A Bayesian Spatial Scan Statistic for Multinomial Data.

Spatial scan statistics are commonly used to detect clustering. We present a Bayesian spatial scan statistic for multinomial data. After validating our method with a simulation study, we use it to detect clusters of SARS-CoV-2 infection/immunity in South Carolina.

Understanding the Role of a Technology and EMR-based Social Determinants of Health Screening Tool and Community-based Resource Connections in Health Care Resource Utilization.

BACKGROUND: Health systems are increasingly investing in social determinants of health (SDoH), but there is limited research on how such efforts impact health care resource use. This study presents pilot work on an SDoH screening and referral platform recently implemented in South Carolina's largest private nonprofit health system. OBJECTIVES: To assess the feasibility and sustainability of SDoH screening and SDoH-related referrals in a large health system and examine how they affect health resource use. RESEARCH DESIGN: Observational study using electronic medical records and SDoH screening data from June 1, 2019 to December 31, 2020. SUBJECTS: Patients (18 y+) engaged in community health, inpatient case management, or ambulatory care and condition management programs. MEASURES: We describe the use of SDoH screening by providers (community health workers, nurse case managers, and social workers) and SDoH referral volumes among patients. We use multivariate analyses to predict changes in emergency department visits, inpatient admission s (length of stay and volume), and primary care visits from referral volume, SDoH screening question responses, and patient characteristics (eg, comorbidities). RESULTS: Of 2687 patients, 662 (24.6%) screened positive for 1 or more SDoH domains. SDoH screening performance remained consistent among providers over time. Six hundred fifty-eight (24.5%) patients received SDoH referrals. Patients receiving an increasing volume of referrals had decreasing primary care visits but their comorbidities moderated this effect. CONCLUSIONS: The study provides initial descriptive information on SDoH needs, implementation of referrals and resource use, guiding SDoH screening implementation in population health, and care management programs.

Emergency department use and geospatial variation in social determinants of health: a pilot study from South Carolina.

BACKGROUND: Health systems are increasingly addressing patients' social determinants of health (SDoH)-related needs and investigating their effects on health resource use. SDoH needs vary geographically; however, little is known about how this geographic variation in SDoH needs impacts the relationship between SDoH needs and health resource use. METHODS: This study uses data from a SDoH survey administered to a pilot patient population in a single health system and the electronic medical records of the surveyed patients to determine if the impact of SDoH needs on emergency department use varies geospatially at the US Census block group level. A Bayesian zero-inflated negative binomial model was used to determine if emergency department visits after SDoH screening varied across block groups. Additionally, the relationships between the number of emergency department visits and the response to each SDoH screening question was assessed using Bayesian negative binomial hurdle models with spatially varying coefficients following a conditional autoregressive (CAR) model at the census block group level. RESULTS: Statistically important differences in emergency department visits after screening were found between block groups. Statistically important spatial variation was found in the association between patient responses to the questions concerning unhealthy home environments (e.g. mold, bugs/rodents, not enough air conditioning/heat) or domestic violence/abuse and the mean number of emergency department visits after the screen. CONCLUSIONS: Notable spatial variation was found in the relationships between screening positive for unhealthy home environments or domestic violence/abuse and emergency department use. Despite the limitation of a relatively small sample size, sensitivity analyses suggest spatially varying relationships between other SDoH-related needs and emergency department use.

Capturing the pool dilution effect in group testing regression: A Bayesian approach.

Group (pooled) testing is becoming a popular strategy for screening large populations for infectious diseases. This popularity is owed to the cost savings that can be realized through implementing group testing methods. These methods involve physically combining biomaterial (eg, saliva, blood, urine) collected on individuals into pooled specimens which are tested for an infection of interest. Through testing these pooled specimens, group testing methods reduce the cost of diagnosing all individuals under study by reducing the number of tests performed. Even though group testing offers substantial cost reductions, some practitioners are hesitant to adopt group testing methods due to the so-called dilution effect. The dilution effect describes the phenomenon in which biomaterial from negative individuals dilute the contributions from positive individuals to such a degree that a pool is incorrectly classified. Ignoring the dilution effect can reduce classification accuracy and lead to bias in parameter estimates and inaccurate inference. To circumvent these issues, we propose a Bayesian regression methodology which directly acknowledges the dilution effect while accommodating data that arises from any group testing protocol. As a part of our estimation strategy, we are able to identify pool specific optimal classification thresholds which are aimed at maximizing the classification accuracy of the group testing protocol being implemented. These two features working in concert effectively alleviate the primary concerns raised by practitioners regarding group testing. The performance of our methodology is illustrated via an extensive simulation study and by being applied to Hepatitis B data collected on Irish prisoners.

Building-level wastewater surveillance of SARS-CoV-2 is associated with transmission and variant trends in a university setting.

The University of South Carolina (UofSC) was among the first universities to include building-level wastewater surveillance of SARS-CoV-2 to complement clinical testing during its reopening in the Fall 2020 semester. In the Spring 2021 semester, 24h composite wastewater samples were collected twice per week from 10 residence halls and the on-campus student isolation and quarantine building. The isolation and quarantine building served as a positive control site. The wastewater was analyzed using RT-ddPCR for the quantification of nucleocapsid genes (N1 and N2) to identify viral transmission trends within residence halls. Log10 SARS-CoV-2 RNA concentrations were compared to both new clinical cases identified in the days following wastewater collection and recovered cases returning to sites during the days preceding sample collection to test temporal and spatial associations. There was a statistically significant positive relationship between the number of cases reported from the sites during the seven-day period following wastewater sampling and the log10 viral RNA copies/L (overall IRR 1.08 (1.02, 1.16) p-value 0.0126). Additionally, a statistically significant positive relationship was identified between the number of cases returning to the residence halls after completing isolation during the seven-day period preceding wastewater sampling and the log10 viral RNA copies/L (overall 1.09 (1.01, 1.17) p-value 0.0222). The statistical significance of both identified cases and recovered return cases on log10 viral RNA copies/L in wastewater indicates the importance of including both types of clinical data in wastewater-based epidemiology (WBE) research. Genetic mutations associated with variants of concern (VOCs) were also monitored. The emergence of the Alpha variant on campus was identified, which contributed to the second wave of COVID-19 cases at UofSC. The study was able to identify sub-community transmission hotspots for targeted intervention in real-time, making WBE cost-effective and creating less of a burden on the general public compared to repeated individual testing methods.

A nowcast model to predict outdoor flea activity in real time for the contiguous United States.

BACKGROUND: The cat flea (Ctenocephalides felis), a parasite commonly found on both dogs and cats, is a competent vector for several zoonotic pathogens, including Dipylidium caninum (tapeworms), Bartonella henselae (responsible for cat scratch disease) and Rickettsia felis (responsible for flea-borne spotted fever). Veterinarians recommend that both cats and dogs be routinely treated with medications to prevent flea infestation. Nevertheless, surveys suggest that nearly one third of pet owners do not routinely administer appropriate preventatives. METHODS: A mathematical model based on weighted averaging over time is developed to predict outdoor flea activity from weather conditions for the contiguous United States. This 'nowcast' model can be updated in real time as weather conditions change and serves as an important tool for educating pet owners about the risks of flea-borne disease. We validate our model using Google Trends data for searches for the term 'fleas.' This Google Trends data serve as a proxy for true flea activity, as validating the model by collecting fleas over the entire USA is prohibitively costly and time-consuming. RESULTS: The average correlation (r) between the nowcast outdoor flea activity predictions and the Google Trends data was moderate: 0.65, 0.70, 0.66, 0.71 and 0.63 for 2016, 2017, 2018, 2019 and 2020, respectively. However, there was substantial regional variation in performance, with the average correlation in the East South Atlantic states being 0.81 while the average correlation in the Mountain states was only 0.45. The nowcast predictions displayed strong seasonal and geographic patterns, with predicted activity generally being highest in the summer months. CONCLUSIONS: The nowcast model is a valuable tool by which to educate pet owners regarding the risk of fleas and flea-borne disease and the need to routinely administer flea preventatives. While it is ideal for domestic cats and dogs to on flea preventatives year-round, many pets remain vulnerable to flea infestation. Alerting pet owners to the local increased risk of flea activity during certain times of the year may motivate them to administer appropriate routine preventives.

Geographic Variations in Urban-Rural Particulate Matter (PM2.5) Concentrations in the United States, 2010-2019.

Fine particulate matter 2.5 (PM2.5) is a widely studied pollutant with substantial health impacts, yet little is known about the urban-rural differences across the United States. Trends of PM2.5 in urban and rural census tracts between 2010 and 2019 were assessed alongside sociodemographic characteristics including race/ethnicity, poverty, and age. For 2010, we identified 13,474 rural tracts and 59,065 urban tracts. In 2019, 13,462 were rural and 59,055 urban. Urban tracts had significantly higher PM2.5 concentrations than rural tracts during this period. Levels of PM2.5 were lower in rural tracts compared to urban and fell more rapidly in rural than urban. Rural tract annual means for 2010 and 2019 were 8.51 [2.24] µg/m3 and 6.41 [1.29] µg/m3, respectively. Urban tract annual means for 2010 and 2019 were 9.56 [2.04] µg/m3 and 7.51 [1.40] µg/m3, respectively. Rural and urban majority Black communities had significantly higher PM2.5 pollution levels (10.19 [1.64] µg/m3 and 9.79 [1.10] µg/m3 respectively), in 2010. In 2019, they were: 7.75 [1.1] µg/m3 and 7.09 [0.78] µg/m3, respectively. Majority Hispanic communities had higher PM2.5 levels and were the highest urban concentration among all races/ethnicities (8.01 [1.73] µg/m3), however they were not the highest rural concentration among all races/ethnicities (6.22 [1.60] µg/m3) in 2019. Associations with higher levels of PM2.5 were found with communities in the poorest quartile and with higher proportions of residents age<15 years old. These findings suggest greater protections for those disproportionately exposed to PM2.5 are needed, such as, increasing the availability of low-cost air quality monitors.

Bayesian group testing regression models for spatial data.

Spatial patterns are common in infectious disease epidemiology. Disease mapping is essential to infectious disease surveillance. Under a group testing protocol, biomaterial from multiple individuals is physically combined into a pooled specimen, which is then tested for infection. If the pool tests negative, all contributing individuals are generally assumed to be uninfected. If the pool tests positive, the individuals are usually retested to determine who is infected. When the prevalence of infection is low, group testing provides significant cost savings over traditional individual testing by reducing the number of tests required. However, the lack of statistical methods capable of producing maps from group testing data has limited the use of group testing in disease mapping. We develop a Bayesian methodology that can simultaneously map disease prevalence using group testing data and identify risk factors for infection. We illustrate its real-world utility using two datasets from vector-borne disease surveillance.

A Hypothesis Test for Detecting Spatial Patterns in Categorical Areal Data.

The vast growth of spatial datasets in recent decades has fueled the development of many statistical methods for detecting spatial patterns. Two of the most commonly studied spatial patterns are clustering, loosely defined as datapoints with similar attributes existing close together, and dispersion, loosely defined as the semi-regular placement of datapoints with similar attributes. In this work, we develop a hypothesis test to detect spatial clustering or dispersion at specific distances in categorical areal data. Such data consists of a set of spatial regions whose boundaries are fixed and known (e.g., counties) associated with a categorical random variable (e.g. whether the county is rural, micropolitan, or metropolitan). We propose a method to extend the positive area proportion function (developed for detecting spatial clustering in binary areal data) to the categorical case. This proposal, referred to as the categorical positive areal proportion function test, can detect various spatial patterns, including homogeneous clusters, heterogeneous clusters, and dispersion. Our approach is the first method capable of distinguishing between different types of clustering in categorical areal data. After validating our method using an extensive simulation study, we use the categorical positive area proportion function test to detect spatial patterns in Boulder County, Colorado USA biological, agricultural, built and open conservation easements.

Spotted Fever Group Rickettsia spp. Molecular and Serological Evidence among Colombian Vectors and Animal Hosts: A Historical Review.

Spotted fever group Rickettsia spp. (SFGR) are a large group of tick-borne bacteria causing important emerging and re-emerging diseases that affect animals and humans. While SFGR are found worldwide, a lack of surveillance and misdiagnosis particularly affect South American countries. Colombia is a high burdened country in South America, yet rickettsioses are not deemed a nationally reportable condition limiting disease-specific public health resources. As mortality rates are high for one Rickettsia pathogen species, there is a great need to better understand the epidemiological and ecological factors that increase SFGR transmission risk regionally. This literature review provides an overview of Colombia-based SFGR studies connecting knowledge about both vectors and hosts.

Human spotted fever group Rickettsia seroprevalence and associated epidemiologic factors among diverse, marginalized populations in South Carolina.

Illness caused by spotted fever group Rickettsia (SFGR) is increasing nationally, with affluent, white residents most likely to be diagnosed. The common under-representativeness of marginalized populations in research studies and these vulnerable populations' health inequities make veritable epidemiologic risk factor profiling challenging, which inhibits equitable public health intervention. The current study leveraged 749 banked sera and associated surveys from a cross-sectional minority-represented COVID-19 study to perform an SFGR seroprevalence investigation. SFGR titers (1:64, 1:128, 1:256, 1:512, and 1:1024) were measured using commercially available indirect fluorescent antibody slides-SFGR positive cases were defined as titers ≥1:128. Multivariable logistic regression and Getis-Ord-Gi* hotspot analyses were used to identify seropositivity-associated factors and determine seropositive clusters. Among a mostly minority and lower socioeconomic population, a 3.4 % SFGR seropositivity was noted at the ≥1:128 titer level. Male gender (Odds Ratio (OR): 3.20; adjusted Odds Ratio (aOR)s: 3.73), age (aOR: 1.05), any frequency of tick bite (OR: 2.29), and spending time working outdoors (OR: 5.05) were associated with SFGR IgG seropositivity. Moreover, the geospatial analysis showed clusters of seropositivity in areas where previous case reports occurred, suggesting potential endemic foci.

Antibody Seroprevalence to Spotted Fever Group Rickettsiae in Miraflores, Colombia: A Cross-Sectional Study in Humans and Dogs.

Tick-borne disease burdens are increasing globally, impacting mostly rural and vulnerable communities. Among the most important emerging tick-borne pathogens are the Rickettsia species within the spotted fever group (SFGR) because of their genetic diversity and high lethality rate. Colombia is highly affected by SFGR despite not being reportable diseases; thus, research and clinical management are neglected. Although some departments have demonstrated high seroprevalence rates, in others, such as Boyacá Department, seroprevalence is unknown. Rickettsioses have not been described in Boyacá since 1943, and conversations with local physicians raised suspicions of recent undiagnosed disease compatible with rickettsiosis in some rural areas of the department, warranting epidemiological investigation. Using biobanked human and canine samples from a previous 2021 vector-borne disease study in Miraflores municipality, Boyacá, we had an opportunity to unearth SFGR's exposure in the region. Samples were evaluated using IgG indirect fluorescent assays against SFGR and complemented by survey questionnaires evaluating associated factors. Findings yielded first-time SFGR serological evidence in Boyacá with a 26.5% seroprevalence among dogs and a 20.4% among humans. Human and dog seroprevalences were positively associated, suggesting the presence of domestic transmission. Owning a greater number of domestic animals (prevalence ratio adjusted for all measured factors [aPR], 1.52) and living near crop fields (aPR, 7.77) were associated with an increased likelihood of household seropositivity. Our findings are consistent with the literature in Colombia, uncovering a suspected region where the disease is endemic. Future studies are warranted to continue defining high-risk areas to determine public health intervention plans.

Employing digital PCR for enhanced detection of perinatal Toxoplasma gondii infection: A cross-sectional surveillance and maternal-infant outcomes study in El Salvador.

Toxoplasma gondii is a parasitic infection that can be transmitted in utero, resulting in fetal chorioretinitis and other long-term neurological outcomes. If diagnosed early, pregnancy-safe chemotherapeutics can prevent vertical transmission. Unfortunately, diagnosis of acute, primary infection among pregnant women remains neglected, particularly in low-and-middle-income countries. Clinically actionable diagnosis is complex due to the commonality of infection during childhood and early adulthood which spawn long-last antibody titers and historically unreliable direct molecular diagnostics. The current study employed a cross-sectional T. gondii perinatal surveillance study using digital PCR, a next generation molecular diagnostic platform, and a maternal-fetal outcomes survey to ascertain the risk of vertical toxoplasmosis transmission in the Western Region of El Salvador. Of 198 enrolled mothers at the time of childbirth, 6.6% had evidence of recent T. gondii infection-85% of these cases were identified using digital PCR. Neonates born to these acutely infected mothers were significantly more likely to meconium aspiration syndrome and mothers were more likely to experience labor and delivery complications. Multivariable logistic regression found higher maternal T. gondii infection odds were associated with the presence of pet cats, the definitive T. gondii host. In closing, this study provides evidence of maternal T. gondii infection, vertical transmission and deleterious fetal outcomes in a vulnerable population near the El Salvador-Guatemala border. Further, this is the first published study to show clinical utility potential of digital PCR for accurate diagnosis of congenital toxoplasmosis cases.

Eco-epidemiology of Rickettsia amblyommatis and Rickettsia parkeri in naturally infected ticks (Acari: Ixodida) from South Carolina.

BACKGROUND: Spotted fever group Rickettsia (SFGR) is the largest group of Rickettsia species of clinical and veterinary importance emerging worldwide. Historically, SFGR cases were linked to Rickettsia rickettsii, the causal agent of Rocky Mountain spotted fever; however, recently discovered species Rickettsia parkeri and Rickettsia amblyommatis have been shown to cause a wide range of clinical symptoms. The role of R. amblyommatis in SFGR eco-epidemiology and the possible public health implications remain unknown. METHODS: This study evaluated statewide tick surveillance and land-use classification data to define the eco-epidemiological relationships between R. amblyommatis and R. parkeri among questing and feeding ticks collected across South Carolina between 2021 and 2022. Questing ticks from state parks and feeding ticks from animal shelters were evaluated for R. parkeri and R. amblyommatis using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) on pooled samples. A Bayesian multivariable logistic regression model for pool testing data was used to assess associations between R. parkeri or R. amblyommatis infection and land-use classification variables among questing ticks. The Spearman correlation was used to evaluate the relationship between the two tested pathogens. RESULTS: The infection prevalence for R. amblyommatis was 24.8% (23.4-26.3%) among questing ticks, and 39.5% (37.4-42.0%) among feeding ticks; conversely, for R. parkeri it was 19.0% (17.6-20.5%) among questing ticks and 22.4% (20.3-24.5%) among feeding ticks. A negative, refractory correlation was found between the species, with ticks significantly more likely to contain one or the other pathogen, but not both simultaneously. The Bayesian analysis revealed that R. amblyommatis infection was positively associated with deciduous, evergreen, and mixed forests, and negatively associated with hay and pasture fields, and emergent herbaceous wetlands. Rickettsia parkeri infection was positively associated with deciduous, mixed, and evergreen forests, herbaceous vegetation, cultivated cropland, woody wetlands, and emergent herbaceous wetlands, and negatively associated with hay and pasture fields. CONCLUSIONS: This is the first study to evaluate the eco-epidemiological factors driving tick pathogenicity in South Carolina. The negative interactions between SFGR species suggest the possible inhibition between the two pathogens tested, which could have important public health implications. Moreover, land-use classification factors revealed environments associated with tick pathogenicity, highlighting the need for tick vector control in these areas.

Perinatal dengue and Zika virus cross-sectional seroprevalence and maternal-fetal outcomes among El Salvadoran women presenting for labor-and-delivery.

BACKGROUND: Despite maternal flavivirus infections' linkage to severe maternal and fetal outcomes, surveillance during pregnancy remains limited globally. Further complicating maternal screening for these potentially teratogenic pathogens is the overwhelming subclinical nature of acute infection. This study aimed to understand perinatal and neonatal risk for poor health outcomes associated with flaviviral infection during pregnancy in El Salvador. METHODS: Banked serologic samples and clinical results obtained from women presenting for labor and delivery at a national referent hospital in western El Salvador March to September 2022 were used for this study. 198 samples were screened for dengue and Zika virus IgM, and statistical analyses analyzed demographic and clinical outcome associations with IgM positivity. RESULTS: This serosurvey revealed a high rate of maternal flavivirus infection-24.2% of women presenting for labor and delivery were dengue or Zika virus IgM positive, suggesting potential infection within pregnancy. Specifically, 20.2% were Zika virus IgM positive, 1.5% were dengue virus IgM positive, and 2.5% were both dengue and Zika virus IgM positive. Women whose home had received mosquito abatement assistance within the last year by the ministry of health were 70% less likely to test IgM positive (aOR = 0.30, 95%CI: 0.10, 0.83). Further, statistical geospatial clustering revealed transmission foci in six primary municipalities. Pregnancy complications and poor birth outcomes were noted among the dengue and/or Zika virus maternal infection group, although these outcomes were not statistically different than the seronegative group. None of the resulting neonates born during this study were diagnosed with congenital Zika syndrome. CONCLUSIONS: The high rate of Zika virus detected among pregnant women and the lack of Zika-specific neonatal outcomes monitoring during a non-outbreak year highlights the need for continued surveillance in Central America and among immigrant mothers presenting for childbirth from these countries. As changing climatic conditions continue to expand the range of the disease vector, asymptomatic screening programs could be vital to early identification of outbreaks and clinical management of cases.

Radiation Exposure during Prostatic Artery Embolization: A Single Institution Review.

ABSTRACT: Prostate artery embolization is a minimally invasive treatment for benign prostatic hyperplasia, and imaging is indispensable for the technical success of this procedure; however, imaging is a major source of radiation exposure for patients and healthcare providers. Radiation emission during prostate artery embolization procedures at a single institution was evaluated to determine radiation exposure with the goal to work toward minimizing exposure. All patients at a single institution that underwent outpatient unilateral/bilateral prostate artery embolization between 4 January 2019 and 16 November 2021 were retrospectively evaluated; data collected included body mass index, prostate volume, and indications for prostate artery embolization. Technical parameters recorded were air kerma, procedure time, fluoroscopy time, number of acquisitions, and intra-procedural imaging modalities. Fisher's t-test, ANOVA, and chi-square analyses were used as appropriate for statistical analysis (P < 0.05). Overall, 56 patients were included in the study. Body mass index (obesity; P = 0.0017) was a significant predictor of increased air kerma; prostate size and bilateral vs. unilateral prostate artery embolization were not significantly associated with increased air kerma despite the number of acquisitions being significantly different between bilateral and unilateral embolization (P = 0.0064). When evaluating radiation exposure during prostate artery embolization, increased body mass index significantly predicted increased air kerma. Contrary to the literature, the extent of embolization (bilateral vs. unilateral) was not associated with increased air kerma regardless of higher acquisitions and procedure time associated with bilateral prostate artery embolization. Increased radiation protection efforts should be considered for patients with higher body mass index to protect patients and practitioners.Health Phys. 124(0):000-000; 2023.

A Hypothesis Test for Detecting Distance-Specific Clustering and Dispersion in Areal Data.

Spatial clustering detection has a variety of applications in diverse fields, including identifying infectious disease outbreaks, pinpointing crime hotspots, and identifying clusters of neurons in brain imaging applications. Ripley's K-function is a popular method for detecting clustering (or dispersion) in point process data at specific distances. Ripley's K-function measures the expected number of points within a given distance of any observed point. Clustering can be assessed by comparing the observed value of Ripley's K-function to the expected value under complete spatial randomness. While performing spatial clustering analysis on point process data is common, applications to areal data commonly arise and need to be accurately assessed. Inspired by Ripley's K-function, we develop the positive area proportion function (PAPF) and use it to develop a hypothesis testing procedure for the detection of spatial clustering and dispersion at specific distances in areal data. We compare the performance of the proposed PAPF hypothesis test to that of the global Moran's I statistic, the Getis-Ord general G statistic, and the spatial scan statistic with extensive simulation studies. We then evaluate the real-world performance of our method by using it to detect spatial clustering in land parcels containing conservation easements and US counties with high pediatric overweight/obesity rates.

Impact of the "39-week rule" on adverse pregnancy outcomes: a statewide analysis.

BACKGROUND: The "39-week rule," adopted by the American College of Obstetricians and Gynecologists circa 2009, discouraged routine elective induction of labor in early-term gestations (37 weeks 0 days-38 weeks 6 days) to decrease the risk of adverse neonatal outcomes. However, little research exists regarding any unintended adverse pregnancy outcomes associated with this policy shift. OBJECTIVE: This study aimed to quantify the difference in incidence of adverse pregnancy outcomes before and after the implementation of the 39-week rule. STUDY DESIGN: Deidentified data from all births in the state of South Carolina from 2000 to 2008 (before the 39-week rule) and from 2013 to 2017 (after statewide implementation and enforcement of the rule) were obtained from the South Carolina Revenue and Fiscal Affairs Office. Demographic data and International Classification of Diseases 9/10 codes were obtained for each birth. Our primary outcome was the incidence of any of the following adverse pregnancy outcomes: cesarean delivery, hypertensive disorders, chorioamnionitis, postpartum hemorrhage, high-degree lacerations, placental abruption, and intensive care unit admission. Propensity score analysis was used to control for age, body mass index, and race. After stratification by propensity score, the Cochran-Mantel-Haenszel test was used to compare the prerule and postrule groups. RESULTS: A total of 633,985 births were eligible for inclusion-412,632 from 2000 to 2008, and 221,353 from 2013 to 2017. There was a significant increase in the primary outcome in the postrule period (39.94% pre vs 42.76% post; P<.01). The incidence of all hypertensive disorders was significantly increased in the postrule period compared with the prerule period (7.75% pre vs 10.1% post; P<.01). The incidence of chorioamnionitis and cesarean delivery also increased in the postrule period (1.45% pre vs 1.92% post; P<.01; 29.6% pre vs 31.82% post; P<.01; respectively). CONCLUSION: There was a significant increase in the primary outcome following the implementation of the 39-week rule. Although the policy shift was driven by a desire to decrease adverse neonatal outcomes, aggregate benefit was not observed for pregnancy outcomes.

Impact of restricting early-term deliveries on adverse neonatal outcomes: a statewide analysis.

BACKGROUND: The "39-Week Rule" was adopted by the American College of Obstetricians and Gynecologists in 2009 to eliminate nonmedically indicated (elective) deliveries before 39 weeks in an effort to improve neonatal outcomes. OBJECTIVE: Our primary objective was to quantify the effect of this policy change on adverse neonatal outcomes among a cohort of term births in South Carolina. STUDY DESIGN: Deidentified data from all births in the state of South Carolina from 2000 to 2008 (before the 39-week rule) and from 2013 to 2017 (after statewide implementation and enforcement of the rule) were obtained from the South Carolina Revenue and Fiscal Affairs Office. Demographic data and International Statistical Classification of Diseases and Related Health Problems Ninth/Tenth Revision codes were obtained for each birth. Our primary outcome was admission to a neonatal intensive care unit. Our secondary outcomes were respiratory morbidities (including respiratory distress syndrome and transient tachypnea of the newborn), hypoxic-ischemic encephalopathy, seizure, sepsis, birth injuries, hyperbilirubinemia, hypoglycemia, and feeding difficulties. Propensity score analysis was used to control for maternal age, body mass index, race, gestational hypertension, infection, placental abruption, and gestational and pregestational diabetes mellitus. After stratification by propensity score, the Cochran-Mantel-Haenszel test was used to compare groups. RESULTS: A total of 620,121 infants were liveborn at term during the 2 study periods. After implementation of the 39-week rule, there was a significant reduction in early-term deliveries. In adjusted analyses, neonatal intensive care unit admission was significantly more common in the postimplementation period. Respiratory morbidities were also significantly more common postimplementation. In contrast, there were significant reductions in birth injuries and hyperbilirubinemia in the postimplementation period. CONCLUSION: Implementation of the 39-week rule was associated temporally with an increase in adverse neonatal outcomes. The outcomes intended to be reduced by the 39-week rule, including neonatal intensive care unit admission and respiratory morbidity, seem to have increased in incidence despite adherence to the proposed guidelines.

The utility of a Bayesian predictive model to forecast neuroinvasive West Nile virus disease in the United States of America, 2022.

Arboviruses (arthropod-borne-viruses) are an emerging global health threat that are rapidly spreading as climate change, international business transport, and landscape fragmentation impact local ecologies. Since its initial detection in 1999, West Nile virus has shifted from being a novel to an established arbovirus in the United States of America. Subsequently, more than 25,000 cases of West Nile neuro-invasive disease have been diagnosed, cementing West Nile virus as an arbovirus of public health importance. Given its novelty in the United States of America, high-risk ecologies are largely underdefined making targeted population-level public health interventions challenging. Using the Centers for Disease Control and Prevention ArboNET neuroinvasive West Nile virus data from 2000-2021, this study aimed to predict neuroinvasive West Nile virus human cases at the county level for the contiguous USA using a spatio-temporal Bayesian negative binomial regression model. The model includes environmental, climatic, and demographic factors, as well as the distribution of host species. An integrated nested Laplace approximation approach was used to fit our model. To assess model prediction accuracy, annual counts were withheld, forecasted, and compared to observed values. The validated models were then fit to the entire dataset for 2022 predictions. This proof-of-concept mathematical, geospatial modelling approach has proven utility for national health agencies seeking to allocate funding and other resources for local vector control agencies tackling West Nile virus and other notifiable arboviral agents.