Decomposing growth in spending finds annual cost of treatment contributed most to spending growth, 1980-2006

Twenty-year trend in comorbidity score among adults aged 50-85 years in Lombardy, Italy: Age-Cohort-Period analysis and future trends

OBJECTIVES

To assess the effects of age, birth cohort, and period on comorbidity rates as well as project their future trends over the next 25 years.

DESIGN

Population-based retrospective observational study.

SETTING

Record linkage from the population-based healthcare utilisation database of Lombardy, Italy, between 2004 and 2023.

PARTICIPANTS

All beneficiaries of the Italian National Health Service (NHS) aged 50-85 years residing in Lombardy. Data were separately analysed for each year from 2004 to 2023, with thus the availability of 20 study populations.

PRIMARY OUTCOME MEASURES

Comorbidities were traced via the medical services provided by the NHS, and the overall quantification was obtained by the Multisource Comorbidity Score, which was developed and validated for the Italian population. The temporal analysis of the 20 yearly temporal comorbidity rates was obtained by the Age-Cohort-Period models. The comorbidities prevalence trends were forecasted from 2025 to 2050.

RESULTS

From 2004 to 2023, the prevalence of comorbidities declined from 46% to 40% in men and from 47% to 42% in women. An increase in prevalence between the ages of 50 and 85 years was observed for both women (from 33% to 63%) and men (from 29% to 67%). A declining prevalence was observed among cohorts born from 1922 to 1970 for both women (by 33%) and men (by 50%). A continued decline in the absolute number and prevalence rate of comorbidities is expected for both women and men until 2050.

CONCLUSIONS

The decline in ageing-related comorbidity prevalence over time may persist up to 2050. Improved medical care and public health initiatives benefiting individuals born in more recent years may counterbalance the expected trend of increasing comorbidity prevalence due to population ageing.

How to Predict Drug Expenditure: A Markov Model Approach with Risk Classes

BACKGROUND

Although pharmaceutical expenditures have been rising for decades, the question of their drivers remains unclear, and long-term projections of pharmaceutical spending are still scarce. We use a Markov approach considering different cost-risk groups to show the possible range of future drug spending in Germany and illustrate the influence of various determinants on pharmaceutical expenditure.

METHODS

We compute different medium and long-term projections of pharmaceutical expenditure in Germany up to 2060 and compare extrapolations with constant shares, time-to-death scenarios, and Markov modeling based on transition probabilities. Our modeling is based on data from a large statutory sickness fund covering around four million insureds. We divide the population into six risk groups according to their share of total pharmaceutical expenditures, determine their cost growth rates, survival and transition probabilities, and compute different scenarios related to changes in life expectancy or spending trends in different cost-risk groups.

RESULTS

If the spending trends in the high-cost groups continue, per-capita expenditure will increase by over 40% until 2040. By 2060, pharmaceutical expenditures could more than double, even if these groups would not benefit from rising life expectancy. By contrast, the isolated effect of demographic change would "only" lead to a long-term increase of around 15%.

CONCLUSION

The long-term development of pharmaceutical spending in Germany will depend mainly on future expenditure and life expectancy trends of particularly high-cost patients. Thus, appropriate pricing of new expensive pharmaceuticals is essential for the sustainability of the German healthcare system.

Health Care Spending on Respiratory Diseases in the United States, 1996-2016

Rationale: Respiratory conditions account for a large proportion of health care spending in the United States. A full characterization of spending across multiple conditions and over time has not been performed. Objectives: To estimate health care spending in the United States for 11 respiratory conditions from 1996 to 2016, providing detailed trends and an evaluation of factors associated with spending growth. Methods: We extracted data from the Institute of Health Metrics and Evaluation's Disease Expenditure Project Database, producing annual estimates in spending for 38 age and sex groups, 7 types of care, and 3 payer types. We performed a decomposition analysis to estimate the change in spending associated with changes in each of five factors (population growth, population aging, disease prevalence, service usage, and service price and intensity). Measurements and Main Results: Total spending across all respiratory conditions in 2016 was $170.8 billion (95% confidence interval [CI], $164.2-179.2 billion), increasing by $71.7 billion (95% CI, $63.2-80.8 billion) from 1996. The respiratory conditions with the highest spending in 2016 were asthma and chronic obstructive pulmonary disease, contributing $35.5 billion (95% CI, $32.4-38.2 billion) and $34.3 billion (95% CI, $31.5-37.3 billion), respectively. Increasing service price and intensity were associated with 81.4% (95% CI, 70.3-93.0%) growth from 1996 to 2016. Conclusions: U.S. spending on respiratory conditions is high, particularly for chronic conditions like asthma and chronic obstructive pulmonary disease. Our findings suggest that service price and intensity, particularly for pharmaceuticals, should be a key focus of attention for policymakers seeking to reduce health care spending growth.

What is the major driver of China's hospital medical expenditure growth? A decomposing analysis

OBJECTIVES

This study aimed to quantify increases in the medical expenditures of public hospitals associated with changes in service use and prices, which could inform policy efforts to curb the future growth of hospital medical expenditures.

DESIGN

Nationwide and provincial data regarding service volume, service price and intensity of public hospitals' outpatient and inpatient care from 2008 to 2018 were extracted from the China Health Statistical Yearbooks, and population size data were obtained from the 2019 China Statistical Yearbook.

METHODS

A decomposition analysis was performed to measure the relative effects of changes in service use (volume or its subcomponent factors) and service price and intensity on the increase in the inpatient and outpatient total medical expenditures of public hospitals from 2008 to 2018.

RESULTS

After adjusting for price inflation, the total medical expenditure of public hospitals increased by approximately threefold from 2008 to 2018. During this period, the increase in service volume was associated with 67.4% of the observed increase in the total medical expenditures in the inpatient sector and 57.2% of the observed increase in the total medical expenditures in the outpatient sector. Most of the service volume effect is due to an increase in the hospital utilisation rate. The growth in the utilisation rate was associated with 73.7% of the observed growth in the total medical expenditures in the inpatient sector and 60.3% of the observed growth in the total medical expenditures in the outpatient sector.

CONCLUSION

Service use, rather than price, appears to be the major driver of increases in medical expenditures in Chinese hospitals. An important policy implication for China and other countries with similar drivers is that the effect of controlling price and intensity growth on containing medical costs could be limited and controlling service utilisation growth could be essential.

Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data

Background

Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases.

Methods

In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment.

Results

Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending.

Conclusions

Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07262-x.

What has driven acute public hospital expenditure growth in South Australia? An analysis of the relative importance of major expenditure drivers between 2006-07 and 2017-18

ObjectiveThe aim of this study was to investigate whether increasing costs of delivering care have driven real growth in acute public hospital expenditure in South Australia (SA) and what has contributed to these real cost increases.MethodsUsing published time-series data, we decomposed inflation-adjusted growth in per capita total acute public hospital recurrent expenditure into its major utilisation and cost components to evaluate their relative contribution over the 12 years to 2017-18.ResultsReal per capita total acute public hospital recurrent expenditure grew by AU$667 (45.2%) over the 12-year period; of this, 86.0% was from real growth in input costs per weighted activity unit, with real growth in the average salaries of hospital staff accounting for AU$247 or 37.0%. Hospital utilisation rates contributed a minor 14.0%.ConclusionOver the 12 years to 2017-18, real growth in average clinical salaries was a more important driver of real growth in per capita total acute public hospital expenditure than rates of hospital utilisation. This would be facilitated by improvements in the scope, accuracy, quality and consistency of published national hospital data.What is known about the topic?Public hospital expenditure is one of the largest and fastest growing areas of government expenditure in Australia. Policy narratives often centre around demand pressures from an increasingly older, overweight, and chronically ill population. Comparatively little attention has been paid to the influence of increases in real input costs within the Australian context.What does this paper add?Real salary growth has been a major driver of acute public hospital recurrent expenditure growth in SA, whereas hospital utilisation rates have played a minor role.What are the implications for practitioners?A clearer understanding of the main drivers of acute public hospital expenditure growth and the resulting benefits to population health is needed to guide the efficient and sustainable use of scarce healthcare resources.

Factors related to the change in Swiss inpatient costs by disease: a 6-factor decomposition

There is currently little systematic knowledge about the contribution of different factors to the increase in health care spending in high-income countries such as Switzerland. The aim of this paper is to decompose inpatient care costs in the Swiss canton of Zurich by 100 diseases and 42 age/sex groups and to assess the contribution of six factors to the change in aggregate costs between 2013 and 2017. These six factors are population size, age and sex structure, inpatient treated prevalence, utilization in terms of stays per patient, length of stay per case, and costs per treatment day. Using detailed inpatient cost data at the case level, we find that the most important contributor to the change in disease-specific costs was a rise in costs per treatment day. For most conditions, this effect was partly offset by a reduction in the average length of stay. Changes in population size accounted for one third of the total increase, but population structure had only a small positive association with costs. The most expensive cases accounted for the largest part of the increase in costs, but the magnitude of this effect differed across diseases. A better understanding of the factors related to cost changes at the disease level over time is essential for the design of targeted health policies aiming at an affordable health care system.

Factors Contributing to the Rising National Cost of Glucose-Lowering Medicines for Diabetes During 2005-2007 and 2015-2017

OBJECTIVE

We examined changes in glucose-lowering medication spending and quantified the magnitude of factors that are contributing to these changes.

RESEARCH DESIGN AND METHODS

Using the Medical Expenditure Panel Survey, we estimated the change in spending on glucose-lowering medications during 2005-2007 and 2015-2017 among adults aged ≥18 years with diabetes. We decomposed the increase in total spending by medication groups: for insulin, by human and analog; and for noninsulin, by metformin, older, newer, and combination medications. For each group, we quantified the contributions by the number of users and cost-per-user. Costs were in 2017 U.S. dollars.

RESULTS

National spending on glucose-lowering medications increased by $40.6 billion (240%), of which insulin and noninsulin medications contributed $28.6 billion (169%) and $12.0 billion (71%), respectively. For insulin, the increase was mainly associated with higher expenditures from analogs (156%). For noninsulin, the increase was a net effect of higher cost for newer medications (+88%) and decreased cost for older medications (-34%). Most of the increase in insulin spending came from the increase in cost-per-user. However, the increase in the number of users contributed more than cost-per-user in the rise of most noninsulin groups.

CONCLUSIONS

The increase in national spending on glucose-lowering medications during the past decade was mostly associated with the increased costs for insulin, analogs in particular, and newer noninsulin medicines, and cost-per-user had a larger effect than the number of users. Understanding the factors contributing to the increase helps identify ways to curb the growth in costs.

Attributing medical spending to conditions: A comparison of methods

To understand the cost burden of medical care it is essential to partition medical spending into conditions. Two broad strategies have been used to measure disease-specific spending. The first attributes each medical claim to the condition that physicians list as its cause. The second decomposes total spending for a person over a year to their cumulative set of health conditions. Traditionally, this has been done through regression analysis. This paper has two contributions. First, we develop a new cost attribution method to attribute spending to conditions using a more flexible attribution approach, based on propensity score analysis. Second, we compare the propensity score approach to the claims-based approach and the regression approach in a common set of beneficiaries age 65 and older in the 2009 Medicare Current Beneficiary Survey. Our estimates show that the three methods have important differences in spending allocation and that the propensity score model likely offers the best theoretical and empirical combination.

Decomposition Analysis of Spending and Price Trends for Biologic Antirheumatic Drugs in Medicare and Medicaid

OBJECTIVE

Billions of public dollars are spent each year on biologic disease-modifying antirheumatic drugs (DMARDs), but the drivers of recent increases in biologic DMARD spending are unclear. This study was undertaken to characterize changes in total spending and unit prices for biologic DMARDs in Medicare and Medicaid programs and quantified the major sources of these spending increases.

METHODS

We accessed drug spending data from years 2012-2016, covering all Medicare Part B (fee-for-service), Medicare Part D, and Medicaid enrollees. After calculating 5-year changes in total spending and unit prices for each biologic DMARD as well as in aggregate, we performed standard decomposition analyses to isolate 4 sources of spending growth: drug prices, uptake (number of recipients), treatment intensity (mean number of doses per claim), and treatment duration (annual number of claims per recipient), both excluding and including time-varying rebates.

RESULTS

From 2012 to 2016, annual spending on public-payer claims for the 10 biologic DMARDs included in this study more than doubled ($3.8 billion to $8.6 billion), with median drug price increases of 51% in Medicare Part D (mean 54%) and 8% in Medicare Part B (mean 21%). With adjustment for general inflation, unit price increases alone accounted for 57% of the 5-year, $3.0 billion spending increase in Part D, while 37% of the spending increase was from increased uptake. Accounting for time-varying rebates, prices were still responsible for 54% of increased spending. Unit prices and spending were lower under Medicaid than under Medicare Part D, though temporal trends and contributors were similar.

CONCLUSION

Postmarket drug price changes alone account for the majority of the recent spending growth in biologic DMARDs. Policy interventions targeting price increases, particularly those under Medicare Part D plans, may help mitigate financial burdens for public payers and biologic DMARD recipients.

Decomposing changes in the growth of U.S. prescription drug use and expenditures, 1999-2016

OBJECTIVE

To analyze factors associated with changes in prescription drug use and expenditures in the United States from 1999 to 2016, a period of rapid growth, deceleration, and resumed above-average growth.

DATA SOURCES/STUDY SETTING

The Medical Expenditure Panel Survey (MEPS), containing household and pharmacy information on over five million prescription drug fills.

STUDY DESIGN

We use nonparametric decomposition to analyze drug use, average payment per fill, and per capita expenditure, tracking the contributions over time of socioeconomic characteristics, health status and treated conditions, insurance coverage, and market factors surrounding the patent cycle.

DATA COLLECTION/EXTRACTION METHODS

Medical Expenditure Panel Survey data were combined with information on drug approval dates and patent status.

PRINCIPAL FINDINGS

Per capita utilization increased by nearly half during 1999-2016, with changes in health status and treated conditions accounting for four-fifths of the increase. In contrast, per capita expenditures more than doubled, with individual characteristics only explaining one-third of the change. Other drivers of spending during this period include the changing pipeline of new drugs, drugs losing exclusivity, and changes in generic competition.

CONCLUSIONS

Long-term trends in treated conditions were the fundamental drivers of medication use, whereas factors involving the patent cycle accelerated and decelerated spending growth relative to trends in use.

High Spending Growth Rates For Key Diseases In 2000-14 Were Driven By Technology And Demographic Factors

We introduce a new source of detailed data on spending by medical condition to analyze US health care spending growth in the period 2000-14. We found that thirty conditions, which represented only 11.5 percent of all conditions studied, accounted for 42 percent of the real growth rate in per capita spending during this period, even though they accounted for only 13 percent of overall spending in 2000. Primary drivers of spending growth included the use of new technologies, a shift toward the provision of preventive-type services, and an aging and more obese population. The health benefits of many new technologies appeared to outweigh the associated expenditures on treatment, which indicates that these are cost-effective and provide a net value to society. However, while these technologies may be of value, new treatments are often more expensive than older ones.

Understanding Trends in Medicare Spending, 2007-2014

OBJECTIVES

To analyze the sources of per-beneficiary Medicare spending growth between 2007 and 2014, including the role of demographic characteristics, attributes of Medicare coverage, and chronic conditions.

DATA SOURCES

Individual-level Medicare spending and enrollment data.

STUDY DESIGN

Using an Oaxaca-Blinder decomposition model, we analyzed whether changes in price-standardized, per-beneficiary Medicare Part A and B spending reflected changes in the composition of the Medicare population or changes in relative spending levels per person.

DATA EXTRACTION METHODS

We identified a 5 percent sample of fee-for-service Medicare beneficiaries age 65 and above from years 2007 to 2014.

RESULTS

Mean payment-adjusted Medicare per-beneficiary spending decreased by $180 between the 2007-2010 and 2011-2014 time periods. This decline was almost entirely attributable to lower spending levels for beneficiaries. Notably, declines in marginal spending levels for beneficiaries with chronic conditions were associated with a $175 reduction in per-beneficiary spending. The decline was partially offset by the increasing prevalence of certain chronic diseases. Still, we are unable to attribute a large share of the decline in spending levels to observable beneficiary characteristics or chronic conditions.

CONCLUSIONS

Declines in spending levels for Medicare beneficiaries with chronic conditions suggest that changing patterns of care use may be moderating spending growth.

Health Care Spending on Diabetes in the U.S., 1996-2013

OBJECTIVE

Health care spending on diabetes in the U.S. has increased dramatically over the past several decades. This research describes health care spending on diabetes to quantify how that spending has changed from 1996 to 2013 and to determine what drivers are increasing spending.

RESEARCH DESIGN AND METHODS

Spending estimates were extracted from the Institute for Health Metrics and Evaluation's Disease Expenditure 2013 database. Estimates were produced for each year from 1996 to 2013 for each of 38 age and sex groups and six types of care. Data on disease burden were extracted from the Global Burden of Disease 2016 study. We analyzed the drivers of spending by measuring the impact of population growth and aging and changes in diabetes prevalence, service utilization, and spending per encounter.

RESULTS

Spending on diabetes in the U.S. increased from $37 billion (95% uncertainty interval $32-$42 billion) in 1996 to $101 billion ($97-$107 billion) in 2013. The greatest amount of health care spending on diabetes in 2013 occurred in prescribed retail pharmaceuticals (57.6% [53.8-62.1%] of spending growth) followed by ambulatory care (23.5% [21.7-25.7%]). Between 1996 and 2013, pharmaceutical spending increased by 327.0% (222.9-456.6%). This increase can be attributed to changes in demography, increased disease prevalence, increased service utilization, and, especially, increases in spending per encounter, which increased pharmaceutical spending by 144.0% (87.3-197.3%) between 1996 and 2013.

CONCLUSIONS

Health care spending on diabetes in the U.S. has increased, and spending per encounter has been the biggest driver. This information can help policy makers who are attempting to control future spending on diabetes.

Advanced Imaging Reduces Cost Compared to Standard of Care in Emergency Department of Triage of Acute Chest Pain

OBJECTIVE

To evaluate medical costs of novel therapies in complex medical settings using registry data.

DATA SOURCE/STUDY SETTING

Primary data, from 2008 to 2010. We used patient registry data to evaluate cost and quality performance of coronary computed tomography angiography (CCTA) in triaging chest pain patients in our tertiary care emergency department and to model financial performance under Medicare's two midnight rule.

STUDY DESIGN

Using generalized linear modeling, we retrospectively compared estimated expenditures for evaluation of low-to-intermediate-risk chest pain for demographic and medically risk matched samples of 894 patients each, triaged with CCTA or local standard of care (SOC) using Medicare reimbursement as a proxy.

DATA COLLECTION/EXTRACTION METHODS

Predefined data elements were downloaded from the hospital mainframe into the CCTA registry, where they were validated and maintained electronically.

PRINCIPLE FINDINGS

We found that predicted standard of care costs were 2.5 times higher on the initial visit and 1.98 times higher over 30 days (p < .001) than those using CCTA. Predicted cost was 1.6 times higher when we applied our two midnight rule model (p < .001).

CONCLUSION

Rapid assessment of treatment using registry data is a promising means of analyzing cost performance in complex health care environments.

Factors Associated With Increases in US Health Care Spending, 1996-2013

IMPORTANCE

Health care spending in the United States increased substantially from 1995 to 2015 and comprised 17.8% of the economy in 2015. Understanding the relationship between known factors and spending increases over time could inform policy efforts to contain future spending growth.

OBJECTIVE

To quantify changes in spending associated with 5 fundamental factors related to health care spending in the United States: population size, population age structure, disease prevalence or incidence, service utilization, and service price and intensity.

DESIGN AND SETTING

Data on the 5 factors from 1996 through 2013 were extracted for 155 health conditions, 36 age and sex groups, and 6 types of care from the Global Burden of Disease 2015 study and the Institute for Health Metrics and Evaluation's US Disease Expenditure 2013 project. Decomposition analysis was performed to estimate the association between changes in these factors and changes in health care spending and to estimate the variability across health conditions and types of care.

EXPOSURES

Change in population size, population aging, disease prevalence or incidence, service utilization, or service price and intensity.

MAIN OUTCOMES AND MEASURES

Change in health care spending from 1996 through 2013.

RESULTS

After adjustments for price inflation, annual health care spending on inpatient, ambulatory, retail pharmaceutical, nursing facility, emergency department, and dental care increased by $933.5 billion between 1996 and 2013, from $1.2 trillion to $2.1 trillion. Increases in US population size were associated with a 23.1% (uncertainty interval [UI], 23.1%-23.1%), or $269.5 (UI, $269.0-$270.0) billion, spending increase; aging of the population was associated with an 11.6% (UI, 11.4%-11.8%), or $135.7 (UI, $133.3-$137.7) billion, spending increase. Changes in disease prevalence or incidence were associated with spending reductions of 2.4% (UI, 0.9%-3.8%), or $28.2 (UI, $10.5-$44.4) billion, whereas changes in service utilization were not associated with a statistically significant change in spending. Changes in service price and intensity were associated with a 50.0% (UI, 45.0%-55.0%), or $583.5 (UI, $525.2-$641.4) billion, spending increase. The influence of these 5 factors varied by health condition and type of care. For example, the increase in annual diabetes spending between 1996 and 2013 was $64.4 (UI, $57.9-$70.6) billion; $44.4 (UI, $38.7-$49.6) billion of this increase was pharmaceutical spending.

CONCLUSIONS AND RELEVANCE

Increases in US health care spending from 1996 through 2013 were largely related to increases in health care service price and intensity but were also positively associated with population growth and aging and negatively associated with disease prevalence or incidence. Understanding these factors and their variability across health conditions and types of care may inform policy efforts to contain health care spending.

Health Care Spending Slowdown From 2000 To 2010 Was Driven By Lower Growth In Cost Per Case, According To A New Data Source

In 2015 the Bureau of Economic Analysis released an experimental set of measures referred to as the Health Care Satellite Account, which tracks national health care spending by medical condition. These statistics improve the understanding of the health care sector by blending medical claims data and survey data to present measures of national spending and cost of treatment by condition. This article introduces key aspects of the new account and uses it to study the health spending slowdown that occurred in the period 2000-10. Our analysis of the account reveals that the slowdown was driven by a reduction of growth in cost per case but that spending trends varied greatly across conditions and differentially affected the slowdown. More than half of the overall slowdown was accounted for by a slowdown in spending on circulatory conditions. However, there were more dramatic slowdowns in spending on categories such as endocrine system and musculoskeletal conditions than in spending on other categories, such as cancers.

Adjusting Health Expenditures for Inflation: A Review of Measures for Health Services Research in the United States

OBJECTIVE

To provide guidance on selecting the most appropriate price index for adjusting health expenditures or costs for inflation.

DATA SOURCES

Major price index series produced by federal statistical agencies.

STUDY DESIGN

We compare the key characteristics of each index and develop suggestions on specific indexes to use in many common situations and general guidance in others.

DATA COLLECTION/EXTRACTION METHODS

Price series and methodological documentation were downloaded from federal websites and supplemented with literature scans.

PRINCIPAL FINDINGS

The gross domestic product implicit price deflator or the overall Personal Consumption Expenditures (PCE) index is preferable to the Consumer Price Index (CPI-U) to adjust for general inflation, in most cases. The Personal Health Care (PHC) index or the PCE health-by-function index is generally preferred to adjust total medical expenditures for inflation. The CPI medical care index is preferred for the adjustment of consumer out-of-pocket expenditures for inflation. A new, experimental disease-specific Medical Care Expenditure Index is now available to adjust payments for disease treatment episodes.

CONCLUSIONS

There is no single gold standard for adjusting health expenditures for inflation. Our discussion of best practices can help researchers select the index best suited to their study.

Estimating Marginal Healthcare Costs Using Genetic Variants as Instrumental Variables: Mendelian Randomization in Economic Evaluation

Accurate measurement of the marginal healthcare costs associated with different diseases and health conditions is important, especially for increasingly prevalent conditions such as obesity. However, existing observational study designs cannot identify the causal impact of disease on healthcare costs. This paper explores the possibilities for causal inference offered by Mendelian randomization, a form of instrumental variable analysis that uses genetic variation as a proxy for modifiable risk exposures, to estimate the effect of health conditions on cost. Well-conducted genome-wide association studies provide robust evidence of the associations of genetic variants with health conditions or disease risk factors. The subsequent causal effects of these health conditions on cost can be estimated using genetic variants as instruments for the health conditions. This is because the approximately random allocation of genotypes at conception means that many genetic variants are orthogonal to observable and unobservable confounders. Datasets with linked genotypic and resource use information obtained from electronic medical records or from routinely collected administrative data are now becoming available and will facilitate this form of analysis. We describe some of the methodological issues that arise in this type of analysis, which we illustrate by considering how Mendelian randomization could be used to estimate the causal impact of obesity, a complex trait, on healthcare costs. We describe some of the data sources that could be used for this type of analysis. We conclude by considering the challenges and opportunities offered by Mendelian randomization for economic evaluation.

Guidelines for Measuring Disease Episodes: An Analysis of the Effects on the Components of Expenditure Growth

OBJECTIVE

To provide guidelines to researchers measuring health expenditures by disease and compare these methodologies' implied inflation estimates.

DATA SOURCE

A convenience sample of commercially insured individuals over the 2003 to 2007 period from Truven Health. Population weights are applied, based on age, sex, and region, to make the sample of over 4 million enrollees representative of the entire commercially insured population.

STUDY DESIGN

Different methods are used to allocate medical-care expenditures to distinct condition categories. We compare the estimates of disease-price inflation by method.

PRINCIPAL FINDINGS

Across a variety of methods, the compound annual growth rate stays within the range 3.1 to 3.9 percentage points. Disease-specific inflation measures are more sensitive to the selected methodology.

CONCLUSION

The selected allocation method impacts aggregate inflation rates, but considering the variety of methods applied, the differences appear small. Future research is necessary to better understand these differences in other population samples and to connect disease expenditures to measures of quality.

[Nutritional status assessment in Geriatrics: Consensus declaration by the Spanish Society of Geriatrics and Gerontology NutritionWork Group]

Ongoing population ageing is one of the factors influencing the increase in the prevalence of undernutrition, as elderly people are a vulnerable group due to their biological, psychological and social characteristics. Despite its high prevalence, undernutrition is underdiagnosed in the geriatric sphere. For this reason, the aim of this consensus document is to devise a protocol for geriatric nutritional assessment. A multidisciplinary team has been set up within the Spanish Society of Geriatrics and Gerontology (in Spanish Sociedad Española de Geriatría y Gerontología [SEGG]) in order to address undernutrition and risk of undernutrition so that they can be diagnosed and treated in an effective manner. The MNA-SF is a practical tool amongst the many validated methods for nutritional screening. Following suspicion of undernutrition, or after establishing the presence of undernutrition, a full assessment will include a detailed nutritional history of the patient. The compilation of clinical-nutritional and dietetic histories is intended to help in identifying the possible risk factors at the root of a patient's undernutrition. Following this, an anthropometric assessment, combined with laboratory data, will describe the patient's physical and metabolic changes associated to undernutrition. Currently, the tendency is for further nutritional assessment through the use of non-invasive techniques to study body composition in association with functional status. The latter is an indirect index for nutritional status, which is very interesting from a geriatrician's point of view. To conclude, correct nutritional screening is the fundamental basis for an early undernutrition diagnosis and to assess the need for nutritional treatment. In order to achieve this, it is fundamental to foster research in the field of nutritional geriatrics, in order to expand our knowledge base and to increasingly practice evidence-based geriatrics.

Explaining the Growth in US Health Care Spending Using State-Level Variation in Income, Insurance, and Provider Market Dynamics

The slowed growth in national health care spending over the past decade has led analysts to question the extent to which this recent slowdown can be explained by predictable factors such as the Great Recession or must be driven by some unpredictable structural change in the health care sector. To help address this question, we first estimate a regression model for state personal health care spending for 1991-2009, with an emphasis on the explanatory power of income, insurance, and provider market characteristics. We then use the results from this simple predictive model to produce state-level projections of health care spending for 2010-2013 to subsequently compare those average projected state values with actual national spending for 2010-2013, finding that at least 70% of the recent slowdown in health care spending can likely be explained by long-standing patterns. We also use the results from this predictive model to both examine the Great Recession's likely reduction in health care spending and project the Affordable Care Act's insurance expansion's likely increase in health care spending.