagnosed diabetes.
To estimate the odds and prevalence of clinically relevant depression in adults with type 1 or type 2 diabetes. Depression is associated with hyperglycemia and an increased risk for diabetic complications; relief of depression is associated with improved glycemic control. A more accurate estimate of depression prevalence than what is currently available is needed to gauge the potential impact of depression management in diabetes.
MEDLINE and PsycINFO databases and published references were used to identify studies that reported the prevalence of depression in diabetes. Prevalence was calculated as an aggregate mean weighted by the combined number of subjects in the included studies. We used chi(2) statistics and odds ratios (ORs) to assess the rate and likelihood of depression as a function of type of diabetes, sex, subject source, depression assessment method, and study design.
A total of 42 eligible studies were identified; 20 (48%) included a nondiabetic comparison group. In the controlled studies, the odds of depression in the diabetic group were twice that of the nondiabetic comparison group (OR = 2.0, 95% CI 1.8-2.2) and did not differ by sex, type of diabetes, subject source, or assessment method. The prevalence of comorbid depression was significantly higher in diabetic women (28%) than in diabetic men (18%), in uncontrolled (30%) than in controlled studies (21%), in clinical (32%) than in community (20%) samples, and when assessed by self-report questionnaires (31%) than by standardized diagnostic interviews (11%).
The presence of diabetes doubles the odds of comorbid depression. Prevalence estimates are affected by several clinical and methodological variables that do not affect the stability of the ORs.
To identify the number of people in the United States with untreated serious mental illness (SMI) and the reasons for their lack of treatment. DATA SOURCE/STUDY DESIGN: The National Comorbidity Survey; cross-sectional, nationally representative household survey.
An operationalization of the SMI definition set forth in the Alcohol, Drug Abuse, and Mental Health Administration Reorganization Act identified individuals with SMI in the 12 months prior to the interview. The presence of SMI then was related to the use of mental health services in the past 12 months.
Of the 6.2 percent of respondents who had SMI in the year prior to interview, fewer than 40 percent received stable treatment. Young adults and those living in nonrural areas were more likely to have unmet needs for treatment. The majority of those who received no treatment felt that they did not have an emotional problem requiring treatment. Among those who did recognize this need, 52 percent reported situational barriers, 46 percent reported financial barriers, and 45 percent reported perceived lack of effectiveness as reasons for not seeking treatment. The most commonly reported reason both for failing to seek treatment (72 percent) and for treatment dropout (58 percent) was wanting to solve the problem on their own.
Although changes in the financing of services are important, they are unlikely by themselves to eradicate unmet need for treatment of SMI. Efforts to increase both self-recognition of need for treatment and the patient centeredness of care also are needed.
A number of self-administered questionnaires are available for assessing depression severity, including the 9-item Patient Health Questionnaire depression module (PHQ-9). Because even briefer measures might be desirable for use in busy clinical settings or as part of comprehensive health questionnaires, we evaluated a 2-item version of the PHQ depression module, the PHQ-2.
The PHQ-2 inquires about the frequency of depressed mood and anhedonia over the past 2 weeks, scoring each as 0 ("not at all") to 3 ("nearly every day"). The PHQ-2 was completed by 6000 patients in 8 primary care clinics and 7 obstetrics-gynecology clinics. Construct validity was assessed using the 20-item Short-Form General Health Survey, self-reported sick days and clinic visits, and symptom-related difficulty. Criterion validity was assessed against an independent structured mental health professional (MHP) interview in a sample of 580 patients.
As PHQ-2 depression severity increased from 0 to 6, there was a substantial decrease in functional status on all 6 SF-20 subscales. Also, symptom-related difficulty, sick days, and healthcare utilization increased. Using the MHP reinterview as the criterion standard, a PHQ-2 score > or =3 had a sensitivity of 83% and a specificity of 92% for major depression. Likelihood ratio and receiver operator characteristic analysis identified a PHQ-2 score of 3 as the optimal cutpoint for screening purposes. Results were similar in the primary care and obstetrics-gynecology samples.
The construct and criterion validity of the PHQ-2 make it an attractive measure for depression screening.
Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
Economic costs of diabetes in the U.S
American Diabetes Association. Economic costs of diabetes in
the U.S. in 2017. Diabetes Care 2018;41:917-928
Medical Expenditure Panel Survey. MEPS Panel 21 longitudinal data file
- Healthcare Agency
- Quality Research
Agency for Healthcare Research and Quality. Medical
Expenditure Panel Survey. MEPS Panel 21 longitudinal data
file. Available from https://www.meps.ahrq.gov/mepsweb/
data_stats/download_data_files_detail.jsp?cboPufNumber=
HC-202. Accessed 1 January 2020
Methodology report #31: sample design of the 2017 Medical Expenditure Panel Survey insurance component
- Healthcare Agency
- Quality Research
Agency for Healthcare Research and Quality. Methodology report
#31: sample design of the 2017 Medical Expenditure Panel
Survey insurance component. Available from https://www.meps.
ahrq.gov/data_files/publications/mr31/mr31.pdf. Accessed 7
April 2020
HC-202: panel 21 longitudinal data file
- Healthcare Agency
- Research
- Quality
- Meps
Agency for Healthcare Research and Quality. MEPS HC-202:
panel 21 longitudinal data file: September 2019. Available from
https://www.meps.ahrq.gov/mepsweb/data_stats/
download_data_files_detail.jsp?cboPufNumber=HC-202.
Accessed 7 April 2020
HC-190: 2016 medical conditions
- Healthcare Agency
- Research
- Quality
- Meps
Agency for Healthcare Research and Quality. MEPS HC-190:
2016 medical conditions. Available from https://meps.ahrq.gov/
data_stats/download_data/pufs/h190/h190doc.shtml. Accessed
7 April 2020
Cobalt: covariate balance tables and plots: R Package
- N Griefer
Griefer N. Cobalt: covariate balance tables and plots: R Package,
version 4.2.2, 2020
Cobalt: covariate balance tables and plots: R Package, version 4.2.2
- Griefer