In the United States, hepatocellular carcinoma (HCC) survival varies with tumor characteristics, patient comorbidities, and treatment. The effect of HCC etiology on survival is less clearly defined. The relationship between HCC etiology and mortality was examined using Surveillance, Epidemiology, and End Results–Medicare data. In a cohort of 11,522 HCC cases diagnosed from 2000 through 2014, etiologies were identified from Medicare data, including metabolic disorders (32.9%), hepatitis C virus (8.2%), alcohol (4.7%), hepatitis B virus (HBV, 2.1%), rare etiologies (0.9%), multiple etiologies (26.7%), and unknown etiology (24.4%). After adjusting for demographics, tumor characteristics, comorbidities and treatment, hazard ratios (HRs) and survival curves by HCC etiology were estimated using Cox proportional hazard models. Compared with HBV‐related HCC cases, higher mortality was observed for those with alcohol‐related HCC (HR 1.49; 95% confidence interval [95% CI] 1.25‐1.77), metabolic disorder–related HCC (HR 1.25; 95% CI 1.07‐1.47), and multiple etiology‐related HCC (HR 1.25; 95% CI 1.07‐1.46), but was not statistically significant for hepatitis C virus–related, rare disorder–related, and HCC of unknown etiology. For all HCC etiologies, there was short median survival ranging from 6.1 months for alcohol to 10.3 months for HBV.Conclusion:More favorable survival was seen with HBV‐related HCC. To the extent that HCC screening is more common among persons with HBV infection compared to those with other etiologic risk factors, population‐based HCC screening, applied evenly to persons across all HCC etiology categories, could shift HCC diagnosis to earlier stages, when cases with good clinical status are more amenable to curative therapy.

Asymptomatic SARS-CoV-2 infection and delayed implementation of diagnostics have led to poorly defined viral prevalence rates in the United States and elsewhere. To address this, we analyzed seropositivity in 9089 adults in the United States who had not been diagnosed previously with COVID-19. Individuals with characteristics that reflected the U.S. population (n = 27,716) were selected by quota sampling from 462,949 volunteers. Enrolled participants (n = 11,382) provided medical, geographic, demographic, and socioeconomic information and dried blood samples. Survey questions coincident with the Behavioral Risk Factor Surveillance System survey, a large probability-based national survey, were used to adjust for selection bias. Most blood samples (88.7%) were collected between 10 May and 31 July 2020 and were processed using ELISA to measure seropositivity (IgG and IgM antibodies against SARS-CoV-2 spike protein and the spike protein receptor binding domain). The overall weighted undiagnosed seropositivity estimate was 4.6% (95% CI, 2.6 to 6.5%), with race, age, sex, ethnicity, and urban/rural subgroup estimates ranging from 1.1% to 14.2%. The highest seropositivity estimates were in African American participants; younger, female, and Hispanic participants; and residents of urban centers. These data indicate that there were 4.8 undiagnosed SARS-CoV-2 infections for every diagnosed case of COVID-19, and an estimated 16.8 million infections were undiagnosed by mid-July 2020 in the United States.

The role of diet in India's rapidly progressing chronic disease epidemic is unclear; moreover, diet may vary considerably across North-South regions. The India Health Study was a multicenter study of men and women aged 35-69, who provided diet, lifestyle, and medical histories, as well as blood pressure, fasting blood, urine, and anthropometric measurements. In each region (Delhi, n = 824; Mumbai, n = 743; Trivandrum, n = 2,247), we identified two dietary patterns with factor analysis. In multiple logistic regression models adjusted for age, gender, education, income, marital status, religion, physical activity, tobacco, alcohol, and total energy intake, we investigated associations between regional dietary patterns and abdominal adiposity, hypertension, diabetes, and dyslipidemia. Across the regions, more than 80% of the participants met the criteria for abdominal adiposity and 10 to 28% of participants were considered diabetic. In Delhi, the "fruit and dairy" dietary pattern was positively associated with abdominal adiposity [highest versus lowest tertile, multivariate-adjusted OR and 95% CI: 2.32 (1.03-5.23); Ptrend = 0.008] and hypertension [2.20 (1.47-3.31); Ptrend < 0.0001]. In Trivandrum, the "pulses and rice" pattern was inversely related to diabetes [0.70 (0.51-0.95); Ptrend = 0.03] and the "snacks and sweets" pattern was positively associated with abdominal adiposity [2.05 (1.34-3.14); Ptrend = 0.03]. In Mumbai, the "fruit and vegetable" pattern was inversely associated with hypertension [0.63 (0.40-0.99); Ptrend = 0.05] and the "snack and meat" pattern appeared to be positively associated with abdominal adiposity. Cardio-metabolic risk factors were highly prevalent in this population. Across all regions, we found little evidence of a Westernized diet; however, dietary patterns characterized by animal products, fried snacks, or sweets appeared to be positively associated with abdominal adiposity. Conversely, more traditional diets in the Southern regions were inversely related to diabetes and hypertension. Continued investigation of diet, as well as other environmental and biological factors, will be needed to better understand the risk profile in this population and potential means of prevention.

Background: Despite substantial research highlighting the importance of exogenous dietary cholesterol intake and endogenous serum cholesterol level in human health, a thorough evaluation of the associations is lacking. Our study objective was to examine overall and cause-specific mortality in relation to dietary and serum cholesterol, as well as egg consumption, and conduct an updated meta-regression analysis of cohort studies. Methods: We conducted a prospective analysis of 27 078 men in the ATBC Study (Alpha-Tocopherol, Beta-Carotene Cancer Prevention). Multivariable-controlled cause-specific Cox proportional hazards regression models were used to calculate hazard ratios and 31-year absolute mortality risk differences. A systematic review and meta-analysis of cohort studies was also performed (PROSPERO [URL: https://www.crd.york.ac.uk/prospero/ ; Unique identifier: CRD42021272756]). Results: Based on 482 316 person-years of follow-up, we identified 22 035 deaths, including 9110 deaths from cardiovascular disease (CVD). Greater dietary cholesterol and egg consumption were associated with increased risk of overall and CVD-related mortality. Hazard ratios for each additional 300 mg cholesterol intake per day were 1.10 and 1.13 for overall and CVD-related mortality, respectively; for each additional 50-g egg consumed daily, hazard ratios were 1.06 and 1.09, respectively, for overall and CVD-related mortality (all P values<0.0001). After multivariable adjustment, higher serum total cholesterol concentrations were associated with increased risk of CVD-related mortality (hazard ratios per 1 SD increment, 1.14; P <0.0001). The observed associations were generally similar across cohort subgroups. The updated meta-analysis of cohort studies on the basis of 49 risk estimates, 3 601 401 participants, and 255 479 events showed consumption of 1 additional 50-g egg daily was associated with significantly increased CVD risk (pooled relative risk, 1.04 [95% CI, 1.00–1.08]; I ² =80.1%). In the subgroup analysis of geographic regions ( P _interaction =0.02), an increase of 50-g egg consumed daily was associated with a higher risk of CVD in US cohorts (pooled relative risk, 1.08 [95% CI, 1.02–1.14]) and appeared related to a higher CVD risk in European cohorts with borderline significance (pooled relative risk, 1.05), but was not associated with CVD risk in Asian cohorts. Conclusions: In this prospective cohort study and updated meta-analysis, greater dietary cholesterol and egg consumption were associated with increased risk of overall and CVD-related mortality. Our findings support restricted consumption of dietary cholesterol as a means to improve long-term health and longevity.

Joinpoint regression can model trends in time-specific estimates from aggregated data. These methods have been developed mainly for nonsurvey data such as cancer registry data assuming that the time-specific estimates are uncorrelated from time point to time point. This independence assumption can be violated for trends in time-specific estimates from complex survey samples due to using the same primary sampling units across time and, therefore, the full variance–covariance matrix of the time-specific estimates should be incorporated into the regression model fitting. This article extends these joinpoint methods for analyzing complex survey data within the National Cancer Institute’s Joinpoint software and empirically compares the extended method to existing methods for analyses of time trends in three surveys.