Trophic Garnishes: Cat–Rat Interactions in an Urban Environment

BACKGROUND:Community interactions can produce complex dynamics with counterintuitive responses. Synanthropic community members are of increasing practical interest for their effects on biodiversity and public health. Most studies incorporating introduced species have been performed on islands where they may pose a risk to the native fauna. Few have examined their interactions in urban environments where they represent the majority of species. We characterized house cat (Felis catus) predation on wild Norway rats (Rattus norvegicus), and its population effects in an urban area as a model system. Three aspects of predation likely to influence population dynamics were examined; the stratum of the prey population killed by predators, the intensity of the predation, and the size of the predator population. METHODOLOGY/PRINCIPAL FINDINGS:Predation pressure was estimated from the sizes of the rat and cat populations, and the characteristics of rats killed in 20 alleys. Short and long term responses of rat population to perturbations were examined by removal trapping. Perturbations removed an average of 56% of the rats/alley but had no negative long-term impact on the size of the rat population (49.6+/-12.5 rats/alley and 123.8+/-42.2 rats/alley over two years). The sizes of the cat population during two years (3.5 animals/alley and 2.7 animals/alley) also were unaffected by rat population perturbations. Predation by cats occurred in 9/20 alleys. Predated rats were predominantly juveniles and significantly smaller (144.6 g+/-17.8 g) than the trapped rats (385.0 g+/-135.6 g). Cats rarely preyed on the larger, older portion of the rat population. CONCLUSIONS/SIGNIFICANCE:The rat population appears resilient to perturbation from even substantial population reduction using targeted removal. In this area there is a relatively low population density of cats and they only occasionally prey on the rat population. This occasional predation primarily removes the juvenile proportion of the rat population. The top predator in this urban ecosystem appears to have little impact on the size of the prey population, and similarly, reduction in rat populations doesn't impact the size of the cat population. However, the selected targeting of small rats may locally influence the size structure of the population which may have consequences for patterns of pathogen transmission

Differentiating ZIP Codes in Syndromic Data; What Can They Tell Us?

OBJECTIVE: To classify visits to NYC emergency departments (ED) into NYC residential, NYC PO Box or commercial building, commuters to NYC, and out-of-town visitors. To describe patterns in each group, to evaluate how they differ, and to consider how the differences can affect syndromic surveillance analyses and results. INTRODUCTION: The NYC Department of Health and Mental Hygiene (DOHMH) ED syndromic surveillance system receives data from 95% of all ED visits in NYC totaling 4 million visits each year. The data include residential ZIP code as reported by the patient. ZIP code information has been used by the DOHMH to separate visits into NYC and non-NYC for analysis; and, a closer examination of non-NYC visits may further inform disease surveillance. METHODS: Visits were initially differentiated into six home ZIP code types. NYC residential ZIP codes, PO Boxes and commercial buildings were identified with 2010 US Census and data from the SAS institute (SAS Institute Inc., Cary, NC, USA). Commuter visits to the EDs were classified as any ZIP codes from the NYC Core Based Statistical Area (CBSA; United States Office of Management and Budget). Out of town visits were identified using with the 2010 US Census. Unknown ZIP codes included all of those ZIP codes that were not identified by any of the previous methods, and missing ZIP codes were those that were blank. ZIP codes were verified with the United States Postal Service website (www.usps.com). Once ZIP codes were categorized, spatial and temporal trends in total ED visits by home ZIP code type were analyzed. RESULTS: Of the approximately 4 million ED visits in NYC during 2011, the number of visits by commuters and out-of-town visitors were 125,236 (3.1%) and 45,158 (1.1%) respectively (Figure 1). There were 4,676 (0.1%) visits with a NYC PO Box or building ZIP codes and 48,077 (1.2%) visits with a missing or non-interpretable ZIP codes. The majority of commuter and out-of-town ED visits occur at a smaller set of hospitals. Out-of town visitors mostly visited hospitals in Manhattan rather than hospitals in the outer boroughs. While the seasonal trends and day-of-week patterns for the NYC residents and the commuters appear to be fairly similar, this is not the case for out-of-town visitors. For example, total ED visit trends correlated well for NYC residents and commuters (r=0.77), but there was no correlation between NYC residents and out-of-town visitors (r=−0.18) over time. The number of ED visits among out-of-town visitors was higher during summer months and the winter holiday season, and this trend may have reflected the larger number of visitors during these periods. Day-of-week patterns were similar for NYC residents and commuters with weekdays associated with larger numbers of visits compared to weekends. However, the opposite was found true for out-of-town visitors with larger number of visits occurring over the weekends compared to weekdays. CONCLUSIONS: Considerable differences in temporal trends were found among out-of-town visitors, NYC residents, and commuters to NYC. Out-of-town visitors also tend to visit EDs located in Manhattan rather than in the outer boroughs. These results suggest that out-of-town visitors represent a unique population ED visitors. Analyzing NYC residents, commuters, and out-of-town visitors separately may provide additional information that could prove useful to daily syndromic surveillance activities

Differentiating ZIP Codes in Syndromic Data; What Can They Tell Us?

NYC emergency departments (ED) visits were categorized into NYC residential, NYC PO Box or commercial building, commuters into NYC, and out-of-town visitors by patient reported ZIP code. Spatial and temporal trends in total ED visits by home ZIP code type were analyzed to describe patterns in each group, and determine how such information might inform daily syndromic surveillance activities. Of the approximately 4 million ED visits in NYC during 2011, the number of visits by commuters and out-of-town visitors were 125,236 (3.1%) and 45,158 (1.1%) respectively. Out-of town visitors were found to mostly go to hospitals in Manhattan rather than the outer boroughs. While the seasonal trends and day-of-week patterns for the NYC residents and the commuters appear to be fairly similar, temporal trends for NYC residents and out-of-town visitors were found to be different. Out-of-town visitors represents a unique subset of the ED population and our results suggest that including a separate analyses of total ED and syndromic visits by out-of-town visitors might provide additional information that could prove useful to daily syndromic surveillance activities

Long-Term Asthma Trend Monitoring in New York City: A Mixed Model Approach

OBJECTIVE: Show the benefits of using a generalized linear mixed model (GLMM) to examine long-term trends in asthma syndrome data. INTRODUCTION: Over the last decade, the application of syndromic surveillance systems has expanded beyond early event detection to include long-term disease trend monitoring. However, statistical methods employed for analyzing syndromic data tend to focus on early event detection. Generalized linear mixed models (GLMMs) may be a useful statistical framework for examining long-term disease trends because, unlike other models, GLMMs account for clustering common in syndromic data, and GLMMs can assess disease rates at multiple spatial and temporal levels (1). We show the benefits of the GLMM by using a GLMM to estimate asthma syndrome rates in New York City from 2007 to 2012, and to compare high and low asthma rates in Harlem and the Upper East Side (UES) of Manhattan. METHODS: Asthma related emergency department (ED) visits, and patient age and ZIP code were obtained from data reported daily to the NYC Department of Health and Mental Hygiene. Demographic data were obtained from 2010 US Census. ZIP codes that represented high and low asthma rates in Harlem and the UES of Manhattan were chosen for closer inspection. The ratio of weekly asthma syndrome visits to total ED visits was modeled with a Poisson GLMM with week and ZIP code random intercepts (2). Age and ethnicity were adjusted for because of their association with asthma rates (3). RESULTS: The GLMM showed citywide asthma rates remained stable from 2007 to 2012, but seasonal differences and significant inter-ZIP code variation were present. The Harlem ZIP code asthma rate that was estimated with the GLMM was significantly higher (5.83%, 95% CI: 3.65%, 9.49%) than the asthma rate in UES ZIP code (0.78%, 95% CI: 0.50%, 1.21%). A linear time component to the GLMM showed no appreciable change over time despite the seasonal fluctuations in asthma rate. GLMM based asthma rates are shown over time (Figure 1). CONCLUSIONS: 1. Disease rates can be estimated at multiple spatial and temporal levels, 2. Standard error adjustment for clustering in syndromic data allows for accurate, statistical assessment of changes over time and differences between subgroups, 3. “Strength borrowed” (4) from the aggregated data informs small subgroups and smooths trends, 4. Integration of covariate data reduces bias in estimated rates. GLMMs have previously been suggested for early event detection with syndromic surveillance data (5), but the versatility of GLMM makes them useful for monitoring long-term disease trends as well. In comparison to GLMMs, standard errors from single level GLMs do not account for clustering and can lead to inaccurate statistical hypothesis testing. Bayesian hierarchical models (6), share many of the strengths of GLMMS, but are more complicated to fit. In the future, GLMMs could provide a framework for grouping similar ZIP codes based on their model estimates (e.g. seasonal trends and influence on overall trend), and analyzing long-term disease trends with syndromic data

Assessing the impact of urban sprawl on Rhizophora mangle in Puerto Rico.

This study examined the affects of urban sprawl on the mangrove ecosystems of Puerto Rico. Data on urban sprawl, water quality, mangrove leaves, and mangrove distribution were analyzed statistically and with GIS software. The data indicated a correlation between urban sprawl and increased water pollution levels. Increased hydrocarbons were found to harm mangrove tree health. Urban expansion greatly reduced mangrove tree distribution until the 1970's. Since, mangrove trees have crept into protected and undeveloped areas, and their distribution has increased

Long-Term Asthma Trend Monitoring in New York City: A Mixed Model Approach

The application of syndromic surveillance systems has expanded beyond early event detection to include long-term disease trend monitoring. To address this wider set of priorities, we propose using a general linear mixed model (GLMM) for examining syndrome trends spatially and over time. With the GLMM, we found that New York City asthma rates varied by ZIP code and fluctuated seasonally, but that annual citywide rates did not change from 2007 to 2012. The GLMM estimated rates at multiple spatial and temporal levels, adjusted for clustering with random effects, and integrated covariate demographic data to reduce bias

Shapefile of census tract boundaries in Chicago in 1920

Shapefile of census tract boundaries in Chicago in 1920. File included in zip file include IL_tract_a.dbf, IL_tract_a.prj, IL_tract_a.sbn, IL_tract_a.sbx, IL_tract_a.shp, IL_tract_a.shp.xml, IL_tract_a.sh

Cancer incidence and mortality trends in Asia based on regions and human development index levels: an analyses from GLOBOCAN 2020

As Asian countries transition socially and economically to higher Human Development Index (HDI) levels, cancer trends are expected to shift to those seen in the Western World. A strong correlation also exists between HDI levels and age-standardized rates (ASR) for the incidence and mortality of cancer. However, there are very few reports on the trends in Asian countries, particularly in Low and Middle-Income Countries (LMICs). In this study, we have investigated the relationship between socioeconomic developments in Asia (determined using HDI levels of countries) and cancer incidence and mortality in these nations. The GLOBOCAN 2020 database was used to study the cancer incidence and mortality data for all cancers combined and those most commonly diagnosed in Asia. The difference in data was analyzed based on region and HDI level. Further, the predictions for cancer incidence and mortality in 2040 according to the GLOBOCAN 2020 were analyzed using the updated HDI stratification described in the UNDP 2020 report. Asia has the highest cancer burden compared to the other regions worldwide. Lung cancer carries the highest cancer incidence and mortality rates in the region. Inequitable distribution of cancer incidence and mortality is seen across regions and HDI levels in Asia. Inequalities in cancer incidence and mortality can only be expected to increase unless innovative and cost-effective interventions are urgently implemented. An effective cancer management plan is needed in Asia, particularly in LMICs, prioritizing effective cancer prevention and control measures for health systems.</p

Fine-scale sociodemographics of Chicago, USA, 1920

Socio-demographic data (including population size, illiteracy, unemployment) of 496 census tracts within the City of Chicago. Data was collected from the 1920 national census

1918 Pandemic Influenza Mortality, Chicago USA

Point location and week of epidemic of 8,031 influenza and pneumonia deaths recorded during the 1918 Spanish flu pandemic within the city of Chicago. Data was digitized from 1920 City of Chicago Department of Health annual report Date last modified: 25-10-2016. Fields include: ID (FID), indicator of pneumonia (0 or 1, 0 indicates an influenza death, 1 an influenza and pneumonia death), x and y coordinates (with units in meters), and week (sequential week of epidemic). See paper for more details