Area Deprivation Index and Rurality in Relation to Lung Cancer Prevalence and Mortality in a Rural State.

BACKGROUND: We sought to describe lung cancer prevalence and mortality in relation to socioeconomic deprivation and rurality. METHODS: We conducted a population-based cross-sectional analysis of prevalent lung cancers from a statewide all-payer claims dataset from 2012 to 2016, lung cancer deaths in Maine from the state death registry from 2012 to 2016, rurality, and area deprivation index (ADI), a geographic area-based measure of socioeconomic deprivation. Analyses examined rate ratios for lung cancer prevalence and mortality according to rurality (small and isolated rural, large rural, or urban) and ADI (quintiles, with highest reflecting the most deprivation) and after adjusting for age, sex, and area-level smoking rates as determined by the Behavioral Risk Factor Surveillance System. RESULTS: Among 1 223 006 adults aged 20 years and older during the 5-year observation period, 8297 received lung cancer care, and 4616 died. Lung cancer prevalence and mortality were positively associated with increasing rurality, but these associations did not persist after adjusting for age, sex, and smoking rates. Lung cancer prevalence and mortality were positively associated with increasing ADI in models adjusted for age, sex, and smoking rates (prevalence rate ratio for ADI quintile 5 compared with quintile 1 = 1.41, 95% confidence interval [CI] =1.30 to 1.54) and mortality rate ratio = 1.59, 95% CI = 1.41 to 1.79). CONCLUSION: Socioeconomic deprivation, but not rurality, was associated with higher lung cancer prevalence and mortality. Interventions should target populations with socioeconomic deprivation, rather than rurality per se, and aim to reduce lung cancer risk via tobacco treatment and control interventions and to improve patient access to lung cancer prevention, screening, and treatment services.

Association between the food and physical activity environment, obesity, and cardiovascular health across Maine counties.

BACKGROUND: Accounting for nearly one-third of all deaths, cardiovascular disease is the leading cause of mortality and morbidity in the United States. Adverse health behaviors are major determinants of this high incidence of disease. Examining local food and physical activity environments and population characteristics in a poor, rural state may highlight underlying drivers of these behaviors. We aimed to identify demographic and environmental factors associated with both obesity and overall poor cardiovascular health (CVH) behaviors in Maine counties. METHODS: Our cross-sectional study analyzed 40,398 Behavioral Risk Factor Surveillance System (BRFSS) 2011-2014 respondents alongside county-level United States Department of Agriculture (USDA) Food Environment Atlas 2010-2012 measures of the built environment (i.e., density of restaurants, convenience stores, grocery stores, and fitness facilities; food store access; and county income). Poor CVH score was defined as exhibiting greater than 5 out of the 7 risk factors defined by the American Heart Association (current smoking, physical inactivity, obesity, poor diet, hypertension, diabetes, and high cholesterol). Multivariable logistic regression models described the contributions of built environment variables to obesity and overall poor CVH score after adjustment for demographic controls. RESULTS: Both demographic and environmental factors were associated with obesity and overall poor CVH. After adjustment for demographics (age, sex, personal income, and education), environmental characteristics most strongly associated with obesity included low full-service restaurant density (OR 1.34; 95% CI 1.24-1.45), low county median household income (OR 1.31; 95% CI 1.21-1.42) and high convenience store density (OR 1.21; 95% CI 1.12-1.32). The strongest predictors of overall poor CVH behaviors were low county median household income (OR 1.30; 95% CI 1.13-1.51), low full-service restaurant density (OR 1.38; 95% CI 1.19-1.59), and low fitness facility density (OR 1.27; 95% CI 1.11-1.46). CONCLUSIONS: In a rural state, both demographic and environmental factors predict overall poor CVH. These findings may help inform communities and policymakers of the impact of both social determinants of health and local environments on health outcomes.

Association Between Rurality and Lung Cancer Treatment Characteristics and Timeliness.

BACKGROUND: Lung cancer is the leading cause of cancer-related mortality in the United States, and rural states bear a greater burden of disease. METHODS: We analyzed tumor registry data to examine relationships between rurality and lung cancer stage at diagnosis and treatment. Cases were from the Maine Cancer Registry from 2012 to 2015, and rurality was defined using rural-urban commuting areas. Multivariable models were used to examine the relationships between rurality and treatment, adjusting for age, sex, poverty, education, insurance status, and cancer stage. RESULTS: We identified 5,338 adults with incident lung cancer; 3,429 (64.2%) were diagnosed at a late stage (III or IV). Rurality was not associated with stage at diagnosis. For patients with early-stage disease (I or II), rurality was not associated with receipt of treatment. However, for patients with late-stage disease, residents of large rural areas received more surgery (10%) compared with metropolitan (9%) or small/isolated rural areas (6%), P = .01. In multivariable analyses, patients in large rural areas received more chemotherapy (OR 1.48; 95% CI: 1.08-2.02) than those in metropolitan areas. Patients with early-stage disease residing in small/ isolated rural areas had delays in treatment (median time to first treatment = 43 days, interquartile range [IQR] 22-68) compared with large rural (34 days, IQR 17-55) and metropolitan areas (35 days, IQR 17-60), P = .0009. CONCLUSION: Rurality is associated with differences in receipt of specific lung cancer treatments and in timeliness of treatment.

Open-access programs for injury categorization using ICD-9 or ICD-10.

BACKGROUND: The article introduces Programs for Injury Categorization, using the International Classification of Diseases (ICD) and R statistical software (ICDPIC-R). Starting with ICD-8, methods have been described to map injury diagnosis codes to severity scores, especially the Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS). ICDPIC was originally developed for this purpose using Stata, and ICDPIC-R is an open-access update that accepts both ICD-9 and ICD-10 codes. METHODS: Data were obtained from the National Trauma Data Bank (NTDB), Admission Year 2015. ICDPIC-R derives CDC injury mechanism categories and an approximate ISS ("RISS") from either ICD-9 or ICD-10 codes. For ICD-9-coded cases, RISS is derived similar to the Stata package (with some improvements reflecting user feedback). For ICD-10-coded cases, RISS may be calculated in several ways: The "GEM" methods convert ICD-10 to ICD-9 (using General Equivalence Mapping tables from CMS) and then calculate ISS with options similar to the Stata package; a "ROCmax" method calculates RISS directly from ICD-10 codes, based on diagnosis-specific mortality in the NTDB, maximizing the C-statistic for predicting NTDB mortality while attempting to minimize the difference between RISS and ISS submitted by NTDB registrars (ISSAIS). Findings were validated using data from the National Inpatient Survey (NIS, 2015). RESULTS: NTDB contained 917,865 cases, of which 86,878 had valid ICD-10 injury codes. For a random 100,000 ICD-9-coded cases in NTDB, RISS using the GEM methods was nearly identical to ISS calculated by the Stata version, which has been previously validated. For ICD-10-coded cases in NTDB, categorized ISS using any version of RISS was similar to ISSAIS; for both NTDB and NIS cases, increasing ISS was associated with increasing mortality. Prediction of NTDB mortality was associated with C-statistics of 0.81 for ISSAIS, 0.75 for RISS using the GEM methods, and 0.85 for RISS using the ROCmax method; prediction of NIS mortality was associated with C-statistics of 0.75-0.76 for RISS using the GEM methods, and 0.78 for RISS using the ROCmax method. Instructions are provided for accessing ICDPIC-R at no cost. CONCLUSIONS: The ideal methods of injury categorization and injury severity scoring involve trained personnel with access to injured persons or their medical records. ICDPIC-R may be a useful substitute when this ideal cannot be obtained.

Behavioral Risk Factors and Regional Variation in Cardiovascular Health Care and Death.

INTRODUCTION: Reducing the burden of death from cardiovascular disease includes risk factor reduction and medical interventions. METHODS: This was an observational analysis at the hospital service area (HSA) level, to examine regional variation and relationships between behavioral risks, health services utilization, and cardiovascular disease mortality (the outcome of interest). HSA-level prevalence of cardiovascular disease behavioral risks (smoking, poor diet, physical inactivity) were calculated from the Behavioral Risk Factor Surveillance System; HSA-level rates of stress tests, diagnostic cardiac catheterization, and revascularization from a statewide multi-payer claims data set from Maine in 2013 (with 606,260 patients aged ≥35 years), and deaths from state death certificate data. Analyses were done in 2016. RESULTS: There were marked differences across 32 Maine HSAs in behavioral risks: smoking (12.4%-28.6%); poor diet (43.6%-73.0%); and physical inactivity (16.4%-37.9%). After adjustment for behavioral risks, rates of utilization varied by HSA: stress tests (28.2-62.4 per 1,000 person-years, coefficient of variation=17.5); diagnostic cardiac catheterization (10.0-19.8 per 1,000 person-years, coefficient of variation=17.3); and revascularization (4.6-6.2 per 1,000 person-years; coefficient of variation=9.1). Strong HSA-level associations between behavioral risk factors and cardiovascular disease mortality were observed: smoking (R2=0.52); poor diet (R2=0.38); and physical inactivity (R2=0.35), and no association between revascularization and cardiovascular disease mortality after adjustment for behavioral risk factors (R2=0.02). HSA-level behavioral risk factors were also strongly associated with all-cause mortality: smoking (R2=0.57); poor diet (R2=0.49); and physical inactivity (R2=0.46). CONCLUSIONS: There is substantial regional variation in behavioral risks and cardiac utilization. Behavioral risk factors are associated with cardiovascular disease mortality regionally, whereas revascularization is not. Efforts to reduce cardiovascular disease mortality in populations should focus on prevention efforts targeting modifiable risk factors.

Cancer incidence in patients with hereditary hemorrhagic telangiectasia.

PURPOSE: Hereditary hemorrhagic telangiectasia (HHT) is a genetic disorder characterized by deficiency in endoglin, an angiogenic protein. We previously showed that HHT, in which systemic endoglin expression is reduced, was associated with better survival outcomes in cancer patients (Duarte et al. in Cancer Epidemiol Biomarkers Prev 23:117-125, 2014). Here, we evaluated whether HHT was associated with reduced cancer incidence. METHODS: A matched case-control analysis using SEER Medicare was conducted to evaluate the effect of HHT on diagnosis with breast, colorectal, lung, or prostate cancer between 2000 and 2007 (n = 633,162). Cancer and non-cancer patients were matched on age, sex, SEER registry region, and length of the ascertainment period for HHT. We assessed crude association using a McNemar's test and then adjusted for demographic variables, cancer type, cancer stage, comorbidities, and ascertainment period with a conditional logistic regression model for cancer incidence. RESULTS: The McNemar's test showed no significant association between HHT and cancer incidence (p = 0.74). Adjusting for covariates with the conditional logistic regression model did not change the result [HHT odds ratio 0.978; 95 % CI (0.795, 1.204)]. The lack of association between HHT and cancer incidence is unexpected given the previously discovered significant association between HHT and improved survival outcomes (Duarte et al. in Cancer Epidemiol Biomarkers Prev 23:117-125, 2014). CONCLUSIONS: We conclude that the protective effect of reduced systemic endoglin expression in cancer is specific to cancer progression through its effect on vascularization and other stromal effects but does not extend to cancer initiation.

Validation of the Patient-Reported Outcome Mortality Prediction Tool (PROMPT).

CONTEXT: The Patient-Reported Outcome Mortality Prediction Tool (PROMPT) estimates six-month mortality risk in elderly patients with declining health, but its external validity has not been established. OBJECTIVES: To prospectively validate the PROMPT in an independent patient cohort and explore its clinical utility. METHODS: The study cohort comprised a diverse sample of 467 patients aged 65 years and older. Model calibration and discrimination were assessed on the original PROMPT and in two updated models. Clinical utility of the final updated PROMPT was examined using decision curve analysis. RESULTS: The validation cohort had a lower six-month mortality rate than the derivation cohort (6.9% vs. 15.0%). Discrimination was virtually unchanged (area under the curve 0.73 compared with 0.75), but calibration was suboptimal (P < 0.05 for the Hosmer-Lemeshow test). The PROMPT, therefore, was updated with a new intercept and slope parameter that significantly improved calibration (Hosmer-Lemeshow statistic of 0.66). Specificity of the PROMPT was high (92% and 97%, respectively, at the 10% and 20% mortality risk thresholds), although sensitivity was modest (53% and 44% at the corresponding thresholds), consistent with diagnostic performance in the derivation sample. Decision curve analysis demonstrated greater net benefit of the updated PROMPT than "treat all" or "treat none" strategies, especially at low to moderate risk thresholds. CONCLUSION: The PROMPT demonstrated good discrimination but poor calibration in an independent heterogeneous clinical population. Model updating improved calibration and diagnostic performance and decision curve analysis demonstrated potential clinical utility of the PROMPT for initiating advance care planning rather than hospice referrals.

An inordinate fondness for Fusarium: phylogenetic diversity of fusaria cultivated by ambrosia beetles in the genus Euwallacea on avocado and other plant hosts.

Ambrosia beetle fungiculture represents one of the most ecologically and evolutionarily successful symbioses, as evidenced by the 11 independent origins and 3500 species of ambrosia beetles. Here we document the evolution of a clade within Fusarium associated with ambrosia beetles in the genus Euwallacea (Coleoptera: Scolytinae). Ambrosia Fusarium Clade (AFC) symbionts are unusual in that some are plant pathogens that cause significant damage in naïve natural and cultivated ecosystems, and currently threaten avocado production in the United States, Israel and Australia. Most AFC fusaria produce unusual clavate macroconidia that serve as a putative food source for their insect mutualists. AFC symbionts were abundant in the heads of four Euwallacea spp., which suggests that they are transported within and from the natal gallery in mandibular mycangia. In a four-locus phylogenetic analysis, the AFC was resolved in a strongly supported monophyletic group within the previously described Clade 3 of the Fusarium solani species complex (FSSC). Divergence-time estimates place the origin of the AFC in the early Miocene ∼21.2 Mya, which coincides with the hypothesized adaptive radiation of the Xyleborini. Two strongly supported clades within the AFC (Clades A and B) were identified that include nine species lineages associated with ambrosia beetles, eight with Euwallacea spp. and one reportedly with Xyleborus ferrugineus, and two lineages with no known beetle association. More derived lineages within the AFC showed fixation of the clavate (club-shaped) macroconidial trait, while basal lineages showed a mix of clavate and more typical fusiform macroconidia. AFC lineages consisted mostly of genetically identical individuals associated with specific insect hosts in defined geographic locations, with at least three interspecific hybridization events inferred based on discordant placement in individual gene genealogies and detection of recombinant loci. Overall, these data are consistent with a strong evolutionary trend toward obligate symbiosis coupled with secondary contact and interspecific hybridization.