2f:I[703058,["/_next/static/immutable/chunks/3bhq_5eh1d760.js","/_next/static/immutable/chunks/1855-ogyi8eh4.js","/_next/static/immutable/chunks/2pfsjpn6k6zlg.js","/_next/static/immutable/chunks/26c7v3_3uwnwu.js","/_next/static/immutable/chunks/25a3ozcrvi1ff.js","/_next/static/immutable/chunks/2r4wx50nvjh_4.js","/_next/static/immutable/chunks/23fsw06hsi5b1.js","/_next/static/immutable/chunks/06_yt270h41rg.js","/_next/static/immutable/chunks/3ezsiz-7shkna.js","/_next/static/immutable/chunks/20x2y96ldu5wx.js","/_next/static/immutable/chunks/2lxntcm3pd1_h.js","/_next/static/immutable/chunks/00dgco8irq3jw.js","/_next/static/immutable/chunks/00nwe-kq0rboc.js","/_next/static/immutable/chunks/2051ffx3xzio9.js"],"PaperListPageClient"] 30:T730,Rural-urban disparities in colorectal cancer (CRC) mortality have been attributed to individual- and population-level factors. Population-level composite measures such as the Social Vulnerability Index (SVI) have been used to investigate rural-urban disparities. However, the contribution of each SVI component to CRC mortality disparities among rural and urban counties is unknown. Via CRC mortality data for all US counties from 1999 to 2020 from the Centers for Disease Control and Prevention, rural-urban differences in county demographics and in 14 SVI factors were examined. Four-way effect decomposition was used to estimate the proportion of the rural-urban CRC mortality disparity mediated by the SVI. Among 2927 counties (rural, 60.7%; urban, 39.3%), rural counties had 11.8% higher CRC mortality than urban counties; 18.6% of that disparity was mediated by low socioeconomic status (SES), 8.8% by household characteristics, and 2.7% by racial/ethnic minority status. Among all counties, poverty (incidence rate ratio [IRR], 3.2; 95% confidence interval [CI], 2.8-3.5), unemployment (IRR, 5.4; 95% CI, 4.4-6.8), lacking a high school diploma (IRR, 2.7; 95% CI, 2.4-2.9), household crowding (IRR, 3.3; 95% CI, 2.5-4.4), and lacking a vehicle (IRR, 3.4; 95% CI, 3.0-3.8) had the greatest impact on CRC mortality. Compared to urban counties, rural counties with a higher proportion of people without a vehicle had a higher risk of CRC mortality (IRR, 4.3; 95% CI, 3.7-5.0; vs. IRR, 1.9; 95% CI, 1.5-2.4). The rural-urban CRC mortality disparity is largely driven by low SES-higher poverty and unemployment, and lower income and education. The relationship between CRC mortality and the SVI is nuanced, and evaluating each component of the SVI may allow for more targeted area-level interventions than evaluating the SVI alone.31:T9d3,Abstract Background Rural–urban disparities in colorectal cancer (CRC) mortality have been attributed to individual‐ and population‐level factors. Population‐level composite measures such as the Social Vulnerability Index (SVI) have been used to investigate rural–urban disparities. However, the contribution of each SVI component to CRC mortality disparities among rural and urban counties is unknown. Methods Via CRC mortality data for all US counties from 1999 to 2020 from the Centers for Disease Control and Prevention, rural–urban differences in county demographics and in 14 SVI factors were examined. Four‐way effect decomposition was used to estimate the proportion of the rural–urban CRC mortality disparity mediated by the SVI. Results : Among 2927 counties (rural, 60.7%; urban, 39.3%), rural counties had 11.8% higher CRC mortality than urban counties; 18.6% of that disparity was mediated by low socioeconomic status (SES), 8.8% by household characteristics, and 2.7% by racial/ethnic minority status. Among all counties, poverty (incidence rate ratio [IRR], 3.2; 95% confidence interval [CI], 2.8–3.5), unemployment (IRR, 5.4; 95% CI, 4.4–6.8), lacking a high school diploma (IRR, 2.7; 95% CI, 2.4–2.9), household crowding (IRR, 3.3; 95% CI, 2.5–4.4), and lacking a vehicle (IRR, 3.4; 95% CI, 3.0–3.8) had the greatest impact on CRC mortality. Compared to urban counties, rural counties with a higher proportion of people without a vehicle had a higher risk of CRC mortality (IRR, 4.3; 95% CI, 3.7–5.0; vs. IRR, 1.9; 95% CI, 1.5–2.4). Conclusions The rural–urban CRC mortality disparity is largely driven by low SES—higher poverty and unemployment, and lower income and education. The relationship between CRC mortality and the SVI is nuanced, and evaluating each component of the SVI may allow for more targeted area‐level interventions than evaluating the SVI alone. 32:T407,While prior studies have assessed sex disparities in ischemic heart disease (IHD) outcomes in the United States, little is known about how these differences vary across states. We analyzed GBD (Global Burden of Disease) data from 2011 to 2021 for all 50 US states. Sex-specific IHD age-standardized mortality rates and IHD age-standardized prevalence rates were used to calculate the mortality-to-prevalence ratios per 100 000 inhabitants per year, enabling comparison of death rates relative to the population at risk. Hawaii, the state with the lowest age-standardized prevalence rate and age-standardized mortality rate in both sexes, served as the reference for In 2011, 30 states had significantly higher mortality-to-prevalence ratios in women than men ( Despite an overall decline, sex disparities in IHD death persist, with Arkansas and Mississippi continuing to bear the highest burden. These disparities are largely driven by overweight/obesity and dietary risk factors, warranting targeted, state-level interventions.33:Tbee, Background While prior studies have assessed sex disparities in ischemic heart disease (IHD) outcomes in the United States, little is known about how these differences vary across states. Methods We analyzed GBD (Global Burden of Disease) data from 2011 to 2021 for all 50 US states. Sex‐specific IHD age‐standardized mortality rates and IHD age‐standardized prevalence rates were used to calculate the mortality‐to‐prevalence ratios per 100 000 inhabitants per year, enabling comparison of death rates relative to the population at risk. Hawaii, the state with the lowest age‐standardized prevalence rate and age‐standardized mortality rate in both sexes, served as the reference for Z score analyses, with 99% ( Z >2.58) or 95% ( Z >1.96) confidence thresholds. Results In 2011, 30 states had significantly higher mortality‐to‐prevalence ratios in women than men ( Z >2.58), indicating an elevated mortality rate in women with IHD. By 2021, only Arkansas (6.7% [95% uncertainty interval, 4.6–9.3]) and Mississippi (7.1% [95% uncertainty interval, 4.8–9.7]) exceeded this threshold, while 11 additional states, mainly in the South, retained moderate disparities ( Z >1.96). Higher mortality‐to‐prevalence ratios were inversely associated with state gross domestic product per capita. High body mass index, processed meat intake, low fiber, and low vegetable intake emerged as the most significant contributors ( Z >1.96, 95% confidence level) to excess IHD death in women but not men. Conclusions Despite an overall decline, sex disparities in IHD death persist, with Arkansas and Mississippi continuing to bear the highest burden. These disparities are largely driven by overweight/obesity and dietary risk factors, warranting targeted, state‐level interventions. 34:T56f,Substantial geographic disparities in melanoma incidence and access to tanning bed facilities exist. In this spatial ecological study, we evaluate the relationship between access to tanning bed facilities and melanoma incidence. New England county-level melanoma incidence rates (2014-2018), tanning bed facilities location, demographic data, socioeconomic data, and geographic data were obtained from the National Cancer Institute State Cancer Profiles, Data Axle, American Community Survey, and United States Geological Survey. Spatial regression models were utilized to determine the association between melanoma incidence and access to tanning beds, adjusting for confounding factors. Adjusted spatial regression identified that for every 1-minute increase in average travel time to tanning facilities within 30 minutes travel time threshold, the melanoma incidence rate decreased by 3.46% in the same county and by 1.92% across New England. We found a negative association between average travel time to tanning facilities and county-level melanoma incidence rate, suggesting that policy aimed at decreasing access to indoor tanning facilities has the potential to reduce melanoma rates. High-risk clusters, with respect to county-level melanoma rate, were also identified. Targeting of interventions and resources to high-risk clusters may reduce geographic melanoma rates disparities.35:T844,Air pollution in North America has intensified due to wildfires in recent years. In 2023, the wildfires in the Canadian province of Quebec caused a southward spread of pollutants, negatively affecting air quality and thereby aggravating certain health conditions in northeastern USA. This study examines the impact of air pollution on atopic dermatitis (AD) and skin health and how wildfires can exacerbate the burden of disease. Carbon monoxide levels measured by the U.S. Environmental Protection Agency (EPA) in the Boston region during the months following the Canadian wildfires of 2023 were collected from the U.S. EPA Outdoor Air Quality webpage. Patient records on dermatology clinic visits for dermatitis and eczema at the Mass General Brigham (MGB) hospital system, 300 miles from the wildfires, were examined, and the data compared with data from the corresponding months in 2019-2022 for historical control. No individual patient data were collected. A notable rise and atypical summer peak in carbon monoxide (CO) levels in the Boston region during 2023 correlated with a spike in AD, dermatitis, and eczema-related dermatology clinic visits within the MGB hospital system, as compared with the prior 4 years. The synchronized atypical peaks of CO levels and AD-related visits during the summer of 2023 highlight the potential impact of acute air pollution events such as wildfires on air quality and the consequences for skin health. Air pollution, exacerbated by wildfires, can damage the skin through the smoke and chemicals utilized for extinguishing fires, which contain multiple potential allergens and irritants to the skin, such as CO, particulate matter (PM2.5), and ammonium phosphates, triggering airway and skin inflammation and flares of AD. This issue disproportionately affects vulnerable populations, including low-income communities and the geriatric and pediatric populations. Healthcare professionals and government agencies must work together to improve air quality and purification policies and initiatives to lower the burden of skin disease, especially for vulnerable communities.25:[[["$","script","0",{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"CollectionPage\",\"name\":\"Ecological Study: study design Research & Clinical Takeaways\",\"description\":\"Explore peer-reviewed papers on Ecological Study (study design). 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Key confounders such as smoking prevalence, stage at diagnosis, and histologic subtype distribution across countries are not accounted for. Data quality varies substantially across WHO and World Bank sources, especially in low- and middle-income countries.","practice_takeaway":"Health system planners should prioritize investments in nursing workforce, radiotherapy infrastructure, and universal health coverage to reduce lung cancer mortality — these factors showed independent benefit beyond physician density alone."}},{"doi":"10.1176/appi.ajp.20250096","pmid":"42337413","title":"Deadly Heat: The Association Between Ambient Temperature and Suicide in Young People in the United States","journal_title":"American Journal of Psychiatry","journal_issn":"0002-953X","published_at":"2026-06-24T00:00:00.000Z","url":"https://doi.org/10.1176/appi.ajp.20250096","source_score":21,"is_retracted":false,"is_open_access":false,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-07-01T13:27:23.757Z","updated_at":"2026-07-01T13:27:23.757Z","tags":[{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"age_group","value":"adolescent","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"age_group","value":"child","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"category","value":"Psychiatry","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"condition","value":"Suicide","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"impact_flag","value":"safety_signal","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"level_of_evidence","value":"4","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"sample_size","value":"county-level data, 1980–2004","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"study_design","value":"Ecological Study","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"warning","value":"Age group tags reflect the full range analyzed (ages 5–65+) for comparison, though the primary focus population is ages 5–24.","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"warning","value":"Sample size tag reflects county-level aggregated data over 25 years; an individual-level N is not reported in the paper.","value_numeric":null},{"paper_doi":"10.1176/appi.ajp.20250096","tag_type":"warning","value":"The study period (1980–2004) predates recent climate trends and social media era, which may limit contemporary relevance.","value_numeric":null}],"summaries":{"what_studied":"This ecological study examined the association between monthly average ambient temperature and suicide rates in young people ages 5–24 across U.S. counties from 1980 to 2004, using fixed-effects regression with seasonal stratification and age-group comparisons (ages 5–65+).","key_result":"Each 1°C rise in average monthly temperature was linked to a **0.75% increase** in youth suicide overall (95% CI: 0.34–1.16), but the effect was far larger in **summer alone: +2.68% per 1°C** (95% CI: 1.42–3.94). Ages **15–24 were most vulnerable**, showing a 2.97% increase per 1°C in summer (95% CI: 1.30–4.65) — greater than any other age group. No significant sociodemographic modifiers (sex, income, race, education, rurality) were identified.","limitations":"- Ecological design prevents individual-level causal inference.\n- Data span 1980–2004, limiting applicability to current climate and social conditions.\n- Analysis restricted to the contiguous U.S., reducing global generalizability.","practice_takeaway":"Clinicians should be alert to heightened suicide risk in teens and young adults (ages 15–24) during summer heat waves, and may want to proactively check in with at-risk youth when temperatures spike. Public health planners should consider heat as a modifiable environmental factor when designing youth mental health interventions."}},{"doi":"10.1111/cea.70355","pmid":"42243985","title":"Worldwide Impact of Human Development and Inequality on the Prevalence of Asthma, Rhinoconjunctivitis and Eczema. Global Asthma Network's Ecological Study","journal_title":"Clinical & Experimental Allergy","journal_issn":"0954-7894","published_at":"2026-06-04T00:00:00.000Z","url":"https://doi.org/10.1111/cea.70355","source_score":1,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-06-09T13:50:30.716Z","updated_at":"2026-06-09T13:50:30.716Z","tags":[{"paper_doi":"10.1111/cea.70355","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"category","value":"Allergy","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"condition","value":"Asthma","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"condition","value":"Eczema","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"condition","value":"Rhinoconjunctivitis","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"impact_flag","value":"practice_changing","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"level_of_evidence","value":"4","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"study_design","value":"Ecological Study","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"warning","value":"Country counts vary by disease and age group (16–25 countries), so comparisons across conditions should be interpreted carefully.","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"warning","value":"No sample size (total N) was reported in the abstract or results; the tag was omitted.","value_numeric":null},{"paper_doi":"10.1111/cea.70355","tag_type":"warning","value":"The 'practice_changing' impact flag is applied cautiously — the ecological design limits direct clinical translation, but the finding that high-HDI/high-GinI intersection drives peak prevalence has clear public health relevance.","value_numeric":null}],"summaries":{"what_studied":"This ecological meta-regression study used Global Asthma Network Phase I data to examine how the Human Development Index (HDI) and Gini inequality index (GinI), alongside climate and geographic variables, explain worldwide prevalence differences in asthma, rhinoconjunctivitis, and eczema across children (6–7 years), adolescents (13–14 years), and their parents/guardians in up to 25 countries.","key_result":"HDI and GinI explained up to ~70% of asthma prevalence variability in children (e.g., current wheeze, severe asthma symptoms) but only ~22% in adolescents; eczema variability was explained up to ~60% in children and ~12% in adolescents; rhinoconjunctivitis was poorly explained (up to ~53% in children, near zero in other groups). Higher prevalence clustered where both high HDI and high GinI intersected — meaning lower-resourced communities within highly developed countries face the greatest risk.","limitations":"Ecological design prevents causal or individual-level inference. Country-level coverage was limited (16–25 countries depending on disease), which may not represent all global regions equally. Residual confounding from unmeasured environmental, genetic, or healthcare-access factors is likely.","practice_takeaway":"Clinicians and public health teams should recognize that patients in deprived communities within high-income countries carry the highest asthma burden — screening and resource allocation should prioritize these groups. The weak explanatory power for rhinoconjunctivitis suggests its drivers differ substantially from asthma and eczema and need separate investigation."}},{"doi":"10.1016/j.aprim.2026.103501","pmid":"42030634","title":"Mental health conditions in Mexico: Diagnosis trends based on hospital records","journal_title":"Atención Primaria","journal_issn":"0212-6567","published_at":"2026-04-23T00:00:00.000Z","url":"https://doi.org/10.1016/j.aprim.2026.103501","source_score":2,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-05-07T22:49:42.346Z","updated_at":"2026-05-07T22:49:42.346Z","tags":[{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"age_group","value":"adult","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"category","value":"Primary Health Care","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"condition","value":"Alzheimer's Disease","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"condition","value":"Anxiety Disorders","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"condition","value":"Dementia","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"condition","value":"Depressive Episode","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"condition","value":"Mental Disorders","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"condition","value":"Obsessive-Compulsive Disorder","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"condition","value":"Schizophrenia","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"condition","value":"Substance Use Disorder","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"impact_flag","value":"limited_evidence","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"level_of_evidence","value":"4","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"sample_size","value":"n=12,561,531","value_numeric":12561531},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"study_design","value":"Ecological Study","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"warning","value":"Coverage is limited to IMSS-insured adults ≥21 years; findings may not generalize to uninsured populations or those covered by other Mexican health systems (e.g., ISSSTE, Seguro Popular/IMSS-Bienestar).","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"warning","value":"The near-tripling of the rate may partly reflect improved detection or coding practices rather than a true epidemiological increase — the ecological design cannot distinguish these.","value_numeric":null},{"paper_doi":"10.1016/j.aprim.2026.103501","tag_type":"warning","value":"The study period (2019–2023) spans the COVID-19 pandemic, which may have substantially influenced both true mental disorder rates and healthcare-seeking behavior, but pandemic-specific effects are not explicitly disaggregated in the abstract.","value_numeric":null}],"summaries":{"what_studied":"This ecological study analyzed spatiotemporal trends in mental disorder diagnoses among adults (≥21 years) with social security (IMSS) coverage in Mexico from 2019 to 2023, using over 12.5 million primary care records.","key_result":"The national mental disorder rate nearly tripled, rising from 2,419 to 7,192 per 100,000 population over five years. Anxiety/OCD, substance use disorders, and depressive episodes were the top diagnoses in both sexes, with notable regional and gender-based variation.","limitations":"- Ecological design limits individual-level inference (ecological fallacy risk).\n- Data restricted to IMSS-insured adults, excluding uninsured and other public health system users, likely undercounting true burden.\n- Analysis relies on administrative diagnosis records, which may reflect detection/coding patterns rather than true prevalence shifts.","practice_takeaway":"The sharp rise in diagnosed mental disorders in Mexican primary care signals a growing need to expand detection capacity and reinforce primary care—especially in high-risk regions. Clinicians and health planners should prepare for increasing caseloads of dementia, Alzheimer's disease, schizophrenia, and delusional disorders alongside anxiety and depression."}},{"doi":"10.1002/cncr.70098","pmid":"41277298","title":"Beyond composite measures of regional vulnerability: Rural–urban colorectal cancer mortality disparities mediated by area‐level characteristics","journal_title":"Cancer","journal_issn":"0008-543X","published_at":"2025-11-24T00:00:00.000Z","url":"https://doi.org/10.1002/cncr.70098","source_score":28,"is_retracted":false,"is_open_access":true,"raw_metadata":{"ooir":{"day":"2025-11-28","doi":"10.1002/cncr.70098","days":"7","issn":"0008-543X","rank":"7","field":"Clinical Medicine","score":"28","category":"Oncology","categories":["Oncology"],"_categories":["Oncology"]},"pubmed":{"doi":"10.1002/cncr.70098","title":"Beyond composite measures of regional vulnerability: Rural-urban colorectal cancer mortality disparities mediated by area-level characteristics.","journal":"Cancer","methods":"","results":"Among 2927 counties (rural, 60.7%; urban, 39.3%), rural counties had 11.8% higher CRC mortality than urban counties; 18.6% of that disparity was mediated by low socioeconomic status (SES), 8.8% by household characteristics, and 2.7% by racial/ethnic minority status. Among all counties, poverty (incidence rate ratio [IRR], 3.2; 95% confidence interval [CI], 2.8-3.5), unemployment (IRR, 5.4; 95% CI, 4.4-6.8), lacking a high school diploma (IRR, 2.7; 95% CI, 2.4-2.9), household crowding (IRR, 3.3; 95% CI, 2.5-4.4), and lacking a vehicle (IRR, 3.4; 95% CI, 3.0-3.8) had the greatest impact on CRC mortality. Compared to urban counties, rural counties with a higher proportion of people without a vehicle had a higher risk of CRC mortality (IRR, 4.3; 95% CI, 3.7-5.0; vs. IRR, 1.9; 95% CI, 1.5-2.4).","abstract":"$30","keywords":["Social Vulnerability Index","colorectal cancer","mortality","rural","social determinants"],"pubmed_id":"41277298","conclusions":"","publication_date":"2025-11-24T00:00:00"},"crossref":{"DOI":"10.1002/cncr.70098","URL":"https://doi.org/10.1002/cncr.70098","ISSN":["0008-543X","1097-0142"],"link":[{"URL":"https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cncr.70098","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"page":null,"type":"journal-article","issue":"23","score":1,"title":["Beyond composite measures of regional vulnerability: Rural–urban colorectal cancer mortality disparities mediated by area‐level characteristics"],"author":[{"given":"Sara","family":"Myers","sequence":"first","affiliation":[{"name":"Department of Surgery Boston Medical Center Chobanian and Avedisian School of Medicine Boston University Boston Massachusetts USA"}]},{"given":"Elizabeth S.","family":"Davis","sequence":"additional","affiliation":[{"name":"Department of Surgery & Patient‐Reported Outcomes, Value and Experience Center Brigham and Women's Hospital Boston Massachusetts USA"}]},{"given":"Jeffrey A.","family":"Franks","sequence":"additional","affiliation":[{"name":"Department of Surgery Boston Medical Center Chobanian and Avedisian School of Medicine Boston University Boston Massachusetts USA"}]},{"given":"Jennifer S.","family":"Davids","sequence":"additional","affiliation":[{"name":"Department of Surgery Boston Medical Center Chobanian and Avedisian School of Medicine Boston University Boston Massachusetts USA"}]},{"given":"Kelly M.","family":"Kenzik","sequence":"additional","affiliation":[{"name":"Department of Surgery Boston Medical Center Chobanian and Avedisian School of Medicine Boston University Boston Massachusetts USA"},{"name":"Department of Surgery & Patient‐Reported Outcomes, Value and Experience Center Brigham and Women's Hospital Boston Massachusetts USA"},{"name":"Slone Epidemiology Center Boston University Boston Massachusetts USA"}]}],"issued":{"date-parts":[[2025,11,24]]},"member":"311","prefix":"10.1002","source":"Crossref","volume":"131","archive":["Portico"],"created":{"date-time":"2025-11-24T08:01:32Z","timestamp":1763971292000,"date-parts":[[2025,11,24]]},"indexed":{"date-time":"2025-12-03T11:16:07Z","timestamp":1764760567386,"date-parts":[[2025,12,3]]},"license":[{"URL":"http://onlinelibrary.wiley.com/termsAndConditions#vor","start":{"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000,"date-parts":[[2025,11,24]]},"delay-in-days":0,"content-version":"vor"}],"subject":[],"abstract":"$31","language":"en","relation":{},"resource":{"primary":{"URL":"https://acsjournals.onlinelibrary.wiley.com/doi/10.1002/cncr.70098"}},"subtitle":[],"assertion":[{"name":"received","group":{"name":"publication_history","label":"Publication History"},"label":"Received","order":0,"value":"2024-12-11"},{"name":"accepted","group":{"name":"publication_history","label":"Publication History"},"label":"Accepted","order":2,"value":"2025-09-02"},{"name":"published","group":{"name":"publication_history","label":"Publication History"},"label":"Published","order":3,"value":"2025-11-24"}],"deposited":{"date-time":"2025-12-03T11:11:24Z","timestamp":1764760284000,"date-parts":[[2025,12,3]]},"issn-type":[{"type":"print","value":"0008-543X"},{"type":"electronic","value":"1097-0142"}],"published":{"date-parts":[[2025,11,24]]},"publisher":"Wiley","reference":[{"DOI":null,"key":"e_1_2_9_2_1","year":null,"issue":null,"author":null,"volume":null,"first-page":null,"unstructured":"Social Determinants of Health.Healthy People 2030. 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analysis limited to New England counties.","practice_takeaway":"Counties with more difficult access to indoor tanning facilities had lower melanoma incidence; policies that reduce access to tanning facilities and targeting identified high‑risk clusters may help lower regional melanoma rates."}},{"doi":"10.1007/s13555-023-01080-1","pmid":"38103119","title":"The Burden of Air Pollution on Skin Health: a Brief Report and Call to Action","journal_title":"Dermatology and Therapy","journal_issn":"2193-8210","published_at":"2023-12-16T00:00:00.000Z","url":"https://doi.org/10.1007/s13555-023-01080-1","source_score":99,"is_retracted":false,"is_open_access":true,"raw_metadata":{"ooir":{"day":"2024-01-11","doi":"10.1007/s13555-023-01080-1","days":"30","issn":"2193-8210","rank":"4","field":"Clinical Medicine","score":"99","category":"Dermatology","categories":["Dermatology"],"_categories":["Dermatology"]},"pubmed":{"doi":"10.1007/s13555-023-01080-1","title":"The Burden of Air Pollution on Skin Health: a Brief Report and Call to Action.","journal":"Dermatology and therapy","methods":"","results":"A notable rise and atypical summer peak in carbon monoxide (CO) levels in the Boston region during 2023 correlated with a spike in AD, dermatitis, and eczema-related dermatology clinic visits within the MGB hospital system, as compared with the prior 4 years.","abstract":"$35","keywords":["Air pollution","Atopic dermatitis","Carbon monoxide","Climate change","Eczema","Public health","Skin health","Vulnerable populations","Wildfires"],"pubmed_id":"38103119","conclusions":"The synchronized atypical peaks of CO levels and AD-related visits during the summer of 2023 highlight the potential impact of acute air pollution events such as wildfires on air quality and the consequences for skin health. 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PM2.5 also increased to 41 μg/m3 in July 2023 versus 6.6 μg/m3 during the prior year.","limitations":"EPA data were limited by sensor coverage and reporting lag (CO available only through July 2023), and did not include other pollutants (e.g., aromatic hydrocarbons, lead). Diagnostic overlap among AD, irritant, and allergic contact dermatitis and the absence of patient-level data may have led to misclassification.","practice_takeaway":"During wildfire-driven air pollution spikes, clinicians should anticipate more AD/dermatitis flares and counsel vulnerable patients on exposure reduction (e.g., staying indoors, N95s, and indoor air filtration) per public health guidance."}}],"initialOffset":8,"fetchConfig":{"kind":"tags","tags":[{"tag_type":"study_design","value":"Ecological Study"}]},"breadcrumbItems":[{"name":"Home","href":"/"},{"name":"Research Hub","href":"/hub"},{"name":"Study Designs","href":"/hub/tag/study_design"},{"name":"Ecological Study"}],"hrefConfig":{"basePath":"/hub/papers","webViewQueryParams":{"hub_ctx":"tag","hub_tag_type":"study_design","hub_tag_value":"Ecological Study"}}}]]

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