Relationship of BMI,Waist Circumference,and Weight Change with Use of Health Services by Older Adults

Objective: To examine the relationship of BMI, waist circumference (WC), and weight change with use of health care services by older adults. Research Methods and Procedures: This was a prospective cohort study conducted from 2001 to 2003 among 2919 persons representative of the non‐institutionalized Spanish population ≥60 years of age. Analyses were performed using logistic regression, with adjustment for age, educational level, size of place of residence, tobacco use, alcohol consumption, and presence of chronic disease. Results: Obesity (BMI ≥ 30 kg/m²) and abdominal obesity (WC >102 cm in men and >88 cm in women) in 2001 were associated with greater use of certain health care services among men and women in the period 2001–2003. Compared with women with WC ≤ 88 cm, women with abdominal obesity were more likely to visit primary care physicians [odds ratio (OR): 1.36; 95% confidence limit (CL): 1.06–1.73] and receive influenza vaccination (OR: 1.30; 95% CL: 1.03–1.63). Weight gain was not associated with greater health service use by either sex, regardless of baseline BMI. Weight loss was associated with greater health service use by obese and non‐obese subjects of both sexes. In comparison with those who reported no important weight change, non‐obese women who lost weight were more likely to visit hospital specialists (OR: 1.45; 95% CL: 1.02–2.06), receive home medical visits (OR: 1.61; 95% CL: 1.06–2.45), be hospitalized (OR: 1.88; 95% CL: 1.29–2.74), and have more than one hospital admission (OR: 2.31; 95% CL: 1.19–4.47). Discussion: Obesity and weight loss are associated with greater health service use among the elderly.     

Obesity among People with and without Mental Retardation across Adulthood

Objective: This study was designed to explore obesity during adulthood and the likelihood of moving out of obesity among 1809 adults without disability and 680 adults with mental retardation who received care at the same primary care practices during the period of 1990 to 2003. Research Method and Procedures: A retrospective observational design using medical records first identified patients with mental retardation (MR) and age‐matched controls without disabilities. Data on BMI collected during each primary care visit allowed exploration of obesity at three levels. Moving out of obesity was defined as having a BMI <25 kg/m². We also abstracted data on age, sex, race, and other medical conditions. Results: For adults 20 to 29 years of age, 33.1% of patients without disability and 21% of patients with MR had a BMI >30 kg/m². Between the ages of 50 and 59 years, 40.5% of the patients without disability and 35.2% of the patients with MR had a BMI >30 kg/m². Patients with mild MR had similar prevalence rates of obesity and patients with severe MR had significantly lower prevalence of obesity compared with the patients without disability through 50 years of age. Throughout the period from 20 to 60 years of age, between 15% and 40% of individuals with and without MR, who were previously obese, were not currently obese. Discussion: Throughout the adult years, an increasing proportion of individuals with and without MR are obese. However, obesity is not a chronic state; many people transition back to a normal body weight.     

The Impact of Obesity on Primary Care Visits

Objective: To investigate the influence of patient obesity on primary care physician practice style. Research Methods and Procedures: This was a randomized, prospective study of 509 patients assigned for care by 105 primary care resident physicians. Patient data collected included sociodemographic information, self‐reported health status (Medical Outcomes Study Short Form‐36), evaluation for depression (Beck Depression Index), and satisfaction. Height and weight were measured to calculate the BMI. Videotapes of the visits were analyzed using the Davis Observation Code (DOC). Results: Regression equations were estimated relating obesity to visit length, each of the 20 individual DOC codes, and the six DOC Physician Practice Behavior Clusters, controlling for patient health status and sociodemographics. Obesity was not significantly associated with the length of the visit, but influenced what happened during the visit. Physicians spent less time educating obese patients about their health (p = 0.0062) and more time discussing exercise (p = 0.0075). Obesity was not related to discussions regarding nutrition. Physicians spent a greater portion of the visit on technical tasks when the patient was obese (p = 0.0528). Mean pre‐visit general satisfaction for obese patients was significantly lower than for non‐obese patients (p = 0.0069); however, there was no difference in post‐visit patient satisfaction. Discussion: Patient obesity impacts the medical visit. Further research can promote a greater understanding of the relationships between obese patients and their physicians.     

Moderate and Severe Obesity Have Large Differences in Health Care Costs

Objective: To analyze health care use and expenditures associated with varying degrees of obesity for a nationally representative sample of individuals 54 to 69 years old. Research Methods and Procedures: Data from the Health and Retirement Study, a nationwide biennial longitudinal survey of Americans in their 50s, were used to estimate multivariate regression models of the effect of weight class on health care use and costs. The main outcomes were total health care expenditures, the number of outpatient visits, the probability of any inpatient stay, and the number of inpatient days. Results: The results indicated that there were large differences in obesity‐related health care costs by degree of obesity. Overall, a BMI of 35 to 40 was associated with twice the increase in health care expenditures above normal weight (about a 50% increase) than a BMI of 30 to 35 (about a 25% increase); a BMI of over 40 doubled health care costs (～100% higher costs above those of normal weight). There was a difference by gender in how health care use and costs changed with obesity class. The primary effect of increasing weight class on health care use appeared to be through elevated use of outpatient health care services. Discussion: Obesity imposes an increasing burden on the health care system, and that burden grows disproportionately large for the most obese segment of the U.S. population. Because the prevalence of severe obesity is increasing much faster than that of moderate obesity, average estimates of obesity effects obscure real consequences for individuals, physician practices, hospitals, and health plans.     

Access and Use of Medical Care among Obese Persons

The prevalence of obesity and severe obesity is growing rapidly, along with obesity‐related comorbidities and mortality. Given the increased health risks associated with obesity, it is vital that obese persons have adequate access to, and make consistent use of, medical care services. Assuming obese persons have access to medical care that is comparable to non‐obese persons, one would expect to observe greater use of medical services among obese persons. In this article we briefly review empirical evidence of the access to and use of medical care among obese persons. Although certain subgroups that tend to have disproportionately high prevalences of obesity (i.e., low socioeconomic status, minority groups) have reduced access to care, no studies have specifically examined whether or not obese persons have the same access to health care as do their lean counterparts. With respect to use of health care services, however, obesity has been consistently linked with greater rates of utilization and increased health care expenditures. Both the increased use and cost appear to be largely a function of treating obesity‐associated comorbidities such as diabetes and hypertension. We conclude that, although it is clear that obesity is associated with both greater use and cost of medical care, the relationship between obesity and access to medical care has not been determined.     

The lifetime medical cost burden of overweight and obesity: implications for obesity prevention

This study quantifies age-specific and lifetime costs for overweight (BMI: 25-29.9), obese I (BMI: 30-34.9), and obese II/III (BMI: >35) adults separately by race/gender strata. We use these results to demonstrate why private sector firms are likely to underinvest in obesity prevention efforts. Not only does the existence of Medicare reduce the economic burden that obesity imposes on private payers, but, from the perspective of a 20-year-old obese adult, the short-term costs of obesity are small. This suggests that legislation that subsidizes wellness programs and/or mandates coverage for obesity treatments might make all firms better off. Ironically, Medicare has a greater incentive to prevent obesity because when an obese 65 year old enters the program, his/her costs are immediate and higher than costs for normal weight individuals.     

Obesity and the Use of Health Care Services

Objective: This study investigated differences in the use of health care services and associated costs between obese and nonobese patients. Research Methods and Procedures: New adult patients (N = 509) were randomly assigned to primary care physicians at a university medical center. Their use of medical services and related charges was monitored for 1 year. Data collected included sociodemographics, self‐reported health status using the Medical Outcomes Study Short Form‐36, evaluation for depression using the Beck Depression Index, and measured height and weight to calculate BMI. Results: Obese patients included a significantly higher percentage of women and had higher mean age, lower mean education, lower mean health status, and higher mean Beck Depression Index scores. Obese patients had a significantly higher mean number of visits to both primary care (p = 0.0005) and specialty care clinics (p = 0.0006), and a higher mean number of diagnostic services (p < 0.0001). Obese patients also had significantly higher primary care (p = 0.0058), specialty clinic (p = 0.0062), emergency department (p = 0.0484), hospitalization (p = 0.0485), diagnostic services (p = 0.0021), and total charges (p = 0.0033). Controlling for health status, depression, age, education, income, and sex, obesity was significantly related to the use of primary care (p = 0.0364) and diagnostic services (p = 0.0075). There was no statistically significant relationship between obesity and medical expenditures in any of the five categories or for total charges. Discussion: Obesity is a chronic condition requiring long‐term management, with an emphasis on prevention. If this critical health issue is not appropriately addressed, the prevalence of obesity and obesity‐related diseases will continue to grow, resulting in escalating use of health care services.     

Association between obesity and patient satisfaction

Objective: To examine the relationship between BMI and patient satisfaction with health care providers using a nationally representative survey. Research Methods and Procedures: This analysis examined the 9914 adult patients who completed the 2000 Medical Expenditure Panel Survey and had visited a health care provider within 12 months of the survey. Linear regression models were employed with patient satisfaction as the dependent variable. The patient satisfaction scale was based on ratings from five questions assessing the quality of provider interactions. The independent variable was BMI, with adjustments for the domains of demographics, social‐economic status, health attitudes and behavior, health status, and health care use. BMI (weight in kilograms/square of height in meters) was classified as normal weight (18.5 to 24.9), overweight (25.0 to 29.9), or obese (≥30.0). Hierarchical models were used to evaluate how each domain modified the BMI‐satisfaction association. Results: Obese patients reported significantly greater satisfaction with their health care providers than their normal‐weight counterparts did (p < 0.05). There were no significant differences in satisfaction between normal‐weight and overweight patients or between overweight and obese patients. The health status domain produced the largest modification in the BMI‐satisfaction relationship. Examination of interaction effects revealed that the association between BMI and satisfaction was confined to older persons. Discussion: In this nationally representative sample of individuals, obese persons were more satisfied than their normal‐weight counterparts. This finding counters those of previous studies. Incomplete adjustments for health care use and insurance status may have led to those conclusions.     

BMI and health status among adults with asthma

Background: A convincing body of literature links obesity with a higher risk for developing adult‐onset asthma. The impact of obesity on asthma severity among adults with pre‐existing asthma, however, is less clear. Methods and Procedures: In a prospective cohort study of 843 adults with severe asthma, we studied the impact of BMI on asthma health status. Results: The prevalence of obesity and overweight were 44% (95% confidence interval (CI) 41–47%) and 28% (95% CI 25–32%). The obese BMI group was associated with a higher risk for daily or near daily asthma symptoms than was the normal BMI group (odds ratio (OR) 1.81; 95% CI 1.10–2.96). Compared to the normal BMI group, generic physical health status was worse in the overweight (mean score decrement ?2.42 points; 95% CI ?4.39 to ?0.45) and the obese groups (?6.31 points; 95% CI ?8.14 to ?4.49). Asthma‐specific quality of life was worse in the underweight (mean score increment 8.66 points; 95% CI 2.53–14.8) and obese groups (4.51 points; 95% CI 2.21–6.81), compared to those with normal BMI. Obese persons also had a higher number of restricted activity days that past month (5.05 days; 95% CI 2.90–7.19 days). Discussion: It appears that obesity has a substantive negative effect on health status among adults with asthma. Further work is needed to clarify the precise mechanisms. Clinicians should counsel dietary modification and weight loss for their overweight and obese patients with asthma.     

Disability prevalence among healthy weight,overweight, and obese adults

Objective:

Obesity is associated with adverse health outcomes in people with and without disabilities. However, little is known about disability prevalence among people who are obese. The purpose of this study is to determine the prevalence and type of disability among adults who are obese.

Design and Methods:

Pooled data from the 2003‐2009 National Health Interview Survey (NHIS) were analyzed to obtain national prevalence estimates of disability, disability type and obesity. The disability prevalence was stratified by body mass index (BMI): healthy weight (BMI 18.5‐<25.0), overweight (BMI 25.0‐<30.0), and obese (BMI ≥ 30.0).

Results:

In this pooled sample, among the 25.4% of US adults who were obese, 41.7% reported a disability. In contrast, 26.7% of those with a healthy weight and 28.5% of those who were overweight reported a disability. The most common disabilities among respondents with obesity were movement difficulty (32.5%) and work limitation (16.6%).

Conclusions:

This research contributes to the literature on obesity by including disability as a demographic in assessing the burden of obesity. Because of the high prevalence of disability among those who are obese, public health programs should consider the needs of those with disabilities when designing obesity prevention and treatment programs.     

Multi-Institutional Sharing of Electronic Health Record Data to Assess Childhood Obesity

Objective

To evaluate the validity of multi-institutional electronic health record (EHR) data sharing for surveillance and study of childhood obesity.

Methods

We conducted a non-concurrent cohort study of 528,340 children with outpatient visits to six pediatric academic medical centers during 2007–08, with sufficient data in the EHR for body mass index (BMI) assessment. EHR data were compared with data from the 2007–08 National Health and Nutrition Examination Survey (NHANES).

Results

Among children 2–17 years, BMI was evaluable for 1,398,655 visits (56%). The EHR dataset contained over 6,000 BMI measurements per month of age up to 16 years, yielding precise estimates of BMI. In the EHR dataset, 18% of children were obese versus 18% in NHANES, while 35% were obese or overweight versus 34% in NHANES. BMI for an individual was highly reliable over time (intraclass correlation coefficient 0.90 for obese children and 0.97 for all children). Only 14% of visits with measured obesity (BMI ≥95%) had a diagnosis of obesity recorded, and only 20% of children with measured obesity had the diagnosis documented during the study period. Obese children had higher primary care (4.8 versus 4.0 visits, p<0.001) and specialty care (3.7 versus 2.7 visits, p<0.001) utilization than non-obese counterparts, and higher prevalence of diverse co-morbidities. The cohort size in the EHR dataset permitted detection of associations with rare diagnoses. Data sharing did not require investment of extensive institutional resources, yet yielded high data quality.

Conclusions

Multi-institutional EHR data sharing is a promising, feasible, and valid approach for population health surveillance. It provides a valuable complement to more resource-intensive national surveys, particularly for iterative surveillance and quality improvement. Low rates of obesity diagnosis present a significant obstacle to surveillance and quality improvement for care of children with obesity.     

The Relationship between Body Mass Index and Hospitalisation Rates,Days in Hospital and Costs: Findings from a Large Prospective Linked Data Study

Background

Internationally there is limited empirical evidence on the impact of overweight and obesity on health service use and costs. We estimate the burden of hospitalisation—admissions, days and costs—associated with above-normal BMI.

Methods

Population-based prospective cohort study involving 224,254 adults aged ≥45y in Australia (45 and Up Study). Baseline questionnaire data (2006-2009) were linked to hospitalisation and death records (median follow-up 3.42y) and hospital cost data. The relationships between BMI and hospital admissions and days were modelled using zero-inflated negative binomial regression; generalised gamma models were used to model costs. Analyses were stratified by sex and age (45-64, 65-79, ≥80y), and adjusted for age, area of residence, education, income, smoking, alcohol-intake and private health insurance status. Population attributable fractions were also calculated.

Results

There were 459,346 admissions (0.55/person-year) and 1,483,523 hospital days (1.76/person-year) during follow-up. For ages 45-64y and 65-79y, rates of admissions, days and costs increased progressively with increments of above-normal BMI. Compared to BMI 22.5-<25kg/m², rates of admissions and days were 1.64-2.54 times higher for BMI 40-50kg/m²; costs were 1.14-1.24 times higher for BMI 27.5-<30kg/m², rising to 1.77-2.15 times for BMI 40-50kg/m². The BMI-hospitalisation relationship was less clear for ≥80y. We estimated that among Australians 45-79y, around 1 in every 8 admissions are attributable to overweight and obesity (2% to overweight, 11% to obesity), as are 1 in every 6 days in hospital (2%, 16%) and 1 in every 6 dollars spent on hospitalisation (3%, 14%).

Conclusions

The dose-response relationship between BMI and hospital use and costs in mid-age and older Australians in the above-normal BMI range suggests even small downward shifts in BMI among these people could result in considerable reductions in their annual health care costs; whether this would result in long-term savings to the health care system is not known from this study.     

BMI and seatbelt use

Objective: Seatbelt use among obese persons may be reduced because seatbelts are uncomfortable. We investigated the association between obesity and seatbelt use with data from the 2002 Behavioral Risk Factor Surveillance System Survey. Research Methods and Procedures: Multivariable logistic regression was used to calculate odds ratios and 95% confidence intervals (CIs) for seatbelt use among overweight (BMI, 25.0 to 29.9), obese (BMI, 30.0 to 39.9), and extremely obese (BMI ≥ 40.0) persons, relative to a non‐overweight/non‐obese reference group (BMI ≤ 24.9), adjusted for age, race, gender, education, and state seatbelt law. Results: Adjusted odds ratios for seatbelt use were 0.89 (95% CI, 0.85 to 0.93) for overweight, 0.69 (0.66 to 0.73) for obese, and 0.45 (95% CI, 0.40 to 0.50) for extremely obese persons. Interaction effects were evident for all covariates, with stronger associations between increasing BMI and decreasing seatbelt use for women, increasing age, higher education, and residence in states with a secondary seatbelt law. There was a linear decrease in seatbelt use with increasing BMI for all subgroups except persons 18 to 24 years old. Discussion: Lack of seatbelt can be added to the list of risk factors associated with obesity. Effective preventive interventions are needed to promote seatbelt use among overweight and obese persons.     

Long-term effects of obesity on employment and work limitations among U.S. Adults, 1986 to 1999

Objective: To determine the relationships between BMI and workforce participation and the presence of work limitations in a U.S. working‐age population. Research Methods and Procedures: We used data from the Panel Study of Income Dynamics, a nationwide prospective cohort, to estimate the effect of obesity in 1986 on employment and work limitations in 1999. Individuals were classified into the following weight categories: underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30). Using multivariable probit models, we estimated the relationships between obesity and both employment and work disability. All analyses were stratified by sex. Results: After adjusting for baseline sociodemographic characteristics, smoking status, exercise, and self‐reported health, obesity was associated with reduced employment at follow‐up [men: marginal effect (ME) ?4.8 percentage points (pp); p < 0.05; women: ME ?5.8 pp; p < 0.10]. Among employed women, being either overweight or obese was associated with an increase in self‐reported work limitations when compared with normal‐weight individuals (overweight: ME +3.9 pp; p < 0.01; obese: ME +12.6 pp; p < 0.01). Among men, the relationship between obesity and work limitations was not statistically significant. Discussion: Obesity appears to result in future productivity losses through reduced workforce participation and increased work limitations. These findings have important implications in the U.S., which is currently experiencing a rise in the prevalence of obesity.     

The Relationship between Body Weight and Health Care among German Women

Objective: To investigate the relationship between body weight and the use of health care services among women from southern Germany. Research Methods and Procedures: Data were drawn from the 1994 to 1995 Monitoring of Trends and Determinants in Cardiovascular Disease Augsburg survey, covering a population‐representative sample of women 25 to 74 years old (n = 2301). Logistic regression models were used to calculate odds ratios (ORs) for the use of medical services by women with overweight (BMI 25.0 to 29.9 kg/m²) or obesity (BMI ≥ 30 kg/m²) in comparison with normal‐weight women (BMI < 25.0 kg/m²). Results: In multivariable analysis, obese women 50 to 74 years old were more likely than normal‐weight women to delay cancer screening procedures, such as manual breast examination and Papanicolaou smear (OR 0.52, 95% confidence interval 0.37 to 0.74) in the previous 12 months. However, the relationship between obesity and cancer screening was not found to be significant in 25‐ to 49‐year‐old women (OR 0.92, 95% confidence interval 0.62 to 1.36). Neither in the 25‐ to 49‐year‐old age group nor in the 50‐ to 74‐year‐old age group were independent relationships between higher body weight and total physician visits, hospitalizations, or medication use observed. Discussion: Obese women tended to use medical services with greater frequency due to obesity‐related diseases. However, postmenopausal women with a BMI ≥ 30 kg/m² were more likely to delay routine cancer screening, putting them at a greater risk for death from breast, cervical, and endometrial cancer. Thus, obese postmenopausal women should be targeted for increased screening.     

Impact of physician BMI on obesity care and beliefs

Using a national cross-sectional survey of 500 primary care physicians conducted between 9 February and 1 March 2011, the objective of this study was to assess the impact of physician BMI on obesity care, physician self-efficacy, perceptions of role-modeling weight-related health behaviors, and perceptions of patient trust in weight loss advice. We found that physicians with normal BMI were more likely to engage their obese patients in weight loss discussions as compared to overweight/obese physicians (30% vs. 18%, P = 0.010). Physicians with normal BMI had greater confidence in their ability to provide diet (53% vs. 37%, P = 0.002) and exercise counseling (56% vs. 38%, P = 0.001) to their obese patients. A higher percentage of normal BMI physicians believed that overweight/obese patients would be less likely to trust weight loss advice from overweight/obese doctors (80% vs. 69%, P = 0.02). Physicians in the normal BMI category were more likely to believe that physicians should model healthy weight-related behaviors-maintaining a healthy weight (72% vs. 56%, P = 0.002) and exercising regularly (73% vs. 57%, P = 0.001). The probability of a physician recording an obesity diagnosis (93% vs. 7%, P < 0.001) or initiating a weight loss conversation (89% vs. 11%, P ≤ 0.001) with their obese patients was higher when the physicians' perception of the patients' body weight met or exceeded their own personal body weight. These results suggest that more normal weight physicians provided recommended obesity care to their patients and felt confident doing so.     

Does High BMI Influence Hospital Charges in Children Undergoing Adenotonsillectomy?

Background: Obesity is a highly prevalent chronic problem with health and fiscal consequences. Data from adults and nonsurgical pediatric patients suggest that obesity has serious implications for the US economy. Objective: Our goal was to describe the impact of BMI on hospital charges in children undergoing adenotonsillectomy (AT). Methods and Procedures: We carried out a retrospective comparative analysis of the electronic anesthesia record and the charges from billing data from a large tertiary institution on children aged 3–18 years who had AT during the year 2005–2007. The main outcome measures were mean total hospital charges, likelihood of admission, and length of hospital stay (LOS). Results: Of 1,643 children, 68.9% were aged <10 years, 76% were whites, and 74.1% had private commercial insurance. Most (75.3%) children were discharged on the day of surgery. Obese and overweight children were more likely to be admitted than their normal‐weight peers (X² = 26.3, P < 0.001). Among those admitted, BMI showed a positive correlation with LOS (r = 0.20, P < 0.001). Obese and overweight patients had significantly higher total hospital charges than their healthy‐weight counterparts (P = 0.001). Anesthesia, postanesthesia care unit (PACU), and pharmacy and laboratory charges were also higher for obese than normal‐weight children (P < 0.05). Discussion: Overweight and obese children undergoing AT accrued higher hospital charges and had longer postoperative LOS than their healthy‐weight peers. If these findings are extendable to other surgical procedures, they could have far‐reaching implications for the US economy.     

Periodic health examination, 1999 update: 1. Detection,prevention and treatment of obesity

OBJECTIVES: (1) To evaluate the evidence relating to the effectiveness of methods to prevent and treat obesity, and (2) to provide recommendations for the prevention and treatment of obesity in adults aged 18 to 65 years and for the measurement of the body mass index (BMI) as part of a periodic health examination. OPTIONS: In adults with obesity (BMI greater than 27) management options include weight reduction, prevention of further weight gain or no intervention. OUTCOMES: The long-term (more than 2 years) effectiveness of (a) methods to prevent obesity and (b) methods to treat obesity. EVIDENCE: MEDLINE was searched for articles published from 1966 to April 1998 that related to the prevention and treatment of obesity; additional articles were identified from the bibliographies of review articles and the listings of Current Contents. Selection criteria were used to limit the analysis to prospective studies with at least 2 years'' follow-up. BENEFITS, HARM AND COSTS: Health benefits of weight reduction were evaluated in terms of alleviation of symptoms, improved management of obesity-related diseases and a reduction in major clinical outcomes. The health risk of weight-reduction methods were briefly evaluated in terms of increased mortality and morbidity. VALUES: The recommendations of this report reflect the commitment of the Canadian Task Force on Preventive Health Care to provide a structured, evidence-based appraisal of whether a manoeuvre should be part of a periodic health examination. RECOMMENDATIONS: (1) Prevention: There is insufficient evidence to recommend in favour of or against community-based obesity prevention programs; however, because of considerable health risks associated with obesity and the limited long-term effectiveness of weight-reduction methods, the prevention of obesity should be a high priority for health care providers (grade C recommendation). (2) Treatment: (a) For obese adults without obesity-related diseases, there is insufficient evidence to recommend in favour of or against weight-reduction therapy because of a lack of evidence supporting the long-term effectiveness of weight-reduction methods (grade C recommendation); (b) for obese adults with obesity-related diseases (e.g., diabetes mellitus, hypertension), weight reduction is recommended because it can alleviate symptoms and reduce drug therapy requirements, at least in the short term (grade B recommendation). (3) Detection: (a) for people without obesity-related diseases, there is insufficient evidence to recommend the inclusion or exclusion of BMI measurement as part of a periodic health examination, and therefore BMI measurement is left to the discretion of individual health care providers (grade C recommendation); (b) for people with obesity-related diseases, BMI measurement is recommended because weight reduction should be considered with a BMI of more than 27 (grade B recommendation). VALIDATION: The findings of this analysis were reviewed through an iterative process by the members of the Canadian Task Force on Preventive Health Care. SPONSORS: The Canadian Task Force on Preventive Health Care is funded through a partnership between the Provincial and Territorial Ministries of Health and Health Canada.     

Obesity and physical activity among Aboriginal Canadians

Objective: To investigate ethnic differences in obesity and physical activity among Aboriginal and non‐Aboriginal Canadians. Methods and Procedures: The sample included 24,279 Canadians (1,176 Aboriginals, 23,103 non‐Aboriginals) aged 2–64 years from the 2004 Canadian Community Health Survey (CCHS). Adult participants were classified as underweight/normal weight, overweight (BMI 25–29.9 kg/m²) or obese (BMI ≥ 30 kg/m²). Children and youth 2–17 years of age were classified as normal weight, overweight or obese based on the International Obesity Task Force criteria. Leisure‐time physical activity levels over the previous 3 months were obtained by questionnaire in those aged 12–64 years. Results: The prevalence of obesity in adults was 22.9% (men: 22.9%; women: 22.9%), and the prevalence was higher among Aboriginals (37.8%) compared to non‐Aboriginals (22.6%). The prevalence of obesity in children and youth was 8.2% (boys: 9.2%; girls: 7.2%), and the prevalence was higher among Aboriginals (15.8%) compared to non‐Aboriginals (8.0%). In both youth and adults, the odds for obesity were higher among Aboriginals (youth: OR = 2.3 (95% CI: 1.4–3.8); adults: OR = 2.4 (95% CI: 1.6–3.6)) after adjustment for a number of covariates. There were no ethnic differences in the prevalence of physical inactivity; however, physical inactivity was a predictor of obesity in both the Aboriginal and non‐Aboriginal samples. Discussion: The prevalence of obesity is higher among Canadian Aboriginals compared to the rest of the population. Further research is required to better delineate the determinants of obesity and the associated health consequences in this population.