Background
Gastric cancer remains one of the 3 most common causes of cancer death worldwide. Understanding the health and economic factors that affect screening cost-effectiveness in different ...countries will help address when and where it makes most sense to screen for gastric cancer.
Methods
We performed a cost-effectiveness analysis using a Markov model to compare screening and surveillance strategies for gastric cancer in Brazil, France, Japan, Nigeria, and the United States. Primary outcome was the incremental cost-effectiveness ratio. We then performed a sensitivity analysis to determine how each variable affected the overall model.
Results
In all countries, the most cost-effective strategies, measured by incremental cost-effectiveness ratio relative to no screening, were screening every 10 years, surveillance of high- and low-risk patients every 5 and 10 years, respectively, and screening every 5 years. Only Japan had at least one cost-effective screening strategy. The most important variables across different screening strategies and countries were starting age of screening, cost of endoscopy, and baseline probability of local gastric cancer at time of diagnosis.
Conclusions
Our model suggests that screening for gastric cancer is cost-effective in countries with higher incidence and lower costs of screening, but screening may still be a viable option in high-risk populations within low incidence countries.
Evidence regarding the association between alcohol use and gastric cancer (GC) has been inconsistent.
Adults who participated in the National Health and Nutrition Examination Survey (NHANES) from ...1999 to 2010 were included. Multivariable regression was used to assess the association between GC and heavy alcohol use (≥5 alcoholic drinks daily).
Of 470,168 individuals surveyed, 342 had a history of GC. Heavy alcohol use was associated with GC (odds ratio 3.13, 95% confidence interval 1.15-8.64) on multivariable analysis.
This is the largest study to our knowledge to demonstrate an association between heavy alcohol use and GC in the United States.
Rare genetic conditions like Down syndrome (DS) are historically understudied. Infection is a leading cause of mortality in DS, along with cardiac anomalies. Currently, it is unknown how the COVID-19 ...pandemic affects individuals with DS. Herein, we report an analysis of individuals with DS who were hospitalized with COVID-19 in New York, New York, USA.
In this retrospective, dual-center study of 7246 patients hospitalized with COVID-19, we analyzed all patients with DS admitted in the Mount Sinai Health System and Columbia University Irving Medical Center. We assessed hospitalization rates, clinical characteristics, and outcomes.
We identified 12 patients with DS. Hospitalized individuals with DS are on average ten years younger than patients without DS. Patients with DS have more severe disease than controls, particularly an increased incidence of sepsis and mechanical ventilation.
We demonstrate that individuals with DS who are hospitalized with COVID-19 are younger than their non-DS counterparts, and that they have more severe disease than age-matched controls. We conclude that particular care should be considered for both the prevention and treatment of COVID-19 in these patients.
To examine associations between sociodemographic and mental health characteristics with household risk for food insecurity during the COVID-19 outbreak.
Cross-sectional online survey analysed using ...univariable tests and a multivariable logistic regression model.
The United States during the week of 30 March 2020.
A convenience sample of 1965 American adults using Amazon's Mechanical Turk platform. Participants reporting household food insecurity prior to the pandemic were excluded from analyses.
One thousand two hundred and fifty participants reported household food security before the COVID-19 outbreak. Among this subset, 41 % were identified as at risk for food insecurity after COVID-19, 55 % were women and 73 % were white. On a multivariable analysis, race, income, relationship status, living situation, anxiety and depression were significantly associated with an incident risk for food insecurity. Black, Asian and Hispanic/Latino respondents, respondents with an annual income <$100 000 and those living with children or others were significantly more likely to be newly at risk for food insecurity. Individuals at risk for food insecurity were 2·60 (95 % CI 1·91, 3·55) times more likely to screen positively for anxiety and 1·71 (95 % CI 1·21, 2·42) times more likely to screen positively for depression.
An increased risk for food insecurity during the COVID-19 pandemic is common, and certain populations are particularly vulnerable. There are strong associations between being at risk for food insecurity and anxiety/depression. Interventions to increase access to healthful foods, especially among minority and low-income individuals, and ease the socioemotional effects of the outbreak are crucial to relieving the economic stress of this pandemic.
Oral direct‐acting antivirals (DAAs) represent a major advance in hepatitis C virus (HCV) treatment. Along with recent updates in HCV screening policy and expansions in insurance coverage, treatment ...demand in the United States is changing rapidly. Our objective was to project the characteristics and number of people needing antiviral treatment and HCV‐associated disease burden in the era of oral DAAs. We used a previously developed and validated Hepatitis C Disease Burden Simulation model (HEP‐SIM). HEP‐SIM simulated the actual clinical management of HCV from 2001 onward, which included antiviral treatment with pegylated interferon (Peg‐IFN)‐based therapies as well as the recent oral DAAs, risk‐based and birth‐cohort HCV screening, and the impact of the Affordable Care Act. We also simulated two hypothetical scenarios—no treatment and treatment with Peg‐IFN‐based therapies only. We estimated that in 2010, 2.5 (95% confidence interval CI, 1.9‐3.1) million noninstitutionalized people were viremic, which dropped to 1.9 (95% CI, 1.4‐2.6) million in 2015, and projected to drop below 1 million by 2020. A total of 1.8 million HCV patients will receive HCV treatment from the launch of oral DAAs in 2014 until 2030. Based on current HCV management practices, it will take 4‐6 years to treat the majority of patients aware of their disease. However, 560,000 patients would still remain unaware by 2020. Even in the oral DAA era, 320,000 patients will die, 157,000 will develop hepatocellular carcinoma, and 203,000 will develop decompensated cirrhosis in the next 35 years. Conclusions: HCV‐associated disease burden will still remain substantial in the era of oral DAAs. Increasing HCV screening and treatment capacity is essential to further decreasing HCV burden in the United States. (Hepatology 2016;64:1442‐1450)
Social distancing policies are currently the best method of mitigating the spread of the COVID-19 pandemic. However, adherence to these policies vary greatly on a county-by-county level. We used ...social distancing adherence (SoDA) estimated from mobile phone data and population-based demographics/statistics of 3054 counties in the United States to determine which demographics features correlate to adherence on a countywide level. SoDA scores per day were extracted from mobile phone data and aggregated from March 16, 2020 to April 14, 2020. 45 predictor features were evaluated using univariable regression to determine their level of correlation with SoDA. These 45 features were then used to form a SoDA prediction model. Persons who work from home prior to the COVID-19 pandemic (β = 0.259, p < 0.00001) and owner-occupied housing unit rate (β = −0.322, p < 0.00001) were the most positively correlated and negatively correlated features to SoDA, respectively. Counties with higher per capita income, older persons, and more suburban areas were positively associated with adherence while counties with higher African American population, high obesity rate, earlier first COVID-19 case/death, and more Republican-leaning residents were negatively correlated with adherence. The base model predicted county SoDA with 90.8% accuracy. The model using only COVID-19-related features predicted with 64% accuracy and the model using the top 25 most substantial features predicted with 89% accuracy. Our results indicate that economic features, health features, and a few other features, such as political affiliation, race, and the time since the first case/death, impact SoDA on a countywide level. These features, combined, can predict adherence with a high level of confidence. Our prediction model could be utilized to inform health policy planning and potential interventions in areas with lower adherence.
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