Who pays the healthcare costs associated with obesity? Among workers, this is largely a question of the incidence of the costs of employer-sponsored coverage. Using data from the National Longitudinal Survey of Youth and the Medical Expenditure Panel Survey, we find that the incremental healthcare costs associated with obesity are passed on to obese workers with employer-sponsored health insurance in the form of lower cash wages. Obese workers without employer-sponsored insurance do not have a wage offset relative to their non-obese counterparts. A substantial part of the lower wages among obese women attributed to labor market discrimination can be explained by their higher health insurance premiums.

Nationally representative data on the quality of care for obese patients in US-ambulatory care settings are limited. We conducted a cross-sectional analysis of the 2005 and 2006 National Ambulatory Medical Care Survey (NAMCS). We examined obesity screening, diagnosis, and counseling during adult visits and associations with patient and provider characteristics. We also assessed performance on 15 previously published ambulatory quality indicators for obese vs. normal/overweight patients. Nearly 50% (95% confidence interval (CI): 46–54%) of visits lacked complete height and weight data needed to screen for obesity using BMI. Of visits by patients with clinical obesity (BMI ≥30.0 kg/m²), 70% (66–74%) were not diagnosed and 63% (59–68%) received no counseling for diet, exercise, or weight reduction. The percentage of visits not being screened (48%), diagnosed (66%), or counseled (54%) for obesity was also notably higher than expected even for patients with known obesity comorbidities. Performance (defined as the percentage of applicable visits receiving appropriate care) on the quality indicators was suboptimal overall. In particular, performance was no better than 50% for eight quality indicators, which are all related to the prevention and treatment of obesity comorbidities, e.g., coronary artery disease, hypertension, hyperlipidemia, asthma, and depression. Performance did not differ by weight status for any of the 15 quality indicators; however, poorer performance was consistently associated with lack of height and weight measurements. In conclusion, many opportunities are missed for obesity screening and diagnosis, as well as for the prevention and treatment of obesity comorbidities, in office-based practices across the United States, regardless of patient and provider characteristics.

The rising U.S. obesity prevalence has disproportionately affected minority children. Previous studies have reported that among African American U.S.-born participants, those with foreign-born parents were significantly less likely to be obese than individuals with U.S.-born parents. Little is known about the children of Hispanic immigrants from Central and South America, and among 2-5 year olds in particular. The current study examined demographic characteristics of 307 children ages 2-5 year olds who participated in a randomized controlled obesity prevention intervention trial in 8 childcare centers in Miami, Florida. Anthropometric data collected included weight, height, waist circumference and body mass index (BMI). Overweight was defined as > 95th %ile for age and at- risk-for-overweight was defined as > 85th to <95th percentile, based on the Centers for Disease Control and Prevention (CDC) guidelines. Obese children were significantly more likely to be born in the US than another country (P<0.0001). Girls were equally as likely as boys to be overweight; 31% of the sample has a BMI percentile > 85th %ile. Children of Central American immigrants were significantly more likely than their Cuban or Caribbean immigrant parent counterparts to be obese (p< 0.01). Obesity prevention interventions need to target children as young as preschool age and should be tailored to the child’s ethnic background, particularly if the child was born in the US and the parents were not.

The main aim of this paper is to describe and estimate a new decomposition of disability trends among working age populations into two parts -- the part of the trend explained by changes in the prevalence of chronic disease and obesity and the part of the trend explained by changes in the prevalence of disability among people with chronic diseases. If most of the changes in disability in this population are due to change in chronic disease prevalence, then there is little room for statutory incentives as an explanation for ADL disability trends. More importantly, such a result would suggest that recent changes in disability in this population are permanent for the affected age cohorts. If this cohort survives to old age, the use of medical care by this group will place great demands on Medicare financing.

On the other hand, if an increase in disability prevalence among those with chronic disease is the explanation, then there arise two possibilities: that chronic diseases have become more severe over time, or that the incidence of even ADL-style disabilities are dependent on cultural, social, and economic determinants such as the disability insurance payments. Such a result would suggest that the increases in ADL disability seen among working age populations might not be permanent.

BACKGROUND: Recent work has shown that rates of severe disability, measured by the inability to perform basic activities of daily living, have been rising in working age populations. At the same time, the prevalence of important chronic diseases has been rising, while others falling, among working age populations. Chronically ill individuals are more likely than others to have activity of daily living limitations.

OBJECTIVE: We examine the extent to which chronic disease trends can explain these disability trends. DATA: We use nationally representative survey data from the 1984-1996 National Interview Survey, which posed a consistent set of questions regarding limitations in activities of daily living over that period.

METHODS: We decompose trends in disability into 2 parts-1 part due to trends in the prevalence of chronic disease and the other due to trends in disability prevalence among those with chronic disease.

RESULTS:: Our primary findings are that for working age populations between 1984 and 1996: (1) disability prevalence fell dramatically among the nonchronically ill; (2) rising obesity prevalence explains about 40% of the rise in disability attributable to trends chronic illness; and (3) rising disability prevalence among the chronically ill explains about 60% of the rise in disability attributable to trends in chronic illness.

CONCLUSIONS: Disability prevention efforts in working age populations should focus on reductions in obesity prevalence and limiting disability among chronically ill populations. Given the rise in disability among these population subgroups, it is unclear whether further substantial declines in elderly disability can be expected.

Context: Without detailed evidence of their effectiveness, pedometers have recently become popular as a tool for motivating physical activity.

Objective: To evaluate the association of pedometer use with physical activity and health outcomes among outpatient adults.

Data Sources: English-language articles from MEDLINE, EMBASE, Sport Discus, PsychINFO, Cochrane Library, Thompson Scientific (formerly known as Thompson ISI), and ERIC (1966-2007); bibliographies of retrieved articles; and conference proceedings.

Study Selection: Studies were eligible for inclusion if they reported an assessment of pedometer use among adult outpatients, reported a change in steps per day, and included more than 5 participants.

Data Extraction and Data Synthesis: Two investigators independently abstracted data about the intervention; participants; number of steps per day; and presence or absence of obesity, diabetes, hypertension, or hyperlipidemia. Data were pooled using random-effects calculations, and meta-regression was performed.

Results: Our searches identified 2246 citations; 26 studies with a total of 2767 participants met inclusion criteria (8 randomized controlled trials [RCTs] and 18 observational studies). The participants' mean (SD) age was 49 (9) years and 85% were women. The mean intervention duration was 18 weeks. In the RCTs, pedometer users significantly increased their physical activity by 2491 steps per day more than control participants (95% confidence interval [CI], 1098-3885 steps per day, P .001). Among the observational studies, pedometer users significantly increased their physical activity by 2183 steps per day over baseline (95% CI, 1571-2796 steps per day, P .0001). Overall, pedometer users increased their physical activity by 26.9% over baseline. An important predictor of increased physical activity was having a step goal such as 10,000 steps per day (P = .001). When data from all studies were combined, pedometer users significantly decreased their body mass index by 0.38 (95% CI, 0.05-0.72; P = .03). This decrease was associated with older age (P = .001) and having a step goal (P = .04). Intervention participants significantly decreased their systolic blood pressure by 3.8 mm Hg (95% CI, 1.7-5.9 mm Hg, P .001). This decrease was associated with greater baseline systolic blood pressure (P = .009) and change in steps per day (P = .08).

Conclusions: The results suggest that the use of a pedometer is associated with significant increases in physical activity and significant decreases in body mass index and blood pressure. Whether these changes are durable over the long term is undetermined.

BACKGROUND: To provide quantitative insight into current U.S. policy choices for cervical cancer prevention, we developed a model of human papillomavirus (HPV) and cervical cancer, explicitly incorporating uncertainty about the natural history of disease. METHODS: We developed a stochastic microsimulation of cervical cancer that distinguishes different HPV types by their incidence, clearance, persistence, and progression. Input parameter sets were sampled randomly from uniform distributions, and simulations undertaken with each set. Through systematic reviews and formal data synthesis, we established multiple epidemiologic targets for model calibration, including age-specific prevalence of HPV by type, age-specific prevalence of cervical intraepithelial neoplasia (CIN), HPV type distribution within CIN and cancer, and age-specific cancer incidence. For each set of sampled input parameters, likelihood-based goodness-of-fit (GOF) scores were computed based on comparisons between model-predicted outcomes and calibration targets. Using 50 randomly resampled, good-fitting parameter sets, we assessed the external consistency and face validity of the model, comparing predicted screening outcomes to independent data. To illustrate the advantage of this approach in reflecting parameter uncertainty, we used the 50 sets to project the distribution of health outcomes in U.S. women under different cervical cancer prevention strategies. RESULTS: Approximately 200 good-fitting parameter sets were identified from 1,000,000 simulated sets. Modeled screening outcomes were externally consistent with results from multiple independent data sources. Based on 50 good-fitting parameter sets, the expected reductions in lifetime risk of cancer with annual or biennial screening were 76% (range across 50 sets: 69-82%) and 69% (60-77%), respectively. The reduction from vaccination alone was 75%, although it ranged from 60% to 88%, reflecting considerable parameter uncertainty about the natural history of type-specific HPV infection. The uncertainty surrounding the model-predicted reduction in cervical cancer incidence narrowed substantially when vaccination was combined with every-5-year screening, with a mean reduction of 89% and range of 83% to 95%. CONCLUSION: We demonstrate an approach to parameterization, calibration and performance evaluation for a U.S. cervical cancer microsimulation model intended to provide qualitative and quantitative inputs into decisions that must be taken before long-term data on vaccination outcomes become available. This approach allows for a rigorous and comprehensive description of policy-relevant uncertainty about health outcomes under alternative cancer prevention strategies. The model provides a tool that can accommodate new information, and can be modified as needed, to iteratively assess the expected benefits, costs, and cost-effectiveness of different policies in the U.S.