Economic Costs of Diabetes in the U.S. in 2022.

OBJECTIVE: This study updates previous estimates of the economic burden of diagnosed diabetes, with calculation of the health resource use and indirect costs attributable to diabetes in 2022. RESEARCH DESIGN AND METHODS: We combine the demographics of the U.S. population in 2022 with diabetes prevalence, from national survey data, epidemiological data, health care cost data, and economic data, into a Cost of Diabetes Economic Model to estimate the economic burden at the population and per capita levels. Health resource use and associated medical costs are analyzed by age, sex, race/ethnicity, comorbid condition, and health service category. Data sources include national surveys (2015-2020 or most recent available), Medicare standard analytic files (2020), and administrative claims data from 2018 to 2021 for a large commercially insured population in the U.S. RESULTS: The total estimated cost of diagnosed diabetes in the U.S. in 2022 is $412.9 billion, including $306.6 billion in direct medical costs and $106.3 billion in indirect costs attributable to diabetes. For cost categories analyzed, care for people diagnosed with diabetes accounts for 1 in 4 health care dollars in the U.S., 61% of which are attributable to diabetes. On average people with diabetes incur annual medical expenditures of $19,736, of which approximately $12,022 is attributable to diabetes. People diagnosed with diabetes, on average, have medical expenditures 2.6 times higher than what would be expected without diabetes. Glucose-lowering medications and diabetes supplies account for ∼17% of the total direct medical costs attributable to diabetes. Major contributors to indirect costs are reduced employment due to disability ($28.3 billion), presenteeism ($35.8 billion), and lost productivity due to 338,526 premature deaths ($32.4 billion). CONCLUSIONS: The inflation-adjusted direct medical costs of diabetes are estimated to rise 7% from 2017 and 35% from 2012 calculations (stated in 2022 dollars). Following decades of steadily increasing prevalence of diabetes, the overall estimated prevalence in 2022 remains relatively stable in comparison to 2017. However, the absolute number of people with diabetes has grown and contributes to increased health care expenditures, particularly per capita spending on inpatient hospital stays and prescription medications. The enormous economic toll of diabetes continues to burden society through direct medical and indirect costs.

Health system-scale language models are all-purpose prediction engines.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1-3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.

The national economic burden of rare disease in the United States in 2019.

BACKGROUND: To provide a comprehensive assessment of the total economic burden of rare diseases (RD) in the United States (U.S.) in 2019. We followed a prevalence-based approach that combined the prevalence of 379 RDs with the per-person direct medical and indirect costs, to derive the national economic burden by patient age and type of RD. To estimate the prevalence and the direct medical cost of RD, we used claims data from three sources: Medicare 5% Standard Analytical File, Transformed Medicaid Statistical Information System, and Optum claims data for the privately insured. To estimate indirect and non-medical cost components, we worked with the rare disease community to design and implement a primary survey. RESULTS: There were an estimated 15.5 million U.S. children (N = 1,322,886) and adults (N = 14,222,299) with any of the 379 RDs in 2019 with a total economic burden of $997 billion, including a direct medical cost of $449 billion (45%), $437 billion (44%) in indirect costs, $73 billion in non-medical costs (7%), and $38 billion (4%) in healthcare costs not covered by insurance. The top drivers for excess medical costs associated with RD are hospital inpatient care and prescription medication; the top indirect cost categories are labor market productivity losses due to absenteeism, presenteeism, and early retirement. CONCLUSIONS: Our findings highlight the scale of the RD economic burden and call for immediate attention from the scientific communities, policy leaders, and other key stakeholders such as health care providers and employers, to think innovatively and collectively, to identify new ways to help improve the care, management, and treatment of these often-devastating diseases.

The Economic Burden of Multiple Sclerosis in the United States: Estimate of Direct and Indirect Costs.

BACKGROUND AND OBJECTIVES: A recent report estimated that approximately 1 million adults were living with multiple sclerosis (MS) in the United States. Although MS is rarely the direct cause of death, its debilitating effects on normal body functions can result in considerable disruption to daily living and life roles including work, physical independence, mobility, social interaction, and participation in leisure activities. This study estimated the total economic burden of MS in the United States in 2019. METHODS: This study used a prevalence-based approach to estimate the national economic burden of MS. Claims from 3 sources (Medicare Current Beneficiary Survey, Medicare Standard Analytical File, and Optum de-identified Normative Health Information System) were used to obtain direct costs and a survey was developed to collect indirect costs (e.g., labor market productivity losses, costs of paid and unpaid caregivers, home modification) from 946 patients with MS (PwMS). Direct medical costs reflected the difference in the total average annual amount paid for PwMS vs matched controls without MS. Future earnings loss due to premature death attributable to MS was calculated using Centers for Disease Control and Prevention mortality data and Medicare claims data. RESULTS: The estimated total economic burden was $85.4 billion, with a direct medical cost of $63.3 billion and indirect and nonmedical costs of $22.1 billion. Retail prescription medication (54%); clinic-administered drugs, medication, and administration (12%); and outpatient care (9%) were the 3 largest components of the direct costs. The average excess per-person annual medical costs for PwMS was $65,612; at $35,154 per person, disease-modifying therapies (DMTs) accounted for the largest proportion of this cost. The cost per DMT user ranged from $57,202 to $92,719, depending on sex-age strata. The average indirect and nonmedical costs were $18,542 per PwMS and $22,875 per PwMS if caregivers' costs were included. Lost earnings due to premature death, presenteeism, and absenteeism losses were the largest indirect cost components. DISCUSSION: MS is a costly chronic disease, with direct costs of prescription drugs and indirect productivity loss being important cost drivers. Our findings suggested that the burden of MS in the United States has been underestimated.

Exploring the Score Equivalence of the English and Chinese Versions of the Brief Assessment Scale for Caregivers.

BACKGROUND: Multilingual outcome measures are used so that research studies are more generalizable across language contexts. OBJECTIVE: To determine the score equivalence of the English and Chinese versions of Brief Assessment Scale for Caregivers (BASC) in Singapore. METHOD: Caregivers of patients with advanced cancer completed the BASC in either English or Chinese. Multivariable linear regression analysis was used to compare the mean BASC total and factor scores between the 2 language versions, with adjustment for possible confounding variables. Equivalence was declared if the 90% confidence interval of the mean scores fell entirely within an equivalence zone of ±0.5 standard deviation. RESULTS: There were 521 ethnic Chinese participants, of whom 214 answered the English version and 307 answered the Chinese version. The BASC total and factor scores met the criteria for equivalence. Cronbach α coefficients were similar and exploratory factor analysis showed similar 2-factor structures for both language versions. CONCLUSION: The English and Chinese versions of the BASC were found to be equivalent in terms of similar adjusted mean scores, Cronbach α, and factor structures.

Two valid and reliable short forms of the Singapore caregiver quality of life scale were developed: SCQOLS-10 and SCQOLS-15.

OBJECTIVES: The Singapore Caregiver Quality of Life Scale (SCQOLS) comprises five domains and 51 items in total. This study aimed to develop and evaluate short forms of SCQOLS. STUDY DESIGN AND SETTING: Data were collected from 612 family caregivers of patients with advanced cancers in Singapore. Exploratory factor analysis and best subset regression were used to identify candidate items for each domain. The short forms were evaluated for measurement properties. RESULTS: Inclusion of at least two items per domain in the short form gave correlation coefficients of at least 0.8 with the corresponding domain scores in the full-length version. The short forms and full-length version had similar correlation coefficients with Negative Personal Impact and Positive Personal Impact measures. Two of the five domains of the 10-item version had Cronbach's alpha ≤0.50 and test-retest reliability ≤0.65. A 15-item version of the scale with two to four items per domain performed satisfactorily in all aspects evaluated. CONCLUSION: A 15-item short form of the SCQOLS (SCQOLS-15) is valid and reliable for the assessment of the overall and domain-specific quality of life of family caregivers. A 10-item short form (SCQOLS-10) may serve as a quick, valid and reliable assessment of the overall level of quality of life.

Validation of the Comprehensive Needs Assessment Tool in Patients with Advanced Cancer.

AIMS: The 59-item Comprehensive Needs Assessment Tool (CNAT) for cancer patients is an English language survey developed in South Korea. The objective of this study was to validate the English version of CNAT in advanced cancer patients in Singapore. METHODS: This was a cross-sectional survey where advanced cancer patients completed the CNAT in English. Confirmatory factor analysis was used to assess construct validity. For known groups validity, independent samples t-test was used to compare CNAT scores based on the Karnofsky performance status and outpatient versus inpatient setting. Cronbach's alpha was used to measure internal consistency. RESULTS: A total of 328 advanced cancer patients were recruited. The mean age was 59.6 years and 49.1% were male. Majority (68.0%) were Chinese, 20.4% were Malay, 7.9% were Indian, and 3.7% were of other ethnicities. The 7-factor model previously established in Korea showed sufficient construct validity with root mean square error of approximation 0.037 and comparative fit index 0.944. All 59 items had a factor loading ≥0.5. Group invariance test showed no difference in the pattern of factor loadings between ethnic Chinese and other ethnic groups (P = 0.155). For known groups validity, there were significant differences in CNAT scores by performance status and outpatient versus inpatient setting. The CNAT total and factor scores showed good internal consistency with Cronbach's alpha of between 0.80 and 0.937. CONCLUSIONS: The CNAT showed construct and known-group validity and internal consistency in this study sample and can be used to assess the unmet needs of advanced cancer patients in the Singapore context.

Validity, Reliability, and Diagnostic Accuracy of the Respiratory Distress Observation Scale for Assessment of Dyspnea in Adult Palliative Care Patients.

BACKGROUND: The prevalence and severity of dyspnea increase at the end of life. Many of these patients have difficulty in reporting their symptoms. Accurate surrogate measures are needed for appropriate assessment and treatment. The Respiratory Distress Observation Scale (RDOS) is proposed as a possible scale although more external validation is needed. We set out to validate the RDOS in the context of palliative care patients near the end of life. MEASURES: We prospectively studied 122 palliative care patients in a tertiary hospital in Singapore. Prior RDOS training was done using a standardized instructional video. Dyspnea was assessed by RDOS, Dyspnea Numerical Rating Scale, and Dyspnea Categorical Scale. Pain was assessed by Pain Numerical Rating Scale. We measured RDOS inter-rater reliability, convergent validity, and divergent validity. We used area under receiver operating characteristics curve (AUC) analysis to examine the discriminant properties of RDOS using dyspnea self-report as benchmark. RESULTS: RDOS had good inter-rater reliability with an intraclass correlation of 0.947 (95% CI 0.919-0.976). It showed moderate-to-strong correlation with Dyspnea Numerical Rating Scale (r = 0.702) and Dyspnea Categorical Scale (r = 0.677) and negligible correlation to Pain Numerical Rating Scale (r = 0.080). It showed good discriminant properties of identifying patients with moderate and severe dyspnea with an AUC of 0.874 (95% CI 0.812-0.936). RDOS ≥ 4 predicted patients with moderate and severe dyspnea with a sensitivity of 76.6%, specificity of 86.2%, positive predictive value of 86.0%, and negative predictive value of 76.9%. CONCLUSIONS: The RDOS shows promise and clinical utility as an observational dyspnea assessment tool. Further studies in uncommunicative patients are needed to determine clinical usefulness and generalizability of results.

Cost of Medical Care of Patients with Advanced Serious Illness in Singapore (COMPASS): prospective cohort study protocol.

BACKGROUND: Advanced cancer significantly impacts quality of life of patients and families as they cope with symptom burden, treatment decision-making, uncertainty and costs of treatment. In Singapore, information about the experiences of advanced cancer patients and families and the financial cost they incur for end-of-life care is lacking. Understanding of this information is needed to inform practice and policy to ensure continuity and affordability of care at the end of life. The primary objectives of the Cost of Medical Care of Patients with Advanced Serious Illness in Singapore (COMPASS) cohort study are to describe changes in quality of life and to quantify healthcare utilization and costs of patients with advanced cancer at the end of life. Secondary objectives are to investigate patient and caregiver preferences for diagnostic and prognostic information, preferences for end-of-life care, caregiver burden and perceived quality of care and to explore how these change as illness progresses and finally to measure bereavement adjustment. The purpose of this paper is to present the COMPASS protocol in order to promote scientific transparency. METHODS: This cohort study recruits advanced cancer patients (n = 600) from outpatient medical oncology clinics at two public tertiary healthcare institutions in Singapore. Patients and their primary informal caregiver are surveyed every 3 months until patients' death; caregivers are followed until 6 months post patient death. Patient medical and billing records are obtained and merged with patient survey data. The treating medical oncologists of participating patients are surveyed to obtain their beliefs regarding care delivery for the patient. DISCUSSION: The study will allow combination of self-report, medical, and cost data from various sources to present a comprehensive picture of the end-of-life experience of advanced cancer patients in a unique Asian setting. This study is responsive to Singapore's National Strategy for Palliative Care which aims to identify opportunities to meet the growing need for high quality care for Singapore's aging population. Results will also be of interest to policy makers and researchers beyond Singapore who are interested to understand and improve the end-of-life experience of cancer patients. TRIAL REGISTRATION: NCT02850640 (Prospectively registered on June 9, 2016).

Does socioeconomic status affect outcomes in early inflammatory arthritis? Data from a canadian multisite suspected rheumatoid arthritis inception cohort.

OBJECTIVE: To assess the effect of socioeconomic status (SES) on outcomes in patients with early inflammatory arthritis, using data from the Canadian Early Arthritis Cohort (CATCH) study. METHODS: In an incident cohort, 2023 patients were recruited, and allocated to low SES or high SES groups based on education and income. Outcomes at baseline and 12 months were analyzed in relation to SES including the 28-joint Disease Activity Score (DAS28), Simplified Disease Activity Index (SDAI), pain, patient's global assessment scale (PtGA), the Health Assessment Questionnaire-Disability Index (HAQ-DI), and the SF12-v2 Health Survey, using the ANOVA, chi-squared test, and regression analyses. RESULTS: The CATCH population had 43% with high school education or less and 37% in the low-income group (< 50,000 Can$ per annum household income). The low-education group had higher DAS28 at baseline (p = 0.045), becoming nonsignificant at 12 months and lower physical component score on SF12-v2 at baseline (p = 0.022). Patients in the low-income group presented with higher HAQ-DI (p = 0.017), pain (p = 0.035), PtGA (p = 0.004), and SDAI (p = 0.022). Low-income versus high-income groups were associated with an OR above the median for HAQ-DI (1.20; 95% CI 1.00-1.45), PtGA (1.27; 95% CI 1.06-1.53), and SDAI (1.25; 95% CI 1.02-1.52) at baseline. The association with low income persisted at 12 months for HAQ-DI (OR 1.30; 95% CI 1.02-1.67), but not for other variables. CONCLUSION: Low SES was initially associated with higher disease activity, pain, and PtGA, and poorer function. At 1 year, outcomes were similar to those with high SES, with the exception of HAQ-DI.

Neyman, Markov processes and survival analysis.

J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.