Robot-Assisted Surgery and Racial and Ethnic Disparities in Post-Prostatectomy Outcomes Among Prostate Cancer Patients.

BACKGROUND: We sought to determine whether the differences in short-term outcomes between patients undergoing robot-assisted radical prostatectomy (RARP) and those treated with open radical prostatectomy (ORP) differ by race and ethnicity. METHODS: This observational study used New York State Cancer Registry data linked to discharge records and included patients undergoing radical prostatectomy for localized prostate cancer during 2008-2018. We used logistic regression to examine the association between race and ethnicity (non-Hispanic White [NHW], non-Hispanic Black [NHB], Hispanic), surgical approach (RARP, ORP), and postoperative outcomes (major events, prolonged length of stay [pLOS], 30-day re-admission). We tested interaction between race and ethnicity and surgical approach on multiplicative and additive scales. RESULTS: The analytical cohort included 18,926 patients (NHW 14,215 [75.1%], NHB 3195 [16.9%], Hispanic 1516 [8.0%]). The average age was 60.4 years (standard deviation 7.1). NHB and Hispanic patients had lower utilization of RARP and higher risks of postoperative adverse events than NHW patients. NHW, NHB, and Hispanic patients all had reduced risks of adverse events when undergoing RARP versus ORP. The absolute reductions in the risks of major events and pLOS following RARP versus ORP were larger among NHB {relative excess risk due to interaction (RERI): major events -0.32 [95% confidence interval (CI) -0.71 to -0.03]; pLOS -0.63 [95% CI -0.98 to -0.35]) and Hispanic (RERI major events -0.27 [95% CI -0.77 to 0.09]; pLOS -0.93 [95% CI -1.46 to -0.51]) patients than among NHW patients. The interaction was absent on the multiplicative scale. CONCLUSIONS: RARP use has not penetrated and benefited all racial and ethnic groups equally. Increasing utilization of RARP among NHB and Hispanic patients may help reduce disparities in patient outcomes after radical prostatectomy.

Neighborhood Deprivation and Neural Tube Defects.

BACKGROUND: Individual measures of socioeconomic status (SES) have been associated with an increased risk of neural tube defects (NTDs); however, the association between neighborhood SES and NTD risk is unknown. Using data from the National Birth Defects Prevention Study (NBDPS) from 1997 to 2011, we investigated the association between measures of census tract SES and NTD risk. METHODS: The study population included 10,028 controls and 1829 NTD cases. We linked maternal addresses to census tract SES measures and used these measures to calculate the neighborhood deprivation index. We used generalized estimating equations to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) estimating the impact of quartiles of census tract deprivation on NTDs adjusting for maternal race-ethnicity, maternal education, and maternal age at delivery. RESULTS: Quartiles of higher neighborhood deprivation were associated with NTDs when compared with the least deprived quartile (Q2: aOR = 1.2; 95% CI = 1.0, 1.4; Q3: aOR = 1.3, 95% CI = 1.1, 1.5; Q4 (highest): aOR = 1.2; 95% CI = 1.0, 1.4). Results for spina bifida were similar; however, estimates for anencephaly and encephalocele were attenuated. Associations differed by maternal race-ethnicity. CONCLUSIONS: Our findings suggest that residing in a census tract with more socioeconomic deprivation is associated with an increased risk for NTDs, specifically spina bifida.

Neighborhood-level Socioeconomic Position During Early Pregnancy and Risk of Gastroschisis.

BACKGROUND: Neighborhood-level socioeconomic position has been shown to influence birth outcomes, including selected birth defects. This study examines the un derstudied association between neighborhood-level socioeconomic position during early pregnancy and the risk of gastroschisis, an abdominal birth defect of increasing prevalence. METHODS: We conducted a case-control study of 1,269 gastroschisis cases and 10,217 controls using data from the National Birth Defects Prevention Study (1997-2011). To characterize neighborhood-level socioeconomic position, we conducted a principal component analysis to construct two indices-Neighborhood Deprivation Index (NDI) and Neighborhood Socioeconomic Position Index (nSEPI). We created neighborhood-level indices using census socioeconomic indicators corresponding to census tracts associated with addresses where mothers lived the longest during the periconceptional period. We used generalized estimating equations to estimate odds ratios (ORs) and 95% confidence intervals (CIs), with multiple imputations for missing data and adjustment for maternal race-ethnicity, household income, education, birth year, and duration of residence. RESULTS: Mothers residing in moderate (NDI Tertile 2 aOR = 1.23; 95% CI = 1.03, 1.48 and nSEPI Tertile 2 aOR = 1.24; 95% CI = 1.04, 1.49) or low socioeconomic neighborhoods (NDI Tertile 3 aOR = 1.28; 95% CI = 1.05, 1.55 and nSEPI Tertile 3 aOR = 1.32, 95% CI = 1.09, 1.61) were more likely to deliver an infant with gastroschisis compared with mothers residing in high socioeconomic neighborhoods. CONCLUSIONS: Our findings suggest that lower neighborhood-level socioeconomic position during early pregnancy is associated with elevated odds of gastroschisis. Additional epidemiologic studies may aid in confirming this finding and evaluating potential mechanisms linking neighborhood-level socioeconomic factors and gastroschisis.

Assessing associations between residential proximity to greenspace and birth defects in the National Birth Defects Prevention Study.

BACKGROUND: Residential proximity to greenspace is associated with various health outcomes. OBJECTIVES: We estimated associations between maternal residential proximity to greenspace (based on an index of vegetation) and selected structural birth defects, including effect modification by neighborhood-level factors. METHODS: Data were from the National Birth Defects Prevention Study (1997-2011) and included 19,065 infants with at least one eligible birth defect (cases) and 8925 without birth defects (controls) from eight Centers throughout the United States. Maternal participants reported their addresses throughout pregnancy. Each address was systematically geocoded and residences around conception were linked to greenspace, US Census, and US Department of Agriculture data. Greenspace was estimated using the normalized difference vegetation index (NDVI); average maximum NDVI was estimated within 100 m and 500 m concentric buffers surrounding geocoded addresses to estimate residential NDVI. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals comparing those in the highest and lowest quartiles of residential NDVI and stratifying by rural/urban residence and neighborhood median income. RESULTS: After multivariable adjustment, for the 500 m buffer, inverse associations were observed for tetralogy of Fallot, secundum atrial septal defects, anencephaly, anotia/microtia, cleft lip ± cleft palate, transverse limb deficiency, and omphalocele, (aORs: 0.54-0.86). Results were similar for 100 m buffer analyses and similar patterns were observed for other defects, though results were not significant. Significant heterogeneity was observed after stratification by rural/urban for hypoplastic left heart, coarctation of the aorta, and cleft palate, with inverse associations only among participants residing in rural areas. Stratification by median income showed heterogeneity for atrioventricular and secundum atrial septal defects, anencephaly, and anorectal atresia, with inverse associations only among participants residing in a high-income neighborhood (aORs: 0.45-0.81). DISCUSSION: Our results suggest that perinatal residential proximity to more greenspace may contribute to a reduced risk of certain birth defects, especially among those living in rural or high-income neighborhoods.

Access to cardiac surgery centers for cardiac and non-cardiac hospitalizations in adolescents and adults with congenital heart defects- a descriptive case series study.

BACKGROUND: Individuals with congenital heart defects (CHDs) are recommended to receive all inpatient cardiac and noncardiac care at facilities that can offer specialized care. We describe geographic accessibility to such centers in New York State and determine several factors associated with receiving care there. METHODS: We used inpatient hospitalization data from the Statewide Planning and Research Cooperative System (SPARCS) in New York State 2008-2013. In the absence of specific adult CHD care center designations during our study period, we identified pediatric/adult and adult-only cardiac surgery centers through the Cardiac Surgery Reporting System to estimate age-based specialized care. We calculated one-way drive and public transit time (in minutes) from residential address to centers using R gmapsdistance package and the Google Maps Distance Application Programming Interface (API). We calculated prevalence ratios using modified Poisson regression with model-based standard errors, fit with generalized estimating equations clustered at the hospital level and subclustered at the individual level. RESULTS: Individuals with CHDs were more likely to seek care at pediatric/adult or adult-only cardiac surgery centers if they had severe CHDs, private health insurance, higher severity of illness at encounter, a surgical procedure, cardiac encounter, and shorter drive time. These findings can be used to increase care receipt (especially for noncardiac care) at pediatric/adult or adult-only cardiac surgery centers, identify areas with limited access, and reduce disparities in access to specialized care among this high-risk population.

Using social security number to identify sub-populations vulnerable to the health impacts from extreme heat in Florida, U.S.

BACKGROUND: Some socioeconomically vulnerable groups may experience disproportionately higher risk of extreme heat illness than other groups, but no study has utilized the presence/absence of a social security number (SSN) as a proxy for vulnerable sub-populations. METHODS: This study focused on the warm season from 2008 to 2012 in Florida, U.S. With a total number of 8,256,171 individual level health outcomes, we devised separate case-crossover models for five heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease), type of health care visit (emergency department (ED) and hospitalization), and patients reporting/not reporting an SSN. Each stratified model also considered potential effect modification by sex, age, or race/ethnicity. RESULTS: Mean temperature raised the odds of five heat-sensitive health outcomes with the highest odds ratios (ORs) for heat-related illness. Sex significantly modified heat exposure effects for dehydration ED visits (Males: 1.145, 95 % CI: 1.137-1.153; Females: 1.110, 95 % CI: 1.103-1.117) and hospitalization (Males: 1.116, 95 % CI: 1.110-1.121; Females: 1.100, 95 % CI: 1.095-1.105). Patients not reporting an SSN between 25 and 44 years (1.264, 95 % CI: 1.192-1.340) exhibited significantly higher dehydration ED ORs than those reporting an SSN (1.146, 95 % CI: 1.136-1.157). We also observed significantly higher ORs for cardiovascular disease hospitalization from the no SSN group (SSN: 1.089, 95 % CI: 1.088-1.090; no SSN: 1.100, 95 % CI: 1.091-1.110). CONCLUSIONS: This paper partially supports the idea that individuals without an SSN could experience higher risks of dehydration (for those 25-45 years), renal disease, and cardiovascular disease than those with an SSN.

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice.

BACKGROUND: Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model. METHODS: We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset. RESULTS: For all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available. CONCLUSIONS: Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice.

Estimating policy-relevant health effects of ambient heat exposures using spatially contiguous reanalysis data.

BACKGROUND: Regional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes. METHODS: We assessed the effect of multi-day lag exposure for maximum near-surface air temperature (Tmax) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008-2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression. RESULTS: We observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in Tmax on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria. CONCLUSION: Reanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public.

Identifying areas at risk of low birth weight using spatial epidemiology: A small area surveillance study.

OBJECTIVES: To assess the geographic distribution of Low Birth Weight (LBW) in New York State among singleton births using a spatial regression approach in order to identify priority areas for public health actions. METHODS: LBW was defined as birth weight less than 2500g. Geocoded data from 562,586 birth certificates in New York State (years 2008-2012) were merged with 2010 census data at the tract level. To provide stable estimates and maintain confidentiality, data were aggregated to yield 1268 areas of analysis. LBW prevalence among singleton births was related with area-level behavioral, socioeconomic and demographic characteristics using a Poisson mixed effects spatial error regression model. RESULTS: Observed low birth weight showed statistically significant auto-correlation in our study area (Moran's I 0.16 p value 0.0005). After over-dispersion correction and accounting for fixed effects for selected social determinants, spatial autocorrelation was fully accounted for (Moran's I-0.007 p value 0.241). The proportion of LBW was higher in areas with larger Hispanic or Black populations and high smoking prevalence. Smoothed maps with predicted prevalence were developed to identify areas at high risk of LBW. Spatial patterns of residual variation were analyzed to identify unique risk factors. CONCLUSION: Neighborhood racial composition contributes to disparities in LBW prevalence beyond differences in behavioral and socioeconomic factors. Small-area analyses of LBW can identify areas for targeted interventions and display unique local patterns that should be accounted for in prevention strategies.

Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates.

An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM2.5) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate concentrations. The general approach for research designed to analyze health impacts of exposure to PM2.5 is to use concentration data from the nearest ground-based air quality monitor(s), which typically have missing data on the temporal and spatial scales due to filter sampling schedules and monitor placement, respectively. To circumvent these data gaps, this research project uses a Hierarchical Bayesian Model (HBM) to generate estimates of PM2.5 in areas with and without air quality monitors by combining PM2.5 concentrations measured by monitors, PM2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM2.5 concentrations. This methodology represents a substantial step forward in the approach for developing representative PM2.5 concentration datasets to correlate with inpatient hospitalizations and emergency room visits data for asthma and inpatient hospitalizations for myocardial infarction (MI) and heart failure (HF) using case-crossover analysis. There were two key objective of this current study. First was to show that the inputs to the HBM could be expanded to include AOD data in addition to data from PM2.5 monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM2.5 air pollutant concentration surfaces which more accurately predict hospital admittance and emergency room visits for MI, asthma, and HF. This study focuses on the New York City, NY metropolitan and surrounding areas during the 2004-2006 time period, in order to compare the health outcome impacts with those from previous studies and focus on any benefits derived from the changes in the HBM model surfaces. Consistent with previous studies, the results show high PM2.5 exposure is associated with increased risk of asthma, myocardial infarction and heart failure. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and estimate of risk as compared to those derived from combining monitor and CMAQ data alone. Thus, this study demonstrates that estimates of PM2.5 concentrations from satellite data can be used to supplement PM2.5 monitor data in the estimates of risk associated with three common health outcomes. Results from this study were inconclusive regarding the potential benefits derived from adding AOD data to the HBM, as the addition of the satellite data did not significantly increase model performance. However, this study was limited to one metropolitan area over a short two-year time period. The use of next-generation, high temporal and spatial resolution satellite AOD data from geostationary and polar-orbiting satellites is expected to improve predictions in epidemiological studies in areas with fewer pollutant monitors or over wider geographic areas.

Lifecourse socioeconomic position and 16 year body mass index trajectories: differences by race and sex.

OBJECTIVE: The aim of this study is to evaluate the association between lifecourse socioeconomic position (SEP) and changes in body mass index (BMI), and assess disparities in these associations across racial/ethnic groups. METHODS: With longitudinal data from 4 waves of the Americans' Changing Lives Study (1986-2002), we employed mixed-effects modeling to estimate BMI trajectories for 1174 Blacks and 2323 White adults. We also estimated associations between these trajectories and lifecourse SEP variables, including father's education, perceived childhood SEP, own education, income, wealth, and financial security. RESULTS: Blacks had higher baseline BMIs, and steeper increases in BMI, compared to Whites. Childhood SEP, as measured by high father's education, was associated with lower baseline BMI among Whites. High education was associated with a lower baseline BMI within both race and sex categories. Income had contrasting effects among men and women. Higher income was associated with higher BMI only among males. Associations between indicators of SEP and BMI trajectories were only found for Whites. CONCLUSIONS: Our study demonstrates that lifecourse SEP may influence adult BMI differently within different racial and sex groups.

Racial and ethnic disparities in robot-assisted radical prostatectomy: testing the physician-level segregated and differential treatment hypotheses.

BACKGROUND: Mechanisms underlying racial and ethnic disparities in robot-assisted radical prostatectomy (RARP) vs open radical prostatectomy (ORP) are unclear. We sought to test 2 physician-level hypotheses: 1) Segregated Treatment and 2) Differential Treatment. METHODS: This observational study used the New York State Cancer Registry linked to discharge records and included patients undergoing radical prostatectomy for localized prostate cancer from October 1, 2008 to December 31, 2018. For hypothesis 1, we examined the association between patient race and ethnicity and treating surgeon RARP use (high-use surgeons, low-use surgeons, and surgeons at non-RARP facilities). For hypothesis 2, we determined the association between patient race and ethnicity and receipt of RARP when matching on treating surgeon, age, year of procedure, and Gleason group. We explored the role of insurance in both analyses. RESULTS: This study included 18 926 patients (8.0% Hispanic, 16.9% non-Hispanic Black, 75.1% non-Hispanic White), with a mean age of 60.4 ± 7.1 years. Compared with non-Hispanic White patients, Hispanic and non-Hispanic Black patients had higher odds of being treated by low-RARP-use surgeons (odds ratio [OR] = 2.16, 95% confidence interval [CI] = 1.20 to 3.88; OR = 1.76, 95% CI = 1.06 to 2.94, respectively) and by surgeons at non-RARP facilities (OR = 4.19, 95% CI = 2.18 to 8.07; OR = 4.60, 95% CI = 2.58 to 8.23, respectively). In the matched cohorts, Hispanic and non-Hispanic Black patients were less likely to receive RARP than non-Hispanic White patients (OR = 0.78, 95% CI = 0.62 to 0.98; OR = 0.75, 95% CI = 0.57 to 1.00, respectively). These associations were partially attenuated after accounting for insurance. CONCLUSIONS: Racial and ethnic disparities in RARP use are related to patients being treated by different surgeons and treated differently by the same surgeons. Identifying and addressing multilevel barriers to equitable surgical treatment is needed to reduce disparities among prostate cancer patients.

Spatial Variation in COVID-19 Mortality in New York City and Its Association with Neighborhood Race, Ethnicity, and Nativity Status.

We examined the association between variation in COVID-19 deaths and spatial differences in the racial, ethnic, and nativity-status composition of New York City neighborhoods, which has received little scholarly attention. Using COVID-19 mortality data (through 31 May 2021) and socioeconomic and demographic data from the American Community Survey at the Zip Code Tabulation Area level as well as United-Hospital-Fund-level neighborhood data from the Community Health Survey of the New York City Department of Health and Mental Hygiene, we employed multivariable Poisson generalized estimating equation models and assessed the association between COVID-19 mortality, racial/ethnic/nativity-status composition, and other ecological factors. Our results showed an association between neighborhood-level racial and ethnic composition and COVID-19 mortality rates that is contingent upon the neighborhood-level nativity-status composition. After multivariable adjustment, ZCTAs with large shares of native-born Blacks and foreign-born Hispanics and Asians were more likely to have higher COVID-19 mortality rates than areas with large shares of native-born Whites. Areas with more older adults and essential workers, higher levels of household crowding, and population with diabetes were also at high risk. Small-area analyses of COVID-19 mortality can inform health policy responses to neighborhood inequalities on the basis of race, ethnicity, and immigration status.

Is gastroschisis associated with county-level socio-environmental quality during pregnancy?

BACKGROUND: Gastroschisis prevalence more than doubled between 1995 and 2012. While there are individual-level risk factors (e.g., young maternal age, low body mass index), the impact of environmental exposures is not well understood. METHODS: We used the U.S. Environmental Protection Agency's Environmental Quality Index (EQI) as a county-level estimate of cumulative environmental exposures for five domains (air, water, land, sociodemographic, and built) and overall from 2006 to 2010. Adjusted odds ratios (aOR) and 95% confidence interval (CI) were estimated from logistic regression models between EQI tertiles (better environmental quality (reference); mid; poorer) and gastroschisis in the National Birth Defects Prevention Study from births delivered between 2006 and 2011. Our analysis included 594 cases with gastroschisis and 4105 infants without a birth defect (controls). RESULTS: Overall EQI was modestly associated with gastroschisis (aOR [95% CI]: 1.29 [0.98, 1.71]) for maternal residence in counties with poorer environmental quality, compared to the reference (better environmental quality). Within domain-specific indices, only the sociodemographic domain (aOR: 1.51 [0.99, 2.29]) was modestly associated with gastroschisis, when comparing poorer to better environmental quality. CONCLUSIONS: Future work could elucidate pathway(s) by which components of the sociodemographic domain or possibly related psychosocial factors like chronic stress potentially contribute to risk of gastroschisis.

Are individual-level risk factors for gastroschisis modified by neighborhood-level socioeconomic factors?

BACKGROUND: Two strong risk factors for gastroschisis are young maternal age (<20 years) and low/normal pre-pregnancy body mass index (BMI), yet the reasons remain unknown. We explored whether neighborhood-level socioeconomic position (nSEP) during pregnancy modified these associations. METHODS: We analyzed data from 1269 gastroschisis cases and 10,217 controls in the National Birth Defects Prevention Study (1997-2011). To characterize nSEP, we applied the neighborhood deprivation index and used generalized estimating equations to calculate odds ratios and relative excess risk due to interaction. RESULTS: Elevated odds of gastroschisis were consistently associated with young maternal age and low/normal BMI, regardless of nSEP. High-deprivation neighborhoods modified the association with young maternal age. Infants of young mothers in high-deprivation areas had lower odds of gastroschisis (adjusted odds ratio [aOR]: 3.1, 95% confidence interval [CI]: 2.6, 3.8) than young mothers in low-deprivation areas (aOR: 6.6; 95% CI: 4.6, 9.4). Mothers of low/normal BMI had approximately twice the odds of having an infant with gastroschisis compared to mothers with overweight/obese BMI, regardless of nSEP (aOR range: 1.5-2.3). CONCLUSION: Our findings suggest nSEP modified the association between gastroschisis and maternal age, but not BMI. Further research could clarify whether the modification is due to unidentified biologic and/or non-biologic factors.

Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study.

Background: The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data. Methods: Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings. Findings: Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]). Interpretation: Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.

The effects of ambient temperature variation on respiratory hospitalizations in summer, New York State.

OBJECTIVE: A time-series study was used to assess the effect of temperature variation during summer on respiratory disease in New York State. METHODS: Daily respiratory admissions were linked with various meteorological indicators including daily and weekly temperature variation from June-August, 1991-2004. Two-stage Bayesian hierarchical models were used to first compute percent excess risks at the region level while controlling for air pollutants and time-varying variables using Poisson generalized additive models, and then to pool statewide estimates together after controlling for regional confounders. RESULTS: This study found that the daily temperature range between maximum and minimum temperature was associated with a 0·27-0·38% increased risk of admission. Minimum temperature (TMIN) above the previous 6-day average was associated with a 0·93% higher risk of respiratory morbidity. Multiday temperature ranges within 5 and 7 days were associated with 0·49 and 0·73% increases in admissions, respectively. CONCLUSIONS: We concluded that daily and multiday temperature variation may increase respiratory hospitalizations with a larger risk associated with TMIN.

Identifying Risk Factors for Hospitalization with Behavioral Health Disorders and Concurrent Temperature-Related Illness in New York State.

Extreme temperature events are linked to increased emergency department visits, hospitalizations, and mortality for individuals with behavioral health disorders (BHD). This study aims to characterize risk factors for concurrent temperature-related illness among BHD hospitalizations in New York State. Using data from the NYS Statewide and Planning Research and Cooperative System between 2005-2019, multivariate log binomial regression models were used in a population of BHD hospitalizations to estimate risk ratios (RR) for a concurrent heat-related (HRI) or cold-related illness (CRI). Dementia (RR 1.65; 95% CI:1.49, 1.83) and schizophrenia (RR 1.38; 95% CI:1.19, 1.60) were associated with an increased risk for HRI among BHD hospitalizations, while alcohol dependence (RR 2.10; 95% CI:1.99, 2.22), dementia (RR 1.52; 95% CI:1.44, 1.60), schizophrenia (RR 1.41; 95% CI:1.31, 1.52), and non-dependent drug/alcohol use (RR 1.20; 95% CI:1.15, 1.26) were associated with an increased risk of CRI among BHD hospitalizations. Risk factors for concurrent HRI among BHD hospitalizations include increasing age, male gender, non-Hispanic Black race, and medium hospital size. Risk factors for concurrent CRI among BHD hospitalizations include increasing age, male gender, non-Hispanic Black race, insurance payor, the presence of respiratory disease, and rural hospital location. This study adds to the literature by identifying dementia, schizophrenia, substance-use disorders, including alcohol dependence and non-dependent substance-use, and other sociodemographic factors as risk factors for a concurrent CRI in BHD hospitalizations.

Coupling freedom from disease principles and early warning from wastewater surveillance to improve health security.

Infectious disease surveillance is vitally important to maintaining health security, but these efforts are challenged by the pace at which new pathogens emerge. Wastewater surveillance can rapidly obtain population-level estimates of disease transmission, and we leverage freedom from disease principles to make use of nondetection of SARS-CoV-2 in wastewater to estimate the probability that a community is free from SARS-CoV-2 transmission. From wastewater surveillance of 24 treatment plants across upstate New York from May through December of 2020, trends in the intensity of SARS-CoV-2 in wastewater correlate with trends in COVID-19 incidence and test positivity (⍴ > 0.5), with the greatest correlation observed for active cases and a 3-day lead time between wastewater sample date and clinical test date. No COVID-19 cases were reported 35% of the time the week of a nondetection of SARS-CoV-2 in wastewater. Compared to the United States Centers for Disease Control and Prevention levels of transmission risk, transmission risk was low (no community spared) 50% of the time following nondetection, and transmission risk was moderate or lower (low community spread) 92% of the time following nondetection. Wastewater surveillance can demonstrate the geographic extent of the transmission of emerging pathogens, confirming that transmission risk is either absent or low and alerting of an increase in transmission. If a statewide wastewater surveillance platform had been in place prior to the onset of the COVID-19 pandemic, policymakers would have been able to complement the representative nature of wastewater samples to individual testing, likely resulting in more precise public health interventions and policies.