Evidence mapPaperPMID 32125402Full record

ArticleJAMA2020

US Health Care Spending by Payer and Health Condition, 1996-2016.

Joseph L Dieleman, Jackie Cao, Abby Chapin, Carina Chen, Zhiyin Li, Angela Liu, Cody Horst, Alexander Kaldjian, Taylor Matyasz, Kirstin Woody Scott and 12 more

10 registry-linked trialsAbstract read
In one paragraph

Article in JAMA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 10 registered trials, which are not on this map. Cited by 631 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
631citing papers in PubMed, 14 pooled it
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07731256 phase2not yet recruitingstarted 2027, after this paper: background citation

A Randomized-Controlled Trial of Semaglutide for Patients With Chronic Low Back Pain and Obesity

Ran2027Enrolled250Registered outcomes13Posted comparisons0ConditionsChronic Lower Back Pain, glp1 Agonist, Lower Back Pain, Obesity (BMI>30)ArmsPlacebo (administered by PDS290 pen-injector), Semaglutide (administered by PDS290 pen-injector)
Open the trial in the graph
NCT02285868 active not recruitingnot on this mapstarted 2026, after this paper: background citation

ATI Evidence-Based Guide Investigating Clinical Services: Rehabilitation and Physical Therapy Patient Outcomes Registry

Typeobservational_patient_registrySponsorATI Holdings, LLCRan2026 to 2029Enrolled4,000,000ConditionsPrimary Body Region (Arranged Most Common to Least), Lumbar/SI, Knee, Shoulder
NCT05431894 nacompletednot on this mapstarted 2021, after this paper: background citation

Behavioral and Recovery Support for 30 Day Post-Discharge Care in Participants With Cardiovascular Diseases

TypeinterventionalSponsorLaguna Health, IncRan2021 to 2022Enrolled408ConditionsCardiovascular DiseasesArmsLaguna Coach Intervention Group
NCT05736393 nacompletednot on this mapstarted 2024, after this paper: background citation

Translation of Robotic Apparel for Alleviating Low Back Pain: Back Pain Consortium (BACPAC)

TypeinterventionalSponsorBoston University Charles River CampusRan2024 to 2025Enrolled44ConditionsLow Back PainArmsBack Exosuit
NCT05986370 narecruitingnot on this mapstarted 2023, after this paper: background citation

The METRIC Study Protocol: an Explanatory Randomized Controlled Trial Investigating the Neurophysiological Mechanisms Underlying the Therapeutic Effects of Spinal Manipulative Therapy for Chronic Primary Low Back Pain

TypeinterventionalSponsorUniversité du Québec à Trois-RivièresRan2023 to 2026Enrolled112ConditionsChronic Low-back PainArmslumbar spinal manipulative therapy, sham spinal manipulative therapy, full spine spinal manipulative therapy
NCT06021613 nacompletednot on this mapstarted 2024, after this paper: background citation

Marijuana and Acute Risk of Arrhythmia- Joint Abstinence and Exposure

TypeinterventionalSponsorUniversity of California, San FranciscoRan2024 to 2025Enrolled108ConditionsPremature Atrial Contractions, Premature Ventricular ContractionsArmsRandomized instructions
NCT06268366 naunknown statusnot on this mapstarted 2024, after this paper: background citation

Effects of Exercise-Based Interventions on Symmio Self-Movement Screen Scores in Untrained Adults_ A Randomized Controlled Trial

TypeinterventionalSponsorUniversity of EvansvilleRan2024 to 2025Enrolled180ConditionsMovement DisordersArmsExercise
NCT06832748 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

Feasibility of a Virtual Reality Based Sensorimotor Training Intervention for Patients with Chronic Traumatic Neck Pain

TypeinterventionalSponsorLuleå Tekniska UniversitetRan2025 to 2025Enrolled20ConditionsNeck PainArmsVR neck training, Endurance neck training
NCT06906107 narecruitingnot on this mapstarted 2025, after this paper: background citation

Validation of a Clinical Prediction Rule to Identify Patients With Neck Pain Likely to Benefit From Cervical Spinal Manipulation: A Randomized Clinical Trial

TypeinterventionalSponsorBaylor UniversityRan2025 to 2026Enrolled160ConditionsNeck Pain Musculoskeletal, Neck Pain Treatment, CervicalgiaArmsCervical Manipulation, Exercise, Mobilization
NCT07681349 nacompletednot on this map

Evaluation of the Effect of Acupuncture on 3 Dimensional Scapular Kinematics and Electromyographic Activity in Patients With Chronic Neck Discomfort

TypeinterventionalSponsorHualien Tzu Chi General HospitalRan2020 to 2021Enrolled50ConditionsNeck Pain ChronicArmsAcupuncture, Sham Acupuncture
3 · Its place in the literature

Who cites it

631 citing papers in PubMed, 14 syntheses or guidelines pooled it.

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  6. Burden of myelodysplastic syndromes: a systematic literature review of economic burden.The European journal of health economics : HEPAC : health economics in prevention and care · 2025
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  12. Models of care for managing non-specific low back pain.The Cochrane database of systematic reviews · 2025
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571 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Joseph L DielemanInstitute for Health Metrics and Evaluation, Seattle, Washington.
Jackie CaoInstitute for Health Metrics and Evaluation, Seattle, Washington.
Abby ChapinInstitute for Health Metrics and Evaluation, Seattle, Washington.
Carina ChenInstitute for Health Metrics and Evaluation, Seattle, Washington.
Zhiyin LiInstitute for Health Metrics and Evaluation, Seattle, Washington.
Angela LiuInstitute for Health Metrics and Evaluation, Seattle, Washington.
Cody HorstInstitute for Health Metrics and Evaluation, Seattle, Washington.
Alexander KaldjianInstitute for Health Metrics and Evaluation, Seattle, Washington.
Taylor MatyaszInstitute for Health Metrics and Evaluation, Seattle, Washington.
Kirstin Woody ScottHarvard Medical School, Boston, Massachusetts.
Anthony L BuiDepartment of Pediatrics, University of Washington, Seattle Children's Hospital, Seattle.
Madeline CampbellFacebook, Menlo Park, California.
Herbert C DuberInstitute for Health Metrics and Evaluation, Seattle, Washington.
Abe C DunnBureau of Economic Analysis, Suitland, Maryland.
Abraham D FlaxmanInstitute for Health Metrics and Evaluation, Seattle, Washington.
Christina FitzmauriceInstitute for Health Metrics and Evaluation, Seattle, Washington.
Mohsen NaghaviInstitute for Health Metrics and Evaluation, Seattle, Washington.
Nafis SadatMicrosoft, Redmond, Washington.
Peter ShiehBureau of Economic Analysis, Suitland, Maryland.
Ellen SquiresThe Improve Group, St Paul, Minnesota.
Kai YeungKaiser Permanente Washington Health Research Institute, Seattle.
Christopher J L MurrayInstitute for Health Metrics and Evaluation, Seattle, Washington.

Funding

NIA NIH HHS P30 AG047845
6 · The paper itself

Abstract

Importance: US health care spending has continued to increase and now accounts for 18% of the US economy, although little is known about how spending on each health condition varies by payer, and how these amounts have changed over time. Objective: To estimate US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Design and Setting: Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were collected to estimate spending for 154 health conditions. Spending growth rates (standardized by population size and age group) were calculated for each type of payer and health condition. Exposures: Ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, and purchase of prescribed pharmaceuticals in a retail setting. Main Outcomes and Measures: National spending estimates stratified by health condition, age group, sex, type of care, and type of payer and modeled for each year from 1996 through 2016. Results: Total health care spending increased from an estimated $1.4 trillion in 1996 (13.3% of gross domestic product [GDP]; $5259 per person) to an estimated $3.1 trillion in 2016 (17.9% of GDP; $9655 per person); 85.2% of that spending was included in this study. In 2016, an estimated 48.0% (95% CI, 48.0%-48.0%) of health care spending was paid by private insurance, 42.6% (95% CI, 42.5%-42.6%) by public insurance, and 9.4% (95% CI, 9.4%-9.4%) by out-of-pocket payments. In 2016, among the 154 conditions, low back and neck pain had the highest amount of health care spending with an estimated $134.5 billion (95% CI, $122.4-$146.9 billion) in spending, of which 57.2% (95% CI, 52.2%-61.2%) was paid by private insurance, 33.7% (95% CI, 30.0%-38.4%) by public insurance, and 9.2% (95% CI, 8.3%-10.4%) by out-of-pocket payments. Other musculoskeletal disorders accounted for the second highest amount of health care spending (estimated at $129.8 billion [95% CI, $116.3-$149.7 billion]) and most had private insurance (56.4% [95% CI, 52.6%-59.3%]). Diabetes accounted for the third highest amount of the health care spending (estimated at $111.2 billion [95% CI, $105.7-$115.9 billion]) and most had public insurance (49.8% [95% CI, 44.4%-56.0%]). Other conditions estimated to have substantial health care spending in 2016 were ischemic heart disease ($89.3 billion [95% CI, $81.1-$95.5 billion]), falls ($87.4 billion [95% CI, $75.0-$100.1 billion]), urinary diseases ($86.0 billion [95% CI, $76.3-$95.9 billion]), skin and subcutaneous diseases ($85.0 billion [95% CI, $80.5-$90.2 billion]), osteoarthritis ($80.0 billion [95% CI, $72.2-$86.1 billion]), dementias ($79.2 billion [95% CI, $67.6-$90.8 billion]), and hypertension ($79.0 billion [95% CI, $72.6-$86.8 billion]). The conditions with the highest spending varied by type of payer, age, sex, type of care, and year. After adjusting for changes in inflation, population size, and age groups, public insurance spending was estimated to have increased at an annualized rate of 2.9% (95% CI, 2.9%-2.9%); private insurance, 2.6% (95% CI, 2.6%-2.6%); and out-of-pocket payments, 1.1% (95% CI, 1.0%-1.1%). Conclusions and Relevance: Estimates of US spending on health care showed substantial increases from 1996 through 2016, with the highest increases in population-adjusted spending by public insurance. Although spending on low back and neck pain, other musculoskeletal disorders, and diabetes accounted for the highest amounts of spending, the payers and the rates of change in annual spending growth rates varied considerably.

Indexed as

AdolescentAdultAgedAged, 80 and overAge DistributionChildChild, PreschoolDiseaseFemaleHealth ExpendituresHealth StatusHumansInfantInsurance, HealthMaleMiddle Aged

Identifiers

PMID32125402
PMCPMC7054840

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

and 4 more above

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.