Interventions to reduce UP should recognize the long-term effects of childhood sexual violence and address the role of low self-esteem on the ability of young sexually active women to effectively and consistently use contraception to prevent UP.
BackgroundPoison control centers (PCCs) hold great potential for saving health care resources particularly by preventing unnecessary medical evaluations. We developed a survey to better identify the needs and experiences of our service community. We hope to use these data to improve PCC outreach education and overall use of our services.MethodA written questionnaire was developed in English and then translated into Spanish. Subjects agreeing to participate were then asked two verbal questions in English: are you at least 18 years of age? And; in what language would you like to complete the questionnaire; English or Spanish? All questionnaires completed by subjects ≥18 years of age were included. Questionnaires with missing responses, other than zip code, were included. Data collected include gender, age, zip code, primary language, ethnicity, education, health insurance status and experiences with the PCC. Subjects were not compensated for participation. Arizona zip codes were divided into “rural” or “urban” based on a census data website. Percentages and odds ratios were determined based on completed responses. Smaller subgroups, for some variables, were combined to increase sample sizes and improve statistical relevance.ResultsOverall, women and subjects with children at home (regardless of ethnicity) were significantly more likely to have heard of the PCC although Blacks and Spanish-speakers were significantly less likely to have heard of the PCC. Similarly, respondents with children at home and those reporting a prior home poisoning (regardless of ethnicity) were significantly more likely to have called the PCC. Blacks were significantly less likely to have called the PCC. These findings were similar among people living in urban zip codes but not statistically significant among rural responders.ConclusionsBased on a small survey, race and language spoken at home were variables identified as being associated with decreased awareness of poison centers. Focusing on these specific groups may assist in efforts to increase PCC penetrance, particularly among urban communities.
Pneumonia is a leading cause of death in New York City (NYC). We identified spatial clusters of pneumonia-associated hospitalisation for persons residing in NYC, aged ⩾18 years during 2010–2014. We detected pneumonia-associated hospitalisations using an all-payer inpatient dataset. Using geostatistical semivariogram modelling, local Moran'sIcluster analyses andχ2tests, we characterised differences between ‘hot spots’ and ‘cold spots’ for pneumonia-associated hospitalisations. During 2010–2014, there were 141 730 pneumonia-associated hospitalisations across 188 NYC neighbourhoods, of which 43.5% (N= 61 712) were sub-classified as severe. Hot spots of pneumonia-associated hospitalisation spanned 26 neighbourhoods in the Bronx, Manhattan and Staten Island, whereas cold spots were found in lower Manhattan and northeastern Queens. We identified hot spots of severe pneumonia-associated hospitalisation in the northern Bronx and the northern tip of Staten Island. For severe pneumonia-associated hospitalisations, hot-spot patients were of lower mean age and a greater proportion identified as non-Hispanic Black compared with cold spot patients; additionally, hot-spot patients had a longer hospital stay and a greater proportion experienced in-hospital death compared with cold-spot patients. Pneumonia prevention efforts within NYC should consider examining the reasons for higher rates in hot-spot neighbourhoods, and focus interventions towards the Bronx, northern Manhattan and Staten Island.
Background New York City (NYC) reported a higher pneumonia and influenza death rate than the rest of New York State during 2010–2014. Most NYC pneumonia and influenza deaths are attributed to pneumonia caused by infection acquired in the community, and these deaths typically occur in hospitals. Methods We identified hospitalizations of New York State residents aged ≥20 years discharged from New York State hospitals during 2010–2014 with a principal diagnosis of community-setting pneumonia or a secondary diagnosis of community-setting pneumonia if the principal diagnosis was respiratory failure or sepsis. We examined mean annual age-adjusted community-setting pneumonia-associated hospitalization (CSPAH) rates and proportion of CSPAH with in-hospital death, overall and by sociodemographic group, and produced a multivariable negative binomial model to assess hospitalization rate ratios. Results Compared with non-NYC urban, suburban, and rural areas of New York State, NYC had the highest mean annual age-adjusted CSPAH rate at 475.3 per 100,000 population and the highest percentage of CSPAH with in-hospital death at 13.7%. NYC also had the highest proportion of CSPAH patients residing in higher-poverty-level areas. Adjusting for age, sex, and area-based poverty, NYC residents experienced 1.3 (95% confidence interval [CI], 1.2–1.4), non-NYC urban residents 1.4 (95% CI, 1.3–1.6), and suburban residents 1.2 (95% CI, 1.1–1.3) times the rate of CSPAH than rural residents. Conclusions In New York State, NYC as well as other urban areas and suburban areas had higher rates of CSPAH than rural areas. Further research is needed into drivers of CSPAH deaths, which may be associated with poverty.
ObjectiveThe objectives of this project were to rapidly build and deploy a web-based reporting platform in response to a canine influenza H3N2 outbreak in New York City (NYC) and provide aggregate data back to the veterinary community as an interactive dashboard.IntroductionData-driven decision-making is a cornerstone of public health emergency response; therefore, a highly-configurable and rapidly deployable data capture system with built-in quality assurance (QA; e.g., completeness, standardization) is critical.1 Additionally, to keep key stakeholders informed of developments during an emergency, data need to be shared in a timely and effective manner. Dynamic data visualization is a particularly useful means of sharing data with healthcare providers and the public.2During Spring 2018, detection of canine influenza H3N2 among dogs in NYC caused concern in the veterinary community. Canine influenza is a highly contagious respiratory infection caused by an influenza A virus.3 However, no central database existed in NYC to monitor the outbreak and no single agency was responsible for data capture. Our team at the NYC Department of Health and Mental Hygiene (DOHMH) partnered with the NYC Veterinary Medical Association (VMA) to monitor the canine influenza H3N2 outbreak by building a web-based reporting platform and interactive dashboard.MethodsThe NYC DOHMH built and deployed a web-based reporting platform to aid veterinarians in reporting cases of canine influenza. We leveraged REDCap Cloud, a cloud-based graphical user interface data capture and management software. REDCap Cloud collected information regarding the provider, owner, dog, residence of dog, illness history, and influenza testing. We leveraged REDCap QA functionality in the form of mandatory questions to ensure data completeness. Several different field types — including dropdown menus, mutually exclusive radio buttons, and multi-select check boxes — were used to ensure data standardization. Skip logic was incorporated to guide users through unique sequences of questions based on the answers they entered. Reporting was voluntary.ResultsAfter requirements were gathered, the REDCap web-based reporting platform was rapidly deployed in approximately two business days. Over the course of one week, multiple versions of the dashboard were produced and the final iteration was completed. The entire system was built on server-side software that is available as free or open-source for individual licenses. The dashboard can be found at the following link: http://www.vmanyc.org/canine_influenza_dashboard.html.A total of 28 cases were reported by 6 providers during June–August 2018. All of the 28 cases were reported from 2 of the 5 NYC counties (boroughs); 17/28 (60.7%) were reported from Brooklyn and 11/28 (39.3%) were reported from Manhattan. We were able to collect mostly complete data by leveraging REDCap QA functionality. The reporting facility was listed in all cases, and an owner was listed in all but two cases. All reported cases used a PCR test for the detection of canine influenza H3N2. One reported case indicated polymerase chain reaction (PCR) test results as “not detected” which suggests that one negative case was reported through the system.ConclusionsUsing REDCap Cloud and R, we were able to rapidly build and deploy a web-based reporting platform and dynamic data visualization during an emergency response to an outbreak of canine influenza H3N2. Our system was used by veterinarians to report 28 cases of canine influenza. Future emergency responses for human disease outbreaks will likely benefit from the experience our team gained during our partnership with the NYC VMA.References1. Centers for Disease Control and Prevention. Public Health Emergency Response Guide for State, Local, and Tribal Public Health Directors. https://emergency.cdc.gov/planning/pdf/cdcresponseguide.pdf.2. Meyer M. The Rise of Healthcare Data Visualization. http://journal.ahima.org/2017/12/21/the-rise-of-healthcare-data-visualization/.3. American Veterinary Medical Association. Canine Influenza FAQ. https://www.avma.org/KB/Resources/FAQs/Pages/Control-of-Canine-Influenza-in-Dogs.aspx.4. Wickham H. R packages. http://r-pkgs.had.co.nz/.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.