A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas.

Importance: Models predicting health care spending and other outcomes from administrative records are widely used to manage and pay for health care, despite well-documented deficiencies. New methods are needed that can incorporate more than 70 000 diagnoses without creating undesirable coding incentives. Objective: To develop a machine learning (ML) algorithm, building on Diagnostic Item (DXI) categories and Diagnostic Cost Group (DCG) methods, that automates development of clinically credible and transparent predictive models for policymakers and clinicians. Design, Setting, and Participants: DXIs were organized into disease hierarchies and assigned an Appropriateness to Include (ATI) score to reflect vagueness and gameability concerns. A novel automated DCG algorithm iteratively assigned DXIs in 1 or more disease hierarchies to DCGs, identifying sets of DXIs with the largest regression coefficient as dominant; presence of a previously identified dominating DXI removed lower-ranked ones before the next iteration. The Merative MarketScan Commercial Claims and Encounters Database for commercial health insurance enrollees 64 years and younger was used. Data from January 2016 through December 2018 were randomly split 90% to 10% for model development and validation, respectively. Deidentified claims and enrollment data were delivered by Merative the following November in each calendar year and analyzed from November 2020 to January 2024. Main Outcome and Measures: Concurrent top-coded total health care cost. Model performance was assessed using validation sample weighted least-squares regression, mean absolute errors, and mean errors for rare and common diagnoses. Results: This study included 35 245 586 commercial health insurance enrollees 64 years and younger (65 901 460 person-years) and relied on 19 clinicians who provided reviews in the base model. The algorithm implemented 218 clinician-specified hierarchies compared with the US Department of Health and Human Services (HHS) hierarchical condition category (HCC) model's 64 hierarchies. The base model that dropped vague and gameable DXIs reduced the number of parameters by 80% (1624 of 3150), achieved an R2 of 0.535, and kept mean predicted spending within 12% ($3843 of $31 313) of actual spending for the 3% of people with rare diseases. In contrast, the HHS HCC model had an R2 of 0.428 and underpaid this group by 33% ($10 354 of $31 313). Conclusions and Relevance: In this study, by automating DXI clustering within clinically specified hierarchies, this algorithm built clinically interpretable risk models in large datasets while addressing diagnostic vagueness and gameability concerns.

Managed competition in the United States: How well is it promoting equity and efficiency?

Managed competition frameworks aim to control healthcare costs and promote access to high-quality health insurance and services through a combination of public policies and market forces. In the United States, managed competition delivery systems are varied and diffused across a patchwork of divided markets and populations. This, coupled with extremely high national health spending per capita, makes a more unified managed competition strategy an appealing alternative to a currently struggling healthcare system. We examine the relative effectiveness of three existing programmes in the U.S. that each rely upon some principles of managed competition: health insurance exchanges instituted by the Affordable Care Act, Medicaid managed care organisations, and Medicare Advantage plans. Although each programme leverages some competitive features, each faces significant hurdles as a candidate for expansion. We highlight these challenges with a survey of academic health economists, and find that provider and insurer consolidation, highly segmented markets, and failing to incentivise competitive efficiencies all dampen the success of existing programmes. Although managed competition for all is a potentially desirable framework for future health reform in the U.S., successful expansion relies on addressing fundamental issues revealed by imperfect existing programmes.

Scope and Incentives for Risk Selection in Health Insurance Markets With Regulated Competition: A Conceptual Framework and International Comparison.

In health insurance markets with regulated competition, regulators face the challenge of preventing risk selection. This paper provides a framework for analyzing the scope (i.e., potential actions by insurers and consumers) and incentives for risk selection in such markets. Our approach consists of three steps. First, we describe four types of risk selection: (a) selection by consumers in and out of the market, (b) selection by consumers between high- and low-value plans, (c) selection by insurers via plan design, and (d) selection by insurers via other channels such as marketing, customer service, and supplementary insurance. In a second step, we develop a conceptual framework of how regulation and features of health insurance markets affect the scope and incentives for risk selection along these four dimensions. In a third step, we use this framework to compare nine health insurance markets with regulated competition in Australia, Europe, Israel, and the United States.

Development and Assessment of a New Framework for Disease Surveillance, Prediction, and Risk Adjustment: The Diagnostic Items Classification System.

Importance: Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Objective: To develop an ICD-10-CM-based classification framework for predicting diverse health care payment, quality, and performance outcomes. Design Setting and Participants: Physician teams mapped all ICD-10-CM diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. Every diagnosis was mapped to at least 1 DXI. Stepwise and weighted least-squares estimation predicted cost and utilization outcomes, and their performance was compared with models built on (1) the Agency for Healthcare Research and Quality Clinical Classifications Software Refined (CCSR) categories, and (2) the Health and Human Services Hierarchical Condition Categories (HHS-HCC) used in the Affordable Care Act Marketplace. Each model's performance was validated using R 2, mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage. Main Outcomes and Measures: Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000. Results: A total of 65 901 460 person-years were split into 90% estimation/10% validation samples (n = 6 604 259). In all, 3223 DXIs were created: 2435 main effects, 772 modifiers, and 16 scaled items. Stepwise regressions predicting annual health care spending (mean [SD], $5821 [$17 653]) selected 76% of the main effect DXIs with no evidence of overfitting. Validated R 2 was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs. Conclusions: In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.

Intermittent claudication treatment patterns in the commercially insured non-Medicare population.

OBJECTIVE: Despite published guidelines and data for Medicare patients, it is uncertain how younger patients with intermittent claudication (IC) are treated. Additionally, the degree to which treatment patterns have changed over time with the expansion of endovascular interventions and outpatient centers is unclear. Our goal was to characterize IC treatment patterns in the commercially insured non-Medicare population. METHODS: The IBM MarketScan Commercial Database, which includes more than 8 billion US commercial insurance claims, was queried for patients newly diagnosed with IC from 2007 to 2016. Patient demographics, medication profiles, and open/endovascular interventions were evaluated. Time trends were modeled using simple linear regression and goodness-of-fit was assessed with coefficients of determination (R2). A patient-centered cohort sample and a procedure-focused dataset were analyzed. RESULTS: Among 152,935,013 unique patients in the database, there were 300,590 patients newly diagnosed with IC. The mean insurance coverage was 4.4 years. The median patients age was 58 years and 56% of patients were male. The prevalence of statin use was 48% among patients at the time of IC diagnosis and increased to 52% among patients after one year from diagnosis. Interventions were performed in 14.3%, of whom 20% and 6% underwent two or more and three or more interventions, respectively. The median time from diagnosis to intervention decreased from 230 days in 2008 days to 49 days in 2016 (R2 = 0.98). There were 16,406 inpatient and 102,925 ambulatory interventions for IC over the study period. Among ambulatory interventions, 7.9% were performed in office-based/surgical centers. The proportion of atherectomies performed in the ambulatory setting increased from 9.7% in 2007 to 29% in 2016 (R2 = 0.94). In office-based/surgical centers, 57.6% of interventions for IC used atherectomy in 2016. Atherectomy was used in ambulatory interventions by cardiologists in 22.6%, surgeons in 15.2%, and radiologists in 13.6% of interventions. Inpatient atherectomy rates remained stable over the study period. Open and endovascular tibial interventions were performed in 7.9% and 7.8% of ambulatory and inpatient IC interventions, respectively. Tibial bypasses were performed in 8.2% of all open IC interventions. CONCLUSIONS: There has been shorter time to intervention in the treatment of younger, commercially insured patients with IC, with many receiving multiple interventions. Statin use was low. Ambulatory procedures, especially in office-based/surgical centers, increasingly used atherectomy, which was not observed in inpatient settings.

Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification.

Importance: On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. Objective: To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. Design, Setting, and Participants: This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance. Diagnoses were mapped using 3 common diagnostic classification systems: World Health Organization (WHO) disease chapters, Department of Health and Human Services Hierarchical Condition Categories (HHS-HCCs), and Agency for Healthcare Research and Quality Clinical Classification System (AHRQ-CCS). Data were analyzed from December 1, 2018, to January 21, 2020. Exposures: US implementation of ICD-10-CM. Main Outcomes and Measures: Monthly rates of individuals with at least 1 diagnosis in a diagnostic classification category per 10 000 eligible members. Results: The analytic sample contained information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnoses; the mean (range) monthly sample size was 22.1 (18.4 to 27.1 ) million individuals. While diagnostic category prevalence changed minimally for WHO disease chapters, the ICD-10-CM transition was associated with level changes of 20% or more among 20 of 127 HHS-HCCs (15.7%) and 46 of 282 AHRQ-CCS categories (16.3%) and with trend changes of 20% or more among 12 of 127 of HHS-HCCs (9.4%) and 27 of 282 of AHRQ-CCS categories (9.6%). For HHS-HCCs, monthly rates of individuals with any acute myocardial infarction diagnosis increased 131.5% (95% CI, 124.1% to 138.8%), primarily because HHS added non-ST-segment-elevation myocardial infarction diagnoses to this category. The HHS-HCC for diabetes with chronic complications increased by 92.4% (95% CI, 84.2% to 100.5%), primarily from including new diabetes-related hypoglycemia and hyperglycemia codes, and the rate for completed pregnancy with complications decreased by 54.5% (95% CI, -58.7% to -50.2%) partly due to removing vaginal birth after cesarean delivery as a complication. Conclusions and Relevance: These findings suggest that the ICD-10-CM transition was associated with large prevalence changes for many diagnostic categories. Diagnostic classification systems developed using ICD-9-CM may need to be refined using ICD-10-CM data to avoid unintended consequences for disease surveillance, performance assessment, and risk-adjusted payments.

Health care utilization and spending among privately insured children with medical complexity.

Children with medical complexity have high health service utilization and health expenditures that can impose significant financial burdens. This study examined these issues for families with children enrolled in US private health plans. Using IBM Watson/Truven Analytics℠ MarketScan® commercial claims and encounters data (2012-2014), we analyzed through regression models, the differences in health care utilization and spending of disaggregated health care services by health plan types and children's medical complexity levels. Children in consumer-driven and high-deductible plans had much higher out-of-pocket spending and cost shares than those in health maintenance organizations and preferred provider organizations (PPOs). Children with complex chronic conditions had higher service utilization and out-of-pocket expenditures while having lower cost shares on various categories of services than those without any chronic condition. Compared to families covered by PPOs, those with high-deductible or consumer-driven plans were 2.7 and 1.7 times more likely to spend over US$1000 out of pocket on their children's medical care, respectively. Families with higher complexity levels were more likely to experience financial burdens from expenditures on children's medical services. In conclusion, policymakers and families with children need to be cognizant of the significant financial burdens that can arise from children's complex medical needs and health plan demand-side cost sharing.

Measuring efficiency of health plan payment systems in managed competition health insurance markets.

Adverse selection in health insurance markets leads to two types of inefficiency. On the demand side, adverse selection leads to plan price distortions resulting in inefficient sorting of consumers across health plans. On the supply side, adverse selection creates incentives for plans to inefficiently distort benefits to attract profitable enrollees. Reinsurance, risk adjustment, and premium categories address these problems. Building on prior research on health plan payment system evaluation, we develop measures of the efficiency consequences of price and benefit distortions under a given payment system. Our measures are based on explicit economic models of insurer behavior under adverse selection, incorporate multiple features of plan payment systems, and can be calculated prior to observing actual insurer and consumer behavior. We illustrate the use of these measures with data from a simulated market for individual health insurance.

Demand elasticities and service selection incentives among competing private health plans.

We examine selection incentives by health plans while refining the selection index of McGuire et al. (2014) to reflect not only service predictability and predictiveness but also variation in cost sharing, risk-adjusted profits, profit margins, and newly-refined demand elasticities across 26 disaggregated types of service. We contrast selection incentives, measured by service selection elasticities, across six plan types using privately-insured claims data from 73 large employers from 2008 to 2014. Compared to flat capitation, concurrent risk adjustment reduces the elasticity by 47%, prospective risk adjustment by 43%, simple reinsurance system by 32%, and combined concurrent risk adjustment with reinsurance by 60%. Reinsurance significantly reduces the variability of individual-level profits, but increases the correlation of expected spending with profits, which strengthens selection incentives.

Social Determinants of Health in Managed Care Payment Formulas.

Importance: Managed care payment formulas commonly allocate more money for medically complex populations, but ignore most social determinants of health (SDH). Objective: To add SDH variables to a diagnosis-based payment formula that allocates funds to managed care plans and accountable care organizations. Design, Setting, and Participants: Using data from MassHealth, the Massachusetts Medicaid and Children's Health Insurance Program, we estimated regression models predicting Medicaid spending using a diagnosis-based and SDH-expanded model, and compared the accuracy of their cost predictions overall and for vulnerable populations. MassHealth members enrolled for at least 6 months in 2013 in fee-for-service (FFS) programs (n = 357 660) or managed care organizations (MCOs) (n = 524 607). Exposures: We built cost prediction models from a fee-for-service program. Predictors in the diagnosis-based model are age, sex, and diagnoses from claims. The SDH model adds predictors describing housing instability, behavioral health issues, disability, and neighborhood-level stressors. Main Outcomes and Measures: Overall model explanatory power and overpayments and underpayments for subgroups of interest for all Medicaid-reimbursable expenditures excepting long-term support services (mean annual cost = $5590 per member). Results: We studied 357 660 people who were FFS participants and 524 607 enrolled in MCOs with a combined 806 889 person-years of experience. The FFS program experience included more men (49.6% vs 43.6%), older patients (mean age of 26.1 years vs 21.6 years), and sicker patients (mean morbidity score of 1.16 vs 0.89) than MCOs. Overall, the SDH model performed well, but only slightly better than the diagnosis-based model, explaining most of the spending variation in the managed care population (validated R2 = 62.4) and reducing underpayments for several vulnerable populations. For example, raw costs for the quintile of people living in the most stressed neighborhoods were 9.6% ($537 per member per year) higher than average. Since greater medical morbidity accounts for much of this difference, the diagnosis-based model underpredicts costs for the most stressed quintile by about 2.1% ($130 per member per year). The expanded model eliminates the neighborhood-based underpayment, as well as underpayments of 72% for clients of the Department of Mental Health (observed costs of about $30 000 per year) and of 7% for those with serious mental illness (observed costs of about $16 000 per year). Conclusions and Relevance: Since October 2016, MassHealth has used an expanded model to allocate payments from a prespecified total budget to managed care organizations according to their enrollees' social and medical risk. Extra payments for socially vulnerable individuals could fund activities, such as housing assistance, that could improve health equity.

Health care demand elasticities by type of service.

We estimate within-year price elasticities of demand for detailed health care services using an instrumental variable strategy, in which individual monthly cost shares are instrumented by employer-year-plan-month average cost shares. A specification using backward myopic prices gives more plausible and stable results than using forward myopic prices. Using 171 million person-months spanning 73 employers from 2008 to 2014, we estimate that the overall demand elasticity by backward myopic consumers is -0.44, with higher elasticities of demand for pharmaceuticals (-0.44), specialists visits (-0.32), MRIs (-0.29) and mental health/substance abuse (-0.26), and lower elasticities for prevention visits (-0.02) and emergency rooms (-0.04). Demand response is lower for children, in larger firms, among hourly waged employees, and for sicker people. Overall the method appears promising for estimating elasticities for highly disaggregated services although the approach does not work well on services that are very expensive or persistent.

Refining Our Understanding of Value-Based Insurance Design and High Cost Sharing on Children.

BACKGROUND: There is significant concern about the financial burdens of new insurance plan designs on families, particularly families with children and youth with special health care needs (CYSHCN). With value-based insurance design (VBID) plans growing in popularity, this study examined the implications of selected VBID cost-sharing features on children. METHODS: We studied children's health care spending patterns in 2 data sets that include high deductible and narrow network plans among others. Medical Expenditure Panel Survey data from 2007 to 2013 on 22 392 children were used to study out-of-pocket (OOP) costs according to CYSHCN, family income, and spending. MarketScan large employer insurance claims data from 2007 to 2014 (N = 4 263 452) were used to test for differences in mean total payments and OOP costs across various health plans. RESULTS: Across the data sets, we found that existing health plans place significant financial burdens on families, particularly lower income households and families with CYSHCN; individuals among the top 10% of OOP spending averaged more than $2000 per child. Although high deductible and consumer-driven plans impose substantial OOP costs on children, they do not significantly reduce spending, whereas health maintenance organizations that use network restrictions and tighter management do. CONCLUSIONS: Our results do not support the conclusion that high cost-sharing features that are common in VBID plans will significantly reduce health care spending on children.

Risk-Adjustment Simulation: Plans May Have Incentives To Distort Mental Health And Substance Use Coverage.

Under the Affordable Care Act, the risk-adjustment program is designed to compensate health plans for enrolling people with poorer health status so that plans compete on cost and quality rather than the avoidance of high-cost individuals. This study examined health plan incentives to limit covered services for mental health and substance use disorders under the risk-adjustment system used in the health insurance Marketplaces. Through a simulation of the program on a population constructed to reflect Marketplace enrollees, we analyzed the cost consequences for plans enrolling people with mental health and substance use disorders. Our assessment points to systematic underpayment to plans for people with these diagnoses. We document how Marketplace risk adjustment does not remove incentives for plans to limit coverage for services associated with mental health and substance use disorders. Adding mental health and substance use diagnoses used in Medicare Part D risk adjustment is one potential policy step toward addressing this problem in the Marketplaces.

Mispricing in the medicare advantage risk adjustment model.

The Centers for Medicare and Medicaid Services (CMS) implemented hierarchical condition category (HCC) models in 2004 to adjust payments to Medicare Advantage (MA) plans to reflect enrollees' expected health care costs. We use Verisk Health's diagnostic cost group (DxCG) Medicare models, refined "descendants" of the same HCC framework with 189 comprehensive clinical categories available to CMS in 2004, to reveal 2 mispricing errors resulting from CMS' implementation. One comes from ignoring all diagnostic information for "new enrollees" (those with less than 12 months of prior claims). Another comes from continuing to use the simplified models that were originally adopted in response to assertions from some capitated health plans that submitting the claims-like data that facilitate richer models was too burdensome. Even the main CMS model being used in 2014 recognizes only 79 condition categories, excluding many diagnoses and merging conditions with somewhat heterogeneous costs. Omitted conditions are typically lower cost or "vague" and not easily audited from simplified data submissions. In contrast, DxCG Medicare models use a comprehensive, 394-HCC classification system. Applying both models to Medicare's 2010-2011 fee-for-service 5% sample, we find mispricing and lower predictive accuracy for the CMS implementation. For example, in 2010, 13% of beneficiaries had at least 1 higher cost DxCG-recognized condition but no CMS-recognized condition; their 2011 actual costs averaged US$6628, almost one-third more than the CMS model prediction. As MA plans must now supply encounter data, CMS should consider using more refined and comprehensive (DxCG-like) models.

Optimal health insurance for multiple goods and time periods.

We examine the efficiency-based arguments for second-best optimal health insurance with multiple treatment goods and multiple time periods. Correlated shocks across health care goods and over time interact with complementarity and substitutability to affect optimal cost sharing. Health care goods that are substitutes or have positively correlated demand shocks should have lower optimal patient cost sharing. Positive serial correlations of demand shocks and uncompensated losses that are positively correlated with covered health services also reduce optimal cost sharing. Our results rationalize covering pharmaceuticals and outpatient spending more fully than is implied by static, one good, or one period models.

Risk selection, risk adjustment and choice: concepts and lessons from the Americas.

Interest has grown worldwide in risk adjustment and risk sharing due to their potential to contain costs, improve fairness, and reduce selection problems in health care markets. Significant steps have been made in the empirical development of risk adjustment models, and in the theoretical foundations of risk adjustment and risk sharing. This literature has often modeled the effects of risk adjustment without highlighting the institutional setting, regulations, and diverse selection problems that risk adjustment is intended to fix. Perhaps because of this, the existing literature and their recommendations for optimal risk adjustment or optimal payment systems are sometimes confusing. In this paper, we present a unified way of thinking about the organizational structure of health care systems, which enables us to focus on two key dimensions of markets that have received less attention: what choices are available that may lead to selection problems, and what financial or regulatory tools other than risk adjustment are used to influence these choices. We specifically examine the health care systems, choices, and problems in four countries: the US, Canada, Chile, and Colombia, and examine the relationship between selection-related efficiency and fairness problems and the choices that are allowed in each country, and discuss recent regulatory reforms that affect choices and selection problems. In this sample, countries and insurance programs with more choices have more selection problems.

Bending the cost curve? Results from a comprehensive primary care payment pilot.

BACKGROUND: There is much interest in understanding how using bundled primary care payments to support a patient-centered medical home (PCMH) affects total medical costs. RESEARCH DESIGN AND SUBJECTS: We compare 2008-2010 claims and eligibility records on about 10,000 patients in practices transforming to a PCMH and receiving risk-adjusted base payments and bonuses, with similar data on approximately 200,000 patients of nontransformed practices remaining under fee-for-service reimbursement. METHODS: We estimate the treatment effect using difference-in-differences, controlling for trend, payer type, plan type, and fixed effects. We weight to account for partial-year eligibility, use propensity weights to address differences in exogenous variables between control and treatment patients, and use the Massachusetts Health Quality Project algorithm to assign patients to practices. RESULTS: Estimated treatment effects are sensitive to: control variables, propensity weighting, the algorithm used to assign patients to practices, how we address differences in health risk, and whether/how we use data from enrollees who join, leave, or change practices. Unadjusted PCMH spending reductions are 1.5% in year 1 and 1.8% in year 2. With fixed patient assignment and other adjustments, medical spending in the treatment group seems to be 5.8% (P=0.20) lower in year 1 and 8.7% (P=0.14) lower in year 2 than for propensity-weighted, continuously enrolled controls; the largest proportional 2-year reduction in spending occurs in laboratory test use (16.5%, P=0.02). CONCLUSIONS: Although estimates are imprecise because of limited data and quasi-experimental design, risk-adjusted bundled payment for primary care may have dampened spending growth in 3 practices implementing a PCMH.

Explaining health care expenditure variation: large-sample evidence using linked survey and health administrative data.

Explaining individual, regional, and provider variation in health care spending is of enormous value to policymakers but is often hampered by the lack of individual level detail in universal public health systems because budgeted spending is often not attributable to specific individuals. Even rarer is self-reported survey information that helps explain this variation in large samples. In this paper, we link a cross-sectional survey of 267 188 Australians age 45 and over to a panel dataset of annual healthcare costs calculated from several years of hospital, medical and pharmaceutical records. We use this data to distinguish between cost variations due to health shocks and those that are intrinsic (fixed) to an individual over three years. We find that high fixed expenditures are positively associated with age, especially older males, poor health, obesity, smoking, cancer, stroke and heart conditions. Being foreign born, speaking a foreign language at home and low income are more strongly associated with higher time-varying expenditures, suggesting greater exposure to adverse health shocks.