Emergency department involvement in the diagnosis of cancer among older adults: a SEER-Medicare study.

BACKGROUND: Internationally, 20% to 50% of cancer is diagnosed through emergency presentation, which is associated with lower survival, poor patient experience, and socioeconomic disparities, but population-based evidence about emergency diagnosis in the United States is limited. We estimated emergency department (ED) involvement in the diagnosis of cancer in a nationally representative population of older US adults, and its association with sociodemographic, clinical, and tumor characteristics. METHODS: We analyzed Surveillance, Epidemiology, and End Results Program-Medicare data for Medicare beneficiaries (≥66 years old) with a diagnosis of female breast, colorectal, lung, and prostate cancers (2008-2017), defining their earliest cancer-related claim as their index date, and patients who visited the ED 0 to 30 days before their index date to have "ED involvement" in their diagnosis, with stratification as 0 to 7 or 8 to 30 days. We estimated covariate-adjusted associations of patient age, sex, race and ethnicity, marital status, comorbidity score, tumor stage, year of diagnosis, rurality, and census-tract poverty with ED involvement using modified Poisson regression. RESULTS: Among 614 748 patients, 23% had ED involvement, with 18% visiting the ED in the 0 to 7 days before their index date. This rate varied greatly by tumor site, with breast cancer at 8%, colorectal cancer at 39%, lung cancer at 40%, and prostate cancer at 7%. In adjusted models, older age, female sex, non-Hispanic Black and Native Hawaiian or Other Pacific Islander race, being unmarried, recent year of diagnosis, later-stage disease, comorbidities, and poverty were associated with ED involvement. CONCLUSIONS: The ED may be involved in the initial identification of cancer for 1 in 5 patients. Earlier, system-level identification of cancer in non-ED settings should be prioritized, especially among underserved populations.

Immortal Time Bias With Time-Varying Exposures in Environmental Epidemiology: A Case Study in Lung Cancer Survival.

Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000-2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-µg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors.

Androgen deprivation therapy and acute kidney injury in patients with prostate cancer undergoing definitive radiotherapy.

BACKGROUND: Androgen deprivation therapy (ADT) is frequently utilized in conjunction with radiotherapy (RT) in the definitive management of prostate cancer. Prior studies have suggested an association between ADT use and acute kidney injury (AKI), however, these included heterogeneous populations undergoing a variety of treatments and relied on billing codes to ascertain the incidence of AKI. METHODS: We analyzed a cohort of 27,868 veterans undergoing definitive RT + /- ADT for prostate cancer between 2001 and 2015 using the Veterans Affairs Informatics and Computing Infrastructure (VINCI). Exposure was defined as use of ADT within one year of diagnosis. The primary outcome was AKI, defined by an increase in serum creatinine to at least 1.5 times the baseline value. AKIs were classified as mild, moderate, or severe in accordance with international guidelines. A multivariate competing risks model was used to account for demographic and oncologic factors as well as medications and procedures known to influence the risk of AKI. RESULTS: Most (n = 18,754) men received RT alone; 9,114 men received RT + ADT. The incidence of AKI at two years after diagnosis was 10.5% in the RT + ADT group and 7.9% in the RT group (Gray's test p < 0.01). Multivariate analysis confirmed ADT usage was associated with an increased risk for any AKI (SHR = 1.24, 95% CI = 1.14-1.36, p < 0.01). ADT was also associated with an increased risk of mild AKI (SHR = 1.13, 95% CI = 1.01-1.27, p = 0.04) and moderate AKI (SHR = 1.45, 95% CI = 1.20-1.76, p < 0.01), though not severe AKI (SHR = 1.33, 95% CI = 0.93-1.91, p = 0.11). CONCLUSIONS: Our findings confirm that use of ADT is associated with an increased risk of AKI in patients undergoing definitive RT for prostate cancer. Clinicians should be alert to the potential for renal dysfunction in this population.

Using the Generalized Synthetic Control Method to Estimate the Impact of Extreme Weather Events on Population Health.

BACKGROUND: Traditional epidemiologic approaches such as time-series or case-crossover designs are often used to estimate the effects of extreme weather events but can be limited by unmeasured confounding. Quasi-experimental methods are a family of methods that leverage natural experiments to adjust for unmeasured confounding indirectly. The recently developed generalized synthetic control method that exploits the timing of an exposure is well suited to estimate the impact of acute environmental events on health outcomes. To demonstrate how this method can be used to study extreme weather events, we examined the impact of the 20-26 October 2007 Southern California wildfire storm on respiratory hospitalizations. METHODS: We used generalized synthetic control to compare the average number of ZIP code-level respiratory hospitalizations during the wildfire storm between ZIP codes that were classified as exposed versus unexposed to wildfire smoke. We considered wildfire exposure eligibility for each ZIP code using fire perimeters and satellite-based smoke plume data. We retrieved respiratory hospitalization discharge data from the Office of Statewide Health Planning and Development. R code to implement the generalized synthetic control method is included for reproducibility. RESULTS: The analysis included 172 exposed and 578 unexposed ZIP codes. We estimated that the average effect of the wildfire storm among the exposed ZIP codes was an 18% (95% confidence interval: 10% to 29%) increase in respiratory hospitalizations. CONCLUSIONS: We illustrate the use of generalized synthetic control to leverage natural experiments to quantify the health impacts of extreme weather events when traditional approaches are unavailable or limited by assumptions.

Association Between Patient Survival and Clinician Variability in Treatment Rates for Aortic Valve Stenosis.

Background Patients with symptomatic severe aortic stenosis (ssAS) have a high mortality risk and compromised quality of life. Surgical/transcatheter aortic valve replacement (AVR) is a Class I recommendation, but it is unclear if this recommendation is uniformly applied. We determined the impact of managing cardiologists on the likelihood of ssAS treatment. Methods and Results Using natural language processing of Optum electronic health records, we identified 26 438 patients with newly diagnosed ssAS (2011-2016). Multilevel, multivariable Fine-Gray competing risk models clustered by cardiologists were used to determine the impact of cardiologists on the likelihood of 1-year AVR treatment. Within 1 year of diagnosis, 35.6% of patients with ssAS received an AVR; however, rates varied widely among managing cardiologists (0%, lowest quartile; 100%, highest quartile [median, 29.6%; 25th-75th percentiles, 13.3%-47.0%]). The odds of receiving AVR varied >2-fold depending on the cardiologist (median odds ratio for AVR, 2.25; 95% CI, 2.14-2.36). Compared with patients with ssAS of cardiologists with the highest treatment rates, those treated by cardiologists with the lowest AVR rates experienced significantly higher 1-year mortality (lowest quartile, adjusted hazard ratio, 1.22, 95% CI, 1.13-1.33). Conclusions Overall AVR rates for ssAS were low, highlighting a potential challenge for ssAS management in the United States. Cardiologist AVR use varied substantially; patients treated by cardiologists with lower AVR rates had higher mortality rates than those treated by cardiologists with higher AVR rates.

Sex disparities in patients with symptomatic severe aortic stenosis.

BACKGROUND: We evaluated whether there is equitable distribution across sexes of treatment and outcomes for aortic valve replacement (AVR), via surgical (SAVR) or transcatheter (TAVR) methods, in symptomatic severe aortic stenosis (ssAS) patients. METHODS: Using de-identified data, we identified 43,822 patients with ssAS (2008-2016). Multivariate competing risk models were used to determine the likelihood of any AVR, while accounting for the competing risk of death. Association between sex and 1-year mortality, stratified by AVR status, was evaluated using multivariate Cox regression models with AVR as a time-dependent variable. RESULTS: Among patients with ssAS, 20,986 (47.9%) were female. Females were older (median age 81 vs. 78, P<0.001), more likely to have body mass index <20 (8.5% vs. 3.5%), and home oxygen use (4.4% vs. 3.4%, P<0001 for all). Overall, 12,129 (27.7%) patients underwent AVR for ssAS. Females were less likely to undergo AVR compared with males (24.1% vs. 31.0%, adjusted hazard ratio [HR] 0.80, 95% confidence interval [CI] 0.77-0.83), but when treated, were more likely to undergo TAVR (37.9% vs. 30.9%, adjusted HR 1.21, 95% CI 1.15-1.27). Untreated females and males had similarly high rates of mortality at 1 year (31.1% vs. 31.3%, adjusted HR 0.98, 95% CI 0.94-1.03). Among those undergoing AVR, females had significantly higher mortality (10.2% vs. 9.4%, adjusted HR 1.24, 95% CI 1.10-1.41), driven by increased SAVR-associated mortality (9.0% vs. 7.6%, adjusted HR 1.43, 95% CI 1.21-1.69). CONCLUSIONS: Treatment rates for ssAS patients remain suboptimal with disparities in female treatment.

Cost Savings Associated With Palliative Care Among Older Adults With Advanced Cancer.

BACKGROUND: There is inconsistent evidence that palliative care intervention decreases total healthcare expenditure at end-of-life for oncology patients. This inconsistent evidence may result from small sample sizes at single institution studies and disparate characterization of costs across studies. Comprehensive studies in population-based datasets are needed to fully understand the impact of palliative care on total healthcare costs. This study analyzed the impact of palliative care on total healthcare costs in a nationally representative sample of advanced cancer patients. METHODS: We conducted a matched cohort study among Medicare patients with metastatic lung, colorectal, breast and prostate cancers. We matched patients who received a palliative care consultation to similar patients who did not receive a palliative care consultation on factors related to both the receipt of palliative care and end of life costs. We compared direct costs between matched patients to determine the per-patient economic impact of a palliative care consultation. RESULTS: Patients who received a palliative care consultation experienced an average per patient cost of $5,834 compared to $7,784 for usual care patients (25% decrease; p < 0.0001). Palliative care consultation within 7 days of death decreased healthcare costs by $451, while palliative care consultation more than 4 weeks from death decreased costs by $4,643. CONCLUSION: This study demonstrates that palliative care has the capacity to substantially reduce healthcare expenditure among advanced cancer patients. Earlier palliative care consultation results in greater cost reductions than consultation in the last week of life.

Racial Differences in the Use of Aortic Valve Replacement for Treatment of Symptomatic Severe Aortic Valve Stenosis in the Transcatheter Aortic Valve Replacement Era.

Background Aortic valve replacement (AVR) is a life-saving treatment for patients with symptomatic severe aortic valve stenosis. We sought to determine whether transcatheter AVR has resulted in a more equitable treatment rate by race in the United States. Methods and Results A total of 32 853 patients with symptomatic severe aortic valve stenosis were retrospectively identified via Optum's deidentified electronic health records database (2007-2017). AVR rates in non-Hispanic Black and White patients were assessed in the year after diagnosis. Multivariate Fine-Gray hazards models were used to evaluate the likelihood of AVR by race, with adjustment for patient factors and the managing cardiologist. Time-trend and 1-year symptomatic severe aortic valve stenosis survival analyses were also performed. From 2011 to 2016, the rate of AVR increased from 20.1% to 37.1%. Overall, Black individuals were less likely than Whites to receive AVR (22.9% versus 31.0%; unadjusted hazard ratio [HR], 0.70; 95% CI, 0.62-0.79; fully adjusted HR, 0.76; 95% CI, 0.67-0.85). Yet, during 2015 to 2016, AVR racial differences were attenuated (29.5% versus 35.2%; adjusted HR, 0.86; 95% CI, 0.74-1.02) because of greater uptake of transcatheter AVR in Blacks than Whites (53.4% of AVRs versus 47.3%; P=0.128). Untreated patients had significantly higher 1-year mortality than those treated (adjusted HR, 0.57; 95% CI, 0.53-0.61), which was consistent by race (interaction P value=0.52). Conclusions Although transcatheter AVR has increased the use of AVR in the United States, treatment rates remain low. Black patients with symptomatic severe aortic valve stenosis were less likely than White patients to receive AVR, yet these differences have recently narrowed.

Quality of care at safety-net hospitals and the impact on pay-for-performance reimbursement.

BACKGROUND: Pay-for-performance reimbursement ties hospital payments to standardized quality-of-care metrics. To the authors' knowledge, the impact of pay-for-performance reimbursement models on hospitals caring primarily for uninsured or underinsured patients remains poorly defined. The objective of the current study was to evaluate how standardized quality-of-care metrics vary by a hospital's propensity to care for uninsured or underinsured patients and demonstrate the potential impact that pay-for-performance reimbursement could have on hospitals caring for the underserved. METHODS: The authors identified 1,703,865 patients with cancer who were diagnosed between 2004 and 2015 and treated at 1344 hospitals. Hospital safety-net burden was defined as the percentage of uninsured or Medicaid patients cared for by that hospital, categorizing hospitals into low-burden, medium-burden, and high-burden hospitals. The authors evaluated the impact of safety-net burden on concordance with 20 standardized quality-of-care measures, adjusting for differences in patient age, sex, stage of disease at diagnosis, and comorbidity. RESULTS: Patients who were treated at high-burden hospitals were more likely to be young, male, Black and/or Hispanic, and to reside in a low-income and low-educated region. High-burden hospitals had lower adherence to 13 of 20 quality measures compared with low-burden hospitals (all P < .05). Among the 350 high-burden hospitals, concordance with quality measures was found to be lowest for those caring for the highest percentage of uninsured or Medicaid patients, minority patients, and less educated patients (all P < .001). CONCLUSIONS: Hospitals caring for uninsured or underinsured individuals have decreased quality-of-care measures. Under pay-for-performance reimbursement models, these lower quality-of-care scores could decrease hospital payments, potentially increasing health disparities for at-risk patients with cancer.

Evaluating the impact of the California 1995 smoke-free workplace law on population smoking prevalence using a synthetic control method.

The objective of this study is to assess the impact of the California 1995 Smoke-Free Workplace Act (SFWA) on cigarette smoking prevalence in the population. We used survey responses related to cigarette smoking from the Behavioral Risk Factor Surveillance System (BRFSS) from 1990 to 2000. We utilize a synthetic control method which creates a weighted combination of control states to produce a single 'synthetic' control group to best approximate the counterfactual trend in California in the absence of the SFWA. Variables known to be associated with smoking were included to weight each state in the pre-intervention period as medians by state and included: distribution of race/ethnicity (White, Black, Asian, Hispanic), sex (Male/Female), marital status (married/unmarried), high school education (yes/no) and employment status (yes/no). We find evidence that there was a small decrease in population smoking prevalence in California in the year immediately following the SFWA, but this effect was not sustained beyond 1995. We hypothesize that one potential explanation for the lack of prolonged impact on population smoking prevalence is that there are sustained effects from the passage of 1989 California Proposition 99, which enacted an excise tax on tobacco products. Understanding how workplace smoking ban legislation affects population smoking behaviors is necessary to better inform policy development in other states and counties and to improve existing policies. Future work should consider the impact of smoking legislation impacts subgroups of the population by socioeconomic status, occupation or race/ethnicity.

Comparison of Hematologic Toxicity and Bone Marrow Compensatory Response in Head and Neck vs. Cervical Cancer Patients Undergoing Chemoradiotherapy.

Background: Hematologic toxicity is a critical problem limiting treatment delivery in cancer patients undergoing concurrent chemoradiotherapy. However, the extent to which anatomic variations in radiation dose limit chemotherapy delivery is poorly understood. A unique natural experiment arises in patients with head and neck and cervical cancer, who frequently undergo identical chemotherapy but receive radiation to different regions of the body. Comparing these cohorts can help elucidate to what extent hematologic toxicity is attributable to marrow radiation as opposed to chemotherapy. Methods: In this longitudinal cohort study, we compared hematologic toxicity and bone marrow compensatory response in 148 patients (90 cervix, 58 head/neck) undergoing chemoradiotherapy with concurrent weekly cisplatin 40 mg/m2. We used linear mixed effect models to compare baseline and time-varying peripheral cell counts and hemoglobin levels between cohorts. To assess bone marrow compensatory response, we measured the change in metabolically active bone marrow (ABM) volume on 18F-fluorodeoxyglucose positron emission tomography/computed tomography. Results: We observed greater reductions in log-transformed lymphocyte, platelet, and absolute neutrophil counts (ANC) for cervix compared to head/neck cancer patients (fixed effects for time-cohort interaction [95% CI]: lymphocytes, -0.06 [-0.09, -0.031]; platelets,-0.028 [-0.051, -0.0047]; ANC, -0.043 [-0.075, -0.011]). Mean ANC nadirs were also lower for cervical vs. head/neck cancer cohorts (2.20 vs. 2.85 × 103 per µL, p < 0.01). Both cohorts exhibited reductions in ABM volume within the radiation field, and increases in ABM volume in out-of-field areas, indicating varying compensatory response to radiation injury. Conclusions: Cervical cancer patients had faster decreases in ANC, lymphocyte, and platelet counts, and lower ANC nadirs, indicating a significant effect of pelvic irradiation on acute peripheral blood cell counts. Both cohorts exhibited a compensatory response with increased out-of-field bone marrow activity.

Evaluation of the Use of Cancer Registry Data for Comparative Effectiveness Research.

Importance: Researchers often analyze cancer registry data to assess for differences in survival among cancer treatments. However, the retrospective, nonrandomized design of these analyses raises questions about study validity. Objective: To examine the extent to which comparative effectiveness analyses using observational cancer registry data produce results concordant with those of randomized clinical trials. Design, Setting, and Participants: In this comparative effectiveness study, a total of 141 randomized clinical trials referenced in the National Comprehensive Cancer Network Clinical Practice Guidelines for 8 common solid tumor types were identified. Data on participants within the National Cancer Database (NCDB) diagnosed between 2004 and 2014, matching the eligibility criteria of the randomized clinical trial, were obtained. The present study was conducted from August 1, 2017, to September 10, 2019. The trials included 85 118 patients, and the corresponding NCDB analyses included 1 344 536 patients. Three Cox proportional hazards regression models were used to determine hazard ratios (HRs) for overall survival, including univariable, multivariable, and propensity score-adjusted models. Multivariable and propensity score analyses controlled for potential confounders, including demographic, comorbidity, clinical, treatment, and tumor-related variables. Main Outcomes and Measures: The main outcome was concordance between the results of randomized clinical trials and observational cancer registry data. Hazard ratios with an NCDB analysis were considered concordant if the NDCB HR fell within the 95% CI of the randomized clinical trial HR. An NCDB analysis was considered concordant if both the NCDB and clinical trial P values for survival were nonsignificant (P ≥ .05) or if they were both significant (P < .05) with survival favoring the same treatment arm in the NCDB and in the randomized clinical trial. Results: Analyses using the NCDB-produced HRs for survival were concordant with those of 141 randomized clinical trials in 79 univariable analyses (56%), 98 multivariable analyses (70%), and 90 propensity score models (64%). The NCDB analyses produced P values concordant with randomized clinical trials in 58 univariable analyses (41%), 65 multivariable analyses (46%), and 63 propensity score models (45%). No clinical trial characteristics were associated with concordance between NCDB analyses and randomized clinical trials, including disease site, type of clinical intervention, or severity of cancer. Conclusions and Relevance: The findings of this study suggest that comparative effectiveness research using cancer registry data often produces survival outcomes discordant with those of randomized clinical trial data. These findings may help provide context for clinicians and policy makers interpreting observational comparative effectiveness research in oncology.

Associations between green space and preterm birth: Windows of susceptibility and interaction with air pollution.

BACKGROUND: Recent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born < 37 completed gestational weeks). In addition, windows of susceptibility during pregnancy have not been explored and potential interactions of green space with air pollution exposures during pregnancy are still unclear. OBJECTIVES: To evaluate the relationships between green space and PTB, identify windows of susceptibility, and explore potential interactions between green space and air pollution. METHODS: Birth certificate records for all births in California (2001-2008) were obtained. The Normalized Difference Vegetation Index (NDVI) was used to characterized green space exposure. Gestational age was treated as a time-to-event outcome; Cox proportional hazard models were applied to estimate the association between green space exposure and PTB, moderately PTB (MPTB, gestational age < 35 weeks), and very PTB (VPTB, gestational age < 30 weeks), after controlling for maternal age, race/ethnicity, education, and median household income. Month-specific green space exposure was used to identify potential windows of susceptibility. Potential interactions between green space and air pollution [fine particulate matter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3)] were examined on both additive and multiplicative scales. RESULTS: In total, 3,753,799 eligible births were identified, including 341,123 (9.09%) PTBs, 124,631 (3.32%) MPTBs, and 22,313 (0.59%) VPTBs. A reduced risk of PTB was associated with increases in residential NDVI exposure in 250 m, 500 m, 1000 m, and 2000 m buffers. In the 2000 m buffer, the association was strongest for VPTB [adjusted hazard ratio (HR) per interquartile range increase in NDVI: 0.959, 95% confidence interval (CI): 0.942-0.976)], followed by MPTB (HR = 0.970, 95% CI: 0.962-0.978) and overall PTB (HR = 0.972, 95% CI: 0.966-0.978). For PTB, green space during the 3rd - 5th gestational months had stronger associations than those in the other time periods, especially during the 4th gestational month (NDVI 2000 m: HR = 0.970, 95% CI: 0.965-0.975). We identified consistent positive additive and multiplicative interactions between decreasing green space and higher air pollution. CONCLUSION: This large study found that maternal exposure to residential green space was associated with decreased risk of PTB, MPTB, and VPTB, especially in the second trimester. There is a synergistic effect between low green space and high air pollution levels on PTB, indicating that increasing exposure to green space may be more beneficial for women with higher air pollution exposures during pregnancy.

Extreme heat episodes and risk of preterm birth in California, 2005-2013.

BACKGROUND: Preterm birth is a leading cause of infant morbidity and mortality. Identifying potentially modifiable triggers toward the end of gestation, such as extreme heat, can improve understanding of the role of acute stress on early deliveries and inform warning systems. In this study we examined the association between extreme heat, variously defined during the last week of gestation, and risk of preterm birth among mothers in California. METHODS: We created a population-based cohort comprised of 1,967,300 mothers who had live, singleton births in California, from May through September 2005-2013. Daily temperature data estimated at the maternal zip code of residence was used to create 12 definitions of extreme heat with varying relative temperatures (75th, 90th, 95th, and 98th percentiles) and durations (at least 2, 3, or 4 consecutive days). We estimated risk of preterm birth (<37 gestational weeks) in relation to exposure to extreme heat during the last week of gestation with multi-level Cox proportional hazard regression models, adjusting for maternal characteristics, sex of neonate, and seasonality. We also included randomly generated data, SAS code, and estimates for reproducibility purposes. RESULTS: Approximately 7% of the cohort had a preterm birth. For all definitions of extreme heat, the risk of preterm birth was consistently higher among mothers who experienced an extreme heat episode during their last week of gestation. Hazard ratios ranged from 1.008 (95% CI: 0.997, 1.021) to 1.128 (95% CI: 1.052, 1.210), with increasing associations as the relative temperature and duration of extreme heat episode increased. CONCLUSION: This study adds to the previous literature by considering multiple definitions of extreme heat and applying a time-to-event framework. Findings suggest that acute exposure to extreme heat during the last week of gestation may trigger an earlier delivery. Implementing heat warning systems targeted toward pregnant women may improve birth outcomes.

Predicting Persistent Opioid Use, Abuse, and Toxicity Among Cancer Survivors.

BACKGROUND: Although opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at risk of persistent opioid use and abuse. METHODS: Within a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and selection operator regression technique. RESULTS: The rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1% to 8.4%); the rate of opioid abuse or dependence was 2.9% (95% CI = 2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI = 2.0% to 2.2%). On multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse opioid-related outcomes including persistent opioid use (area under the curve [AUC] = 0.85), future diagnoses of opioid abuse or dependence (AUC = 0.87), and admission for opioid abuse or toxicity (AUC = 0.78). CONCLUSION: This study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer patients.

Respiratory hospitalizations and wildfire smoke: a spatiotemporal analysis of an extreme firestorm in San Diego County, California.

Wildfire smoke adversely impacts respiratory health as fine particles can penetrate deeply into the lungs. Epidemiological studies of differential impacts typically target population subgroups in terms of vulnerability to wildfire smoke. Such information is useful to customize smoke warnings and mitigation actions for specific groups of individuals. In addition to individual vulnerability, it is also important to assess spatial patterns of health impacts to identify vulnerable communities and tailor public health actions during wildfire smoke events. METHODS: We assess the spatiotemporal variation in respiratory hospitalizations in San Diego County during a set of major wildfires in 2007, which led to a substantial public health burden. We propose a spatial within-community matched design analysis, adapted to the study of wildfire impacts, coupled with a Bayesian Hierarchical Model, that explicitly considers the spatial variation of respiratory health associated with smoke exposure, compared to reference periods before and after wildfires. We estimate the signal-to-noise ratio to ultimately gauge the precision of the Bayesian model output. RESULTS: We find the highest excess hospitalizations in areas covered by smoke, mainly ZIP codes contained by and immediately downwind of wildfire perimeters, and that excess hospitalizations tend to follow the distribution of smoke plumes across space (ZIP codes) and time (days). CONCLUSIONS: Analyzing the spatiotemporal evolution of exposure to wildfire smoke is necessary due to variations in smoke plume extent, particularly in this region where the most damaging wildfires are associated with strong wind conditions.

Associations between prevalent multimorbidity combinations and prospective disability and self-rated health among older adults in Europe.

BACKGROUND: Multimorbidity is associated with greater likelihood of disability, health-related quality of life, and mortality, greater than the risk attributable to individual diseases. The objective of this study is to examine the association between unique multimorbidity combinations and prospective disability and poor self-rated health (SRH) in older adults in Europe. METHODS: We conducted a prospective analysis using data from the Survey of Health, Ageing and Retirement in Europe in 2013 and 2015. We used hierarchical models to compare respondents with multiple chronic conditions to healthy respondents and respondents reporting only one chronic condition and made within-group comparisons to examine the marginal contribution of specific chronic condition combinations. RESULTS: Less than 20% of the study population reported having zero chronic conditions, while 50% reported having at least two chronic conditions. We identified 380 unique disease combinations among people who reported having at least two chronic conditions. Over 35% of multimorbidity could be attributed to five specific multimorbidity combinations, and over 50% to ten specific combinations. Overall, multimorbidity combinations that included high depressive symptoms were associated with increased odds of reporting poor SRH, and increased rates of ADL-IADL disability. CONCLUSIONS: Multimorbidity groups that include high depressive symptoms may be more disabling than combinations that include only somatic conditions. These findings argue for a continued integration of both mental and somatic chronic conditions in the conceptualization of multimorbidity, with important implications for clinical practice and healthcare delivery.

Ambient Fine Particulate Matter and Preterm Birth in California: Identification of Critical Exposure Windows.

Exposure to ambient fine particulate matter (particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5)) during pregnancy is associated with preterm birth (PTB), a leading cause of infant morbidity and mortality. Results from studies attempting to identify etiologically relevant exposure periods of vulnerability have been inconsistent, possibly because of failure to consider the time-to-event nature of the outcome and lagged exposure effects of PM2.5. In this study, we aimed to identify critical exposure windows for weekly PM2.5 exposure and PTB in California using California birth cohort data from 2005-2010. Associations were assessed using distributed-lag Cox proportional hazards models. We assessed effect-measure modification by race/ethnicity by calculating the weekly relative excess risk due to interaction. For a 10-µg/m3 increase in PM2.5 exposure over the entire period of gestation, PTB risk increased by 11% (hazard ratio = 1.11, 95% confidence interval: 1.09, 1.14). Gestational weeks 17-24 and 36 were associated with increased vulnerability to PM2.5 exposure. We find that non-Hispanic black mothers may be more susceptible to effects of PM2.5 exposure than non-Hispanic white mothers, particularly at the end of pregnancy. These findings extend our knowledge about the existence of specific exposure periods during pregnancy that have the greatest impact on preterm birth.

A quantile regression approach to examine fine particles, term low birth weight, and racial/ethnic disparities.

BACKGROUND: Exposure to fine particulate matter (PM2.5) during pregnancy has been shown to be associated with reduced birth weight and racial/ethnic minorities have been found to be more vulnerable. Previous studies have focused on the mean value of birth weight associated with PM2.5, which may mask meaningful differences. We applied a quantile regression approach to investigate the variation by percentile of birth weight and compared non-Hispanic (NH) Black, NH White, and Hispanic mothers. METHODS: Data for singleton births in California from October 24, 2005 to February 27, 2010 were collected from the birth records accessed from the California Department of Public Health. Air pollution monitoring data collected by the California Air Resources Board and interpolated for each zip code using an inverse-distance weighting approach, and linked to maternal zip code of residence reported on the birth certificate. Multilevel linear regression models were conducted with mother's residential zip code tabulation area as a random effect. Multilevel quantile regression models were used to analyze the association at different percentiles of birth weight (5th, 10th, 25th, 50th, 75th, 90th, 95th), as well as examine the heterogeneity in this association between racial/ethnic groups. RESULTS: Linear regression revealed that a 10 µg/m3 increase in PM2.5 exposure during pregnancy is associated with a mean birth weight decrease of 7.31 g [95% confidence interval (CI): 8.10, 6.51] and NH Black mothers are the most vulnerable. Results of the quantile regression are not constant across quantiles. For NH Black mothers whose infants had the lowest birthweight of less than 2673 g (5th percentile), a 10 µg/m3 increase in PM2.5 exposure is associated with a decrease of 18.57 g [95% CI: 22.23, 14.91], while it is associated with a decrease of 7.77 g [95% CI: 8.73, 6.79] for NH White mothers and 7.76 [8.52, 7.00] decrease for Hispanic mothers at the same quantile. CONCLUSION: Results of the quantile regression revealed greater disparities, particularly for infants with the lowest birth weight. By identifying vulnerable populations, we can promote and implement policies to confront these health disparities.

Identifying windows of susceptibility for maternal exposure to ambient air pollution and preterm birth.

Maternal exposure to ambient air pollution has been associated with preterm birth (PTB), however, entire pregnancy or trimester-specific associations were generally reported, which may not sufficiently identify windows of susceptibility. Using birth registry data from Guangzhou, a megacity of southern China (population ~14.5 million), including 469,975 singleton live births between January 2015 and July 2017, we assessed the association between weekly air pollution exposure and PTB in a retrospective cohort study. Daily average concentrations of PM2.5, PM10, NO2, SO2, and O3 from 11 monitoring stations were used to estimate district-specific exposures for each participant based on their district residency during pregnancy. Distributed lag models (DLMs) incorporating Cox proportional hazard models were applied to estimate the association between weekly maternal exposure to air pollutant and PTB risk (as a time-to-event outcome), after controlling for temperature, seasonality, and individual-level covariates. We also considered moderate PTB (32-36 gestational weeks) and very PTB (28-31 gestational weeks) as outcomes of interest. Hazard ratios (HRs) and 95% confidential intervals (95% CIs) were calculated for an interquartile range (IQR) increase in air pollutants during the study period. An IQR increase in PM2.5 exposure during the 20th to 28th gestational weeks (27.0 µg/m3) was significantly associated with PTB risk, with the strongest effect in the 25th week (HR = 1.034, 95% CI:1.010-1.059). The significant exposure windows were the 19th-28th weeks for PM10, the 18th-31st weeks for NO2, and the 23rd-31st weeks for O3, respectively. The strongest associations were observed in the 25th week for PM10 (IQR = 37.0 µg/m3; HR = 1.048, 95% CI:1.034-1.062), the 26th week for NO2 (IQR = 29.0 µg/m3; HR = 1.060, 95% CI:1.028-1.094), and in the 28th week for O3 (IQR = 90.0 µg/m3; HR = 1.063, 95% CI:1.046-1.081). Similar patterns were observed for moderate PTB (32-36 gestational weeks) and very PTB (28-31 gestational weeks) for PM2.5, PM10, NO2 exposure, but the effects were greater for very PTB. We did not observe any association between pregnancy SO2 exposure and the risk of PTB. Our results suggest that middle to late pregnancy is the most susceptible air pollution exposure window for air pollution and PTB among women in Guangzhou, China.