OBJECTIVES: To review the diagnostic performance of contemporary imaging modalities for determining local disease extent and nodal metastasis in patients with newly diagnosed cervical cancer.
METHODS: Pubmed and Embase databases were searched for studies published from 2000 to 2019 that used ultrasound (US), CT, MRI, and/or PET for evaluating various aspects of local extent and nodal metastasis in patients with newly diagnosed cervical cancer. Sensitivities and specificities from the studies were meta-analytically pooled using bivariate and hierarchical modeling.
RESULTS: Of 1311 studies identified in the search, 115 studies with 13,999 patients were included. MRI was the most extensively studied modality (MRI, CT, US, and PET were evaluated in 78, 12, 9, and 43 studies, respectively). Pooled sensitivities and specificities of MRI for assessing all aspects of local extent ranged between 0.71-0.88 and 0.86-0.95, respectively. In assessing parametrial invasion (PMI), US demonstrated pooled sensitivity and specificity of 0.67 and 0.94, respectively-performance levels comparable with those found for MRI. MRI, CT, and PET performed comparably for assessing nodal metastasis, with low sensitivity (0.29-0.69) but high specificity (0.88-0.98), even when stratified to anatomical location (pelvic or paraaortic) and level of analysis (per patient vs. per site).
CONCLUSIONS: MRI is the method of choice for assessing any aspect of local extent, but where not available, US could be of value, particularly for assessing PMI. CT, MRI, and PET all have high specificity but poor sensitivity for the detection of lymph node metastases.
KEY POINTS: • Magnetic resonance imaging is the method of choice for assessing local extent. • Ultrasound may be helpful in determining parametrial invasion, especially in lower-resourced countries. • Computed tomography, magnetic resonance imaging, and positron emission tomography perform similarly for assessing lymph node metastasis, with high specificity but low sensitivity.
We estimate that there will be 13·7 million new cases of childhood cancer globally between 2020 and 2050. At current levels of health system performance (including access and referral), 6·1 million (44·9%) of these children will be undiagnosed. Between 2020 and 2050, 11·1 million children will die from cancer if no additional investments are made to improve access to health-care services or childhood cancer treatment. Of this total, 9·3 million children (84·1%) will be in low-income and lower-middle-income countries. This burden could be vastly reduced with new funding to scale up cost-effective interventions. Simultaneous comprehensive scale-up of interventions could avert 6·2 million deaths in children with cancer in this period, more than half (56·1%) of the total number of deaths otherwise projected. Taking excess mortality risk into consideration, this reduction in the number of deaths is projected to produce a gain of 318 million life-years. In addition, the global lifetime productivity gains of US$2580 billion in 2020-50 would be four times greater than the cumulative treatment costs of $594 billion, producing a net benefit of $1986 billion on the global investment: a net return of $3 for every $1 invested. In sum, the burden of childhood cancer, which has been grossly underestimated in the past, can be effectively diminished to realise massive health and economic benefits and to avert millions of needless deaths.
Importance: Advances in childhood and adolescent cancer treatment have been associated with increased rates of cure during the past 3 decades; however, improvement in adult life expectancy for these individuals has not yet been reported.
Objectives: To project long-term survival and assess whether life expectancy will improve among adult survivors of childhood cancer who were treated in more recent decades.
Design, Setting, and Participants: A microsimulation model of competing mortality risks was developed using data from the Childhood Cancer Survivor Study on 5-year survivors of childhood cancer diagnosed between 1970 and 1999. The model included (1) late recurrence, (2) treatment-related late effects (health-related [subsequent cancers, cardiac events, pulmonary conditions, and other] and external causes), and (3) US background mortality rates.
Exposures: Treatment subgroups (no treatment or surgery only, chemotherapy alone, radiotherapy alone, and radiotherapy with chemotherapy) and individuals with acute lymphoblastic leukemia during childhood by era (1970-1979, 1980-1989, and 1990-1999).
Main Outcomes and Measures: Conditional life expectancy (defined as the number of years a 5-year survivor can expect to live), cumulative cause-specific mortality risk, and 10-year mortality risks conditional on attaining ages of 30, 40, 50, and 60 years.
Results: Among the hypothetical cohort of 5-year survivors of childhood cancer representative of the Childhood Cancer Survivor Study participants (44% female and 56% male; mean [SD] age at diagnosis, 7.3 [5.6] years), conditional life expectancy was 48.5 years (95% uncertainty interval [UI], 47.6-49.6 years) for 5-year survivors diagnosed in 1970-1979, 53.7 years (95% UI, 52.6-54.7 years) for those diagnosed in 1980-1989, and 57.1 years (95% UI, 55.9-58.1 years) for those diagnosed in 1990-1999. Compared with individuals without a history of cancer, these results represented a gap in life expectancy of 25% (95% UI, 24%-27%) (16.5 years [95% UI, 15.5-17.5 years]) for those diagnosed in 1970-1979, 19% (95% UI, 17%-20%) (12.3 years [95% UI, 11.3-13.4 years]) for those diagnosed in 1980-1989, and 14% (95% UI, 13%-16%) (9.2 years [95% UI, 8.3-10.4 years]) for those diagnosed in 1990-1999. During the 3 decades, the proportion of survivors treated with chemotherapy alone increased (from 18% in 1970-1979 to 54% in 1990-1999), and the life expectancy gap in this chemotherapy-alone group decreased from 11.0 years (95% UI, 9.0-13.1 years) to 6.0 years (95% UI, 4.5-7.6 years). In contrast, during the same time frame, only modest improvements in the gap in life expectancy were projected for survivors treated with radiotherapy (21.0 years [95% UI, 18.5-23.2 years] to 17.6 years [95% UI, 14.2-21.2 years]) or with radiotherapy and chemotherapy (17.9 years [95% UI, 16.7-19.2 years] to 14.8 years [95% UI, 13.1-16.7 years]). For the largest group of survivors by diagnosis-those with acute lymphoblastic leukemia-the gap in life expectancy decreased from 14.7 years (95% UI, 12.8-16.5 years) in 1970-1979 to 8.0 years (95% UI, 6.2-9.7 years).
Conclusions and Relevance: Evolving treatment approaches are projected to be associated with improved life expectancy after treatment for pediatric cancer, in particular among those who received chemotherapy alone for their childhood cancer diagnosis. Despite improvements, survivors remain at risk for shorter lifespans, especially when radiotherapy was included as part of their childhood cancer treatment.
BACKGROUND: Although the national obesity epidemic has been well documented, less is known about obesity at the U.S. state level. Current estimates are based on body measures reported by persons themselves that underestimate the prevalence of obesity, especially severe obesity.
METHODS: We developed methods to correct for self-reporting bias and to estimate state-specific and demographic subgroup-specific trends and projections of the prevalence of categories of body-mass index (BMI). BMI data reported by 6,264,226 adults (18 years of age or older) who participated in the Behavioral Risk Factor Surveillance System Survey (1993-1994 and 1999-2016) were obtained and corrected for quantile-specific self-reporting bias with the use of measured data from 57,131 adults who participated in the National Health and Nutrition Examination Survey. We fitted multinomial regressions for each state and subgroup to estimate the prevalence of four BMI categories from 1990 through 2030: underweight or normal weight (BMI [the weight in kilograms divided by the square of the height in meters], <25), overweight (25 to <30), moderate obesity (30 to <35), and severe obesity (≥35). We evaluated the accuracy of our approach using data from 1990 through 2010 to predict 2016 outcomes.
RESULTS: The findings from our approach suggest with high predictive accuracy that by 2030 nearly 1 in 2 adults will have obesity (48.9%; 95% confidence interval [CI], 47.7 to 50.1), and the prevalence will be higher than 50% in 29 states and not below 35% in any state. Nearly 1 in 4 adults is projected to have severe obesity by 2030 (24.2%; 95% CI, 22.9 to 25.5), and the prevalence will be higher than 25% in 25 states. We predict that, nationally, severe obesity is likely to become the most common BMI category among women (27.6%; 95% CI, 26.1 to 29.2), non-Hispanic black adults (31.7%; 95% CI, 29.9 to 33.4), and low-income adults (31.7%; 95% CI, 30.2 to 33.2).
CONCLUSIONS: Our analysis indicates that the prevalence of adult obesity and severe obesity will continue to increase nationwide, with large disparities across states and demographic subgroups. (Funded by the JPB Foundation.).
OBJECTIVE: This study aimed to estimate the cost-effectiveness and impact on childhood obesity of installation of chilled water dispensers ("water jets") on school lunch lines and to compare water jets' cost, reach, and impact on water consumption with three additional strategies.
METHODS: The Childhood Obesity Intervention Cost Effectiveness Study(CHOICES) microsimulation model estimated the cost-effectiveness of water jets on US childhood obesity cases prevented in 2025. Also estimated were the cost, number of children reached, and impact on water consumption of the installation of water jets and three other strategies.
RESULTS: Installing water jets on school lunch lines was projected to reach 29.6 million children (95% uncertainty interval [UI]: 29.4 million-29.8 million), cost $4.25 (95% UI: $2.74-$5.69) per child, prevent 179,550 cases of childhood obesity in 2025 (95% UI: 101,970-257,870), and save $0.31 in health care costs per dollar invested (95% UI: $0.15-$0.55). In the secondary analysis, installing cup dispensers next to existing water fountains was the least costly but also had the lowest population reach.
CONCLUSIONS: Installating water jet dispensers on school lunch lines could also save almost half of the dollars needed for implementation via a reduction in obesity-related health care costs. School-based interventions to promote drinking water may be relatively inexpensive strategies for improving child health.
An excise tax of 1 peso per liter on sugar-sweetened beverages was implemented in Mexico in 2014. We estimated the cost-effectiveness of this tax and an alternative tax scenario of 2 pesos per liter. We developed a cohort simulation model calibrated for Mexico to project the impact of the tax over ten years. The current tax is projected to prevent 239,900 cases of obesity, 39 percent of which would be among children. It could also prevent 61,340 cases of diabetes, lead to gains of 55,300 quality-adjusted life-years, and avert 5,840 disability-adjusted life-years. The tax is estimated to save $3.98 per dollar spent on its implementation. Doubling the tax to 2 pesos per liter would nearly double the cost savings and health impact. Countries with comparable conditions could benefit from implementing a similar tax.
Importance: Eating disorders (EDs) are common psychiatric disorders associated with high mortality. However, data on ED disease dynamics and treatment coverage are sparse.
Objectives: To model the individual-level disease dynamics of ED from birth to age 40 years and to estimate the association of increased treatment coverage with ED-related mortality.
Design, Setting, and Participants: In this decision analytical model study, an individual-level Markov state transition model was empirically calibrated in April 2019 using a Bayesian approach to synthesize available clinical and epidemiologic ED data. The simulation model was calibrated to nationally representative US survey data from 2007 and 2011. A virtual cohort of 100 000 individuals (50 000 [50%] male) was modeled from birth to age 40 years for 4 ED diagnoses: anorexia nervosa, bulimia nervosa, binge eating disorder, and other specified feeding and eating disorders.
Exposures: Age-specific ED incidence and mortality rates and background (all-cause) mortality.
Main Outcomes and Measures: The main outcomes were age-specific 12-month and lifetime ED prevalence and number of deaths per 100 000 general population individuals by age 40 years. The mean and 95% uncertainty intervals (UIs) of 1000 simulations, accounting for stochastic and parameter uncertainty, are reported.
Results: The highest estimated mean annual prevalence of ED occurred at approximately age 21 years for both male individuals (7.4%; 95% UI, 3.5%-11.5%) and female individuals (10.3%; 95% UI, 7.0%-14.2%), with lifetime mean prevalence estimates increasing to 14.3% (95% UI, 9.7%-19.0%) for male individuals and 19.7% (95% UI, 15.8%-23.9%) for female individuals by age 40 years. Ninety-five percent of first-time cases occurred by age 25 years. Current treatment coverage averts an estimated mean of 41.7 deaths per 100 000 people (95% UI, 13.0-82.0 deaths per 100 000 people) by age 40 years, whereas increasing treatment coverage for all patients with ED could avert an estimated mean of 70.5 deaths per 100 000 people by age 40 years (95% UI, 26.0-143.0 deaths per 100 000 people).
Conclusions and Relevance: In this simulation modeling study, the estimated lifetime prevalence of ED was high, with approximately 1 in 7 male and 1 in 5 female individuals having an ED by age 40 years. The initial onset of EDs was highly concentrated during adolescence and young adulthood, suggesting that this is a critical period for prevention efforts. However, the high estimated prevalence of recurring ED later in life highlights the importance of identification and treatment of ED at older ages as well. These findings suggest that increasing treatment coverage could substantially reduce ED-related mortality.
OBJECTIVE: To evaluate the potential cost-effectiveness of and stakeholder perspectives on a sugar-sweetened beverage (SSB) excise tax and a Supplemental Nutrition Assistance Program (SNAP) policy that would not allow SSB purchases in Maine, US.
DESIGN: A cost-effectiveness simulation model combined with stakeholder interviews.
SETTING: Maine, US.
PARTICIPANTS: Microsimulation of the Maine population in 2015 and interviews with stakeholders (n = 14). Study conducted from 2013 to 2017.
MAIN OUTCOME MEASURES: Health care cost savings, net costs, and quality-adjusted life-years (QALYs) from 2017 to 2027. Stakeholder positions on policies. Retail SSB cost and implementation cost data were collected.
ANALYSIS: Childhood Obesity Intervention Cost-Effectiveness Study project microsimulation model with uncertainty analysis to estimate cost-effectiveness. Thematic stakeholder interview coding.
RESULTS: Over 10 years, the SSB and SNAP policies were projected to reduce health care costs by $78.3 million (95% uncertainty interval [UI], $31.7 million-$185 million) and $15.3 million (95% UI, $8.32 million-$23.9 million), respectively. The SSB and SNAP policies were projected to save 3,560 QALYs (95% UI, 1,447-8,361) and 749 QALYs (95% UI, 415-1,168), respectively. Stakeholders were more supportive of SSB taxes than the SNAP policy because of equity concerns associated with the SNAP policy.
CONCLUSIONS AND IMPLICATIONS: Cost-effectiveness analysis provided evidence of potential health improvement and cost savings to state-level stakeholders weighing broader implementation considerations.
BACKGROUND: Accurate childhood cancer survival estimates are crucial for policy makers and clinicians for priority-setting and planning decisions. However, observed survival estimates are lacking for many countries, and when available, wide variation in outcomes is reported. Understanding the barriers to optimising survival can help improve childhood cancer outcomes. We aimed to provide estimates of global childhood cancer survival, accounting for the impact of multiple factors that affect cancer outcomes in children.
METHODS: We developed a microsimulation model to simulate childhood cancer survival for 200 countries and territories worldwide, accounting for clinical and epidemiologic factors, including country-specific treatment variables, such as availability of chemotherapy, radiation, and surgery. To ensure model results were consistent with reported survival data, we calibrated the model to estimates from the CONCORD-2 and CONCORD-3 studies using an Approximate Bayesian Computation approach. We estimated 5-year net survival for diagnosed cases of childhood cancer in each country and territory and estimated potential survival gains of seven policy interventions focused on improving treatment availability and delivery (ie, increasing the availability of chemotherapy, radiation, general surgery, neurosurgery, or ophthalmic surgery, reducing treatment abandonment, and improving the quality of care to the mean of high-income countries) implemented in isolation or as packages.
FINDINGS: Our model estimated that, for diagnosed cases, global 5-year net childhood cancer survival is currently 37·4% (95% uncertainty interval 34·7-39·8), with large variation by region, ranging from 8·1% (4·4-13·7) in eastern Africa to 83·0% (81·6-84·4) in North America. Among the seven policy interventions modelled, each individually provided small gains, increasing global 5-year net survival to between 38·4% (35·8-40·9) and 44·6% (41·7-47·4). 5-year net survival increased more substantially when policy interventions were bundled into packages that improved service delivery (5-year net survival 50·2% [47·3-53·0]) or that expanded treatment access (54·1% [50·1-58·5]). A comprehensive systems approach consisting of all policy interventions yielded superadditive gains with a global 5-year net survival of 53·6% (51·5-55·6) at 50% scale-up and 80·8% (79·5-82·1) at full implementation.
INTERPRETATION: Childhood cancer survival varies widely by region, with especially poor survival in Africa. Although expanding access to treatment (chemotherapy, radiation, and surgery) and addressing financial toxicity are essential, investments that improve the quality of care, at both the health-system and facility level, are needed to improve childhood cancer outcomes globally.
FUNDING: Boston Children's Hospital, Dana-Farber Cancer Institute, Harvard TH Chan School of Public Health, Harvard Medical School, National Cancer Institute, SickKids, St Jude Children's Research Hospital, Union for International Cancer Control, Children with Cancer UK Davidson and O'Gorman Fellowship.
BACKGROUND: Accurate estimates of childhood cancer incidence are important for policy makers to inform priority setting and planning decisions. However, many countries do not have cancer registries that quantify the incidence of childhood cancer. Moreover, even when registries do exist, they might substantially underestimate the true incidence, since children with cancer might not be diagnosed. We therefore aimed to provide estimates of total childhood cancer incidence accounting for underdiagnosis.
METHODS: We developed a microsimulation model to simulate childhood cancer incidence for 200 countries and territories worldwide, taking into account trends in population growth and urbanicity, geographical variation in cancer incidence, and health system barriers to access and referral that contribute to underdiagnosis. To ensure model results were consistent with epidemiological data, we calibrated the model to publicly available cancer registry data using a Bayesian approach in which the observed data are fixed and the model parameters (cancer incidence and probabilities of health system access and referral) are random variables. We estimated the total incidence of childhood cancer (diagnosed and undiagnosed) in each country in 2015 and projected the number of cases from 2015 to 2030.
FINDINGS: Our model estimated that there were 397 000 (95% uncertainty interval [UI] 377 000-426 000) incident cases of childhood cancer worldwide in 2015, of which only 224 000 (95% UI 216 000-237 000) were diagnosed. This finding suggests that 43% (172 000 of 397 000) of childhood cancer cases were undiagnosed globally, with substantial variation by region, ranging from 3% in western Europe (120 of 4300) and North America (300 of 10 900) to 57% (43 000 of 76 000) in western Africa. In south Asia (including southeastern Asia and south-central Asia), the overall proportion of undiagnosed cases was estimated to be 49% (67 000 of 137 000). Taking into account population projections, we estimated that there will be 6·7 million (95% UI 6·3-7·2) cases of childhood cancer worldwide from 2015 to 2030. At current levels of health system performance, we estimated that 2·9 million (95% UI 2·7-3·3) cases of childhood cancer will be missed between 2015 and 2030.
INTERPRETATION: Childhood cancer is substantially underdiagnosed, especially in south Asia and sub-Saharan Africa (including western, eastern, and southern Africa). In addition to improving treatment for childhood cancer, health systems must be strengthened to accurately diagnose and effectively care for all children with cancer. As countries expand universal health coverage, these estimates of total incidence will hopefully help guide efforts to appropriately increase health system capacity to ensure access to effective childhood cancer care.
FUNDING: Boston Children's Hospital, Dana-Farber Cancer Institute, Harvard T H Chan School of Public Health, Harvard Medical School, National Cancer Institute, SickKids, St Jude Children's Research Hospital, and Union for International Cancer Control.
BACKGROUND: Although the current obesity epidemic has been well documented in children and adults, less is known about long-term risks of adult obesity for a given child at his or her present age and weight. We developed a simulation model to estimate the risk of adult obesity at the age of 35 years for the current population of children in the United States.
METHODS: We pooled height and weight data from five nationally representative longitudinal studies totaling 176,720 observations from 41,567 children and adults. We simulated growth trajectories across the life course and adjusted for secular trends. We created 1000 virtual populations of 1 million children through the age of 19 years that were representative of the 2016 population of the United States and projected their trajectories in height and weight up to the age of 35 years. Severe obesity was defined as a body-mass index (BMI, the weight in kilograms divided by the square of the height in meters) of 35 or higher in adults and 120% or more of the 95th percentile in children.
RESULTS: Given the current level of childhood obesity, the models predicted that a majority of today's children (57.3%; 95% uncertainly interval [UI], 55.2 to 60.0) will be obese at the age of 35 years, and roughly half of the projected prevalence will occur during childhood. Our simulations indicated that the relative risk of adult obesity increased with age and BMI, from 1.17 (95% UI, 1.09 to 1.29) for overweight 2-year-olds to 3.10 (95% UI, 2.43 to 3.65) for 19-year-olds with severe obesity. For children with severe obesity, the chance they will no longer be obese at the age of 35 years fell from 21.0% (95% UI, 7.3 to 47.3) at the age of 2 years to 6.1% (95% UI, 2.1 to 9.9) at the age of 19 years.
CONCLUSIONS: On the basis of our simulation models, childhood obesity and overweight will continue to be a major health problem in the United States. Early development of obesity predicted obesity in adulthood, especially for children who were severely obese. (Funded by the JPB Foundation and others.).
OBJECTIVES: To estimate the cost-effectiveness and population impact of the national implementation of the Study of Technology to Accelerate Research (STAR) intervention for childhood obesity.
METHODS: In the STAR cluster-randomized trial, 6- to 12-year-old children with obesity seen at pediatric practices with electronic health record (EHR)-based decision support for primary care providers and self-guided behavior-change support for parents had significantly smaller increases in BMI than children who received usual care. We used a microsimulation model of a national implementation of STAR from 2015 to 2025 among all pediatric primary care providers in the United States with fully functional EHRs to estimate cost, impact on obesity prevalence, and cost-effectiveness.
RESULTS: The expected population reach of a 10-year national implementation is ∼2 million children, with intervention costs of $119 per child and $237 per BMI unit reduced. At 10 years, assuming maintenance of effect, the intervention is expected to avert 43 000 cases and 226 000 life-years with obesity at a net cost of $4085 per case and $774 per life-year with obesity averted. Limiting implementation to large practices and using higher estimates of EHR adoption improved both cost-effectiveness and reach, whereas decreasing the maintenance of the intervention's effect worsened the former.
CONCLUSIONS: A childhood obesity intervention with electronic decision support for clinicians and self-guided behavior-change support for parents may be more cost-effective than previous clinical interventions. Effective and efficient interventions that target children with obesity are necessary and could work in synergy with population-level prevention strategies to accelerate progress in reducing obesity prevalence.
BACKGROUND: Evidence on immunization costs is a critical input for cost-effectiveness analysis and budgeting, and can describe variation in site-level efficiency. The Expanded Program on Immunization Costing and Financing (EPIC) Project represents the largest investigation of immunization delivery costs, collecting empirical data on routine infant immunization in Benin, Ghana, Honduras, Moldova, Uganda, and Zambia.
METHODS: We developed a pooled dataset from individual EPIC country studies (316 sites). We regressed log total costs against explanatory variables describing service volume, quality, access, other site characteristics, and income level. We used Bayesian hierarchical regression models to combine data from different countries and account for the multi-stage sample design. We calculated output elasticity as the percentage increase in outputs (service volume) for a 1% increase in inputs (total costs), averaged across the sample in each country, and reported first differences to describe the impact of other predictors. We estimated average and total cost curves for each country as a function of service volume.
RESULTS: Across countries, average costs per dose ranged from $2.75 to $13.63. Average costs per child receiving diphtheria, tetanus, and pertussis ranged from $27 to $139. Within countries costs per dose varied widely-on average, sites in the highest quintile were 440% more expensive than those in the lowest quintile. In each country, higher service volume was strongly associated with lower average costs. A doubling of service volume was associated with a 19% (95% interval, 4.0-32) reduction in costs per dose delivered, (range 13% to 32% across countries), and the largest 20% of sites in each country realized costs per dose that were on average 61% lower than those for the smallest 20% of sites, controlling for other factors. Other factors associated with higher costs included hospital status, provision of outreach services, share of effort to management, level of staff training/seniority, distance to vaccine collection, additional days open per week, greater vaccination schedule completion, and per capita gross domestic product.
CONCLUSIONS: We identified multiple features of sites and their operating environment that were associated with differences in average unit costs, with service volume being the most influential. These findings can inform efforts to improve the efficiency of service delivery and better understand resource needs.
Participation in recommended levels of physical activity promotes a healthy body weight and reduced chronic disease risk. To inform investment in prevention initiatives, we simulate the national implementation, impact on physical activity and childhood obesity and associated cost-effectiveness (versus the status quo) of six recommended strategies that can be applied throughout childhood to increase physical activity in US school, afterschool and childcare settings. In 2016, the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES) systematic review process identified six interventions for study. A microsimulation model estimated intervention outcomes 2015-2025 including changes in mean MET-hours/day, intervention reach and cost per person, cost per MET-hour change, ten-year net costs to society and cases of childhood obesity prevented. First year reach of the interventions ranged from 90,000 youth attending a Healthy Afterschool Program to 31.3 million youth reached by Active School Day policies. Mean MET-hour/day/person increases ranged from 0.05 MET-hour/day/person for Active PE and Healthy Afterschool to 1.29 MET-hour/day/person for the implementation of New Afterschool Programs. Cost per MET-hour change ranged from cost saving to $3.14. Approximately 2500 to 110,000 cases of children with obesity could be prevented depending on the intervention implemented. All of the six interventions are estimated to increase physical activity levels among children and adolescents in the US population and prevent cases of childhood obesity. Results do not include other impacts of increased physical activity, including cognitive and behavioral effects. Decision-makers can use these methods to inform prioritization of physical activity promotion and obesity prevention on policy agendas.
Health utility, a summary measure of quality of life, has not been previously used to compare outcomes among childhood cancer survivors and individuals without a cancer history. We estimated health utility (0, death; 1, perfect health) using the Short Form-6D (SF-6D) in survivors (n = 7105) and siblings of survivors (n = 372) (using the Childhood Cancer Survivor Study cohort) and the general population (n = 12 803) (using the Medical Expenditures Panel Survey). Survivors had statistically significantly lower SF-6D scores than the general population (mean = 0.769, 95% confidence interval [CI] = 0.766 to 0.771, vs mean = 0.809, 95% CI = 0.806 to 0.813, respectively, ITALIC! P< .001, two-sided). Young adult survivors (age 18-29 years) reported scores comparable with general population estimates for people age 40 to 49 years. Among survivors, SF-6D scores were largely determined by number and severity of chronic conditions. No clinically meaningful differences were identified between siblings and the general population (mean = 0.793, 95% CI = 0.782 to 0.805, vs mean = 0.809, 95% CI = 0.806 to 0.813, respectively). This analysis illustrates the importance of chronic conditions on long-term survivor quality of life and provides encouraging results on sibling well-being. Preference-based utilities are informative tools for outcomes research in cancer survivors.
OBJECTIVE: To estimate the cost-effectiveness of noncardia gastric adenocarcinoma (NCGA) screening strategies based on new biomarker and endoscopic technologies.
DESIGN: Using an intestinal-type NCGA microsimulation model, we evaluated the following one-time screening strategies for US men: (1) serum pepsinogen to detect gastric atrophy (with endoscopic follow-up of positive screen results), (2) endoscopic screening to detect dysplasia and asymptomatic cancer (with endoscopic mucosal resection (EMR) treatment for detected lesions) and (3) Helicobacter pylori screening and treatment. Screening performance, treatment effectiveness, cancer and cost data were based on published literature and databases. Subgroups included current, former and never smokers. Outcomes included lifetime cancer risk and incremental cost-effectiveness ratios (ICERs), expressed as cost per quality-adjusted-life-year (QALY) gained.
RESULTS: Screening the general population at age 50 years reduced the lifetime intestinal-type NCGA risk (0.24%) by 26.4% with serum pepsinogen screening, 21.2% with endoscopy and EMR and 0.2% with H. pylori screening/treatment. Targeting current smokers reduced the lifetime risk (0.35%) by 30.8%, 25.5%, and 0.1%, respectively. For all subgroups, serum pepsinogen screening was more effective and more cost-effective than all other strategies, although its ICER varied from $76,000/QALY (current smokers) to $105,400/QALY (general population). Results were sensitive to H. pylori prevalence, screen age and serum pepsinogen test sensitivity. Probabilistic sensitivity analysis found that at a $100,000/QALY willingness-to-pay threshold, the probability that serum pepsinogen screening was preferred was 0.97 for current smokers.
CONCLUSIONS: Although not warranted for the general population, targeting high-risk smokers for serum pepsinogen screening may be a cost-effective strategy to reduce intestinal-type NCGA mortality.
BACKGROUND: State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity.
METHODS: Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods.
RESULTS: Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%-in contrast, most states were below 30% in CDC maps.
CONCLUSIONS: Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic.
BACKGROUND/OBJECTIVES: State-specific obesity prevalence data are critical to public health efforts to address the childhood obesity epidemic. However, few states administer objectively measured body mass index (BMI) surveillance programs. This study reports state-specific childhood obesity prevalence by age and sex correcting for parent-reported child height and weight bias.
SUBJECTS/METHODS: As part of the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES), we developed childhood obesity prevalence estimates for states for the period 2005-2010 using data from the 2010 US Census and American Community Survey (ACS), 2003-2004 and 2007-2008 National Survey of Children's Health (NSCH) (n=133 213), and 2005-2010 National Health and Nutrition Examination Surveys (NHANES) (n=9377; ages 2-17). Measured height and weight data from NHANES were used to correct parent-report bias in NSCH using a non-parametric statistical matching algorithm. Model estimates were validated against surveillance data from five states (AR, FL, MA, PA and TN) that conduct censuses of children across a range of grades.
RESULTS: Parent-reported height and weight resulted in the largest overestimation of childhood obesity in males ages 2-5 years (NSCH: 42.36% vs NHANES: 11.44%). The CHOICES model estimates for this group (12.81%) and for all age and sex categories were not statistically different from NHANES. Our modeled obesity prevalence aligned closely with measured data from five validation states, with a 0.64 percentage point mean difference (range: 0.23-1.39) and a high correlation coefficient (r=0.96, P=0.009). Estimated state-specific childhood obesity prevalence ranged from 11.0 to 20.4%.
CONCLUSION: Uncorrected estimates of childhood obesity prevalence from NSCH vary widely from measured national data, from a 278% overestimate among males aged 2-5 years to a 44% underestimate among females aged 14-17 years. This study demonstrates the validity of the CHOICES matching methods to correct the bias of parent-reported BMI data and highlights the need for public release of more recent data from the 2011 to 2012 NSCH.
PURPOSE: Best-worst scaling (BWS) is a survey method for assessing individuals' priorities. It identifies the extremes-best and worst items, most and least important factors, biggest and smallest influences-among sets. In this article, we demonstrate an application of BWS in a primary care setting to illustrate its use in identifying patient priorities for services.
METHODS: We conducted a BWS survey in 2014 in Boston, Massachusetts, to assess the relative importance of 10 previously identified attributes of Papanicolaou (Pap) testing services among women experiencing homelessness. Women were asked to evaluate 11 sets of 5 attributes of Pap services, and identify which attribute among each set would have the biggest and smallest influence on promoting uptake. We show how frequency analysis can be used to analyze results.
RESULTS: In all, 165 women participated, a response rate of 72%. We identified the most and least salient influences on encouraging Pap screening based on their frequency of report among our sample, with possible standardized scores ranging from+1.0 (biggest influence) to -1.0 (smallest influence). Most important was the availability of support for issues beyond health (+0.39), while least important was the availability of accommodations for personal hygiene (-0.27).
CONCLUSIONS: BWS quantifies patient priorities in a manner that is transparent and accessible. It is easily comprehendible by patients and relatively easy to administer. Our application illustrates its use in a vulnerable population, showing that factors beyond those typically provided in health care settings are highly important to women in seeking Pap screening. This approach can be applied to other health care services where prioritization is helpful to guide decisions.