Disparities in Reportable Communicable Disease Incidence by Census Tract-Level Poverty, New York City, 2006-2013

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

Machine learning for predicting hepatitis B or C virus infection in diabetic patients

Highly prevalent hepatitis B and hepatitis C virus (HBV and HCV) infections have been reported among individuals with diabetes. Given the frequently asymptomatic nature of hepatitis and the challenges associated with screening in some vulnerable populations such as diabetes patients, we conducted an investigation into the performance of various machine learning models for the identification of hepatitis in diabetic patients while also evaluating the significance of features. Analyzing NHANES data from 2013 to 2018, machine learning models were evaluated; random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO) along with stacked ensemble model. We performed hyperparameter tuning to improve the performance of the model, and selected important predictors using the best performance model. LASSO showed the highest predictive performance (AUC-ROC = 0.810) rather than other models. Illicit drug use, poverty, and race were highly ranked as predictive factors for developing hepatitis in diabetes patients. Our study demonstrated that a machine-learning-based model performed optimally in the detection of hepatitis among diabetes patients, achieving high performance. Furthermore, models and predictors evaluated from the current study, we expect, could be supportive information for developing screening or treatment methods for hepatitis care in diabetes patients.

Risk factors for Lyme disease resulting from residential exposure amidst emerging Ixodes scapularis populations: A neighbourhood-level analysis of Ottawa, Ontario

Lyme disease is an emerging health threat in Canada due to the continued northward expansion of the main tick vector, Ixodes scapularis. It is of particular concern to populations living in expanding peri-urban areas where residential development and municipal climate change response impact neighbourhood structure and composition. The objective of this study was to estimate associations of socio-ecological characteristics with residential Lyme disease risk at the neighbourhood scale. We used Lyme disease case data for 2017-2020 reported for Ottawa, Ontario to determine where patients' residential property, or elsewhere within their neighbourhood, was the suspected site of tick exposure. Cases meeting this exposure definition (n = 118) were aggregated and linked to neighbourhood boundaries. We calculated landscape characteristics from composited and classified August 2018 PlanetScope satellite imagery. Negative binomial generalized linear models guided by a priori hypothesized relationships explored the association between hypothesized interactions of landscape structure and the outcome. Increases in median household income, the number of forest patches, the proportion of forested area, forest edge density, and mean forest patch size were associated with higher residential Lyme disease incidence at the neighbourhood scale, while increases in forest shape complexity and average distance to forest edge were associated with reduced incidence (P<0.001). Among Ottawa neighbourhoods, the combined effect of forest shape complexity and average forest patch size was associated with higher residential Lyme disease incidence (P<0.001). These findings suggest that Lyme disease risk in residential settings is associated with urban design elements. This is particularly relevant in urban centres where local ecological changes may impact the presence of emerging tick populations and how residents interact with tick habitat. Further research into the mechanistic underpinnings of these associations would be an asset to both urban development planning and public health management.

A spatio-temporal hierarchical model to account for temporal misalignment in American Community Survey explanatory variables

The American Community Survey (ACS) is one of the most vital public sources for demographic and socioeconomic characteristics of communities in the United States and is administered by the U.S. Census Bureau every year. The ACS publishes 5-year estimates of community characteristics for all geographical areas and 1-year estimates for areas with population of at least 65,000. Many epidemiological and public health studies use 5-year ACS estimates as explanatory variables in models. However, doing so ignores the uncertainty and averages over variability during the time-period which may lead to biased estimates of covariate effects of interest. In this paper, we propose a Bayesian hierarchical model that accounts for the uncertainty and disentangles the temporal misalignment in the ACS multi-year time-period estimates. We show via simulation that our proposed model more accurately recovers covariate effects compared to models that ignore the temporal misalignment. Lastly, we implement our proposed model to quantify the relationship between yearly, county-level characteristics and the prevalence of frequent mental distress for counties in North Carolina from 2014 to 2018.

Wastewater surveillance beyond COVID-19: a ranking system for communicable disease testing in the tri-county Detroit area, Michigan, USA

Introduction

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been utilized to monitor the disease in the United States through routine national, statewide, and regional monitoring projects. A significant canon of evidence was produced showing that wastewater surveillance is a credible and effective tool for disease monitoring. Hence, the application of wastewater surveillance can extend beyond monitoring SARS-CoV-2 to encompass a diverse range of emerging diseases. This article proposed a ranking system for prioritizing reportable communicable diseases (CDs) in the Tri-County Detroit Area (TCDA), Michigan, for future wastewater surveillance applications at the Great Lakes Water Authority's Water Reclamation Plant (GLWA's WRP).

Methods

The comprehensive CD wastewater surveillance ranking system (CDWSRank) was developed based on 6 binary and 6 quantitative parameters. The final ranking scores of CDs were computed by summing the multiplication products of weighting factors for each parameter, and then were sorted based on decreasing priority. Disease incidence data from 2014 to 2021 were collected for the TCDA. Disease incidence trends in the TCDA were endowed with higher weights, prioritizing the TCDA over the state of Michigan.

Results

Disparities in incidences of CDs were identified between the TCDA and state of Michigan, indicating epidemiological differences. Among 96 ranked CDs, some top ranked CDs did not present relatively high incidences but were prioritized, suggesting that such CDs require significant attention by wastewater surveillance practitioners, despite their relatively low incidences in the geographic area of interest. Appropriate wastewater sample concentration methods are summarized for the application of wastewater surveillance as per viral, bacterial, parasitic, and fungal pathogens.

Discussion

The CDWSRank system is one of the first of its kind to provide an empirical approach to prioritize CDs for wastewater surveillance, specifically in geographies served by centralized wastewater collection in the area of interest. The CDWSRank system provides a methodological tool and critical information that can help public health officials and policymakers allocate resources. It can be used to prioritize disease surveillance efforts and ensure that public health interventions are targeted at the most potentially urgent threats. The CDWSRank system can be easily adopted to geographical locations beyond the TCDA.

Hepatitis Risk in Diabetes Compared to Non-Diabetes and Relevant Factors: A Cross-Sectional Study with National Health and Nutrition Examination Survey (NHANES), 2013-2018

This study aimed to identify the development of hepatitis B or C infection in diabetes patients compared to those without and to elucidate factors associated with the prevalence of hepatitis B or C infection in diabetes. We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2013-2018. As evaluation factors, we included variables such as age, race, illicit drug use, and poverty. The diabetic group had a significantly higher prevalence of hepatitis B or C infection than the non-diabetic group (odds ratio (OR) = 1.73; 95% confidence interval (CI), 1.36-2.21, p < 0.01). In multivariate Cox regression, non-poverty and non-illicit drug use were lower risk factors contributing to hepatitis development in diabetes (hazard ratio (HR) = 0.50; 95% CI, 0.32-0.79, p < 0.01, and HR = 0.05; 95% CI, 0.03-0.08, p < 0.01, respectively). Logistic regression also showed that these factors were significant contributors to hepatitis development in the diabetic group (p < 0.01). In patients with diabetes, the development of hepatitis was higher than that in those without, and hepatitis development was influenced by poverty and illicit drug use. This may provide supporting evidence of response strategies for diabetes to care for hepatitis development in advance.

Social divisions and risk perception drive divergent epidemics and large later waves

During infectious disease outbreaks, individuals may adopt protective measures like vaccination and physical distancing in response to awareness of disease burden. Prior work showed how feedbacks between epidemic intensity and awareness-based behaviour shape disease dynamics. These models often overlook social divisions, where population subgroups may be disproportionately impacted by a disease and more responsive to the effects of disease within their group. We develop a compartmental model of disease transmission and awareness-based protective behaviour in a population split into two groups to explore the impacts of awareness separation (relatively greater in- vs. out-group awareness of epidemic severity) and mixing separation (relatively greater in- vs. out-group contact rates). Using simulations, we show that groups that are more separated in awareness have smaller differences in mortality. Fatigue (i.e. abandonment of protective measures over time) can drive additional infection waves that can even exceed the size of the initial wave, particularly if uniform awareness drives early protection in one group, leaving that group largely susceptible to future infection. Counterintuitively, vaccine or infection-acquired immunity that is more protective against transmission and mortality may indirectly lead to more infections by reducing perceived risk of infection and therefore vaccine uptake. Awareness-based protective behaviour, including awareness separation, can fundamentally alter disease dynamics. Social media summary: Depending on group division, behaviour based on perceived risk can change epidemic dynamics & produce large later waves.

Association between serum cotinine and volatile organic compounds (VOCs) in adults living with HIV, HBV, or HCV (NHANES 2005-2018)

Although people all know that nicotine in tobacco smoke is the key to cause health damage, they ignore the synergistic effect of a large number of Volatile Organic Compounds (VOCs) produced by incomplete tobacco combustion on nicotine or cotinine metabolism. Our aim is to investigate the association between serum VOCs and cotinine in smokers infected with HIV, HBV or HCV. National Health and Nutrition Examination Survey (NHANES 2005-2018) database, including 13,652 nationally representative subjects' sociodemographic characteristics and serological indicators, was used in this study. Smokers living with human immunodeficiency virus (HIV), hepatitis B virus (HBV) or hepatitis C virus (HCV) were compared to non-infected population. The correlation between VOCs and cotinine as well as the effects of VOCs on cotinine metabolism were analyzed by Spearman correlation analysis and multivariable logistic regression analysis, respectively. Among HIV, HBV, or HCV infected smokers with the largest exposure dose to tobacco, the intensity of the association between VOCs and cotinine was the strongest. The results of multivariable binary logistic regression showed that high concentrations of 1,2-Dichlorobenzene (OR:1.036, CI:1.009-1.124), Benzene (OR:1.478, CI:1.036-2.292), Carbon Tetrachloride (OR:1.576, CI:1.275-2.085) and 2,5-Dimethylfuran (OR:1.091, CI:1.030-1.157) in blood might be independent risk factors leading to the increase of serum metabolite cotinine in smokers.

Seroprevalence of hepatitis E virus infection in the Americas: Estimates from a systematic review and meta-analysis

Background

Hepatitis E virus (HEV) infection is responsible for inflammatory liver disease and can cause severe health problems. Because the seroprevalence of HEV varies within different population groups and between regions of the continent, we conducted a systematic review on the topic in order to provide evidence for targeted prevention strategies.

Methods

We performed a systematic review in PubMed, SCIELO, LILACS, EBSCO, and Cochrane Library and included reports up to 25 May 2021 (PROSPERO registration number: CRD42020173934). We assessed the risk of bias, publication bias, and heterogeneity between studies and conducted a random-effect meta-analysis for proportions using a (binomial-normal) generalized linear mixed model (GLMM) fitted by Maximum Likelihood (ML). We also reported other characteristics like genotype and risk factors.

Results

Of 1212 identified records, 142 fulfilled the inclusion criteria and were included in the qualitative analysis and 132 in the quantitative analysis. Our random-effects GLMM pooled overall estimate for past infection (IgG) was 7.7% (95% CI 6.4%–9.2%) with high heterogeneity (I2 = 97%). We found higher seroprevalence in certain population groups, for example in people with pig related exposure for IgG (ranges from 6.2%–28% and pooled estimate of 13.8%, 95% CI: 7.6%–23.6%), or with diagnosed or suspected acute viral hepatitis for IgM (ranges from 0.3%–23.9% and pooled estimate of 5.5%, 95% CI: 2.0%–14.1%). Increasing age, contact with pigs and meat products, and low socioeconomic conditions are the main risk factors for HEV infection. Genotype 1 and 3 were documented across the region.

Conclusion

HEV seroprevalence estimates demonstrated high variability within the Americas. There are population groups with higher seroprevalence and reported risk factors for HEV infection that need to be prioritized for further research. Due to human transmission and zoonotic infections in the region, preventive strategies should include water sanitation, occupational health, and food safety.

Association between living in municipalities with high crowding conditions and poverty and mortality from COVID-19 in Mexico

Background

The World Health Organization stated a pandemic by severe acute respiratory syndrome coronavirus SARS-Cov2 (COVID-19) on March, 2020 with devastating implications for populations, healthcare systems, and economies globally.

Objective

The present study explores the association between patients living in municipalities with crowding conditions and poverty and mortality from COVID-19 in Mexico; specifically evaluating the socioeconomic characteristics of the municipality in which the patients reside and some individual characteristics.

Methods

In the present study, we examined public information collected from the National Epidemiological Surveillance System informing all persons tested for SARS-CoV-2 and published by the Ministry of Health. The present analysis was restricted to those with the date of registration to October 12, 2021. The association between the main exposures (overcrowded conditions and poverty) and the outcomes of interest (death by COVID-19) was explored using Cox proportional hazard regression models, including frailty penalties to accommodate multilevel data and random effects for the municipality of case occurrence.

Results

A total of 9619917 subjects were included in the Epidemiological Surveillance System for viral respiratory disease platform. Of those for which results were available, 6141403 were negative for COVID-19 and 3478514 were positive for COVID-19; with a total of 273216 deaths in those who tested positive. Among those positive to COVID-19 mean age was 46.9. Patients living in municipalities with high rates of crowding conditions increased the risk of dying from COVID-19 by 8% (95% CI: 1.03, 1.14). Individuals living in municipalities with indigenous background was associated with an increased risk of dying from COVID-19 (HR = 1.10; 95% CI: 1.04, 1.17). Individuals living in municipalities with illiteracy (HR = 1.09; 95% CI: 1.03, 1.11), poverty (HR = 1.17; 95% CI: 1.14, 1.19), food insecurity (HR = 1.094; 95% CI 1.02, 1.06), limited access to social security (HR = 1.10; 95% CI: 1.08, 1.13) and health services (HR = 1.06; 95% CI: 1.04, 1.08) had a higher risk of mortality from COVID-19.

Conclusion

Our data suggest that patients living in municipalities with higher rates of crowding conditions and higher rates of poverty had elevated risk of mortality from COVID-19. In Mexico, the COVID-19 pandemic is a systemic crisis linked to human development since we have seen that it affects less developed and more vulnerable municipalities. Policies to reduce vulnerabilities and develop strategies to deal with health crises like the current one needs to be considered.

Disparities in Incidence and Severity of Shigella Infections Among Children-Foodborne Diseases Active Surveillance Network (FoodNet), 2009-2018

BACKGROUND

Shigella infections are an important cause of diarrhea in young children and can result in severe complications. Disparities in Shigella infections are well documented among US adults. Our objective was to characterize disparities in incidence and severity of Shigella infections among US children.

METHODS

We analyzed laboratory-diagnosed Shigella infections reported to FoodNet, an active, population-based surveillance system in 10 US sites, among children during 2009-2018. We calculated the incidence rate stratified by sex, age, race/ethnicity, Shigella species, and disease severity. Criteria for severe classification were hospitalization, bacteremia, or death. The odds of severe infection were calculated using logistic regression.

RESULTS

During 2009-2018, 10 537 Shigella infections were reported in children and 1472 (14.0%) were severe. The incidence rate was 9.5 infections per 100 000 child-years and the incidence rate of severe infections was 1.3 per 100 000 child-years. Incidence was highest among children aged 1-4 years (19.5) and lowest among children aged 13-17 years (2.3); however, children aged 13-17 years had the greatest proportion of severe infections (21.2%). Incidence was highest among Black (16.2 total; 2.3 severe), Hispanic (13.1 total; 2.3 severe), and American Indian/Alaska Native (15.2 total; 2.5 severe) children. Infections caused by non-sonnei species had higher odds of severity than infections caused by Shigella sonnei (adjusted odds ratio 2.58; 95% confidence interval 2.12-3.14).

CONCLUSIONS

The incidence and severity of Shigella infections among US children vary by age, race/ethnicity, and Shigella species, warranting investigation of unique risk factors among pediatric subpopulations.

Overcrowding and COVID-19 mortality across U.S. counties: Are disparities growing over time?

A growing line of research underscores that sociodemographic factors may contribute to disparities in the impact of COVID-19. Further, stages of disease theory suggests that disparities may grow as the pandemic unfolds and more advantaged areas are better able to apply growing knowledge and mitigation strategies. In this paper, we focus on the role of county-level household overcrowding on disparities in COVID-19 mortality in U.S. counties. We examine this relationship across three theoretically important periods of the pandemic from April–October 2020, that mark both separate stages of community knowledge and national mortality levels. We find evidence that the percentage of overcrowded households is a stronger predictor of COVID-19 mortality during later periods of the pandemic. Moreover, despite a relationship between overcrowding and poverty at the county-level, overcrowding plays an independent role in predicting COVID-19 mortality. Our findings underscore that areas disadvantaged by overcrowding may be more vulnerable to the effects of COVID-19 and that this vulnerability may lead to changing disparities over time.

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County-level overcrowding is positively associated with COVID-19 mortality rates.

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The overcrowding-mortality relationship is strongest during June and July.

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Overcrowding and poverty additively shape county-level COVID-19 mortality.

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Social disparities in mortality may change with new knowledge and disease spread.

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Policies must consider how social disadvantages shape COVID-19 mortality over time.

Rapid Emergence of SARS-CoV-2 in the Greater New York Metropolitan Area: Geolocation, Demographics, Positivity Rates, and Hospitalization for 46 793 Persons Tested by Northwell Health

BACKGROUND

In March 2020, the greater New York metropolitan area became an epicenter for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The initial evolution of case incidence has not been well characterized.

METHODS

Northwell Health Laboratories tested 46 793 persons for SARS-CoV-2 from 4 March through 10 April. The primary outcome measure was a positive reverse transcription-polymerase chain reaction test for SARS-CoV-2. The secondary outcomes included patient age, sex, and race, if stated; dates the specimen was obtained and the test result; clinical practice site sources; geolocation of patient residence; and hospitalization.

RESULTS

From 8 March through 10 April, a total of 26 735 of 46 793 persons (57.1%) tested positive for SARS-CoV-2. Males of each race were disproportionally more affected than females above age 25, with a progressive male predominance as age increased. Of the positive persons, 7292 were hospitalized directly upon presentation; an additional 882 persons tested positive in an ambulatory setting before subsequent hospitalization, a median of 4.8 days later. Total hospitalization rate was thus 8174 persons (30.6% of positive persons). There was a broad range (>10-fold) in the cumulative number of positive cases across individual zip codes following documented first caseincidence. Test positivity was greater for persons living in zip codes with lower annual household income.

CONCLUSIONS

Our data reveal that SARS-CoV-2 incidence emerged rapidly and almost simultaneously across a broad demographic population in the region. These findings support the premise that SARS-CoV-2 infection was widely distributed prior to virus testing availability.

Rural-urban differences in estimated life expectancy associated with neighborhood-level cumulative social and environmental determinants

Diverse neighborhood-level environmental and social impacts on health are well documented. While studies typically examine these impacts individually, examining potential health impacts from multiple sources as a whole can provide a broader context of overall neighborhood-level health impacts compared to examining each component independently. This study examined the association between cumulative neighborhood-level potential health impacts on health and expected life expectancy within neighborhoods (census tracts) across Texas using the Neighborhood Potential Health Impact Score tool. Among urban census tract neighborhoods, a difference of nearly 5 years was estimated between neighborhoods with the least health promoting cumulative health impacts compared to neighborhoods with the most health promoting cumulative health impacts. Differences were observed between rural and urban census tract neighborhoods, with rural areas having less variability in expected life expectancy associated with neighborhood-level cumulative potential health impacts compared to urban areas.

The intersectional effect of poverty, home ownership, and racial/ethnic composition on mean childhood blood lead levels in Milwaukee County neighborhoods

Environmental conditions that contribute to childhood lead exposure are spatially patterned. Socioeconomic and racial inequities in childhood lead exposure have been well documented, however childhood lead exposure in Milwaukee is understudied. As a segregated rustbelt metropolitan area with childhood lead exposure concerns, Milwaukee is uniquely positioned to evaluate the synergistic effects of racial and economic drivers of childhood lead exposure. Using surveillance data from the Wisconsin Department of Health Services, Division of Public Health and the US Census Bureau, this cross-sectional study determined the intersectional effect of poverty, home ownership, and racial/ethnic composition on childhood lead exposure in Milwaukee County neighborhoods using linear regression adjusting for average census tract housing age and number of children. The final analytical sample consisted of 48,393 individual childhood blood lead levels aggregated to 215 Milwaukee County census tracts. Census tracts with mean childhood blood lead levels greater than or equal to 5 μg/dL were predominantly low home ownership, high poverty, and majority non-White census tracts. The effects of low home ownership, high poverty, and majority non-White census tracts were synergistic, producing 1.78 (95% CI: 1.44, 2.11) μg/dL higher mean childhood blood lead level than high home ownership, low poverty, and majority White census tracts (referent). This research reveals that social determinants at the neighborhood level co-occur and interact to produce inequities in childhood lead exposure. Lead prevention efforts should align with equity-focused housing and economic policies that target primary prevention in neighborhoods disproportionately burdened by childhood lead exposure.

Human Papillomavirus 16/18-Associated Cervical Lesions: Differences by Area-Based Measures of Race and Poverty

INTRODUCTION

This analysis evaluates trends in cervical lesions with human papillomavirus 16/18 detected by area-based measures of race, ethnicity, and poverty during 2008-2015.

METHODS

Trends in the proportion of lesions with human papillomavirus 16/18 detected among residents of New Haven County, Connecticut were examined by area-based measures of race, ethnicity, and poverty. Area-based measures are aggregate descriptors of census tract characteristics useful for measuring differences in health outcomes in the context of where people live. Multivariable logistic regression modeling was conducted, adjusted for individual-level race, ethnicity, and insurance status to assess the independent effects of area-based measures. Data were analyzed in 2018-2019.

RESULTS

Among women aged 21-24 years and 25-29 years, significant declines in the proportion of lesions with human papillomavirus 16/18 were observed. Among women aged 21-24 years, declines began earlier and were greater in magnitude in areas of lower poverty (OR=0.55, 95% CI=0.36, 0.85 for 2010-2012 vs 2008-2009 and OR=0.30, 95% CI=0.18, 0.51 for 2013-2015 vs 2008-2009) compared with higher poverty (OR=1.66, 95% CI=0.86, 3.21 and OR=0.48, 95% CI=0.19, 1.20). Similar patterns were observed for women aged 25-29 years, and for area-based measures of race and ethnicity.

CONCLUSIONS

Differences were observed in declines in the proportion of human papillomavirus 16/18 lesions by area-based measures since the introduction of human papillomavirus vaccines, with greater and earlier declines in areas with fewer residents living in poverty and racial minorities. Ongoing human papillomavirus vaccine impact monitoring is necessary to track differences by sociodemographic characteristics.

Disparities in Shigellosis Incidence by Census Tract Poverty, Crowding, and Race/Ethnicity in the United States, FoodNet, 2004-2014

Background

Shigella causes an estimated 500000 enteric illnesses in the United States annually, but the association with socioeconomic factors is unclear.

Methods

We examined possible epidemiologic associations between shigellosis and poverty using 2004–2014 Foodborne Diseases Active Surveillance Network (FoodNet) data. Shigella cases (n = 21246) were geocoded, linked to Census tract data from the American Community Survey, and categorized into 4 poverty and 4 crowding strata. For each stratum, we calculated incidence by sex, age, race/ethnicity, and FoodNet site. Using negative binomial regression, we estimated incidence rate ratios (IRRs) comparing the highest to lowest stratum.

Results

Annual FoodNet Shigella incidence per 100000 population was higher among children <5 years old (19.0), blacks (7.2), and Hispanics (5.6) and was associated with Census tract poverty (incidence rate ratio [IRR], 3.6; 95% confidence interval [CI], 3.5–3.8) and household crowding (IRR, 1.8; 95% CI, 1.7–1.9). The association with poverty was strongest among children and persisted regardless of sex, race/ethnicity, or geographic location. After controlling for demographic variables, the association between shigellosis and poverty remained significant (IRR, 2.3; 95% CI, 2.0–2.6).

Conclusions

In the United States, Shigella infections are epidemiologically associated with poverty, and increased incidence rates are observed among young children, blacks, and Hispanics.

Relationship Between Census Tract–Level Poverty and Domestically Acquired Salmonella Incidence: Analysis of Foodborne Diseases Active Surveillance Network Data, 2010–2016

Background

The relationships between socioeconomic status and domestically acquired salmonellosis and leading Salmonella serotypes are poorly understood.

Methods

We analyzed surveillance data from laboratory-confirmed cases of salmonellosis from 2010–2016 for all 10 Foodborne Disease Active Surveillance Network (FoodNet) sites, having a catchment population of 47.9 million. Case residential data were geocoded, linked to census tract poverty level, and then categorized into 4 groups according to census tract poverty level. After excluding those reporting international travel before illness onset, age-specific and age-adjusted salmonellosis incidence rates were calculated for each census tract poverty level, overall and for each of the 10 leading serotypes.

Results

Of 52 821geocodable Salmonella infections (&gt;96%), 48 111 (91.1%) were domestically acquired. Higher age-adjusted incidence occurred with higher census tract poverty level (P &lt; .001; relative risk for highest [≥20%] vs lowest [&lt;5%] census tract poverty level, 1.37). Children &lt;5 years old had the highest relative risk (2.07). Although this relationship was consistent by race/ethnicity and by serotype, it was not present in 5 FoodNet sites or among those aged 18–49 years.

Conclusion

Children and older adults living in higher-poverty census tracts have had a higher incidence of domestically acquired salmonellosis. There is a need to understand socioeconomic status differences for risk factors for domestically acquired salmonellosis by age group and FoodNet site to help focus prevention efforts.

Disparities in Zika Virus Testing and Incidence Among Women of Reproductive Age-New York City, 2016

CONTEXT

The New York City Department of Health and Mental Hygiene (NYC DOHMH) performs surveillance for reportable diseases, including Zika virus (ZIKV) infection and disease, to inform public health responses. Incidence rates of other mosquito-borne diseases related to international travel are associated with census tract poverty level in NYC, suggesting that high poverty areas might be at higher risk for ZIKV infections.

OBJECTIVES

We assessed ZIKV testing rates and incidence of travel-associated infection among reproductive age women in NYC to identify areas with high incidence and low testing rates and assess the effectiveness of public health interventions.

DESIGN

We analyzed geocoded ZIKV surveillance data collected by NYC DOHMH. Women aged 15 to 44 years tested during January-July 2016 (n = 4733) were assigned to census tracts, which we grouped by poverty level and quartile of the number of persons born in countries or territories with mosquito-borne ZIKV transmission as a proxy for risk of travel to these areas. We calculated crude ZIKV testing rates, incidence rates, and incidence rate ratios (IRRs).

SETTING

New York City.

RESULTS

Eight percent of patients (n = 376) tested had evidence of ZIKV infection. Cumulative incidence was higher both in areas with higher versus lower poverty levels (IRR = 2.4; 95% confidence interval [CI], 2.0-3.0) and in areas with the largest versus smallest populations of persons born in countries or territories with mosquito-borne ZIKV transmission (IRR = 11.3; 95% CI, 6.2-20.7). Initially, ZIKV testing rates were lowest in higher poverty areas with the largest populations of persons born in countries or territories with mosquito-borne ZIKV transmission (15/100 000), but following targeted interventions, testing rates were highest in these areas (80/100 000).

CONCLUSIONS

Geocoded data enabled us to identify communities with low testing but high ZIKV incidence rates, intervene to promote testing and reduce barriers to testing, and measure changes in testing rates.

Simulation of Legionnaires' disease prospective spatiotemporal cluster detection, Allegheny County, Pennsylvania, USA

Legionnaires' disease (LD) incidence in the USA has quadrupled since 2000. Health departments must detect LD outbreaks quickly to identify and remediate sources. We tested the performance of a system to prospectively detect simulated LD outbreaks in Allegheny County, Pennsylvania, USA. We generated three simulated LD outbreaks based on published outbreaks. After verifying no significant clusters existed in surveillance data during 2014-2016, we embedded simulated outbreak-associated cases into 2016, assigning simulated residences and report dates. We mimicked daily analyses in 2016 using the prospective space-time permutation scan statistic to detect clusters of ⩽30 and ⩽180 days using 365-day and 730-day baseline periods, respectively. We used recurrence interval (RI) thresholds of ⩾20, ⩾100 and ⩾365 days to define significant signals. We calculated sensitivity, specificity and positive and negative predictive values for daily analyses, separately for each embedded outbreak. Two large, simulated cooling tower-associated outbreaks were detected. As the RI threshold was increased, sensitivity and negative predictive value decreased, while positive predictive value and specificity increased. A small, simulated potable water-associated outbreak was not detected. Use of a RI threshold of ⩾100 days minimised time-to-detection while maximizing positive predictive value. Health departments should consider using this system to detect community-acquired LD outbreaks.

Incidence of Lyme Disease Diagnosis in a Maryland Medicaid Population, 2004-2011

The epidemiology of Lyme disease has been examined utilizing insurance claims from privately insured individuals; however, it is unknown whether reported patterns vary among the publicly insured. We examined trends in incidence rates of first Lyme disease diagnosis among 384,652 Maryland Medicaid recipients enrolled from July 2004 to June 2011. Age-, sex-, county-, season-, and year-specific incidence rates were calculated, and mixed-effects multiple logistic regression models were used to study the relationship between Lyme disease diagnosis and these variables. The incidence rate in our sample was 97.65 cases per 100,000 person-years (95% confidence interval (CI): 91.53, 104.06), and there was a 13% average annual increase in the odds of a Lyme disease diagnosis (odds ratio = 1.13, 95% CI: 1.09, 1.17; P < 0.001). Incidence rates for males and females were not significantly different, though males were significantly more likely to be diagnosed during high-season months (relative risk (RR) = 1.24, 95% CI: 1.06, 1.44) and less likely to be diagnosed during low-season months (RR = 0.63, 95% CI: 0.46, 0.87) than females. Additionally, adults were significantly more likely than children to be diagnosed during low-season months (RR = 1.59, 95% CI: 1.19, 2.12). While relatively rare in this study sample, Lyme disease diagnoses do occur in a Medicaid population in a Lyme-endemic state.

Detecting spatial clusters of HIV and hepatitis coinfections

BACKGROUND

People with HIV infection in the United States are often affected by chronic viral hepatitis. These coinfected people with either HBV or HCV are at increased risk for serious, life-threatening complications. Coinfections with viral hepatitis may also complicate the delivery of anti-retroviral therapy (ART) by escalating the risk of drug-related hepatoxicity. According to the Centers for Disease Control and Prevention (CDC), approximately 10 percent of people with HIV in the United States also have HBV, and 25 percent also have HCV coinfection. With the advent of highly active antiretroviral therapy (HAART) and the increased life-expectancy of HIV patients, clinicians are more likely to be confronted with issues related to co-infection and the management challenges that they present, especially in resource-limited settings. The purpose of this analysis was to identify geographical clusters of HIV- (HBV/HCV) co-infection and compared to the geographical clusters of not co-infected using DC, Department of Health surveillance data. The results of the analysis will be used to target resources to areas at risk.

METHODS

HIV and Hepatitis surveillance data were matched among cases diagnosed between 1980 and 2016. HIV-hepatitis co-infected and the not co-infected spatial clusters were detected using discrete Poisson model. Kulldorff's spatial scan statistic method was implemented in the free software tool called SaTScan which has been widely adopted for detecting disease cluster. The analysis was conducted by tracts, but for visualization, ease of interpretation and assist in policy making the tract map was overlaid with the ward map using ArcGIS 10.5.1.

RESULTS

Between 1980 and 2016, there were 12,965 diagnosed cases of HIV, of which 2,316 HIV/Hepatitis matches were identified. Of the 2316 co-infected people living in DC, 25 percent (N = 590) of people had HBV, and 75 percent (N = 1,726) had HCV. Out of 12,965 diagnosed cases, remaining 10,649 did not have any co-infections (not co-infected). IDU (27.16 percent) and MSM (32.86 percent) were the highest mode of transmission for co-infected population. African-American were reported 83.64 percent (N = 1,937) among co-infection population. Three clusters were identified for both co-infected population in DC. The largest cluster radius for co-infected analysis covers wards 6, 7 and 8 as well as large parts of 2 and 5 (p < 0.001). Multiple clusters were identified for not co-infected population (p < 0.001). IDU (n = 450) was the highest mode of transmission for the co-infected clusters. For all clusters combined of not co-infected population highest mode of transmission were MSM (n = 2,534). This analysis also showed racial disparity, economic deprivation and lack of education were prominent in the co-infected clusters.

CONCLUSION

We identified locations of high risk clusters where enhanced hepatitis and HIV prevention, treatment, and care can help combat the epidemic. The clusters radius expands into the neighboring state of Maryland as well. The findings from this analysis will be used to target area based public health policy and healthcare interventions for HIV-hepatitis. It is recommended based on the analysis that needle exchange programs can successfully control new HIV infections as well as hepatitis co-infections.

Identifying Areas at Greatest Risk for Recent Zika Virus Importation - New York City, 2016

INTRODUCTION

The New York City Department of Health and Mental Hygiene sought to detect and minimize the risk of local, mosquito-borne Zika virus (ZIKV) transmission. We modeled areas at greatest risk for recent ZIKV importation, in the context of spatially biased ZIKV case ascertainment and no data on the local spatial distribution of persons arriving from ZIKV-affected countries.

METHODS

For each of 14 weeks during June-September 2016, we used logistic regression to model the census tract-level presence of any ZIKV cases in the prior month, using eight covariates from static sociodemographic census data and the latest surveillance data, restricting to census tracts with any ZIKV testing in the prior month. To assess whether the model discriminated better than random between census tracts with and without recent cases, we compared the area under the receiver operating characteristic (ROC) curve for each week's fitted model versus an intercept-only model applied to cross-validated data. For weeks where the ROC contrast test was significant at P < 0.05, we output and mapped the model-predicted individual probabilities for all census tracts, including those with no recent testing.

RESULTS

The ROC contrast test was significant for 8 of 14 weekly analyses. No covariates were consistently associated with the presence of recent cases. Modeled risk areas fluctuated across these 8 weeks, with Spearman correlation coefficients ranging from 0.30 to 0.93, all P < 0.0001. Areas in the Bronx and upper Manhattan were in the highest risk decile as of late June, while as of late August, the greatest risk shifted to eastern Brooklyn.

CONCLUSION

We used observable characteristics of areas with recent, known travel-associated ZIKV cases to identify similar areas with no observed cases that might also be at-risk each week. Findings were used to target public education and Aedes spp. mosquito surveillance and control. These methods are applicable to other conditions for which biased case ascertainment is suspected and knowledge of how cases are geographically distributed is important for targeting public health activities.

The Relationship Between Census Tract Poverty and Shiga Toxin-Producing E. coli Risk, Analysis of FoodNet Data, 2010-2014

Background

The relationship between socioeconomic status and Shiga toxin-producing Escherichia coli (STEC) is not well understood. However, recent studies in Connecticut and New York City found that as census tract poverty (CTP) decreased, rates of STEC increased. To explore this nationally, we analyzed surveillance data from laboratory-confirmed cases of STEC from 2010-2014 for all Foodborne Disease Active Surveillance Network (FoodNet) sites, population 47.9 million.

Methods

Case residential data were geocoded and linked to CTP level (2010-2014 American Community Survey). Relative rates were calculated comparing incidence in census tracts with <20% of residents below poverty with those with ≥20%. Relative rates of age-adjusted 5-year incidence per 100 000 population were determined for all STEC, hospitalized only and hemolytic-uremic syndrome (HUS) cases overall, by demographic features, FoodNet site, and surveillance year.

Results

There were 5234 cases of STEC; 26.3% were hospitalized, and 5.9% had HUS. Five-year incidence was 10.9/100 000 population. Relative STEC rates for the <20% compared with the ≥20% CTP group were >1.0 for each age group, FoodNet site, surveillance year, and race/ethnic group except Asian. Relative hospitalization and HUS rates tended to be higher than their respective STEC relative rates.

Conclusions

Persons living in lower CTP were at higher risk of STEC than those in the highest poverty census tracts. This is unlikely to be due to health care-seeking or diagnostic bias as it applies to analysis limited to hospitalized and HUS cases. Research is needed to better understand exposure differences between people living in the lower vs highest poverty-level census tracts to help direct prevention efforts.

Legionnaires' Disease Outbreaks and Cooling Towers, New York City, New York, USA

Surveillance will determine whether a new law regulating cooling towers reduces the incidence of Legionnaires’ disease.

The incidence of Legionnaires’ disease in the United States has been increasing since 2000. Outbreaks and clusters are associated with decorative, recreational, domestic, and industrial water systems, with the largest outbreaks being caused by cooling towers. Since 2006, 6 community-associated Legionnaires’ disease outbreaks have occurred in New York City, resulting in 213 cases and 18 deaths. Three outbreaks occurred in 2015, including the largest on record (138 cases). Three outbreaks were linked to cooling towers by molecular comparison of human and environmental Legionella isolates, and the sources for the other 3 outbreaks were undetermined. The evolution of investigation methods and lessons learned from these outbreaks prompted enactment of a new comprehensive law governing the operation and maintenance of New York City cooling towers. Ongoing surveillance and program evaluation will determine if enforcement of the new cooling tower law reduces Legionnaires’ disease incidence in New York City.

Cigarette Smoking in Persons Living with Hepatitis C: The National Health and Nutrition Examination Survey (NHANES), 1999-2014

BACKGROUND

Cigarette smoking is common in persons living with hepatitis C (hepatitis C+), but national statistics on this harmful practice are lacking. A better understanding of smoking behaviors in hepatitis C+ individuals may help in the development of targeted treatment strategies.

METHODS

We extracted data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2014. Hepatitis C+ were compared with hepatitis C- adults in the entire sample and in the subset of current smokers. Measures included demographics, current smoking, cigarettes/day, nicotine dependence, other tobacco use, substance use, and medical and psychiatric comorbidities.

RESULTS

Complete smoking and hepatitis C virus (HCV) data were available for 39,472 (90.1%) of 43,793 adult participants in NHANES during the study years. Hepatitis C+ smoked at almost triple the rate of hepatitis C- adults (62.4% vs 22.9%), with no significant difference between hepatitis C+ men and women (64.5% vs 58.2%). Hepatitis C+ smokers were more likely to smoke daily than hepatitis C- smokers (87.5% vs 80.0%), but had similar levels of nicotine dependence. Hepatitis C+ smokers were more likely to be older (mean age: 47.1 vs 41.5 years), male (69.4% vs 54.4%), Black (21.2% vs 12.1%), less educated (any college: 31.8% vs 42.9%), poor (mean family monthly poverty index: 1.80 vs 2.47), uninsured (43.9% vs 30.4%), use drugs (cocaine: 11.1% vs 3.2%; heroin: 4.0% vs 0.6%), and be depressed (33.2% vs 13.5%). Multivariate analyses revealed significant associations of both hepatitis C infection and cigarette smoking with current depression and hypertension.

CONCLUSIONS

There is a cigarette smoking epidemic embedded within the hepatitis C epidemic in the United States. The sociodemographic profile of hepatitis C+ smokers suggests that the implementation of effective tobacco treatment will be challenging. Thoughtful treatment strategies that are mindful of the unique characteristics of this group are needed.

Deaths From Pneumonia-New York City, 1999-2015

Background

"Pneumonia and influenza" are the third leading cause of death in New York City. Since 2012, pneumonia and influenza have been the only infectious diseases listed among the 10 leading causes of death in NYC. Most pneumonia and influenza deaths in NYC list pneumonia as the underlying cause of death, not influenza. We therefore analyzed death certificate data for pneumonia in NYC during 1999-2015.

Methods

We calculated annualized pneumonia death rates (overall and by sociodemographic subgroup) and examined the etiologic agent listed.

Results

There were 41 400 pneumonia deaths during the study period, corresponding to an annualized age-adjusted death rate of 29.7 per 100 000 population. Approximately 17.5% of pneumonia deaths specified an etiologic agent. Age-adjusted pneumonia death rate declined over the study period and across each borough. Males had an annualized age-adjusted pneumonia death rate 1.5 (95% confidence interval [CI], 1.5-1.5) times that of females. Non-Hispanic blacks had an annualized age-adjusted pneumonia death rate 1.2 (95% CI, 1.2-1.2) times that of non-Hispanic whites. The annualized pneumonia death rate increased with age group above 5-24 years and neighborhood-level poverty. Staten Island had an annualized age-adjusted pneumonia death rate 1.3 (95% CI, 1.2-1.3) times that of Manhattan. In the multivariable analysis, pneumonia deaths were more likely to occur among males, non-Hispanic blacks, persons aged ≥65 years, residents of neighborhoods with higher poverty levels, and in Staten Island.

Conclusions

While the accuracy of death certificates is unknown, investigation is needed to understand why certain populations are disproportionately recorded as dying from pneumonia in NYC.

Neighborhood Inequalities in Hepatitis C Mortality: Spatial and Temporal Patterns and Associated Factors

Deaths attributable to hepatitis C (HCV) infection are increasing in the USA even as highly effective treatments become available. Neighborhood-level inequalities create barriers to care and treatment for many vulnerable populations. We seek to characterize citywide trends in HCV mortality rates over time and identify and describe neighborhoods in New York City (NYC) with disproportionately high rates and associated factors. We used a multiple cause of death (MCOD) definition for HCV mortality. Cases identified between January 1, 2006, and December 31, 2014, were geocoded to NYC census tracts (CT). We calculated age-adjusted HCV mortality rates and identified spatial clustering using a local Moran's I test. Temporal trends were analyzed using joinpoint regression. A multistep global and local Poisson modeling approach was used to test for neighborhood associations with sociodemographic indicators. During the study period, 3697 HCV-related deaths occurred in NYC, with an average annual percent increase of 2.6% (p = 0.02). The HCV mortality rates ranged from 0 to 373.6 per 100,000 by CT, and cluster analysis identified significant clustering of HCV mortality (I = 0.23). Regression identified positive associations between HCV mortality and the proportion of non-Hispanic black or Hispanic residents, neighborhood poverty, education, and non-English-speaking households. Local regression estimates identified spatially varying patterns in these associations. The rates of HCV mortality in NYC are increasing and vary by neighborhood. HCV mortality is associated with many indicators of geographic inequality. Results identified neighborhoods in greatest need for place-based interventions to address social determinants that may perpetuate inequalities in HCV mortality.

Racial and socioeconomic disparities in viral suppression among persons living with HIV in New York City

PURPOSE

To examine differences in racial disparities across levels of neighborhood poverty and differences in socioeconomic disparities by race/ethnicity in viral suppression among persons living with HIV (PLWH).

METHODS

Using HIV surveillance data, we categorized and geocoded PLWH who were in care in New York City (NYC). Multilevel binomial regression techniques were used to model viral suppression with a two-level hierarchical structure, by including age, transmission risk, year of diagnosis, race/ethnicity, census tract poverty, and an interaction term of race/ethnicity and census tract poverty in the model.

RESULTS

There were 30,638 Blacks, 22,921 Hispanics, and 11,695 Whites living with HIV and retained in care in NYC, 2014. Compared with Blacks living in the most impoverished neighborhoods (≥30% residents living below the federal poverty level) who had the lowest proportion of viral suppression, with 75% in males and 76% in females, Whites living in the least impoverished neighborhoods (<10% residents living below the federal poverty level) had the highest, with 92% in males (prevalence ratio = 1.16; 95% confidence interval: 1.13, 1.18) and 90% in females (PR = 1.14; 95% CI: 1.09, 1.19).

CONCLUSIONS

By examining racial and socioeconomic disparities simultaneously, we were able to detect both disparities in viral suppression among PLWH in NYC.

Burden of Adult Community-acquired, Health-care-Associated, Hospital-Acquired, and Ventilator-Associated Pneumonia: New York City, 2010 to 2014

BACKGROUND

Although pneumonia is a leading cause of death in New York City (NYC), limited data exist about the settings in which pneumonia is acquired across NYC. Cases of pneumonia acquired in community settings are more likely to be preventable with vaccines and treatable with first-line antibiotics than those acquired in noncommunity settings. The objective of this study was to estimate the burden of hospitalizations associated with community-acquired (CAP), health-care-associated (HCAP), hospital-acquired (HAP), and ventilator-associated (VAP) pneumonia from 2010 to 2014.

METHODS

This retrospective analysis was performed by using an all-payer reporting system of hospital discharges that included NYC residents aged ≥ 18 years. Pneumonia-associated hospitalizations were defined as any hospitalization that included a diagnostic code for pneumonia among any of the discharge diagnoses. Using published clinical guidelines, we classified hospitalizations into mutually exclusive categories of CAP, HCAP, HAP, and VAP and defined pneumonia acquired in the community setting as the combination of CAP and HCAP.

RESULTS

Of 4,614,108 hospitalizations during the reporting period, 283,927 (6.2%) involved pneumonia. Among pneumonia-associated hospitalizations, 154,158 (54.3%) were CAP, 85,656 (30.2%) were HCAP, 39,712 (14.0%) were HAP, and 4,401 (1.6%) were VAP. Death during hospitalization occurred in 7.9% of CAP-associated hospitalizations, compared with 15.6% of HCAP-associated hospitalizations, 20.7% of HAP-associated hospitalizations, and 21.6% of VAP-associated hospitalizations.

CONCLUSIONS

Most pneumonia-associated hospitalizations in NYC involve pneumonias acquired in the community setting. Although 15.6% of pneumonia-associated hospitalizations were categorized as HAP or VAP, these pneumonias accounted for > 25% of deaths from pneumonia-associated hospitalizations. Public health pneumonia prevention efforts need to target both community and hospital settings.

HEALTH SURVEY OF FREE-RANGING RACCOONS (PROCYON LOTOR) IN CENTRAL PARK, NEW YORK, NEW YORK, USA: IMPLICATIONS FOR HUMAN AND DOMESTIC ANIMAL HEALTH

We conducted health assessments on 113 free-ranging raccoons ( Procyon lotor ) in Central Park, New York City, US, in February 2010, September 2010, and November 2011 in conjunction with a trap-vaccinate-release program to control a raccoon rabies epizootic. Five individuals were sampled at two time points for 118 raccoon examinations in total. We tested 13 of 13 and 8 of 13 euthanized raccoons for rabies and canine distemper virus (CDV), respectively, by antigen testing on brain tissue; all were negative for both viruses. Endoparasitism was the most common necropsy finding, with definitive identification of Baylisascaris procyonis in six of eight (75%) necropsied raccoons. Multiple intestinal parasites were detected in feces of living raccoons, including ascarid-type ova in 25 of 80 (31%) raccoons, with B. procyonis confirmed in one sample. Median blood lead level was 7.3 μg/dL (n=104). Rabies virus neutralizing antibody titer was ≥0.5 IU/mL in 9 of 88 (10%) raccoons naive to rabies vaccination and in 13 of 20 (65%) previously vaccinated raccoons. The majority of raccoons we tested were seropositive for canine parvovirus-2 (54/59, 92%) and Toxoplasma gondii (39/60, 65%). Fewer were seropositive for Rickettsia rickettsii (3/30, 10%). None were seropositive for CDV (n=108), canine adenovirus-1 (n=60), or Borrelia burgdorferi (n=30). Ectoparasites found during 16 of 118 (13.6%) physical examinations included Ixodes texanus ticks (15/118, 12.7%) and Trichodectes octomaculatus lice (1/118, 0.8%). We detected Campylobacter jejuni in 5 of 79 (6%) fecal samples. We detected 11 Salmonella enterica serotypes in 70 of 111 (63.1%) enteric cultures, the most common of which were Salmonella Newport (20/70, 29%) and Salmonella Oranienburg (20/70, 29%). These results indicate that raccoons in Central Park likely are involved in the environmental occurrence and potential disease transmission of a variety of infectious and noninfectious diseases of concern for human, wildlife, and domestic animal health.

Defining Clinical Excellence in Adult Infectious Disease Practice

Excellence in clinical care should be recognized and rewarded. A recent paradigm defined clinical excellence through seven key domains. This work examines ID clinical excellence in these domains, and in doing so highlights the important skill sets of ID physicians.

Clinical excellence should be recognized, particularly in the current climate that appropriately prioritizes relationship-centered care. In order to develop a recognition model, a definition of clinical excellence must be created and agreed upon. A paradigm recently suggested by C. Christmas describes clinical excellence through the following domains: diagnostic acumen, professionalism and humanism, communication and interpersonal skills, skillful negotiation of the healthcare system, knowledge, taking a scholarly approach to clinical practice, and having passion for clinical medicine. This work references examples of infectious disease (ID) clinical excellence across Christmas' domains and, in doing so, both examines how the definition of clinical excellence applies to ID practice and highlights the importance of ID physicians. Emphasizing such aspirational standards may not only inspire trainees and practicing physicians to pursue their own fulfilling clinical ID careers, it may also encourage health systems to fully value outstanding ID physicians who labor tirelessly to provide patients with exceptional care.

Impact of a Simulation Exercise on Pharmacy Student Attitude toward Poverty

Objective. To evaluate the impact of a simulation on pharmacy student attitudes toward poverty using the Attitude toward Poverty (ATP) Short Form scale. Methods. Second-year pharmacy students participated in the 3-hour Missouri Association for Community Action Poverty Simulation. Students completed a survey of the ATP Short Form scale prior to and following participation in the simulation. Results. Significant improvements in attitude were noted in 15 of 21 ATP Short Form items. Improvements in the stigma and structural domains were significant while improvement in the personal deficiency domain was not significant. Conclusions. This poverty simulation exercise positively altered pharmacy student attitudes toward poverty. When combined with didactic and experiential curriculum, this simulation may enhance student achievement of the 2013 Center for the Advancement of Pharmacy Education (CAPE) outcome subdomain of cultural sensitivity.