Association between trajectories of adherence to endocrine therapy and risk of treated breast cancer recurrence among US nonmetastatic breast cancer survivors.

BACKGROUND: Endocrine therapy is the mainstay treatment for breast cancer (BC) to reduce BC recurrence risk. During the first year of endocrine therapy use, nearly 30% of BC survivors are nonadherent, which may increase BC recurrence risk. This study is to examine the association between endocrine therapy adherence trajectories and BC recurrence risk in nonmetastatic BC survivors. METHODS: This retrospective cohort study included Medicare beneficiaries in the United States (US) with incident nonmetastatic BC followed by endocrine therapy initiation in 2010-2019 US Surveillance, Epidemiology, and End Results linked Medicare data. We calculated monthly fill-based proportion of days covered in the first year of endocrine therapy. We applied group-based trajectory models to identify distinct endocrine therapy adherence patterns. After the end of the first-year endocrine therapy trajectory measurement period, we estimated the risk of time to first treated BC recurrence within 4 years using Cox proportional hazards models. RESULTS: We identified 5 trajectories of adherence to endocrine therapy in BC Stages 0-I subgroup (n = 28,042) and in Stages II-III subgroup (n = 7781). A trajectory of discontinuation before 6 months accounted for 7.0% in Stages 0-I and 5.8% in Stages II-III subgroups, and this trajectory was associated with an increased treated BC recurrence risk compared to nearly perfect adherence (Stages 0-I: adjusted hazard [aHR] = 1.84, 95% CI = 1.46-2.33; Stages II-III: aHR = 1.38, 95% CI = 1.07-1.77). CONCLUSIONS: Nearly 7% of BC survivors who discontinued before completing 6 months of treatment was associated with an increased treated BC recurrence risk compared to those with nearly perfect adherence among Medicare nonmetastatic BC survivors.

Association between trajectories of prescription opioid use and risk of opioid use disorder and overdose among US nonmetastatic breast cancer survivors.

PURPOSE: To examine the association between prescription opioid use trajectories and risk of opioid use disorder (OUD) or overdose among nonmetastatic breast cancer survivors by treatment type. METHODS: This retrospective cohort study included female nonmetastatic breast cancer survivors with at least 1 opioid prescription fill in 2010-2019 Surveillance, Epidemiology and End Results linked Medicare data. Opioid mean daily morphine milligram equivalents (MME) calculated within 1.5 years after initiating active breast cancer therapy. Group-based trajectory models identified distinct opioid use trajectory patterns. Risk of time to first OUD/overdose event within 1 year after the trajectory period was calculated for distinct trajectory groups using Cox proportional hazards models. Analyses were stratified by treatment type. RESULTS: Four opioid use trajectories were identified for each treatment group. For 38,030 survivors with systemic endocrine therapy, 3 trajectories were associated with increased OUD/overdose risk compared with early discontinuation: minimal dose (< 5 MME; adjusted hazard ratio [aHR] = 1.73 [95% CI 1.43-2.09]), very low dose (5-25 MME; 2.67 [2.05-3.48]), and moderate dose (51-90 MME; 6.20 [4.69-8.19]). For 9477 survivors with adjuvant chemotherapy, low-dose opioid use was associated with higher OUD/overdose risk (aHR = 7.33 [95% CI 2.52-21.31]) compared with early discontinuation. For 3513 survivors with neoadjuvant chemotherapy, the differences in OUD/OD risks across the 4 trajectories were not significant. CONCLUSIONS: Among Medicare nonmetastatic breast cancer survivors receiving systemic endocrine therapy or adjuvant chemotherapy, compared with early discontinuation, low-dose or moderate-dose opioid use were associated with six- to sevenfold higher OUD/overdose risk. Breast cancer survivors at high-risk of OUD/overdose may benefit from targeted interventions (e.g., pain clinic referral).

Real-world economic evaluation of prospective rapid whole-genome sequencing compared to a matched retrospective cohort of critically ill pediatric patients in the United States.

There is an increasing demand for supporting the adoption of rapid whole-genome sequencing (rWGS) by demonstrating its real-world value. We aimed to assess the cost-effectiveness of rWGS in critically ill pediatric patients with diseases of unknown cause. Data were collected prospectively of patients admitted to the Nicklaus Children's Hospital's intensive care units from March 2018 to September 2020, with rWGS (N = 65). Comparative data were collected in a matched retrospective cohort with standard diagnostic genetic testing. We determined total costs, diagnostic yield (DY), and incremental cost-effectiveness ratio (ICER) adjusted for selection bias and right censoring. Sensitivity analyses explored the robustness of ICER through bootstrapping. rWGS resulted in a diagnosis in 39.8% while standard testing in 13.5% (p = 0.026). rWGS resulted in a mean saving per person of $100,440 (SE = 26,497, p < 0.001) and a total of $6.53 M for 65 patients. rWGS in critically ill pediatric patients is cost-effective, cost-saving, shortens diagnostic odyssey, and triples the DY of traditional approaches.

RSV testing practice and positivity by patient demographics in the United States: integrated analyses of MarketScan and NREVSS databases.

BACKGROUND: RSV-incidence estimates obtained from routinely-collected healthcare data (e.g., MarketScan) are commonly adjusted for under-reporting using test positivity reported in national Surveillance Systems (NREVSS). However, NREVSS lacks detail on patient-level characteristics and the validity of applying a single positivity estimate across diverse patient groups is uncertain. We aimed to describe testing practices and test positivity across subgroups of private health insurance enrollees in the US and illustrate the possible magnitude of misclassification when using NREVSS to correct for RSV under ascertainment. METHODS: Using billing records, we determined distributions of RSV-test claims and test positivity among a national sample of private insurance enrollees. Tests were considered positive if they coincided with an RSV-diagnosis. We illustrated the influence of positivity variation across sub-populations when accounting for untested acute respiratory infections. RESULTS: Most tests were for children (age 0-4: 65.8%) and outpatient encounters (78.3%). Test positivity varied across age (0-4: 19.8%, 5-17: 1.8%, adults: 0.7%), regions (7.6-16.1%), settings (inpatient 4.7%, outpatient 14.2%), and test indication (5.0-35.9%). When compared to age, setting or indication-specific positivity, bias due to using NREVSS positivity to correct for untested ARIs ranged from - 76% to 3556%. CONCLUSIONS: RSV-test positivity depends on the characteristics of patients for whom those tests were ordered. NREVSS-based correction for RSV-under-ascertainment underestimates the true incidence among children and overestimate rates among adults. Demographic-specific detail on testing practice and positivity can improve the accuracy of RSV-incidence estimates.

Cost-effectiveness of CYP2C19-guided antiplatelet therapy in patients with acute coronary syndrome and percutaneous coronary intervention informed by real-world data.

Current guidelines recommend dual antiplatelet therapy (DAPT) consisting of aspirin and a P2Y12 inhibitors following percutaneous coronary intervention (PCI). CYP2C19 genotype can guide DAPT selection, prescribing ticagrelor or prasugrel for loss-of-function (LOF) allele carriers (genotype-guided escalation). Cost-effectiveness analyses (CEA) are traditionally grounded in clinical trial data. We conduct a CEA using real-world data using a 1-year decision-analytic model comparing primary strategies: universal empiric clopidogrel (base case), universal ticagrelor, and genotype-guided escalation. We also explore secondary strategies commonly implemented in practice, wherein all patients are prescribed ticagrelor for 30 days post PCI. After 30 days, all patients are switched to clopidogrel irrespective of genotype (nonguided de-escalation) or to clopidogrel only if patients do not harbor an LOF allele (genotype-guided de-escalation). Compared with universal clopidogrel, both universal ticagrelor and genotype-guided escalation were superior with improvement in quality-adjusted life years (QALY's). Only genotype-guided escalation was cost-effective ($42,365/QALY) and demonstrated the highest probability of being cost-effective across conventional willingness-to-pay thresholds. In the secondary analysis, compared with the nonguided de-escalation strategy, although genotype-guided de-escalation and universal ticagrelor were more effective, with ICER of $188,680/QALY and $678,215/QALY, respectively, they were not cost-effective. CYP2C19 genotype-guided antiplatelet prescribing is cost-effective compared with either universal clopidogrel or universal ticagrelor using real-world implementation data. The secondary analysis suggests genotype-guided and nonguided de-escalation may be viable strategies, needing further evaluation.

Self-assessed life expectancy among older adults in Côte d'Ivoire.

BACKGROUND: The purpose of this study was to estimate individuals' expected longevity based on self-assessed survival probabilities and determine the predictors of such subjective life expectancy in a sample of elderly people (50 years and older) in Côte d'Ivoire. METHODS: Paper-based questionnaires were administered to a sample (n = 267) of older adults residing in the city of Dabou, Côte d'Ivoire in May 2017. Information on subjective expectations regarding health, comorbidities, and self-assessed survival probabilities was collected. We estimated self-assessed life expectancy and its determinants using a two-pronged approach by: (i) estimating individuals' life expectancy using the self-assessed survival probabilities (SSPs), and (ii) applying a finite mixture of regression models to form homogenous groups of individuals (clusters/components) and investigate the determinants. A spline-based approach was used to estimate the overall distribution of life expectancy for each individual using two to four points of self-assessed survival probabilities. A finite mixture of regression models was used to identify homogeneous groups of individuals (i.e. clusters/components) of the overall subjective life expectancy distribution of the study participants. RESULTS: The mean subjective life expectancy in older people varied according to four components/clusters. The average subjective life expectancy among the elderly was 79.51, 78.89, 80.02, and 77.79 years in the first, second, third, and fourth component of the subjects' overall subjective life expectancy, respectively. The effect of sociodemographic characteristics, comorbidities, and lifestyle on subjective life expectancy varied across components. For instance, a U-shape relationship between household per capita income and subjective life expectancy was found for individuals classified into the third component, and an inverse U-shape relationship was found for individuals classified into the fourth component. CONCLUSIONS: We extended the estimation of subjective life expectancy by accounting for heterogeneity in the distribution of the estimated subjective life expectancy. This approach improved the usual methods for estimating individual subjective life expectancies and may provide insight into the elderly's perception of aging, which could be used to forecast the demand for health services and long-term care needs.

A quantitative analysis of the effect of continuity of care on 30-day readmission and in-hospital mortality among patients with acute ischemic stroke.

BACKGROUND: Continuity of care is a core element of high-quality patient care in a primary care setting and one of a national priority. OBJECTIVE: To assess and quantify the impact of continuity of care on 30-day readmissions, 30-day inpatient mortality, and hospital length of stay (LOS), among hospitalized patients with acute ischemic stroke disease. DESIGN AND SUBJECTS: Observational retrospective cohort (n = 356,134) using a 2.75% random sample (n=1,036,753) from the State of Florida Agency for Health Care Administration (AHCA) database from 2006 to 2016. MEASURES: We assessed continuity of care using an integrated continuity of care CoC score, calculated by merging three standard indices of continuity of care - Bice-Boxerman Continuity of Care Index (COCI), Herfindahl Index (HI), and Usual Provider of Care (UPC) Index via a Principal Component Analysis (PCA). We measured 30-day hospital readmissions, 30-day inpatient mortality, and LOS. RESULTS: Our analysis revealed that hospital LOS was significantly affected by CoC. The statistically significant average treatment effect (ATEs), expressed in risk difference (RD), ranged between 0.27 [95%CI: (0.07, 0.48)] and 1.0 day [95%CI: (0.57, 1.43)]. A similar trend was observed for 30-day readmission (ATEs ranging from 0.0067 [95%CI: (0.0002, 0.0132) to 0.0071 [95%CI: (0.0005, 0.0136)]), and inpatient mortality (ATEs ranging from 0.0006 [95% confidence interval (CI): (0.0001, 0.0012)] to 0.0007 [95%CI: (0.0001, 0.0012)]). CONCLUSIONS: Our findings suggest a strong association between continuity of care and clinical outcomes. Continuity of care leads to a reduction in mortality, rehospitalization, and hospital length of stay.

Multicriteria Decision Analysis to Support Health Technology Assessment Agencies: Benefits, Limitations, and the Way Forward.

OBJECTIVE: Recent years have witnessed an increased interest in the use of multicriteria decision analysis (MCDA) to support health technology assessment (HTA) agencies for setting healthcare priorities. However, its implementation to date has been criticized for being "entirely mechanistic," ignoring opportunity costs, and not following best practice guidelines. This article provides guidance on the use of MCDA in this context. METHODS: The present study was based on a systematic review and consensus development. We developed a typology of MCDA studies and good implementation practice. We reviewed 36 studies over the period 1990 to 2018 on their compliance with good practice and developed recommendations. We reached consensus among authors over the course of several review rounds. RESULTS: We identified 3 MCDA study types: qualitative MCDA, quantitative MCDA, and MCDA with decision rules. The types perform differently in terms of quality, consistency, and transparency of recommendations on healthcare priorities. We advise HTA agencies to always include a deliberative component. Agencies should, at a minimum, undertake qualitative MCDA. The use of quantitative MCDA has additional benefits but also poses design challenges. MCDA with decision rules, used by HTA agencies in The Netherlands and the United Kingdom and typically referred to as structured deliberation, has the potential to further improve the formulation of recommendations but has not yet been subjected to broad experimentation and evaluation. CONCLUSION: MCDA holds large potential to support HTA agencies in setting healthcare priorities, but its implementation needs to be improved.

Economic evaluation of sequencing strategies in HER2-positive metastatic breast cancer in Mexico: a contrast between public and private payer perspectives.

PURPOSE: Breast cancer is the most common malignancy among women in Mexico. A large proportion of Mexican patients present with advanced disease, and 25% have HER2-positive tumors. We performed a cost-effectiveness analysis of different sequencing strategies of HER2-targeted agents in Mexico according to various payer perspectives. METHODS: A Markov model was constructed to evaluate the cost-effectiveness of four different HER2-targeted treatment sequences among patients with HER2-positive metastatic breast cancer treated in Mexico according to three public and one private payer perspectives. Patients were followed weekly over their remaining life expectancies within the model. Health states considered were progression-free survival (PFS) 1st-3rd lines, and death. Transition probabilities between states were based on published trials. Cost data were obtained from official publications from Mexican healthcare institutions. The evaluated outcomes were PFS, OS, costs, QALYs, and incremental cost effectiveness ratio (ICER). RESULTS: In the public payer perspective, sequences containing pertuzumab or T-DM1 were not cost-effective when compared with a sequence including the combination of trastuzumab/docetaxel as first line without subsequent T-DM1 or pertuzumab, even when utilizing alternate definitions for willingness to pay thresholds. In the private payer perspective, a sequence containing T-DM1 but not pertuzumab proved cost-effective at a lower clinical effectiveness. CONCLUSIONS: In Mexico, the use of at least three lines of trastuzumab in combination with other therapies, but not with pertuzumab or TDM-1, represents the most cost-effective option for patients covered by the public healthcare system, and this sequence should be made available for all patients.

Cost-effectiveness analysis of 1st through 3rd line sequential targeted therapy in HER2-positive metastatic breast cancer in the United States.

PURPOSE: Based on available phase III trial data, we performed a cost-effectiveness analysis of different treatment strategies that can be used in patients with newly diagnosed HER2-positive metastatic breast cancer (mBC). PATIENTS AND METHODS: We constructed a Markov model to assess the cost-effectiveness of four different HER2 targeted treatment sequences in patients with HER2-positive mBC treated in the U.S. The model followed patients weekly over their remaining life expectancies. Health states considered were progression-free survival (PFS) 1st to 3rd lines, and death. Transitional probabilities were based on published phase III trials. Cost data (2015 US dollars) were captured from the U.S. Centers for Medicare and Medicaid Services (CMS) drug payment table and physician fee schedule. Health utility data were extracted from published studies. The outcomes considered were PFS, OS, costs, QALYs, the incremental cost per QALY gained ratio, and the net monetary benefit. Deterministic and probabilistic sensitivity analyses assessed the uncertainty around key model parameters and their joint impact on the base-case results. RESULTS: The combination of trastuzumab, pertuzumab, and docetaxel (THP) as first-line therapy, trastuzumab emtansine (T-DM1) as second-line therapy, and lapatinib/capecitabine third-line resulted in 1.81 QALYs, at a cost of $335,231.35. The combination of trastuzumab/docetaxel as first line without subsequent T-DM1 or pertuzumab yielded 1.41 QALYs, at a cost of $175,240.69. The least clinically effective sequence (1.27 QALYs), but most cost-effective at a total cost of $149,250.19, was trastuzumab/docetaxel as first-line therapy, T-DM1 as second-line therapy, and trastuzumab/lapatinib as third-line therapy. CONCLUSION: Our results suggest that THP as first-line therapy, followed by T-DM1 as second-line therapy, would require at least a 50 % reduction in the total drug acquisition cost for it to be considered a cost-effective strategy.

A review of systematic reviews of the cost-effectiveness of hormone therapy, chemotherapy, and targeted therapy for breast cancer.

Breast cancer is a global health concern. In fact, breast cancer is the primary cause of death among women worldwide and constitutes the most expensive malignancy to treat. As health care resources are finite, decisions regarding the adoption and coverage of breast cancer treatments are increasingly being based on "value for money," i.e., cost-effectiveness. As the evidence about the cost-effectiveness of breast cancer treatments is abundant, therefore difficult to navigate, systematic reviews of published systematic reviews offer the advantage of bringing together the results of separate systematic reviews in a single report. As a consequence, this paper presents an overview of systematic reviews of the cost-effectiveness of hormone therapy, chemotherapy, and targeted therapy for breast cancer to inform policy and reimbursement decision-making. A systematic review was conducted of published systematic reviews documenting cost-effectiveness analyses of breast cancer treatments from 2000 to 2014. Systematic reviews identified through a literature search of health and economic databases were independently assessed against inclusion and exclusion criteria. Systematic reviews of original evaluations were included only if they targeted breast cancer patients and specific breast cancer treatments (hormone therapy, chemotherapy, and targeted therapy only), documented incremental cost-effectiveness ratios, and were reported in the English language. The search strategy used a combination of these key words: "breast cancer," "systematic review/meta-analysis," and "cost-effectiveness/economics." Data were extracted using predefined extraction forms and qualitatively appraised using the assessment of multiple systematic reviews (AMSTAR) tool. The literature search resulted in 511 bibliographic records, of which ten met our inclusion criteria. Five reviews were conducted in the early-stage breast cancer setting and five reviews in the metastatic setting. In early-stage breast cancer, evidence about trastuzumab value differed by age. Trastuzumab was cost-effective only in women with HER2-positive breast cancer younger than 65 years and over a life-time horizon. The cost-effectiveness of trastuzumab in HER2-positive metastatic breast cancer yielded conflicting results. The same conclusions were reached in comparisons between vinorelbine and taxanes. In both early stage and advanced/metastatic breast cancer, newer aromatase inhibitors (AIs) have proved cost-effective compared to older treatments. This overview of systematic reviews shows that there is heterogeneity in the evidence concerning the cost-effectiveness of hormone therapy, chemotherapy, and targeted therapy for breast cancer. The cost-effectiveness of these treatments depends not only on the comparators but the context, i.e., adjuvant or metastatic setting, subtype of patient population, and perspective adopted. Decisions involving the cost-effectiveness of breast cancer treatments could be made easier and more transparent by better harmonizing the reporting of economic evaluations assessing the value of these treatments.

Application of multicriteria decision analysis in health care: a systematic review and bibliometric analysis.

BACKGROUND: The use of Multi-Criteria Decision Analysis (MCDA) in health care has become common. However, the literature lacks systematic review trend analysis on the application of MCDA in health care. AIM: To systematically identify applications of MCDA to the areas of health care, and to report on publication trends. METHODS: English language studies published from January 1, 1980 until October 1, 2013 were included. Electronic databases searches were supplemented by searching conference proceedings and relevant journals. Studies considered for inclusion were those using MCDA techniques within the areas of health care, and involving the participation of decision makers. A bibliometric analysis was undertaken to present the publication trends. RESULTS: A total of 66 citations met the inclusion criteria. An increase in publication trend occurred in the years 1990, 1997, 1999, 2005, 2008, and 2012. For the remaining years, the publication trend was either steady or declining. The trend shows that the number of publications reached its highest peak in 2012 (n = 9). Medical Decision Making was the dominant with the highest number published papers (n = 7). The majority of the studies were conducted in the US (n = 29). Medical Decision Making journal published the highest number of articles (n = 7). Analytic Hierarchy Process (n = 33) was the most used MCDA technique. Cancer was the most researched disease topic (n = 12). The most covered area of application was diagnosis and treatment (n = 26). CONCLUSION: The review shows that MCDA has been applied to a broad range of areas in the health care, with the use of a variety of methodological approaches. Further research is needed to develop practice guidelines for the appropriate application and reporting of MCDA methods.

Multicriteria decision analysis in oncology.

BACKGROUND: There has been a growing interest in the development and application of alternative decision-making frameworks within health care, including multicriteria decision analysis (MCDA). Even though the literature includes several reviews on MCDA methods, applications of MCDA in oncology are lacking. AIM: The aim of this paper is to discuss a rationale for the use of MCDA in oncology. In this context, the following research question emerged: How can MCDA be used to develop a clinical decision support tool in oncology? METHODS: In this paper, a brief background on decision making is presented, followed by an overview of MCDA methods and process. The paper discusses some applications of MCDA, proposes research opportunities in the context of oncology and presents an illustrative example of how MCDA can be applied to oncology. FINDINGS: Decisions in oncology involve trade-offs between possible benefits and harms. MCDA can help analyse trade-off preferences. A wide range of MCDA methods exist. Each method has its strengths and weaknesses. Choosing the appropriate method varies depending on the source and nature of information used to inform decision making. The literature review identified eight studies. The analytical hierarchy process (AHP) was the most often used method in the identified studies. CONCLUSION: Overall, MCDA appears to be a promising tool that can be used to assist clinical decision making in oncology. Nonetheless, field testing is desirable before MCDA becomes an established decision-making tool in this field.

Using quality-adjusted progression-free survival as an outcome measure to assess the benefits of cancer drugs in randomized-controlled trials: case of the BOLERO-2 trial.

The aim of this study is to estimate the quality-adjusted progression-free survival (QAPFS) as an effectiveness measure for the treatment arms of the BOLERO-2 trial. For each treatment arm of the trial, QAPFS was estimated by multiplying the overall health utility weights associated with progression-free survival (PFS) (accounting for utility decrements associated with the adverse events of treatments) by the corresponding mean PFS time. Health utility data were obtained from the literature, while mean PFS times were estimated through a survival analysis of the reconstructed individual patient data of the BOLERO-2 trial. PFS (robust mean, (95 % robust confidence interval)) was 44.73 weeks (41.03; 48.43) for Everolimus + Exemestane and 22.98 weeks (19.88; 26.08) for Placebo + Exemestane. The QAPFS (robust mean, (95 % robust confidence interval)) for the treatment arms of the trial was 30.09 (27.60; 32.58) for Everolimus + Exemestane and 16.27 (14.07; 18.46) for Placebo + Exemestane, respectively. Using QAPFS as an outcome measure provides a complete picture of the benefit induced by the treatment arms of the BOLERO-2 trial. The benefit of Everolimus + Exemestane over Placebo + Exemestane observed in the trial is maintained in this analysis. The approach and estimates obtained as part of our analysis can serve as a basis for cost effectiveness analyses of the treatment arms of the BOLERO-2 trial.

Cost-effectiveness analysis of everolimus plus exemestane versus exemestane alone for treatment of hormone receptor positive metastatic breast cancer.

Everolimus in combination with exemestane significantly improved progression-free survival compared to exemestane alone in patients previously treated with non-steroidal aromatase inhibitors in the BOLERO-2 trial. As a result, this combination has been approved by the food and drug administration to treat postmenopausal women with hormone receptor positive and HER2 negative metastatic breast cancer. A cost-effectiveness analysis was conducted to determine whether everolimus represents good value for money, utilizing data from BOLERO-2. A decision-analytic model was used to estimate the incremental cost-effectiveness ratio between treatment arms of the BOLERO-2 trial. Costs were obtained from the Center for Medicare Services drug payment table and physician fee schedule. Benefits were expressed as quality-adjusted progression-free survival weeks (QAPFW) and quality-adjusted progression-free years (QAPFY), with utilities/disutilities derived from the literature. Deterministic and probabilistic sensitivity analyses were performed. A willingness to pay threshold of 1-3 times the per capita gross domestic product was adopted, as per the definition of the World Health Organization. The U.S. per capita gross domestic product in 2013 was $49,965; thus, a threshold varying between $49,965 and $149,895 was considered. Everolimus/exemestane had an incremental benefit of 11.88 QAPFW (0.22 QAPFY) compared to exemestane and an incremental cost of $60,574. This translated into an ICER of $265,498.5/QAPFY. Univariate sensitivity analyses showed important variations of the ICER, ranging between $189,836.4 and $530,947/QAPFY. A tornado analysis suggested that the key drivers of our model, by order of importance, included health utility value for stable disease, everolimus acquisition costs, and transition probabilities from the stable to the progression states. The Monte-Carlo simulation showed results that were similar to the base-case analysis. This cost-effectiveness analysis showed that everolimus plus exemestane is not cost-effective compared to exemestane alone. Further research is needed to investigate the cost-effectiveness of the drug combination within sub-groups of the population studied in BOLERO-2.

Estimating and Rewarding the Value of Healthcare Interventions Beyond the Healthcare Sector: A Conceptual Framework.

BACKGROUND: Evaluating healthcare interventions for their impacts beyond health outcomes may result in recognition of changes in human capital, income level, tax revenue, and government spending, which could affect economic growth and population health. In this paper, we document instances where current health technology assessment (HTA) practices fail to account for the impacts of healthcare interventions on broader society beyond the healthcare sector. METHODS: We propose a novel conceptual framework, highlighting its three components (distributional cost-effectiveness analysis [DCEA], input-output model, and voting scheme) and their contributions to capturing the economic and societal ripple effects of healthcare interventions. This manuscript also outlines a case study in which the framework is applied to the reassessment of a previously evaluated digital health therapeutic for the treatment of opioid use disorder (OUD) compared with standard of care, demonstrating its practical application. RESULTS: The DCEA health value metric indicates that digital therapeutic is more equitable, favoring socioeconomically disadvantaged groups, while standard of care exacerbates health inequality by benefiting the already advantaged. Additionally, digital therapeutic shows potential for boosting productivity, raising income, and creating jobs, supporting its consideration by employer-sponsored health plans to optimize resource allocation for treating OUD. CONCLUSION: The conceptual framework provides insights for enhancing HTAs to incorporate the broader economic and societal impacts of healthcare interventions. By integrating DCEA, extended HTA analysis with input-output modeling, and a voting scheme, decision makers can make informed choices aligned with societal priorities, although further research and validation are necessary for practical implementation across diverse healthcare contexts.

Economic Burden of Medically Attended Respiratory Syncytial Virus Infections Among Privately Insured Children Under 5 Years of Age in the USA.

BACKGROUND: The cost of medically attended RSV LRI (lower respiratory infection) is critical in determining the economic value of new RSV immunoprophylaxes. However, most studies have focused on intermittent RSV encounters, not the episode of care that captures the entirety of RSV illness. METHODS: We created age- and condition-specific cohorts of children under 5 years of age using MarketScan® data (2015-2019). We contrasted aggregating healthcare costs over RSV-LRTI episodes to ascertaining costs based on RSV-specific encounters only. Economic burden was estimated by multiplying costs per encounter or per episode by their respective incidence rates. RESULTS: Average cost was higher per episode than per encounter regardless of settings (inpatient: $28,586 vs. $18,056 and outpatient/ED: $2099 vs. $407 for infants). Across ages, the economic burden was highest for infants and RSV-LRTI requiring inpatient care, but the burden in outpatient/ED settings was disproportionately higher than costs due to higher incidence rates (for inpatient vs. outpatient episodes: $226,403 vs. $101,269; for inpatient vs. outpatient encounters: $151,878 vs. $38,819 per 1000 infant-years). For high-risk children, cost and burden were up to 3-10 times higher, respectively. CONCLUSIONS: With a comprehensive stratification by settings and risk condition, the encounter- versus episode-based estimates provide a robust range for policymakers' economic appraisal of new RSV immunoprophylaxes.

Predictors of 30-day readmission, mortality, and length of stay for hospitalized U.S. patients with Alzheimer's and related dementias from 2010 to 2015.

OBJECTIVES: Examine predictors of clinical and resource utilization outcomes associated with Alzheimer's disease and related dementias (ADRD), stratified by patient severity profiles. METHODS: Cross-sectional study of adults (30+ year old) with ADRD discharged from US hospitals to home health care (HHC) and identified from the 2010-2015 Nationwide Readmissions Database (NRD) using ICD 9th-10th codes. Outcomes of interest included 30-day hospital readmissions, in-hospital mortality, and hospital length of stay (LOS). Covariates consisted of sociodemographic and clinical variables. Multiple logistic regressions (for readmissions and mortality) and generalized linear regressions (for LOS) were used to examine associations between outcomes and study covariates, stratified by patient severity profiles. RESULTS: Of 164,598 ADRD patients, 3,848 were mild, 68803 were moderate, 72428 were severe, and 19,519 were extreme. The 30-day readmission rate was 3.2%, death rate was 14.5%, and LOS was 3.0 days, (95%, CI: 15.0, 17.0) to 5.0 days, (95%, CI: 18.0, 19.0), all with a p-value<0.0001. Across outcomes and severity levels, the top predictors included number of diagnoses, gender, hospital bed size, primary and secondary diagnoses, and income size. CONCLUSIONS: Severe and extreme stages of HHC discharge may lead to increased readmissions, death, LOS, and costs. Specialized care is needed to reduce these negative outcomes in the ADRD patient population.

Development and validation of a tool to predict high-need, high-cost patients hospitalised with ischaemic heart disease.

OBJECTIVE: To develop and validate a tool to predict patients with ischaemic heart disease (IHD) at risk of excessive healthcare resource utilisation. DESIGN: A retrospective cohort study. SETTING: We identified patients through the State of Florida Agency for Health Care Administration (N=586 518) inpatient dataset. PARTICIPANTS: Adult patients (at least 40 years of age) admitted to the hospital with a diagnosis of IHD between 1 January 2007 and 31 December 2016. PRIMARY OUTCOME MEASURES: We identified patients whose healthcare utilisation is higher than presumed (analysis of residuals) and used logistic regression (binary and multinomial) in estimating the predictive models to classify individual as high-need, high-care (HNHC) patients relative to inpatient visits (frequency of hospitalisation), cost and hospital length of stay. Discrimination power, prediction accuracy and model improvement for the binary logistic model were assessed using receiver operating characteristic statistic, the Brier score and the log-likelihood (LL)-based pseudo-R2, respectively. LL-based pseudo-R2 and Brier score were used for multinomial logistic models. RESULTS: The binary logistic model had good discrimination power (c-statistic=0.6496), an accuracy of probabilistic predictions (Brier score) of 0.0621 and an LL-based pseudo-R2 of 0.0338 in the development cohort. The model performed similarly in the validation cohort (c-statistic=0.6480), an accuracy of probabilistic predictions (Brier score) of 0.0620 and an LL-based pseudo-R2 of 0.0380. A user-friendly Excel-based HNHC risk predictive tool was developed and readily available for clinicians and policy decision-makers. CONCLUSIONS: The Excel-based HNHC risk predictive tool can accurately identify at-risk patients for HNHC based on three measures of healthcare expenditures.

Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data.

There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79-0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73-0.75), the logistic regression 0.64 (95% CI: 0.63-0.65) and the weighted ensemble model 0.77 (95% CI: 0.76-0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.