US Spending on Personal Health Care and Public Health, 1996-2013.

doi:10.1001/JAMA.2016.16885

US Spending on Personal Health Care and Public Health,

1996-2013

Joseph L. Dieleman, PhD; Ranju Baral, PhD; Maxwell Birger, BS; Anthony L. Bui, MPH; Anne Bulchis, MPH; Abigail Chapin, BA; Hannah Hamavid, BA;

Cody Horst, BS; Elizabeth K. Johnson, BA; Jonathan Joseph, BS; Rouselle Lavado, PhD; Liya Lomsadze, BS; Alex Reynolds, BA; Ellen Squires, BA;

Madeline Campbell, BS; Brendan DeCenso, MPH; Daniel Dicker, BS; Abraham D. Flaxman, PhD; Rose Gabert, MPH; Tina Highfill, MA;

Mohsen Naghavi, MD, MPH, PhD; Noelle Nightingale, MLIS; Tara Templin, BA; Martin I. Tobias, MBBCh; Theo Vos, MD; Christopher J. L. Murray, MD, DPhil

IMPORTANCE

US health care spending has continued to increase, and now accounts for more

than 17% of the US economy. Despite the size and growth of this spending, little is known

about how spending on each condition varies by age and across time.

OBJECTIVE To systematically and comprehensively estimate US spending on personal health

care and public health, according to condition, age and sex group, and type of care.

DESIGN AND SETTING Government budgets, insurance claims, facility surveys, household

surveys, and official US records from 1996 through 2013 were collected and combined. In

total, 183 sources of data were used to estimate spending for 155 conditions (including

cancer, which was disaggregated into 29 conditions). For each record, spending was

extracted, along with the age and sex of the patient, and the type of care. Spending was

adjusted to reflect the health condition treated, rather than the primary diagnosis.

EXPOSURES Encounter with US health care system.

MAIN OUTCOMES AND MEASURES National spending estimates stratified by condition, age

and sex group, and type of care.

RESULTS From 1996 through 2013, $30.1 trillion of personal health care spending was

disaggregated by 155 conditions, age and sex group, and type of care. Among these 155

conditions, diabetes had the highest health care spending in 2013, with an estimated

$101.4 billion (uncertainty interval [UI], $96.7 billion-$106.5 billion) in spending,

including 57.6% (UI, 53.8%-62.1%) spent on pharmaceuticals and 23.5% (UI, 21.7%-25.7%)

spent on ambulatory care. Ischemic heart disease accounted for the second-highest

amount of health care spending in 2013, with estimated spending of $88.1 billion

(UI, $82.7 billion-$92.9 billion), and low back and neck pain accounted for the third-highest

amount, with estimated health care spending of $87.6 billion (UI, $67.5 billion-$94.1 billion).

The conditions with the highest spending levels varied by age, sex, type of care, and year.

Personal health care spending increased for 143 of the 155 conditions from 1996 through

2013. Spending on low back and neck pain and on diabetes increased the most over the 18

years, by an estimated $57.2 billion (UI, $47.4 billion-$64.4 billion) and $64.4 billion

(UI, $57.8 billion-$70.7 billion), respectively. From 1996 through 2013, spending

on emergency care and retail pharmaceuticals increased at the fastest rates (6.4%

[UI, 6.4%-6.4%] and 5.6% [UI, 5.6%-5.6%] annual growth rate, respectively), which

were higher than annual rates for spending on inpatient care (2.8% [UI, 2.8%–2.8%] and

nursing facility care (2.5% [UI, 2.5%-2.5%]).

CONCLUSIONS AND RELEVANCE Modeled estimates of US spending on personal health care

and public health showed substantial increases from 1996 through 2013; with spending on

diabetes, ischemic heart disease, and low back and neck pain accounting for the highest

amounts of spending by disease category. The rate of change in annual spending varied

considerably among different conditions and types of care. This information may have

implications for efforts to control US health care spending.

JAMA. 2016;316(24):2627-2646. doi:

10.1001/jama.2016.16885

Editorial page 2604

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Author Affiliations: Author

affiliations are listed at the end of this

article.

Corresponding Author: Joseph L.

Dieleman, PhD, Institute for

Health Metrics and Evaluation,

University of Washington, 2301 Fifth

Ave, Ste 600, Seattle, WA 98121

(

dieleman@uw.edu).

Research

JAMA |

Original Investigation

(Reprinted) 2627

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H

ealth care spending in the United States is greater than

in any other country in the world.

1

According to offi-

cial US estimates, spending on health care reached

$2.9 trillion in 2014, amounting to more than 17% of the US

economy and more than $9110 per person.

2

Between 2013 and

2014 alone, spending on health care increased 5.3%.

2

Despite the resources spent on health c are, much re-

mains unknown about how much is spent for each condition,

or how spending on these conditions differs across ages and

time. Understanding how health care spending varies can help

health system researchers and policy makers identify which

conditions, age and sex groups, and types of care are driving

spending increases. In particular, this information can be used

to identify where new technologies and processes may yield

a potential return on investment.

The objective of this study was to systematically and com-

prehensively estimat e US spending on personal health care and

public health, according to condition (ie, disease or health cat-

egory), age and sex group, and type of care.

Methods

Conceptual Framework

This project received review and approval from the Univer-

sity of Washington institutional review board, and because data

was used from a deidentified database, informed consent was

waived. The strategy of this research was to use nationally rep-

resentative data containing information about patient inter-

actions with the health care system to estimate spending by

condition, age and sex group, and type of health care. Data were

scaled to reflect the official US government estimate of per-

sonal health care spending for each type of care for each year

of the study. These official estimates, reported in the National

Health Expenditure Accoun ts (NHEA), disaggregate total health

spending into personal health spending, government public

health activities, investment, and 2 administrative cost cat-

egories associated with public health insurance such as Medi-

care and Medic aid. Personal health spending, which com-

posed 89.5% of total health spending in 2013, was the focus

of this study and was defined in the NHEA as “the total amount

spent to treat individuals with specific medical conditions.”

3

In addition to estimating personal health care spending, this

study also made preliminary estimates disaggregating feder-

ally funded public health spending.

The NHEA divided total personal health care spending into

10 mutually exclusive types of care, which included hospital

care, physician and clinical services, nursing facility care, and

prescribed retail pharmaceutic al spending, among others.

These types of care are not routinely ascribed to specific health

conditions.

2

To better align the NHEA personal health spend-

ing accounts with health system encounter data, spending

fractions from the Medical Expenditure Panel Survey

4

and

methods described by Roehrig

5

were used to group these 10

categories into 6 types of personal health care: inpatient care,

ambulatory care, emergency department care, nursing facil-

ity care, and dental care, along with spending on prescribed

retail pharmaceuticals. Ambulatory care included health care

in urgent care facilities, and prescribed retail pharmaceuti-

cals only included prescribed medicine that was purchased in

a retail setting, rather than that provided during an inpatient

or ambulator y care v isit. Spending on physicians was in-

cluded in inpatient, ambulatory, emergency department care,

and nursing facility care, depending on the type of care pro-

vided. Together, health care spending incurred in these 6 types

of care constituted between 84.0% and 85.2% of annual per-

sonal health care spending from 1996 through 2013.

2

Across

all 18 years of this study, personal health c are spending

that fell outside of the 6 types of care tracked was on over-

the-counter pharmaceutic als (6.6%), nondurable and du-

rable medical dev ices (5.1%), and home health (3.6%). A

detailed Supplement provides additional information about all

the methods used for this analysis.

Spending on the 6 types of personal health care was then

disaggregated across 155 mutually exclusive and collec tively

exhaustiv e conditions and 38 age and sex groups. Each sex was

divided into 19 5-year age groups, with the exception of the

group aged 0 to 4 years, which was split into 2 c ategories

(<1 year and 1-4 years) for more granular analysis. Of the 155

conditions, 140 were based on the disease categories used in

the Global Burden of Disease (GBD) 2013 study.

6

The remain-

ing 15 conditions were associated with substantial health care

spending but were not underlying conditions of health bur-

den, and were thus excluded from the GBD or included as a

part of other underlying conditions. Examples of these addi-

tional categories include well visits, routine dental visits, preg-

nancy and postpartum care, septicemia, renal failure, and treat-

ment of 4 major risk fac tors—hypertension, hyperlipidemia,

obesity, and tobacco use. For these 4 risk f actors, spending on

the treatment of the risk factor was reported separately,

whereas spending on the treatment of diseases the risk factor

may have caused were allocated to the actual disease. For ex-

ample, spending on statins for hyperlipidemia was consid-

ered spending on the treatment of each risk factor, and spend-

ing on treatment of ischemic heart disease (IHD) reported

spending for the treatment of the disease. Spending on these

4 risk factors was reported separately bec ause of the large

amount of spending associated with these risk factors and the

ability to estimate this spending in the underly ing health sys-

tem encounter data. Spending on treatment of other risk fac-

tors, such as dietary risks or high fasting glucose, was allo-

cated to the conditions resulting from these risks. All 155

conditions of health care spending and the major spending in

each c ategory is shown in eTables 8.1, 9.1, and 10.1 of the

Supplement. More information about the framework of this

study is included in section 1 of the Supplement.

Data

For the 6 types of personal health care tracked in this study,

encounter-level mic rodata were used to determine the

amount of resources spent on each condition and age and sex

group for each year. An encounter was defined as an interac-

tion with the medical system, such as an inpatient or nursin g

care facility admission; an emergency department, dental,

or ambulator y care v isit; or the purchase of a prescribed

pharmaceutical.

7

Health care spending, patient age and sex,

Research

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type of care, and patient diagnoses were extracted from

insurance claims, facility surveys, and household surveys. In

addition, sample weights were used to make the studies

nationally representative. Table 1 reports all microdata

sources used for this study. Together, these sources included

more than 163 million health system encounters.

The Medical Expenditure Panel Survey began in 1996.

4

The

Medical Expenditure Panel Survey was used as an input into the

ambulatory, dental, emergency department, and prescribed re-

tail pharmaceutical spending estimates. Because of the impor-

tance of the Medical Expenditure Panel Survey to this analy-

sis, this study made annual estimates extending back to 1996

but not before. More information about the data sources used

for this study is included in section 2 of the

Supplement.

Identifying the Condition of Health Care Spending

In these microdata, households, physicians, or health system

administrators reported a primary diagnosis using Interna-

tional Classification of Diseases, Ninth Revision (ICD-9) cod-

ing. In the rare case that the primary diagnosis was not iden-

tified and more than 1 diagnosis was reported, the diagnosis

listed first was assumed the primary diagnosis unless an in-

jury diagnosis was included. With the exception of injuries oc-

curring within a medical facility, injury codes, such as “fall”

and “street or highway accident,” were prioritized over other

diagnoses. This was done because many data sources report

injuries separately from other diagnoses and it was unclear

which diagnosis was the primary.

ICD-9 diagnoses were grouped to form 155 conditions using

methods described in the GBD study.

6

ICD-9 diagnoses related

to the na ture of an injury (rather than the condition) or diag-

noses providing imprecise information, such as “certain early

complications of trauma” and “care involving use of rehabili-

tation procedures,” were proportionally redistributed to 1 of the

155 condition categories using methods developed for the

GBD.

6,8

More informa tion about how encounters were strati-

fied by condition is included in section 3 of the

Supplement.

Estimating Spending

Spending on encounters with the same primary diagnosis, age

and sex group, year, and type of health care were aggregated.

Sampling weights were used to ensure that the estimates re-

mained nationally representative.

On average, comorbidities make health c are more com-

plicated and more expensive.

9-11

Attributing all of the re-

sources used in a health care encounter to the primary diag-

nosis biases the estimates.

7

To account for the presence of

comorbidities, a previously developed regression-based

Table 1. Health System Encounter and Claims Data Sources Used to Disaggregate Spending by Condition,

Age and Sex Groups, and Type of Care

Microdata Source Years Observations Metric

a

Mean Patient-Weighted

Metric

b

Ambulatory Care

MEPS 1996-2013 2 680 505 Spending ($US billions) 302.68

Visits (thousands) 1 601 515.67

NAMCS/NHAMCS 1996-2011 955 958 Visits (thousands) 98 469.18

MarketScan

c

2000, 2010, 2012 1 134 628 128 Treated prevalence NA

Inpatient Care

NIS 1996-2012 128 223 548 Spending ($US billions) 781.50

Bed days (thousands) 167 161.94

MarketScan

c

2000, 2010, 2012 65 679 028 Treated prevalence NA

Emergency Department Care

MEPS 1996-2013 89 462 Spending ($US billions) 30.47

Visits (thousands) 45 457.97

NHAMCS 1996-2011 464 279 Visits (thousands) 82 089.07

MarketScan

c

2000, 2010, 2012 77 566 041 Treated prevalence NA

Nursing Facility Care

Medicare Claims

Data

d

1999-2001, 2002,

2004, 2006, 2008,

2010, 2012

25 449 729 Spending ($US billions) 30.44

Bed days (thousands) 68 451.04

NNHS 1997, 1999, 2004 23 428 Spending ($US billions) 50.50

Bed days (thousands) 403 564.31

MarketScan

c

2000, 2010, 2012 7 735 120 Treated prevalence NA

MCBS 1999-2011 12 608 021

Dental Care

MEPS 1996-2013 488 922 Spending ($US billions) 69.46

Visits (thousands) 278 481.55

Prescribed Retail Pharmaceuticals

MEPS 1996-2013 4 908 359 Spending ($US billions) 189.37

Visits (thousands) 2 748 649.75

Abbreviations: MCBS, Medicare

Current Beneficiaries Survey;

MEPS, Medical Expenditure Panel

Survey; NA, not applicable;

NAMCS, National Ambulatory

Medical Care Survey;

NHAMCS, National Hospital

Ambulatory Medical Care

Survey; NIS, National Inpatient

Sample; NNHS, National Nursing

Home Survey.

a

Metric indicates what each data

source was used to estimate

or model.

b

Mean patient-weighted metric

is the average across time for the

measurement of each metric. This

measurement was adjusted to be

nationally representative using the

provided survey patient-weights.

c

MarketScan was developed by

Truven Health Analytics.

d

Medicare Claims Data refers to the

Limited Data Set from the Center for

Medicare & Medicaid Services.

Spending on US Health Care, 1996-2013

Original Investigation Research

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method was used to adjust health care spending. As a conse-

quence, conditions that are often accompanied by costly co-

morbidities decreased after comorbidity adjustment, whereas

conditions often considered comorbidities increased after ad-

justment. Thus, the adjusted spending estimates reflect the

spending attributed to each condition, rather than the spend-

ing attributed to primary diagnoses. More information about

adjusting the spending estimates for the presence of comor-

bidities is included in section 5 of the

Supplement.

The spendin g estimates for each type of care wer e scaled

to reflect the adjusted annual health care spending reported by

the NHEA. This procedure is common, as no single data source

offers a census of spending in all health care settings.

12,13

This

scaling procedure assumed that the spending captured in data

used for this study was representati ve of spending in the total

population. Spending was adjusted for infla tion before an y mod-

eling, and all estimates are reported in 2015 US dollars. More in-

formation about scaling these estimates to reflect the NHEA type

of care total is included in section 5 of the

Supplement.

Addressing Data Nonrepresentativeness

Several data limitations made additional adjustments neces-

sary. First, health care charges, rather than spending, were re-

ported in the National Inpatient Sample, which was used to

measure inpatient care spending.

14

Because actual spending

is generally a f raction of the charge, charge data were ad-

justed to reflect actual spending using a prev iously devel-

oped regression-based adjustment.

15

This adjustment was

stratified by condition, primary payer, and year because the

average amount paid per $1 charged varied systematically

across these dimensions. This adjustment allowed high-

quality inpatient charge data to be used and is described in sec-

tion 5 of the Supplement.

Second, to address concerns related to small sample sizes

and undersampled rare conditions, a Bayesian hierarchical

model was applied. For all types of care except prescribed re-

tail pharmaceuticals and emergency department care, 2 or 3 data

sources were combined to generate spendin g estimates with

complete time and age trends, and to leverage the strength of

each data source. A large number of models were considered

for this process. The final model was selected because of its flex -

ibility, responsiveness to patterns in the raw data, and ability

to combine disparate data to produce a single estimate. The

model was employedindependen tly for each condition,se x, and

type of care combination. More information about this model-

ing is included in section 4 of the

Supplement.

The third adjustment addressed the fact that ambulatory

and inpatient care data sources used for this study underes-

timate spending at specialty mental health and substance abuse

facilities.

4,14

To address this problem, spending on these types

of care was split into portions that reflect mental health spend-

ing and substance abuse spending, and spending was scaled

to an appropriate total reported by the US Substance Abuse and

Mental Health Services Administration.

16

This adjustment en-

sured that the total spending on mental health and substance

abuse in these settings was commensurate with official US rec-

ords. More information about this adjustment is included in

section 5 of the

Supplement.

Fourth, nursing fac ility care data were adjusted to ac-

count for differences in short-term and long-term stays. US

Medicare reimburses nursing facilities for up to 100 days of care

after a qualifying hospital event. To incorporate the best data

available, Medicare data were used to measure spending for

these short-term nursing facility stays, and 2 other sources of

nationally representative data were used to estimate spend-

ing for nursing facility stays longer than 100 days.

17-19

Spend-

ing on short-term and long-term nursing facility stays were

added together and formed the total amount of spending in

nursing facility c are. This adjustment ensured the best data

available were used to measure spending in nursing facili-

ties, and ensured that disparate patterns of health care spend-

ing in short-term and long-term nursing facility care were con-

sidered. More information about this adjustment is included

in section 5 of the

Supplement.

Quantifying Uncertainty for Personal Health Care Spending

For all types of care, uncertainty intervals (UIs) were calcu-

lated by bootstrapping the underlying encounter-level data

1000 times. The entire estimation process was completed for

each bootstrap sample independently, and 1000 estimates were

generated for each condition, age and sex group, year, and type

of care. The estimates reported in this article are the mean of

these 1000 estimates. A UI was constructed using the 2.5th and

97.5th percentiles. Bootstrapping methods assume that the em-

pirical distribution of errors in the sample data approximates

the population ’s distribution. This may not be true for our most

disaggregated estimates. Furthermore, bootstrapping meth-

ods capture only some types of uncertainty and do not reflect

the uncertainty associated with some modeling and process

decisions. Because of these limitations, the reported UIs should

not be considered precise. Furthermore, the UIs have not been

derived analytically or been calibrated to reflect a specific de-

gree of uncertainty. The UIs are included to reflect relative un-

certainty across the disparate set of measurements. More in-

formation about generating UIs for personal health spending

estimates is included in section 6 of the

Supplement.

Estimating Federal Public Health Care Spending

In addition to the 6 types of personal health care spending, this

study also generated preliminary estimates disaggregating fed-

erally funded public health spending by condition, age and sex

group, and year from 1996 through 2013. Encounter-level data

did not exist for public health spending. Instead, federal pub-

lic health program budget data were extracted from the 4 pri-

mary federal agencies providing public health funding: the

Health Resources and Services Administration, the Centers for

Disease Control and Prevention, the Substance Abuse and

Mental Health Services Administration, and the US Food and

Drug Administration. For each of these agencies, individual

programs were mapped to the associated conditions. Spend-

ing estimates were extracted from audited appropriations re-

ports. A series of linear regressions was used to fill in pro-

gram spending when not available. Population estimates and

program-specific information were used to disaggregate pro-

gram spending across age and sex groups. Because the NHEA

does not include resources transferred to state and local public

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health offices in its estimate of federal public health spend-

ing, disaggregated public health spending estimates were not

scaled. More information about how public health spending

was estimated is included in section 7 of the

Supplement. All

data manipulation and statistical analyses were completed

using Stata (StataCorp), version 13.1; R (R Foundation), ver-

sion 3.3.1; Python (Python Software Foundation), version 3.5.1;

and PyMC2,

20

version 2.3.6.

21,22

Results

Conditions Leading to the Most Personal Health Care

Spending in 2013

Among the aggregated condition categories (Table 2), cardio-

vascular disease, which includes IHD and cerebrovascular

disease but excludes spending on the treatment of hyperlip-

idemia and hypertension, was the largest category of spend-

ing, w ith an estimated $231.1 billion ( UI, $218.5 billion-

$240.7 billion) spent in 2013. Of this spending, 5 7.3% (UI,

52.6%-60.9%) was in an inpatient setting, whereas 65.2%

(UI, 61.3%-68.2%) was for patients 65 years and older. Diabe-

tes, urogenital, blood, and endocrine diseases made up the

second-largest category with an estimated $224.5 billion (UI,

$216.4 billion-$233.5 billion), and the spending was spread

relatively evenly ac ross ambulator y care, prescribed retail

pharmaceuticals, and inpatient care. Of the aggregated con-

ditions, spending on the risk factors (the treatment of hyper-

tension, hyperlipidemia, and obesity, and tobacco cessation)

and musc uloskeletal disorders were estimated to inc rease

the fastest, with estimated rates of 6.6% (UI, 5.9%-7.3%) and

5.4% (UI, 4.7%-6.0%), respectively.

In 2013, among all 155 conditions, the 20 top conditions

accounted for an estimated 57.6% (UI, 56.9%-58.3%) of per-

sonal health c are spending, which totaled $1.2 trillion

(Table 3). More resources were estimated to be spent on dia-

betes than an y other condition, with an estima t ed $101.4 bil-

lion (UI, $96.7 billion-$106.5 billion) spent in 2013. Pre-

scribed retail pharmaceutic al spending accounted for an

estimated 57.6% (UI, 53.8%-62.1%) of total diabetes health

care spending, whereas an estimated 87.1% (UI, 83.0%-

91.6%) of spending on diabetes was inc urred by those 45

years and older. IHD was estimated to account for the

second-highest amount of health care spending, a t $88.1 bil-

lion (UI, $82.7 billion-$92.9 billion). Most IHD spending

occurred in inpatient care settin gs (56.5% [UI, 51.7%-60.6%])

and was account ed for by those 65 years or older (61.2% [UI,

57.0%-64.8%]). Spending on IHD excludes spending on the

treatment of hypertension and hyperlipidemia, both of

which contribute to IHD and for which treatment often

requires substantial spending on prescribed retail pharma-

ceuticals. Spending on the treatment of these 2 risk factors in

2013 was estimated to be $83.9 billion ( UI, $80.2 billion-

$88.8 billion) and $51.8 billion (UI, $48.9 billion-$54.6 bil-

lion), respectively. Low back and neck pain was estimated to

be the third-largest condition of health c are spending, at

$87.6 billion (UI, $67.5 billion-$94.1 billion), with the majority

of this spending (60.5% [UI, 49.3%-63. 8 %]) in ambulatory care.

Table 2. Personal Health Care Spending in the United States by Aggregated Condition Category for 2013

a

Rank

b

Aggregated Condition Category

2013 Spending

(Billions of

Dollars), $

Annualized Rate

of Change,

1996-2013, %

2013 Spending by Type of Care, % 2013 Spending by Age, %

Ambulatory

Care Inpatient Care Pharmaceuticals

Emergency

Care

Nursing Facility

Care <20 Years ≥65 Years

1 Cardiovascular diseases 231.1 1.2 18.4 57.3 6.2 2.7 15.3 0.9 65.2

2 Diabetes, urogenital, blood, and endocrine diseases 224.5 5.1 31.5 23.0 31.0 4.2 10.3 3.5 42.6

3 Other noncommunicable diseases 191.7 3.1 43.0 11.3 6.5 2.8 3.2 15.3 32.9

4 Mental and substance abuse disorders 187.8 3.7 52.1 19.0 20.9 1.6 6.5 19.8 12.8

5 Musculoskeletal disorders 183.5 5.4 47.7 37.0 6.2 3.3 5.9 1.9 40.0

6 Injuries 168.0 3.3 34.5 33.7 0.7 25.1 6.1 14.1 27.5

7 Communicable, maternal, neonatal, and nutritional disorders 164.9 3.7 21.7 58.1 2.1 6.2 11.8 23.8 36.6

8 Well care 155.5 2.9 28.7 36.5 3.0 0.5 0.1 37.7 5.1

9 Treatment of risk factors 140.8 6.6 35.6 3.5 53.6 1.1 6.2 0.6 50.0

10 Chronic respiratory diseases 132.1 3.7 31.1 26.7 28.4 4.7 9.0 14.5 39.0

11 Neoplasms 115.4 2.5 42.0 51.2 1.0 1.2 4.6 3.0 46.3

12 Neurological disorders 101.3 4.0 26.3 15.0 12.3 3.5 43.0 2.4 58.8

13 Digestive diseases 99.4 2.9 20.6 60.8 5.5 6.4 6.7 6.0 39.3

14 Cirrhosis 4.2 5.1 7.8 88.5 0.0 0.0 3.6 1.3 19.6

All conditions 2100.1 3.5 33.6 33.2 13.7 4.9 9.3 11.1 37.9

a

Reported in 2015 US dollars. Uncertainty intervals are reported in the Supplement.

b

Ranked from highest spending to lowest spending.

Spending on US Health Care, 1996-2013 Original Investigation Research

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US Spending on Personal Health Care and Public Health, 1996-2013.

Citations

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

The State of US Health, 1990-2016: Burden of Diseases, Injuries, and Risk Factors Among US States

Medical Costs of Fatal and Nonfatal Falls in Older Adults.

Clinical practice guidelines for the management of non-specific low back pain in primary care: an updated overview

Prevalence of Parent-Reported ADHD Diagnosis and Associated Treatment Among U.S. Children and Adolescents, 2016.

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Annual Medical Spending Attributable To Obesity: Payer-And Service-Specific Estimates

Prevalence, expenditures, and complications of multiple chronic conditions in the elderly

Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013 : quantifying the epidemiological transition

Monetary Costs of Dementia in the United States

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Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

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