State-Specific Prevalence of Severe Obesity Among Adults in the US Using Bias Correction of Self-Reported Body Mass Index.

INTRODUCTION: Adults with severe obesity are at increased risk for poor metabolic health and may need more intensive clinical and community supports. The prevalence of severe obesity is underestimated from self-reported weight and height data. We examined severe obesity prevalence among US adults by sociodemographic characteristics and by state after adjusting for self-report bias. METHODS: Using a validated bias-correction method, we adjusted self-reported body mass index (BMI) data from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) by using measured data from the National Health and Nutrition Examination Survey. We compared bias-corrected prevalence of severe obesity (BMI ≥40) with self-reported estimates by sociodemographic characteristics and state. RESULTS: Self-reported BRFSS data significantly underestimated the prevalence of severe obesity compared with bias-corrected estimates. In 2020, 8.8% of adults had severe obesity based on the bias-corrected estimates, whereas 5.3% of adults had severe obesity based on self-reported data. Women had a significantly higher prevalence of bias-corrected severe obesity (11.1%) than men (6.5%). State-level prevalence of bias-corrected severe obesity ranged from 5.5% (Massachusetts) to 13.2% (West Virginia). Based on bias-corrected estimates, 16 states had a prevalence of severe obesity greater than 10%, a level not seen in the self-reported estimates. CONCLUSION: Self-reported BRFSS data underestimated the overall prevalence of severe obesity by 40% (5.3% vs 8.8%). Accurate state-level estimates of severe obesity can help public health and health care decision makers prioritize and plan to implement effective prevention and treatment strategies for people who are at high risk for poor metabolic health.

Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity.

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.).

Global costs, health benefits, and economic benefits of scaling up treatment and imaging modalities for survival of 11 cancers: a simulation-based analysis.

BACKGROUND: In addition to increased availability of treatment modalities, advanced imaging modalities are increasingly recommended to improve global cancer care. However, estimates of the costs and benefits of investments to improve cancer survival are scarce, especially for low-income and middle-income countries (LMICs). In this analysis, we aimed to estimate the costs and lifetime health and economic benefits of scaling up imaging and treatment modality packages on cancer survival, both globally and by country income group. METHODS: Using a previously developed model of global cancer survival, we estimated stage-specific cancer survival and life-years gained (accounting for competing mortality) in 200 countries and territories for patients diagnosed with one of 11 cancers (oesophagus, stomach, colon, rectum, anus, liver, pancreas, lung, breast, cervix uteri, and prostate) representing 60% of all cancer diagnoses between 2020 and 2030 (inclusive of full years). We evaluated the costs and health and economic benefits of scaling up packages of treatment (chemotherapy, surgery, radiotherapy, and targeted therapy), imaging modalities (ultrasound, x-ray, CT, MRI, PET, single-photon emission CT), and quality of care to the mean level of high-income countries, separately and in combination, compared with no scale-up. Costs and benefits are presented in 2018 US$ and discounted at 3% annually. FINDINGS: For the 11 cancers studied, we estimated that without scale-up (ie, with current availability of treatment, imaging, and quality of care) there will be 76·0 million cancer deaths (95% UI 73·9-78·6) globally for patients diagnosed between 2020 and 2030, with more than 70% of these deaths occurring in LMICs. Comprehensive scale-up of treatment, imaging, and quality of care could avert 12·5% (95% UI 9·0-16·3) of these deaths globally, ranging from 2·8% (1·8-4·3) in high-income countries to 38·2% (32·6-44·5) in low-income countries. Globally, we estimate that comprehensive scale-up would cost an additional $232·9 billion (95% UI 85·9-422·0) between 2020 and 2030 (representing a 6·9% increase in cancer treatment costs), but produce $2·9 trillion (1·8-4·0) in lifetime economic benefits, yielding a return of $12·43 (6·47-33·23) per dollar invested. Scaling up treatment and quality of care without imaging would yield a return of $6·15 (2·66-16·71) per dollar invested and avert 7·0% (3·9-10·3) of cancer deaths worldwide. INTERPRETATION: Simultaneous investment in cancer treatment, imaging, and quality of care could yield substantial health and economic benefits, especially in LMICs. These results provide a compelling rationale for the value of investing in the global scale-up of cancer care. FUNDING: Harvard TH Chan School of Public Health and National Cancer Institute.

The impact of scaling up access to treatment and imaging modalities on global disparities in breast cancer survival: a simulation-based analysis.

BACKGROUND: Female breast cancer is the most commonly diagnosed cancer in the world, with wide variations in reported survival by country. Women in low-income and middle-income countries (LMICs) in particular face several barriers to breast cancer services, including diagnostics and treatment. We aimed to estimate the potential impact of scaling up the availability of treatment and imaging modalities on breast cancer survival globally, together with improvements in quality of care. METHODS: For this simulation-based analysis, we used a microsimulation model of global cancer survival, which accounts for the availability and stage-specific survival impact of specific treatment modalities (chemotherapy, radiotherapy, surgery, and targeted therapy), imaging modalities (ultrasound, x-ray, CT, MRI, PET, and single-photon emission computed tomography [SPECT]), and quality of cancer care, to simulate 5-year net survival for women with newly diagnosed breast cancer in 200 countries and territories in 2018. We calibrated the model to empirical data on 5-year net breast cancer survival in 2010-14 from CONCORD-3. We evaluated the potential impact of scaling up specific imaging and treatment modalities and quality of care to the mean level of high-income countries, individually and in combination. We ran 1000 simulations for each policy intervention and report the means and 95% uncertainty intervals (UIs) for all model outcomes. FINDINGS: We estimate that global 5-year net survival for women diagnosed with breast cancer in 2018 was 67·9% (95% UI 62·9-73·4) overall, with an almost 25-times difference between low-income (3·5% [0·4-10·0]) and high-income (87·0% [85·6-88·4]) countries. Among individual treatment modalities, scaling up access to surgery alone was estimated to yield the largest survival gains globally (2·7% [95% UI 0·4-8·3]), and scaling up CT alone would have the largest global impact among imaging modalities (0·5% [0·0-2·0]). Scaling up a package of traditional modalities (surgery, chemotherapy, radiotherapy, ultrasound, and x-ray) could improve global 5-year net survival to 75·6% (95% UI 70·6-79·4), with survival in low-income countries improving from 3·5% (0·4-10·0) to 28·6% (4·9-60·1). Adding concurrent improvements in quality of care could further improve global 5-year net survival to 78·2% (95% UI 74·9-80·4), with a substantial impact in low-income countries, improving net survival to 55·3% (42·2-67·8). Comprehensive scale-up of access to all modalities and improvements in quality of care could improve global 5-year net survival to 82·3% (95% UI 79·3-85·0). INTERPRETATION: Comprehensive scale-up of treatment and imaging modalities, and improvements in quality of care could improve global 5-year net breast cancer survival by nearly 15 percentage points. Scale-up of traditional modalities and quality-of-care improvements could achieve 70% of these total potential gains, with substantial impact in LMICs, providing a more feasible pathway to improving breast cancer survival in these settings even without the benefits of future investments in targeted therapy and advanced imaging. FUNDING: Harvard T H Chan School of Public Health, and National Cancer Institute P30 Cancer Center Support Grant to Memorial Sloan Kettering Cancer Center.

Estimating the impact of the COVID-19 pandemic on diagnosis and survival of five cancers in Chile from 2020 to 2030: a simulation-based analysis.

BACKGROUND: The COVID-19 pandemic has strained health system capacity worldwide due to a surge of hospital admissions, while mitigation measures have simultaneously reduced patients' access to health care, affecting the diagnosis and treatment of other diseases such as cancer. We estimated the impact of delayed diagnosis on cancer outcomes in Chile using a novel modelling approach to inform policies and planning to mitigate the forthcoming cancer-related health impacts of the pandemic in Chile. METHODS: We developed a microsimulation model of five cancers in Chile (breast, cervix, colorectal, prostate, and stomach) for which reliable data were available, which simulates cancer incidence and progression in a nationally representative virtual population, as well as stage-specific cancer detection and survival probabilities. We calibrated the model to empirical data on monthly detected cases, as well as stage at diagnosis and 5-year net survival. We accounted for the impact of COVID-19 on excess mortality and cancer detection by month during the pandemic, and projected diagnosed cancer cases and outcomes of stage at diagnosis and survival up to 2030. For comparison, we simulated a no COVID-19 scenario in which the impacts of COVID-19 on excess mortality and cancer detection were removed. FINDINGS: Our modelling showed a sharp decrease in the number of diagnosed cancer cases during the COVID-19 pandemic, with a large projected short-term increase in future diagnosed cases. Due to the projected backlog in diagnosis, we estimated that in 2021 there will be an extra 3198 cases (95% uncertainty interval [UI] 1356-5017) diagnosed among the five modelled cancers, an increase of nearly 14% compared with the no COVID-19 scenario, falling to a projected 10% increase in 2022 with 2674 extra cases (1318-4032) diagnosed. As a result of delayed diagnosis, we found a worse stage distribution for detected cancers in 2020-22, which is estimated to lead to 3542 excess cancer deaths (95% UI 2236-4816) in 2022-30, compared with the no COVID-19 scenario, among the five modelled cancers, most of which (3299 deaths, 2151-4431) are projected to occur before 2025. INTERPRETATION: In addition to a large projected surge in diagnosed cancer cases, we found that delays in diagnosis will result in worse cancer stage at presentation, leading to worse survival outcomes. These findings can help to inform surge capacity planning and highlight the importance of ensuring appropriate health system capacity levels to detect and care for the increased cancer cases in the coming years, while maintaining the timeliness and quality of cancer care. Potential delays in treatment and adverse impacts on quality of care, which were not considered in this model, are likely to contribute to even more excess deaths from cancer than projected. FUNDING: Harvard TH Chan School of Public Health. TRANSLATIONS: For the Spanish and Portuguese translations of the abstract see Supplementary Materials section.

Medical imaging and nuclear medicine: a Lancet Oncology Commission.

The diagnosis and treatment of patients with cancer requires access to imaging to ensure accurate management decisions and optimal outcomes. Our global assessment of imaging and nuclear medicine resources identified substantial shortages in equipment and workforce, particularly in low-income and middle-income countries (LMICs). A microsimulation model of 11 cancers showed that the scale-up of imaging would avert 3·2% (2·46 million) of all 76·0 million deaths caused by the modelled cancers worldwide between 2020 and 2030, saving 54·92 million life-years. A comprehensive scale-up of imaging, treatment, and care quality would avert 9·55 million (12·5%) of all cancer deaths caused by the modelled cancers worldwide, saving 232·30 million life-years. Scale-up of imaging would cost US$6·84 billion in 2020-30 but yield lifetime productivity gains of $1·23 trillion worldwide, a net return of $179·19 per $1 invested. Combining the scale-up of imaging, treatment, and quality of care would provide a net benefit of $2·66 trillion and a net return of $12·43 per $1 invested. With the use of a conservative approach regarding human capital, the scale-up of imaging alone would provide a net benefit of $209·46 billion and net return of $31·61 per $1 invested. With comprehensive scale-up, the worldwide net benefit using the human capital approach is $340·42 billion and the return per dollar invested is $2·46. These improved health and economic outcomes hold true across all geographical regions. We propose actions and investments that would enhance access to imaging equipment, workforce capacity, digital technology, radiopharmaceuticals, and research and training programmes in LMICs, to produce massive health and economic benefits and reduce the burden of cancer globally.

Forecasting essential childhood cancer drug need: An innovative model-based approach.

BACKGROUND: Childhood cancer outcomes in low-income and middle-income countries have not kept pace with advances in care and survival in high-income countries. A contributing factor to this survival gap is unreliable access to essential drugs. METHODS: The authors created a tool (FORx ECAST) capable of predicting drug quantity and cost for 18 pediatric cancers. FORx ECAST enables users to estimate the quantity and cost of each drug based on local incidence, stage breakdown, treatment regimen, and price. Two country-specific examples are used to illustrate the capabilities of FORx ECAST to predict drug quantities. RESULTS: On the basis of domestic public-sector price data, the projected annual cost of drugs to treat childhood cancer cases is 0.8 million US dollars in Kenya and 3.0 million US dollars in China, with average median price ratios of 0.9 and 0.1, respectively, compared with costs sourced from the Management Sciences for Health (MSH) International Medical Products Price Guide. According to the cumulative chemotherapy cost, the most expensive disease to treat is acute lymphoblastic lymphoma in Kenya, but a higher relative unit cost of methotrexate makes osteosarcoma the most expensive diagnosis to treat in China. CONCLUSIONS: FORx ECAST enables needs-based estimates of childhood cancer drug volumes to inform health system planning in a wide range of contexts. It is broadly adaptable, allowing decision makers to generate results specific to their needs. The resultant estimates of drug need can help equip policymakers and health governance institutions with evidence-informed data to advance innovative procurement strategies that drive global improvements in childhood cancer drug access.

Estimating the impact of treatment and imaging modalities on 5-year net survival of 11 cancers in 200 countries: a simulation-based analysis.

BACKGROUND: Accurate survival estimates are important for cancer control planning. Although observed survival estimates are unavailable for many countries, where they are available, wide variations are reported. Understanding the impact of specific treatment and imaging modalities can help decision makers to effectively allocate resources to improve cancer survival in their local context. METHODS: We developed a microsimulation model of stage-specific cancer survival in 200 countries and territories for 11 cancers (oesophagus, stomach, colon, rectum, anus, liver, pancreas, lung, breast, cervix uteri, and prostate) comprising 60% of global diagnosed cancer cases. The model accounts for country-specific availability of treatment (chemotherapy, surgery, radiotherapy, and targeted therapy) and imaging modalities (ultrasound, x-ray, CT, MRI, PET, single-photon emission CT), as well as quality of care. We calibrated the model to reported survival estimates from CONCORD-3 (which reports global trends in cancer survival in 2000-14). We estimated 5-year net survival for diagnosed cancers in each country or territory and estimated potential survival gains from increasing the availability of individual treatment and imaging modalities, and more comprehensive packages of scale-up of these interventions. We report the mean and 95% uncertainty intervals (UIs) for all outcomes, calculated as the 2·5 and 97·5 percentiles of the simulation results. FINDINGS: The estimated global 5-year net survival for all 11 cancers combined is 42·6% (95% uncertainty interval 40·3-44·3), with survival in high-income countries being an average of 12 times (range 4-17) higher than that in low-income countries. Expanding availability of surgery or radiotherapy or improving quality of care would yield the largest survival gains in low-income (2·5-3·4 percentage point increase in survival) and lower-middle-income countries (2·4-6·1 percentage point increase), whereas upper-middle-income and high-income countries are more likely to benefit from improved availability of targeted therapy (0·7 percentage point increase for upper-middle income and 0·4 percentage point increase for high income). Investing in medical imaging will also be necessary to achieve substantial survival gains, with traditional modalities estimated to provide the largest gains in low-income settings, while MRI and PET would yield the largest gains in higher-income countries. Simultaneous expansion of treatment, imaging, and quality of care could improve 5-year net survival by more than ten times in low-income countries (3·8% [95% UI 0·5-9·2] to 45·2% [40·2-52·1]) and could more than double 5-year net survival in lower-middle-income countries (20·1% [7·2-31·7] to 47·1% [42·8-50·8]). INTERPRETATION: Scaling up both treatment and imaging availability could yield synergistic survival gains for patients with cancer. Expanding traditional modalities in lower-income settings might be a feasible pathway to improve survival before scaling up more modern technologies. FUNDING: Harvard T H Chan School of Public Health.

The role and contribution of treatment and imaging modalities in global cervical cancer management: survival estimates from a simulation-based analysis.

BACKGROUND: Cervical cancer is the fourth most common cancer among women worldwide, causing more than 300 000 deaths globally each year. In addition to screening and prevention, effective cancer treatment is needed to reduce cervical cancer mortality. We discuss the role of imaging in cervical cancer management and estimate the potential survival effect of scaling up imaging in several different contexts. METHODS: Using a previously developed microsimulation model of global cancer survival, we estimated stage-specific cervical cancer 5-year net survival in 200 countries and territories. We evaluated the potential survival effect of scaling up treatment (chemotherapy, surgery, radiotherapy, and targeted therapy), and imaging modalities (ultrasound, x-ray, CT, MRI, PET, and single photon emission CT [SPECT]) to the mean level of high-income countries, both individually and in combination. FINDINGS: We estimate global cervical cancer 5-year net survival as 42·1% (95% uncertainty interval [UI] 33·8-48·5). Among individual imaging modalities, expanding MRI would yield the largest 5-year survival gains globally (data are absolute percentage point increase in survival 0·6, 95% UI 0·1-2·1), scaling up ultrasound would yield the largest gains in low-income countries (0·5, 0·0-3·7), expanding CT and x-ray would have the greatest effect in Latin America (0·8, 0·0-3·4) and Oceania (0·4, 0·0-3·2), and expanding PET would yield the largest gains in high-income countries (0·2, 0·0-0·8). Scaling up SPECT did not show major changes in any region. Among individual treatment modalities, scaling up radiotherapy would yield the largest absolute percentage point gains in low-income countries (5·2, 0·3-13·5), and expanding surgery would have the largest effect in lower-middle-income countries (7·4, 0·3-21·1) and upper-middle-income countries (0·8, 0·0-2·9). Estimated survival gains in high-income countries were very modest. However, the gains from expanding any single treatment or imaging modality individually were small across all income levels and geographical settings. Scaling up all treatment modalities could improve global 5-year net survival to 52·4% (95% UI 44·6-62·0). In addition to expanding treatment, improving quality of care could raise survival to 57·5% (51·2-63·5), and the cumulative effect of scaling up all imaging modalities together with expanded treatment and quality of care could improve 5-year net survival for cervical cancer to 62·5% (57·7-67·8). INTERPRETATION: Comprehensive scale-up of treatment, imaging, and quality of care could substantially improve global cervical cancer 5-year net survival, with quality of care and imaging improvements each contributing about 25% of the total potential gains. These findings suggest that a narrow focus on the availability of treatment modalities could forgo substantial survival gains. Investments in imaging equipment, personnel, and quality of care efforts will also be needed to successfully scale up cervical cancer treatment worldwide. FUNDING: Harvard T H Chan School of Public Health and National Cancer Institute.

Sustainable care for children with cancer: a Lancet Oncology Commission.

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.

Simulation of Growth Trajectories of Childhood Obesity into Adulthood.

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.).

Diagnostic performance of conventional and advanced imaging modalities for assessing newly diagnosed cervical cancer: systematic review and meta-analysis.

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.

Estimating the total incidence of global childhood cancer: a simulation-based analysis.

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.

Global childhood cancer survival estimates and priority-setting: a simulation-based analysis.

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.

The cost determinants of routine infant immunization services: a meta-regression analysis of six country studies.

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.

Using cost-effectiveness analysis to prioritize policy and programmatic approaches to physical activity promotion and obesity prevention in childhood.

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.

Global maternal mortality projections by urban/rural location and education level: a simulation-based analysis.

Background: Maternal mortality remains a challenge in global health, with well-known disparities across countries. However, less is known about disparities in maternal health by subgroups within countries. The aim of this study is to estimate maternal health indicators for subgroups of women within each country. Methods: In this simulation-based analysis, we used the empirically calibrated Global Maternal Health (GMatH) microsimulation model to estimate a range of maternal health indicators by subgroup (urban/rural location and level of education) for 200 countries/territories from 1990 to 2050. Education levels were defined as low (less than primary), middle (less than secondary), and high (completed secondary or higher). The model simulates the reproductive lifecycle of each woman, accounting for individual-level factors such as family planning preferences, biological factors (e.g., anemia), and history of maternal complications, and how these factors vary by subgroup. We also estimated the impact of scaling up women's education on projected maternal health outcomes compared to clinical and health system-focused interventions. Findings: We find large subgroup differences in maternal health outcomes, with an estimated global maternal mortality ratio (MMR) in 2022 of 292 (95% UI 250-341) for rural women and 100 (95% UI 84-116) for urban women, and 536 (95% UI 450-594), 143 (95% UI 117-174), and 85 (95% UI 67-108) for low, middle, and high education levels, respectively. Ensuring all women complete secondary school is associated with a large impact on the projected global MMR in 2030 (97 [95% UI 76-120]) compared to current trends (167 [95% UI 142-188]), with especially large improvements in countries such as Afghanistan, Chad, Madagascar, Niger, and Yemen. Interpretation: Substantial subgroup disparities present a challenge for global maternal health and health equity. Outcomes are especially poor for rural women with low education, highlighting the need to ensure that policy interventions adequately address barriers to care in rural areas, and the importance of investing in social determinants of health, such as women's education, in addition to health system interventions to improve maternal health for all women. Funding: John D. and Catherine T. MacArthur Foundation, 10-97002-000-INP.