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Investors are in control of U.S. health care | STAT

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Health care costs are surging. ACA Marketplace premiums will rise 18%  next year— even more for the millions set to lose their enhanced federal subsidies without congressional action. Meanwhile, big employers expect their premiums — already about $25,000 for family coverage — to increase 9% in 2026. Gargantuan health care profits are the flip side of those unsustainable cost increases.

Already, the average American lays out more than $17,000 for health care, a figure that includes the insurance premiums they and their employers pay as well as copayments, deductibles, and the taxes that fund Medicare, Medicaid, and other public programs. Where five decades ago, U.S. health costs were middle-of-the- pack among wealthy nations, today Americans spend about 50% more on health care than the average Canadian, Australian, or European.

Our exorbitant spending doesn’t buy us better health, or better (or even more) care. Americans die, on average, 4.1 years younger than people in other wealthy nations. We get less hospital care, fewer doctor visits, and fewer prescription drugs. And while everyone in those nations has health coverage, 27 million Americans are uninsured today, a number set to rise to 40 million once the health care cuts in the One Big Beautiful Bill take effect.

It’s fashionable to blame out-of-control health spending on our aging population, increasing rates of chronic illnesses, innovative drugs and medical technology, and expanded insurance coverage, but other nations that spend far less also face those cost challenges. 

Instead, as we highlight in The Lancet this week, the real culprits are huge profits for health care investors, a gigantic bureaucracy deployed to extract those profits, and health services skewed to what’s profitable rather than what’s needed. The market competition, managed care, and “corporate efficiency” that economists prescribed as the cure for health care inflation instead accelerated it, with corporations’ takeover of health care at the heart of the costly bloat.

Starting in the 1980s, politicians, responding to policy wonks’ advice (and insurance industry lobbyists), subcontracted more and more of Medicare and Medicaid to private managed care insurers. The initial trickle of government dollars flowing to private insurers as “premiums” paid to Medicare Advantage and Medicaid Managed Care plans is now a torrent; $923 billion in 2023. Those subcontracts were touted as money-savers. But we now know (thanks to Congress’ official Medicare payment commission) that Medicare Advantage plans have overcharged Medicare by at least $615 billion.

Insurance executives engineered those overpayments by gaming the risk-adjustment system that determines how much the government pays them. First, they cherry-pick enrollees likely to use little care. Then they exaggerate how sick their enrollees are (so-called “upcoding”), boosting their payments from the taxpayers. In 2023 alone, Medicare Advantage plans “pocketed $50 billion from Medicare for diseases no doctor treated.” Today, by our calculations, the majority of private insurers’ revenues come from government coffers, not from selling private coverage.

Although ideologues claimed their market-based strategies would unleash the cost-cutting power of competition, the opposite happened; health care competition has all but disappeared. Giant systems have bought up hospitals, clinics, and practices, cornering the market in most regions of the country and creating quasi-monopolies that jack up prices and profits.

And the health insurance giants that have come to dominate the market wield even more power. UnitedHealth, once mainly an insurer, has turned itself into a vast health care conglomerate — employing tens of thousands of doctors, running pharmacies and home care agencies, and processing payments for a third of Americans. CVS Health, and recently Amazon, have followed the same playbook. This “vertical integration,” in which the same corporation owns the insurer, the doctors, and the pharmacy, turns every part of care into a profit center and lets companies reward their own clinics, punish independent doctors who advocate for patients, and steer prescriptions to their own pharmacies that pocket drug rebates— all at patients’ expense.

Between 2001-2022, health care corporations diverted at least $2.6 trillion from patient care to shareholder payouts. But market-driven health care wastes money in other ways too. The drive for profit has bred useless but expensive bureaucracy. More than 900,000 insurance and health care workers do tasks that help no one — upcoding, marketing, billing, denying coverage. Private insurers’ overhead (the share of their premiums that doesn’t pay for care) averages 10.3%, five-fold higher than the 2% overhead of traditional Medicare in the U.S. and Canada’s Medicare for All system. And hospitals and clinics deploy an army of clerks and managers (costing billions) to maximize billings and fight with insurers. Overall, nearly one-third of the dollars Americans spend on health care goes for administration, twice as much as in Canada.

Profit-driven distortions in care waste billions more, and — even worse — cost thousands of lives each year. A raft of studies show that primary care saves lives, but it’s a money-loser for hospitals and clinics; profits are much higher for expensive services like hip replacements and cancer chemotherapy. That’s why, in Boston you can get an appointment with an orthopedic surgeon in 12 days but have to wait 69 days see a family doctor, and why one Boston hospital (where thousands are on waiting lists for primary care) is building a new cancer hospital next door to an existing top-ranked one.

Almost all Americans agree that our country faces a health care cost crisis. Addressing that crisis will require recognizing that our failed experiment with profit-driven health care brought us to this point. The remedy is straightforward: decommercialized national health insurance: a single, public insurance plan that covers everyone, financed through progressive taxes, with no copayments or deductibles. Administrative costs would plummet, savings could be redirected to strengthen primary care and public health, and health — not profit — would again be the system’s organizing principle. That kind of reform would save both money and lives, and it would put communities, not investors, in control of health care.

Steffie Woolhandler, M.D., M.P.H., and David U. Himmelstein, M.D., are both general internists, distinguished professors of public health at the City University of New York’s Hunter College and lecturers in medicine at Harvard Medical School. Adam Gaffney, M.D., M.P.H., is a pulmonary and critical care physician at the Cambridge Health Alliance and an assistant professor of medicine at Harvard Medical School. Danny McCormick, M.D., M.P.H., is a primary care doctor at the Cambridge Health Alliance and associate professor of medicine at Harvard Medical School. 

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sarcozona
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“market competition, managed care, and “corporate efficiency” that economists prescribed as the cure for health care inflation instead accelerated it, with corporations’ takeover of health care at the heart of the costly bloat.”
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Implications of a New Obesity Definition Among the All of Us Cohort | Research, Methods, Statistics | JAMA Network Open | JAMA Network

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Obesity has traditionally been defined as elevated body mass index (BMI; calculated as weight in kilograms divided by height in square meters), yet BMI alone is an imprecise measure of adiposity. Recognizing this limitation, a recent consensus guideline published in The Lancet Diabetes & Endocrinology proposed a new definition of obesity that incorporates anthropometrics and/or direct measures of body fat to better differentiate adipose tissue excess.1 Developed by an international commission of experts spanning multiple specialties and countries, this guideline has already been endorsed by at least 76 professional organizations, marking a significant shift in how obesity will be conceptualized and classified.

The new definition allows for classification of obesity based on any of the following criteria: (1) elevated BMI plus at least 1 elevated anthropometric measure (eg, waist circumference, waist-to-hip ratio, and/or waist-to-height ratio) or BMI greater than 40; (2) at least 2 elevated anthropometric measures, irrespective of BMI; or (3) excess body fat as assessed by dual-energy x-ray absorptiometry or similar modalities. The guideline also introduces the concepts of clinical and preclinical obesity to differentiate individuals with vs without obesity-associated organ dysfunction and/or physical limitation. The consensus group suggests lower urgency and intensity of care for preclinical obesity, with pharmacologic and surgical interventions reserved for select cases. This approach represents a notable departure from current clinical practice, recent clinical trials, and US Food and Drug Administration (FDA)–approved use of modern antiobesity medications, which have traditionally considered all individuals with obesity to be candidates for therapy.2-7

The new obesity definition may have major ramifications for patients, clinicians, payers, and policymakers. Nonetheless, its clinical implications and relevance to long-term health outcomes have yet to be comprehensively evaluated in a large cohort. In this study, we applied the new obesity definition to the US-based All of Us (AoU) cohort with the following objectives: (1) to determine the prevalence of obesity and clinical obesity under the new definition, including variations by age, sex, and race; (2) to compare characteristics of individuals who meet criteria for obesity based on elevated BMI plus anthropometric measures vs elevated anthropometric measures alone; and (3) to evaluate longitudinal health outcomes across obesity subgroups compared with individuals without obesity. This analysis delineates the clinical and practical relevance of the new obesity definition and highlights areas for further investigation.

We leveraged the AoU cohort to examine the clinical implications of the new obesity guideline. All US adults able to provide consent were eligible to enroll in the AoU research program.8,9 We analyzed a controlled tier dataset (version 8; C2024Q3R4; February 3, 2025) for participants enrolled between May 31, 2017, and September 30, 2023 (median follow-up, 4.0 [IQR, 1.7-4.7 years). All participants provided written informed consent, and the institutional review board at Massachusetts General Hospital, Boston, approved this study. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

We included individuals aged 18 to 80 years at baseline with complete physical measurements and electronic health record (EHR) data available (eMethods and eFigures 1 and 2 in Supplement 1). Characteristics were similar between participants included in this analysis and those excluded due to missing anthropometric data (eTable 1 in Supplement 1).

At baseline, participants underwent standardized measurements of height, weight, waist circumference, and hip circumference. Participants also completed health surveys, including demographic assessments (eg, self-reported sex, race and ethnicity), and could complete additional health surveys over time. Race was classified as American Indian or Alaska Native, Asian, Black or African American, Middle Eastern or North African, White, and other race (including Native Hawaiian or Other Pacific Islander, multiracial, did not identify with any group, and preferred not to answer); ethnicity was classified as Hispanic or Latinx or non–Hispanic or Latinx. These data were collected to compare obesity prevalence and phenotypes by race and to adjust for race in multivariable analyses. Longitudinal EHR data from all encounters before and after enrollment, including International Classification of Diseases, Ninth Revision (ICD-9), and International and Statistical Classification of Diseases, Tenth Revision (ICD-10), codes and clinical laboratory results, were abstracted from partnered health care organizations (eMethods and eTables 2-6 in Supplement 1).

The traditional definition of obesity was applied at baseline using race-specific BMI cutoffs per World Health Organization criteria.10 ,11 The new definition was also applied, which we further subclassified into 2 mutually exclusive phenotypes: (1) BMI-plus-anthropometric obesity, defined as BMI above the traditional obesity threshold plus at least 1 elevated anthropometric measure or BMI greater than 40, and (2) anthropometric-only obesity, defined as at least 2 elevated anthropometric measures with BMI below the traditional obesity threshold.1 BMI, waist circumference, waist-to-hip ratio, and waist-to-height ratio were evaluated according to sex- and/or race-specific thresholds (eMethods in Supplement 1).

Per the new definition, obesity was categorized at baseline as clinical or preclinical based on the presence of at least 1 prespecified manifestation of organ dysfunction and/or physical limitation (termed organ dysfunction throughout).1 These conditions were captured using ICD codes, survey data, and/or clinical laboratory results within the year prior to baseline (eMethods and eTable 2 in Supplement 1). Sensitivity analyses using ICD codes alone or from any time before baseline yielded similar prevalence estimates of clinical obesity.

Incident diabetes, cardiovascular events, and all-cause mortality were evaluated as longitudinal outcomes. Incident diabetes was defined using ICD codes, survey data, and/or clinical laboratory results among individuals without diabetes at baseline or within the first 6 months of follow-up (eMethods and eTable 3 in Supplement 1). Cardiovascular events were defined as myocardial infarction, stroke, or acute heart failure per ICD codes (eMethods and eTable 6 in Supplement 1). Eligibility for obesity pharmacotherapy was defined by current BMI-based indications as (1) BMI of 30 or higher or (2) BMI of 27 or higher plus hypertension, dyslipidemia, obstructive sleep apnea, or cardiovascular disease (using ICD codes, questionnaires, and/or laboratory data) per obesity guidelines, recent clinical trials, and FDA guidance (eTable 7 in Supplement 1).2,4-7

Baseline characteristics were summarized using medians (IQR) or proportions. Groups were compared using a Wilcoxon rank sum test and χ2 test. Logistic regression models estimated odds ratios (ORs) with 95% CI for organ dysfunction by obesity phenotype. Cause-specific Cox proportional hazards regression models estimated adjusted hazard ratios (AHRs) with 95% CIs for time-to-event outcomes, accounting for age, sex, race, and tobacco use (for cardiovascular events and all-cause mortality) (eMethods in Supplement 1). Analyses were performed using R, version 4.2.2 (R Program for Statistical Computing) on the AoU Researcher Workbench platform. Two-sided P < .05 indicated statistical significance.

Prevalence of Obesity Per the New Definition

A total of 301 026 participants (183 633 [61.0%] female and 117 393 [39.0%] male; median age, 54 [IQR, 38-65] years) were included in the analysis. In terms of race, 4409 participants (1.5%) were American Indian or Alaska Native, 9037 (3.0%) were Asian, 59 347 (19.7%) were Black or African American, 1745 (0.6%) were Middle Eastern or North African, 160 158 (53.2%) were White, and 66 330 (22.0%) were of other race. In terms of ethnicity, a total of 54 308 participants (18.0%) identified as Hispanic or Latinx and 238 933 (79.4%) identified as non–Hispanic or Latinx.

Using traditional BMI-based criteria, 128 992 individuals (42.9%) had obesity. Under the new definition, obesity prevalence increased nearly 60% to 206 361 individuals (68.6%) (Figure 1A). Nearly all individuals with obesity by the traditional definition also met criteria for BMI-plus-anthropometric obesity by the new definition. Among the overall sample, only 678 participants (0.2%) no longer met criteria for obesity per the new classification due to high BMI despite nonelevated anthropometric measures. In contrast, 78 047 participants (25.9%) did not have obesity per the traditional definition but were reclassified as having anthropometric-only obesity per the new framework.

Figure 1.  Prevalence of Obesity by the Traditional and New Definitions Among the All of Us Cohort

The inset highlights the low prevalence of high body mass index (BMI) but nonelevated anthropometric measures under the traditional BMI-based obesity definition.

While obesity prevalence by the new definition was similar between sexes, the distribution of obesity phenotypes differed by sex with a higher frequency of anthropometric-only obesity in male vs female participants (38 157 of 117 393 [32.5%] vs 39 890 of 183 633 [21.7%]; P < .001) (Figure 1A). Across racial groups, obesity prevalence increased by a similar absolute percentage when transitioning from the traditional to the new definition, with the highest relative increase among Asian individuals (90.3% relative increase from 2439 [27.0%] to 4641 [51.4%] of 9037 participants) (Figure 1B). By the new definition, obesity was more prevalent with older age, affecting 15 991 of 36 396 individuals aged 18 to 29 years (43.9%) and 35 268 of 45 018 individuals 70 years or older (78.3%) (P for trend < .001).

Differential Characteristics of New Obesity Phenotypes

Under the new definition, compared with individuals with BMI-plus-anthropometric obesity, individuals with anthropometric-only obesity were older (median age, 54 [IQR, 40-64] vs 60 [IQR, 48-69] years, respectively; P < .001), were more commonly male (44 555 of 128 314 [34.7%] vs 38 157 of 78 047 [48.9%], respectively; P < .001), and had greater attainment of higher education (45 201 of 128 314 [35.2%] vs 35 124 of 78 047 [45.0%], respectively; P < .001) and income (eg, >$150 000, 9318 of 128 314 [7.3%] vs 9753 of 78 047 [12.5%], respectively; P < .001) (Table and eTable 8 in Supplement 1). Among those with obesity, the proportion with anthropometric-only obesity increased with age, affecting 4298 of 15 991 individuals aged 18 to 29 years (26.9%) and 18 671 of 35 268 individuals 70 years or older (52.9%) (P for trend < .001) (eFigure 3 in Supplement 1).

Table.  Characteristics of Obesity Categories and Obesity Phenotypes Per the New Definition of Obesitya

CharacteristicObesity category, No. (%) valuebObesity phenotype, No. (%) valuec
Overall (N = 301 026)Obesity absent (n = 94 665)Obesity present (n = 206 361)Anthropometric-only (n = 78 047)BMI-plus- anthropometric (n = 128 314)
Age, median (IQR), y54 (38-65)46 (31-61)56 (43-66)<.00160 (48-69)54 (40-64)<.001
Sex
Female183 633 (61.0)59 984 (63.4)123 649 (59.9)<.00139 890 (51.1)83 759 (65.3)<.001
Male117 393 (39.0)34 681 (36.6)82 712 (40.1)38 157 (48.9)44 555 (34.7)
Race
American Indian or Alaska Native4409 (1.5)877 (0.9)3532 (1.7)<.0011243 (1.6)2289 (1.8)<.001
Asian9037 (3.0)4396 (4.6)4641 (2.2)2257 (2.9)2384 (1.9)
Black or African American59 347 (19.7)16 717 (17.7)42 630 (20.7)12 638 (16.2)29 992 (23.4)
Middle Eastern or North African1745 (0.6)805 (0.9)940 (0.5)424 (0.5)516 (0.4)
White160 158 (53.2)54 316 (57.4)105 842 (51.3)44 110 (56.5)61 732 (48.1)
Otherd66 330 (22.0)17 554 (18.5)48 776 (23.6)17 375 (22.3)31 401 (24.5)
Ethnicity
Hispanic or Latinx54 308 (18.0)13 977 (14.8)40 331 (19.5)<.00114 219 (18.2)26 112 (20.4)<.001
Non–Hispanic or Latinx238 933 (79.4)78 349 (82.8)160 584 (77.8)61 568 (78.9)99 016 (77.2)
Missing7785 (2.6)2339 (2.5)5446 (2.6)2260 (2.9)3186 (2.5)
Smoking status
Never smoker172 287 (57.2)58 012 (61.3)114 275 (55.4)<.00140 745 (52.2)73 530 (57.3)<.001
Ever smoker69 023 (22.9)16 738 (17.7)52 285 (25.3)20 733 (26.6)31 552 (24.6)
Current smoker49 589 (16.5)16 599 (17.5)32 990 (16.0)13 917 (17.8)19 073 (14.9)
Missing10 127 (3.4)3316 (3.5)6811 (3.3)2652 (3.4)4159 (3.2)
BMI, median (IQR)28.7 (24.6-33.9)23.5 (21.4-25.7)31.6 (28.0-36.5)<.00127.0 (25.2-28.5)35.0 (32.1-39.6)<.001
Waist circumference, median (IQR), cm96.0 (84.0-108.2)79.0 (73.5-84.2)103.1 (95.0-113.8)<.00195.3 (90.2-101.0)110.0 (101.5-119.6)<.001
Hip circumference, median (IQR), cm106.7 (99.0-116.8)98.0 (92.5-103.0)111.9 (104.1-122.0)<.001103.0 (98.2-107.6)119.0 (111.8-128.2)<.001
Elevated waist circumference
Yes165 315 (54.9)209 (0.2)165 106 (80.0)<.00145 080 (57.8)120 026 (93.5)<.001
No135 711 (45.1)94 456 (99.8)41 255 (20.0)32 967 (42.2)8288 (6.5)
Elevated WHR
Yes175 840 (58.4)9158 (9.7)166 682 (80.8)<.00170 907 (90.9)95 775 (74.6)<.001
No125 186 (41.6)85 507 (90.3)39 679 (19.2)7140 (9.1)32 539 (25.4)
Elevated waist-to-height ratio
Yes227 810 (75.7)21 991 (23.2)205 819 (99.7)<.00177 963 (99.9)127 856 (99.6)<.001
No73 216 (24.3)72 674 (76.8)542 (0.3)84 (0.1)458 (0.4)
Classification by traditional definition
No obesity172 034 (57.1)93 987 (99.3)78 047 (37.8)<.00178 047 (100)0<.001
Obesity128 992 (42.9)678 (0.7)128 314 (62.2)0128 314 (100)

Among 78 047 individuals with anthropometric-only obesity, 17 426 (22.3%) had a BMI traditionally classified as normal or underweight, whereas the remainder fell within the traditional overweight category (eFigure 4 in Supplement 1). All 3 anthropometric measures were elevated among 37 856 of 78 047 people with anthropometric-only obesity (48.5%) and 92 200 of 128 314 with BMI-plus-anthropometric obesity (71.9%) (eFigure 5 in Supplement 1).

Prevalence of Clinical Obesity Per the New Definition

By the new definition, 108 650 of 301 026 participants overall (36.1%) and 108 650 of 206 361 participants with obesity (52.7%) had clinical obesity (eFigures 6 and 7 in Supplement 1). Clinical obesity prevalence was comparable between sexes, but increased with age, both in the overall cohort and among those with obesity (both P for trend < .001) (eFigure 8 in Supplement 1). Specifically, among participants aged 18 to 29 years, 3107 of 36 396 overall (8.5%) and 3107 of 15 991 with obesity (19.4%) met criteria for clinical obesity. In contrast, among participants 70 years or older, 24 498 of 45 018 overall (54.4%) and 24 498 of 35 268 with obesity (69.5%) had clinical obesity. Clinical obesity was least common among Asian individuals, comprising 1784 of 9037 individuals overall (19.7%) and 1784 of 4641 with obesity (38.4%) (P < .001 each) (eFigure 9 in Supplement 1). Per traditional BMI categories, among 76 460 individuals with normal weight, 9388 (12.3%) and 7902 (10.3%) were classified as having preclinical and clinical obesity, respectively, under the new definition. Among 91 644 individuals with overweight, 31 403 (34.3%) and 29 218 (31.9%) met criteria for preclinical and clinical obesity, respectively (eFigure 10 in Supplement 1).

Clinical Obesity by Obesity Phenotype

Individuals with BMI-plus-anthropometric obesity had a higher proportion of clinical obesity (71 457 of 128 314 [55.7%] vs 37 193 of 78 047 [47.7%]; P < .001) (Figure 2A) and a greater number of manifestations of organ dysfunction (median for BMI-plus-anthropometric obesity, 1 [IQR, 0-2]; median for anthropometric-only obesity, 0 [IQR, 0-1]; P < .001) (eFigure 11 in Supplement 1) compared with those with anthropometric-only obesity. Relatedly, the frequency of organ dysfunction among people with obesity progressively increased with higher BMI among male and female participants from normal weight (47.8% and 43.9%, respectively) to overweight (50.4% and 46.0%, respectively), obesity class 1 (54.7% and 48.5%, respectively), obesity class 2 (63.0% and 55.3%, respectively), and obesity class 3 (65.3% and 62.6%, respectively) (P for trend < .001) (Figure 2B and eFigure 12 in Supplement 1). In a multivariable model adjusting for age, sex, and race, odds ratios of organ dysfunction were 3.31 (95% CI, 3.24-3.37) for individuals with BMI-plus-anthropometric obesity and 1.76 (95% CI, 1.73-1.80) for those with anthropometric-only obesity vs individuals without obesity. Odds were comparable across sexes and races, although sex- and race-specific patterns of organ dysfunction emerged (eFigures 13 and 14 in Supplement 1). In the overall cohort, the most common manifestations of clinical obesity were hypertension, physical limitation, and obstructive sleep apnea (Figure 2C and eFigure 15 and eTable 9 in Supplement 1).

Figure 2.  Differential Characteristics of New Obesity Phenotypes

BMI indicates body mass index.

Eligibility for Obesity Pharmacotherapy

Excluding individuals with diabetes who were otherwise candidates for glucagonlike peptide-1 receptor agonists (GLP1RAs), 111 467 of 249 235 individuals in the overall cohort (44.7%) met current BMI-based eligibility criteria for obesity pharmacotherapy.2 ,4-7 Notably, 15 495 of 69 894 individuals with clinical obesity (22.2%) by the new definition did not meet these criteria. Conversely, 57 068 of 111 467 participants eligible for obesity pharmacotherapy per BMI-based indications (51.2%) did not have clinical obesity (eFigure 16 in Supplement 1).

Longitudinal Health Outcomes by Obesity Definition and Phenotype

By the traditional and new definitions, obesity conferred elevated risks of incident diabetes (AHRs, 2.60 [95% CI, 2.50-2.70] vs 3.21 [95% CI, 3.03-3.39]), cardiovascular events (AHRs, 1.39 [95% CI, 1.34-1.45] vs 1.70 [95% CI, 1.62-1.80]), and all-cause mortality (AHRs 1.10 [95% CI, 1.03-1.18] vs 1.21 [95% CI, 1.12-1.31]), with higher AHRs under the new framework (eFigure 17 in Supplement 1). Using the new definition, BMI-plus-anthropometric obesity carried the greatest risk of incident diabetes (AHR, 3.95 [95% CI, 3.73-4.18]), followed by anthropometric-only obesity (AHR, 2.12 [95% CI, 1.99-2.27]), compared with no obesity (Figure 3A and B). In contrast, risks of cardiovascular events (BMI-plus-anthropometric AHR, 1.81 [95% CI, 1.72-1.92]; anthropometric-only AHR, 1.55 [95% CI, 1.46-1.65]) and all-cause mortality (AHRs, 1.22 [95% CI, 1.12-1.34] and 1.20 [95% CI, 1.09-1.31], respectively) were similarly elevated across obesity phenotypes relative to no obesity (Figure 3C and D and eFigure 18 in Supplement 1).

Figure 3.  Longitudinal Risks of Adverse Health Outcomes by New Obesity Phenotype in the All of Us Cohort

A and C, Cumulative incidence plots with shaded 95% CIs depict the probability of each longitudinal outcome by obesity phenotype. B and D, Forest plots display hazard ratios (HRs) with 95% CIs for each longitudinal health outcome among subgroups. The unadjusted model includes only the exposure variable (obesity phenotype). The demographics model adjusted for age, sex, and race, with additional adjustment for smoking status in the analysis of cardiovascular (CV) events. BMI indicates body mass index.

Longitudinal Health Outcomes by Clinical Obesity Status

Compared with individuals without obesity or organ dysfunction, clinical obesity according to the new definition conferred the highest risk of incident diabetes (AHR, 6.11 [95% CI, 5.67-6.60]), followed by preclinical obesity (AHR, 3.32 [95% CI, 3.08-3.58]) and organ dysfunction in the absence of obesity (AHR, 2.50 [95% CI, 2.25-2.78]) (Figure 4A and B). In contrast, clinical obesity (AHR, 5.88 [95% CI, 5.38-6.43]) and organ dysfunction in the absence of obesity (AHR, 4.68 [95% CI, 4.22-5.19]) were both associated with a highly increased risk of cardiovascular events, whereas the risk elevation associated with preclinical obesity was more moderate (AHR, 1.40 [95% CI, 1.27-1.55]) (Figure 4C and D). Similarly, clinical obesity (AHR, 2.71 [95% CI, 2.41-3.05]) and organ dysfunction in the absence of obesity (AHR, 2.82 [95% CI, 2.45-3.26]) were both associated with increased risk of all-cause mortality, whereas there was no risk elevation observed for preclinical obesity (AHR, 1.09 [95% CI, 0.96-1.25]) (eFigure 19 in Supplement 1). Associations were consistent across age strata (eFigure 20 in Supplement 1).

Figure 4.  Longitudinal Risks of Adverse Health Outcomes by Clinical Obesity Status Per the New Definition of Obesity in the All of Us Cohort

A and C, Cumulative incidence plots with shaded 95% CIs depict the probability of each longitudinal outcome among individuals with clinical obesity, preclinical obesity, and no obesity with or without organ dysfunction. B and D, Forest plots display hazard ratios (HRs) with 95% CIs for each longitudinal health outcome among these 4 subgroups. The unadjusted model includes only the exposure variable (obesity and organ dysfunction status). The demographics model adjusted for age, sex, and race, with additional adjustment for smoking status in the analysis of cardiovascular (CV) events. BMI indicates body mass index.

In this analysis, we applied the Lancet Commission’s new definition of obesity to AoU, a large, diverse US cohort. Under this framework, 68.6% of participants met criteria for obesity, a 60% increase in prevalence from the traditional BMI-based definition. This rise was entirely driven by inclusion of individuals with anthropometric-only obesity, defined as having at least 2 elevated anthropometric measures despite a nonelevated BMI. Meanwhile, nearly all individuals classified as having obesity by the traditional definition met criteria under the new framework for BMI-plus-anthropometric obesity, defined as having an elevated BMI plus at least 1 elevated anthropometric measure or BMI of greater than 40. Our findings suggest that the new obesity definition effectively stratified individuals at high-risk of organ dysfunction and long-term complications while exposing potential public health implications and areas for further study.

Based on the new definition, approximately 1 in 4 participants in the AoU cohort met criteria for anthropometric-only obesity. Notably, an estimated 1 in 4 of these individuals had BMI in the traditionally normal (nonoverweight) range. Despite their nonelevated BMI, individuals with anthropometric-only obesity exhibited a heightened prevalence of organ dysfunction and risk of incident diabetes compared with those without obesity, although to a moderately lesser extent than those with BMI-plus-anthropometric obesity. Meanwhile, risks of cardiovascular events and mortality were similarly increased across obesity phenotypes.

These findings support the new definition of obesity by identifying individuals with anthropometric-only obesity as having a heightened risk of adverse health outcomes. Concordant with our data, prior studies have repeatedly shown that central adiposity, independently of BMI, is a key factor associated with cardiometabolic disease.12-15 However, our data also suggest that anthropometric-only obesity (reflecting excess abdominal adiposity) and BMI-plus-anthropometric obesity (reflecting excess total body adiposity) may represent distinct clinical entities with unique pathophysiologic mechanisms and outcomes. In this regard, risk factors and treatment strategies that have been studied in the context of traditional BMI-based obesity may be more applicable to BMI-plus-anthropometric obesity, whereas the drivers and optimal management of anthropometric-only obesity remain less well understood. The sociodemographic differences that we observed between subgroups highlight the importance of investigating the variable contributions of aging, sex hormones, genetics, and social determinants of health to each obesity phenotype to inform targeted prevention strategies. Furthermore, as BMI appears to compound the adverse effects of central adiposity, our findings underscore the continued need to address elevated BMI as a cornerstone of obesity management. At the same time, clinical trials are critically needed to evaluate interventions that specifically target excess abdominal adiposity in individuals with anthropometric-only obesity who may not derive the same benefit from traditional weight-centric approaches. Notably, lifestyle interventions such as exercise16-18 and pharmacologic therapies including tesamorelin19-21 have been shown to reduce visceral adiposity without significantly altering body weight and thus may warrant further investigation in the context of anthropometric-only obesity.

Among the AoU cohort, we found that approximately half of participants classified as having obesity under the new definition also exhibited organ dysfunction and/or physical limitation consistent with clinical obesity. Our analyses suggest that the new definition of clinical obesity appropriately designates individuals with obesity who are at the highest long-term risk of incident diabetes, cardiovascular events, and mortality. However, our findings also demonstrate that preclinical obesity is not a benign entity. Individuals with preclinical obesity exhibited an increased risk of diabetes and cardiovascular events compared with those without obesity or organ dysfunction. Moreover, clinical obesity prevalence increased with age, suggesting that preclinical obesity may progress to clinical obesity over time. Further research is needed to distinguish which individuals with preclinical obesity are at highest risk of adverse health outcomes to allow for targeted, cost-effective interventions among this heterogenous population.

While the definition of clinical obesity appropriately designates individuals with obesity who face the highest long-term health risks, our findings also underscore the substantial risks conferred by organ dysfunction itself, even in the absence of obesity. This was particularly evident for cardiovascular events and mortality, whereby individuals with organ dysfunction, irrespective of obesity status, exhibited comparably elevated risk. Since obesity was associated with an increased likelihood of organ dysfunction in our analysis, these findings support a framework in which obesity may contribute to organ dysfunction, which in turn may predispose to downstream clinical events. This interpretation aligns with established cardiovascular risk prediction tools such as the Pooled Cohort Equation,22 the Systemic Coronary Risk Evaluation 2 (SCORE2),23 and the Framingham Risk Score,24 which incorporate markers of organ dysfunction but not BMI. At the same time, given the elevated risks of diabetes and cardiovascular events that we observed among individuals with preclinical obesity, organ dysfunction may not be required for adiposity to confer harm. Further research is needed to determine whether treating obesity can reverse organ dysfunction and whether this may translate to improved long-term outcomes.

Across racial groups, implementation of the new obesity definition led to the greatest relative rise in obesity prevalence among Asian individuals. Distinct profiles of clinical obesity emerged by race, with metabolic dysfunction being a predominant manifestation among this group. This aligns with prior observations that certain Asian populations have heightened risks of diabetes and cardiovascular disease relative to other racial groups.25-29

Strikingly, nearly 80% of participants 70 years or older in the AoU cohort met criteria for obesity, which constitutes a doubling in obesity prevalence from the traditional BMI-based definition. Furthermore, over half of individuals 70 years or older exhibited clinical obesity. The predisposition to anthropometric-only obesity in older adults aligns with prior studies that have shown a tendency toward central fat accumulation with older age and the menopausal transition.30-33 Clinical obesity conferred a similarly elevated risk of incident diabetes, cardiovascular events, and mortality across age groups, providing a rationale for guideline-directed pharmacologic and/or surgical management among older adults. These findings may have significant public health implications, presaging cost challenges for health payer systems, including Medicare.

Finally, the new definition may substantially reshape obesity pharmacotherapy prescribing patterns. Approximately 45% of our cohort met current BMI-based indications for antiobesity medication, which falls midway between the prevalence of clinical obesity and overall obesity under the new framework. Since the new guideline recommends pharmacologic therapy for all individuals with clinical obesity and select individuals with preclinical obesity,1 overall use of obesity pharmacotherapy may remain comparable to current levels. However, the composition of the treatment-eligible population is likely to shift; as many as half of those currently eligible for medication may no longer qualify due to absence of clinical obesity (eg, a person with BMI of 34 and no organ dysfunction), whereas approximately 1 in 4 individuals with clinical obesity who are newly designated for treatment would not meet eligibility criteria for recent GLP1RA clinical trials4,5 or current FDA-labeled indications7 (eg, a person with BMI of 24, central adiposity, and organ dysfunction). Thus, implementation of the new framework may have significant ramifications for patients, including existing GLP1RA users,34 and creates a compelling need to evaluate use of antiobesity medications within this redefined target population.

Strengths and Limitations

To our knowledge, this is the first study to comprehensively examine the clinical implications of the new obesity definition and its relevance to long-term health outcomes. The study’s strengths include a large, diverse sample and evaluation of both cross-sectional and longitudinal health conditions. The AoU cohort provides access to longitudinal EHR data, which may be less well-captured in other cohorts. Although Black or African American individuals are somewhat overrepresented,35 the obesity prevalence in our sample using traditional BMI-based criteria (42.9%) closely aligns with Centers for Disease Control and Prevention estimates (40.3%),36 supporting the generalizability of our findings.

A key limitation of our analysis is its reliance on ICD codes, survey responses, and laboratory results to classify clinical obesity, which may not fully capture organ dysfunction or physical limitation. Nonetheless, our findings remained robust in sensitivity analyses using alternate diagnostic criteria and data capture windows. Additionally, while the guideline specifies that organ dysfunction must be secondary to obesity, we could not establish causality. Consequently, we classified all individuals with both obesity and organ dysfunction as having clinical obesity, reflecting how the guideline is likely to be applied in clinical practice where establishing causality is also challenging. Ideally, causality would be inferred if organ dysfunction improved following treatment of obesity.37 However, this presumes reversibility and introduces a circular dilemma whereby diagnosis is required to justify treatment, yet treatment response is required to confirm diagnosis. Last, in the absence of a gold standard for defining obesity, we relied on the new framework’s ability to stratify future health risk as an indirect measure of clinical utility. While the new obesity framework is more complex and resource-intensive than the traditional BMI-based definition, its capacity to appropriately risk-stratify individuals may justify the effort. Smart tools integrated into EHR systems and online calculators could enhance adoption.

In this cohort study, implementation of a new obesity definition resulted in a substantial rise in obesity prevalence, particularly among older adults, that may have profound public health and financial implications. Our findings support inclusion of anthropometric-only obesity within the new obesity definition and affirm the value of clinical obesity in identifying individuals at highest risk of adverse health outcomes. At the same time, our results highlight critical gaps in knowledge regarding anthropometric-only obesity, preclinical obesity, and the shifting target population for obesity pharmacotherapy, underscoring the need for further research to inform evidence-based care of these groups.

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BMI misses more small people who are unwell than it misidentifies big people are fine.
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Chiac

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From Wikipedia, the free encyclopedia

Chiac
Native toCanada
RegionAcadians in southeastern New-Brunswick
Language codes
ISO 639-3
GlottologNone

Chiac (or Chiak, Chi’aq), is a patois of Acadian French spoken mostly in southeastern New Brunswick, Canada.[1] Chiac is frequently characterized and distinguished from other forms of Acadian French by its borrowings from English and is thus often mistakenly considered a form of Franglais.

The word "Chiac" can also sometimes be used to refer to ethnic Acadians of rural southeastern New Brunswick, who are not considered French Canadian historically and ethnically because of their separate and distinctive history. They are considered ethnically as "Chiac-Acadian"[2] or simply "Chiac".

As a major modern variety of Acadian-French, Chiac shares most phonological particularities of the dialect. However, Chiac contains far more English loanwords compared to other Canadian French dialects. Many of its words also have roots in the Eastern Algonquian languages, most notably Mi'kmaq. Loanwords generally follow French conjugation patterns; "Ej j'va aller watcher un movie" uses the English-derived loanword "watch" as if it were an "-er" verb. The most common loans are basic lexical features (nouns, adjectives, verb stems), but a few conjunctions and adverbs are borrowed from English ("but, so, anyway").

Chiac originated in the community of specific ethnic Acadians, known as "Chiacs, Chiaks or Chi'aq",[2] living on the southeast coast of New Brunswick, specifically near the Shediac Bay area.

While some[who?] believe that Chiac dates back as far as the 17th or the 18th centuries, others[who?] believe it developed in the 20th century, in reaction to the dominance of English-language media in Canada, the lack of French-language primary and secondary education, the increased urbanization of Moncton, and contact with the dominant Anglophone community in the area.[citation needed] The origin of the word "Chiac" is not known; some speculate that it is an alteration of "Shediac" or "Es-ed-ei-ik".

Geographic distribution

[edit]

Chiac is mostly spoken by native speakers of Acadian French in the southeastern region of New Brunswick. Its speakers are primarily located in the Westmorland County of southeastern New Brunswick and further north along the coast in adjacent Kent County.

Further north along the coast, Acadian French resembling Quebec French is more common as the border with Quebec is approached. To the immediate east, west, and south, fully bilingual speakers of French and English are found, and the regions beyond typically have unilingual Anglophones.

Acadian writers, poets, and musicians such as Lisa LeBlanc, Radio Radio,[3] Fayo,[4] Cayouche, Les Hay Babies, 1755, Antonine Maillet[5] and many others have produced works in Chiac.

Chiac is also featured in Acadieman, a comedy about "The world's first Acadian Superhero" by Dano Leblanc.[6]

  1. ^ "Chiac | The Canadian Encyclopedia". <a href="http://www.thecanadianencyclopedia.ca" rel="nofollow">www.thecanadianencyclopedia.ca</a>. Retrieved 2021-06-15.
  2. ^ Jump up to: a b Dorion, Leah; Préfontaine, Darren R.; Barkwell, Lawrence J. (1999). "VI: Métis Culture and Language". Resources for Métis Researchers (PDF). Gabriel Dumont Institute and The Louis Riel Institute. p. 14. Chiac, the little-known mixed Algonquian-Acadian French language of the Metis people in Maritime Canada bears a remarkable similarity in syntax to Michif
  3. ^ Radio Radio: Comment ça va?, 21 January 2013, retrieved 2022-03-17
  4. ^ Laberge, Corinne (2007-06-28). "Le monde de Fayo". Retrieved 2007-08-09.
  5. ^ Morrow, Martin (18 February 2023). "Acadian actor Viola Léger embodied the iconic character La Sagouine". The Globe and Mail. Retrieved 17 January 2024. La Sagouine's use of chiac, the type of Acadian French native to rural New Brunswick.
  6. ^ "C'est la vie". C'est la vie. 2006-12-08.
  • King, Ruth. "Overview and Evaluation of Acadie's joual," in Social Lives in Language – Sociolinguistics and multilingual speech communities: Celebrating the Work of Gillian Sankoff edited by Miriam Meyerhoff and Naomi Nagy (2008) pp 137ff
  • Chiac: an example of dialect change and language transfer in Acadian French. National Library of Canada, 1987.
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Local artist creates several sand carvings on public beach at Witty’s Lagoon (PHOTOS)

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DRC Ebola outbreak puts neighbours on high alert

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Health authorities battling to contain an outbreak of Ebola in the Democratic Republic of Congo (DRC) are urging neighbouring countries to be vigilant and strengthen response plans to stop the disease spreading across borders.

Since the start of the outbreak in late August, 48 confirmed and probable cases have been reported and 31 people have died in the Bulape health zone, in DRC’s southwestern Kasai province, according to the World Health Organization (WHO).

About 50 WHO experts in disease surveillance, clinical care, infection prevention and control, logistics and community engagement are working alongside Congolese response teams in the affected area, where a treatment centre has been set up.

Although the disease is currently limited to this remote area, the WHO says it is working with nearby countries to ensure they are ready to rapidly detect the virus and implement control measures.

Angola, which shares a land border with Kasai province, is the top priority, due to the high risk of cross-border spread. Burundi, the Central African Republic, Congo, Rwanda, South Sudan, Uganda, Tanzania and Zambia are considered at moderate risk.

Charles Njuguna, regional advisor for strengthening country readiness at WHO Africa, said the WHO had provided ministries of health with the tools to carry out “readiness assessments” to help develop response plans and identify priorities.

“Seven of them have already completed the entire process. We are following up with other countries,” he told an online press conference Thursday (18 September), adding that the level of preparedness in these countries was currently “moderate”.

A key priority will be surveillance at ports of entry, particularly at the Angola-DRC border.

“We all know that diseases do not need permission to cross borders, they do not need visas,” Njuguna added.

“So we are working with Angola to build capacity at the ports of entry.”

WHO is also working with the International Organization for Migration on the ground in Angola to monitor the movement of people between affected areas and country borders.

Vaccination campaign

Central to efforts to contain the outbreak is a vaccination campaign underway in Bulape health zone where at least 600 people have received the Ervebo vaccine since 14 September.

Groups most at risk of infection, such as frontline health workers and people who have been in contact with those infected, are the priority targets for the operation, planned by Congolese health authorities, the WHO and the United Nations Children’s Fund (UNICEF).

About 50 WHO experts in disease surveillance, clinical care, infection prevention and control, logistics and community engagement are working alongside Congolese response teams in the affected area, where a treatment centre has been set up.

The Africa Centres for Disease Control and Prevention (Africa CDC) has also mobilised a field team to support the DRC’s health ministry in setting up community-based surveillance.

Justus Nsio Mbeta, epidemiologist and deputy DRC country director for Africa CDC, told SciDev.Net: “This team, alongside the government, will recruit community relays, train them to search for and detect suspected cases in households during their home visits, and to be able to list contacts and follow them.”

‘Don’t panic’

Health authorities say building public trust and communicating with local communities is essential to assuage fears.

One of the challenges, says Mbeta, is that people are fearful of being found to have been in contact with an infected person.

“There is strong resistance and reluctance from the community to be denounced as a contact,” said Mbeta.

“The sick are still hiding in the community,” he added.

He says community relay teams will go “from household to household” to reassure people and advise on what to do when a person is infected or develops clinical signs of Ebola.

According to the WHO, the follow-up of contacts of confirmed cases has improved from 19 per cent a fortnight ago to more than 90 per cent. Nearly 950 contacts are currently being followed in the health zone.

“At the beginning, when the investigation was still ongoing, panic among the population was reported, but after we had confirmation [that it was Ebola] and put in place the necessary measures, and thanks to our continued engagement with the population, we did not record any new incidents of panic and displacement,” said Otim Patrick Ramadan, WHO programme manager for emergency response.

He reassured local populations: “There is no need to be afraid, no need to panic and the epidemic can be quickly contained if all the measures recommended on the ground are followed.”

These measures include putting contacts of infected people under observation for 21 days and vaccination for those deemed at risk, he said.

“Sick people should be reported quickly and taken to a health facility so that they do not expose those living under the same roof,” he added.

“They are being taken care of and any death must be reported. People should not participate in dangerous burials.”

Zaire strain

The first confirmed case in the outbreak was a 34-year-old pregnant woman, admitted on 20 August to the Bulape General Hospital with symptoms including fever, vomiting and bleeding.

Samples analysed at the National Institute of Biomedical Research in the capital Kinshasa confirmed a diagnosis of Ebola virus disease, which was identified as the Zaire strain, which can be effectively prevented with the Ervebo vaccine.

Experts say sequencing of the virus and rapid identification of the strain was crucial in facilitating a quick response to the outbreak.

Anticipating possible shortages of vaccine stocks, WHO says the International Coordinating Group on Vaccine Provision has approved the shipment of about 45,000 additional doses to the DRC.

Yap Boum, deputy head of the incident management support team at Africa CDC, said continued vigilance was critical.

“The Ebola outbreak in Kasai continues to pose a major threat to health systems, even as treatment and vaccination capacities have improved,” he told the press conference Thursday.

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