Given the continued burden of COVID-19 worldwide, there is a high unmet need for data on the effect of social distancing and face mask use to mitigate the risk of COVID-19. We examined the ...association of community-level social distancing measures and individual face mask use with risk of predicted COVID-19 in a large prospective U.S. cohort study of 198,077 participants. Individuals living in communities with the greatest social distancing had a 31% lower risk of predicted COVID-19 compared with those living in communities with poor social distancing. Self-reported 'always' use of face mask was associated with a 62% reduced risk of predicted COVID-19 even among individuals living in a community with poor social distancing. These findings provide support for the efficacy of mask-wearing even in settings of poor social distancing in reducing COVID-19 transmission. Despite mass vaccination campaigns in many parts of the world, continued efforts at social distancing and face mask use remain critically important in reducing the spread of COVID-19.
Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep ...learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.
The moulding together of artificial intelligence (AI) and the geographic/geographic information systems (GIS) dimension creates GeoAI. There is an emerging role for GeoAI in health and healthcare, as ...location is an integral part of both population and individual health. This article provides an overview of GeoAI technologies (methods, tools and software), and their current and potential applications in several disciplines within public health, precision medicine, and Internet of Things-powered smart healthy cities. The potential challenges currently facing GeoAI research and applications in health and healthcare are also briefly discussed.
Purpose
To conduct the first epidemiologic study prospectively examining the association between particulate matter air pollution < 2.5 µm in diameter (PM
2.5
) exposure and hepatocellular carcinoma ...(HCC) risk in the U.S.
Methods
Surveillance, Epidemiology, and End Results (SEER) provided information on HCC cases diagnosed between 2000 and 2014 from 16 population-based cancer registries across the U.S. Ambient PM
2.5
exposure was estimated by linking the SEER county with a spatial PM
2.5
model using a geographic information system. Poisson regression with robust variance estimation was used to calculate incidence rate ratios and 95% confidence intervals (CIs) for the association between ambient PM
2.5
exposure per 10 µg/m
3
increase and HCC risk adjusting for individual-level age at diagnosis, sex, race, year of diagnosis, SEER registry, and county-level information on health conditions, lifestyle, demographic, socioeconomic, and environmental factors.
Results
Higher levels of ambient PM
2.5
exposure were associated with a statistically significant increased risk for HCC (
n
= 56,245 cases; adjusted IRR per 10 µg/m
3
increase = 1.26, 95% CI 1.08, 1.47;
p
< 0.01).
Conclusions
If confirmed in studies with individual-level PM
2.5
exposure and risk factor information, these results suggest that ambient PM
2.5
exposure may be a risk factor for HCC in the U.S.
To examine the association between ultraviolet radiation (UVR) exposure and the risk of herpes zoster (HZ) in 3 prospective cohorts.
We included 205,756 participants from the Health Professionals ...Follow-up Study (HPFS; 1986-2008), Nurses' Health Study (NHS; 1996-2012), and Nurses' Health Study II (NHS II; 1991-2013). Ambient UVR exposure was based on updated geocoded address histories linked with a high-resolution spatiotemporal ultraviolet model. Incident HZ cases were identified by self-reported clinician diagnosis. Sunburn history and medical, lifestyle, and dietary factors were assessed using biennial questionnaires. Multivariable Cox proportional hazards models were used.
A total of 24,201 cases of HZ occurred during 3,626,131 person-years. Ambient UVR exposure was associated with a higher risk of HZ in men (HPFS: multivariable-adjusted hazard ratio MVHR comparing highest vs lowest quintiles, 1.14; 95% CI, 1.02-1.29; P=.03 for trend) but not in women (NHS: MVHR, 0.99; 95% CI, 0.93-1.05; NHS II: MVHR, 0.96; 95% CI, 0.90-1.03). A higher lifetime number of severe sunburns was associated with a higher risk of HZ in all cohorts (HPFS: MVHR for ≥10 sunburns vs none, 1.08; 95% CI, 0.96-1.20; P=.02 for trend; NHS: MVHR, 1.14; 95% CI, 1.05-1.22; P=.01 for trend; NHS II: MVHR, 1.13; 95% CI, 1.00-1.28; P<.001 for trend).
Ambient UVR exposure was associated with a higher risk of HZ in men but not in women. A history of severe sunburn was associated with a modest increased risk of HZ in men and women, possibly because of immunosuppression from overexposure to the sun.
Although adherence to healthy dietary guidelines has been associated with a reduced risk of several health outcomes, including cardiovascular diseases, type 2 diabetes, and some cancers, little is ...known about the role of dietary patterns in the development of hepatocellular carcinoma (HCC). We prospectively assessed the associations of three key commonly used a priori dietary patterns—the Alternative Healthy Eating Index‐2010 (AHEI‐2010), Alternate Mediterranean Diet (AMED), and Dietary Approaches to Stop Hypertension (DASH)—with risk of incident HCC in the Health Professionals Follow‐Up Study (HPFS) and the Nurses’ Health Study (NHS), two large prospective cohort studies. Diet was assessed almost every 4 years using validated food frequency questionnaires (FFQs). Multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards regression. During up to 32 years of follow‐up, 160 incident HCC cases were identified. After adjustment for most HCC risk factors, participants in the highest tertile of Alternative Healthy Eating Index‐2010 (AHEI‐2010) had a multivariable HR of 0.61 (95% CI, 0.39‐0.95; Ptrend = 0.03), compared with those in the lowest tertile. There was a suggestive, but nonsignificant, inverse association for Alternate Mediterranean Diet (AMED; HR = 0.75; 95% CI, 0.49‐1.15; Ptrend = 0.18) and a null association for Dietary Approaches to Stop Hypertension (DASH; HR = 0.90; 95% CI, 0.59‐1.36; Ptrend = 0.61) in relation to the risk of HCC development. Conclusion: Our findings suggest that better adherence to the AHEI‐2010 may decrease the risk of developing HCC among U.S. adults. Future studies are needed to replicate our results, examine these associations in other populations, and elucidate the underlying mechanisms.
Background and Aims
Little is known about the role of low‐carbohydrate diets (LCDs) in the development of hepatocellular carcinoma (HCC). We prospectively evaluated the associations between ...plant‐based and animal‐based LCDs and risk of HCC in the Nurses’ Health Study (NHS) and the Health Professionals Follow‐up Study (HPFS).
Approach and Results
Dietary intake was assessed every 4 years using validated food frequency questionnaires. Cox proportional hazards regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). HRs are shown for a 1‐standard deviation (SD) increment with variables modeled as continuous. During 3,664,769 person‐years of follow‐up, there were 156 incident HCC cases. Although there were no associations between overall or animal‐based LCD score and risk of HCC, plant‐based LCD score was inversely associated with HCC risk (HR, 0.83; 95% CI, 0.70‐0.98; Ptrend = 0.03). Carbohydrate intake, especially from refined grains (HR, 1.18; 95% CI, 1.00‐1.39; Ptrend = 0.04), was positively, while plant fat (HR, 0.78; 95% CI, 0.65‐0.95; Ptrend = 0.01) was inversely associated with HCC risk. Substituting 5% of energy from plant fat and protein for carbohydrate (HR, 0.74; 95% CI, 0.58‐0.93; Ptrend = 0.01) or refined grains (HR, 0.70; 95% CI, 0.55‐0.90; Ptrend = 0.006) was associated with lower HCC risk. In conclusion, a plant‐based LCD and dietary restriction of carbohydrate from refined grains were associated with a lower risk of HCC. Substituting plant fat and protein for carbohydrate, particularly refined grains, may decrease HCC incidence.
Conclusions
Our findings support a potential benefit in emphasizing plant sources of fat and protein in the diet for HCC primary prevention; additional studies that carefully consider hepatitis B and C virus infections and chronic liver diseases are needed to confirm our findings.
Type 2 diabetes (T2D) is a risk factor for hepatocellular carcinoma (HCC). However, it is unknown whether T2D duration or additional metabolic comorbidities further contribute to HCC risk. From the ...Nurses' Health Study (NHS), 120,826 women were enrolled in 1980, and from the Health Professionals Follow‐up Study (HPFS), 50,284 men were enrolled in 1986 and followed through 2012. Physician‐diagnosed T2D was ascertained at baseline and updated biennially. Cox proportional hazards regression models were used to calculate age‐ and multivariable‐adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for incident HCC. Over 32 years of follow‐up (4,488,410 person‐years), we documented 112 cases of HCC (69 women, 43 men). T2D was associated with an increased HCC risk (multivariable HR, 4.59; 95% CI, 2.98‐7.07), as was an increasing T2D duration (Ptrend < 0.001). Compared to nondiabetics, the multivariable HRs for HCC were 2.96 (95% CI, 1.57‐5.60) for 0‐<2 years; 6.08 (95% CI, 2.96‐12.50) for 2‐<10 years; and 7.52 (95% CI, 3.88‐14.58) for ≥10 years. Increasing number of metabolic comorbidities (T2D, obesity, hypertension, and dyslipidemia) was associated with increased HCC risk (Ptrend < 0.001); compared to individuals without metabolic comorbidity, those with four metabolic comorbidities had an 8.1‐fold increased HCC risk (95% CI, 2.48‐26.7). In T2D, neither insulin use nor oral hypoglycemic use was significantly associated with HCC risk (HR, 2.04 95% CI, 0.69‐6.09 and HR, 1.45 95% CI, 0.69‐3.07, respectively). Conclusion: T2D is independently associated with increased risk for HCC in two prospective cohorts of U.S. men and women. This risk is enhanced with prolonged diabetes duration and with comorbid metabolic conditions, suggesting the importance of insulin resistance in the pathogenesis of HCC. (Hepatology 2018;67:1797‐1806)
Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease. Particulate matter air pollution <2.5 μm in diameter (PM2.5) is a ubiquitous exposure primarily produced ...from fossil fuel combustion. Previous epidemiologic studies have been mixed. The objective of this study was to examine the association between ambient PM2.5 exposure and NAFLD among hospitalized patients in the Nationwide Inpatient Sample (NIS).
We conducted a cross-sectional analysis of hospitalizations from 2001 to 2011 using the NIS, the largest nationally representative all-payer inpatient care administrative database in the United States. Average annual PM2.5 exposure was estimated by linking census tracts (based on NIS-provided hospital ZIP Codes) with a spatiotemporal exposure model. Clinical conditions were identified using hospital discharge diagnosis codes. Multivariable logistic regression incorporating discharge weights was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between PM2.5 exposure and odds of NAFLD among hospitalized patients adjusting for age, sex, race/ethnicity, year, individual- and area-level socioeconomic status, urbanicity, region, obesity, diabetes, metabolic syndrome, impaired fasting glucose, dyslipidemia, hypertension, obstructive sleep apnea, and smoking.
There were 269,705 hospitalized patients with NAFLD from 2001 to 2011 (total unweighted n = 45,433,392 hospitalizations). Higher ambient PM2.5 exposure was associated with increased odds of NAFLD among hospitalized patients (adjusted OR: 1.24 per 10 μg/m3 increase, 95% CI 1.15–1.33, p < 0.01). There were statistically significant interactions between PM2.5 exposure and age, race/ethnicity, diabetes, smoking, and region, with stronger positive associations among patients who were aged ≥45 years, non-Hispanic White or Asian/Pacific Islander, non-diabetics, non-smokers, or in the Midwest and West regions, respectively.
In this nationwide cross-sectional analysis of the NIS database, there was a positive association between ambient PM2.5 exposure and odds of NAFLD among hospitalized patients. Future research should examine the effects of long-term historical PM2.5 exposure and incident NAFLD cases.
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•Emerging evidence suggests air pollution may increase NAFLD risk.•Hospitalizations across the United States from 2001-2011 were examined.•Ambient PM2.5 exposure was estimated using a spatiotemporal exposure model.•Higher PM2.5 exposure was associated with increased odds of NAFLD among hospitalized patients.
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