Capturing emergency dispatch address points as geocoding candidates to quantify delimited confidence in residential geolocation.

BACKGROUND: In response to citizens' concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients' residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation. RESULTS: We defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62-1.00) and ED_sRGDP (0.36-1.00) varied across counties and were lower in rural counties (p < 0.05); sRGDP correlated with ED_sRGDP (r = 0.42, p < 0.001). The discussion covered multiple conceptual and economic issues attendant to quantifying confidence in residential geolocation and presented a set of organizing principles for future work. CONCLUSIONS: Our methodology produces simple metrics - sRGDP - to capture confidence in residential geolocation via leveraging ED address points as CEL. Two facts demonstrate the usefulness of sRGDP as area-based summary metrics: sRGDP variability between counties and the overall lower quality of residential geolocation in rural vs. urban counties. Low sRGDP for the cancer cases within the area of interest helps manage expectations for the uncertainty in cancer incidence data. By supplementing cancer incidence data with sRGDP and ED_sRGDP, CCRs can demonstrate transparency in geocoding success, which may help win citizen trust.

Big data from small animals: integrating multi-level environmental data into the Dog Aging Project.

Environmental exposures can have large impacts on health outcomes. While many resources have been dedicated to understanding how humans are influenced by the environment, few efforts have been made to study the role of built and natural environmental features on animal health. The Dog Aging Project (DAP) is a longitudinal community science study of aging in companion dogs. Using a combination of owner-reported surveys and secondary sources linked through geocoded coordinates, DAP has captured home, yard and neighbourhood variables for over 40,000 dogs. The DAP environmental data set spans four domains: the physical and built environment; chemical environment and exposures; diet and exercise; and social environment and interactions. By combining biometric data, measures of cognitive function and behaviour, and medical records, DAP is attempting to use a big-data approach to transform the understanding of how the surrounding world affects the health of companion dogs. In this paper, the authors describe the data infrastructure developed to integrate and analyse multi-level environmental data that can be used to improve the understanding of canine co-morbidity and aging.

L'impact des expositions environnementales sur la santé est parfois considérable. Si diverses ressources ont été consacrées à décrire l'influence de l'environnement sur les humains, les efforts visant à étudier l'effet des paramètres environnementaux, tant naturels qu'anthropiques, sur la santé animale sont plus rares. Le Dog Aging Project (DAP) est une étude scientifique longitudinale à base communautaire portant sur le vieillissement du chien de compagnie. À partir d'observations notifiées par les propriétaires de chiens et de sources secondaires reliées par des coordonnées de géocodage, le DAP a réuni des variables sur le foyer d'habitation, l'environnement extérieur immédiat et le voisinage de plus de 40 000 chiens. Les séries de données environnementales du DAP couvrent quatre domaines : l'environnement physique et bâti ; l'environnement chimique et les expositions ; le régime alimentaire et la dépense physique ; et les interactions et l'environnement social. En combinant les données biométriques, les mesures du fonctionnement cognitif et comportemental et les dossiers médicaux, le DAP cherche à utiliser l'approche des mégadonnées pour transformer notre perception de la manière dont le monde qui nous entoure affecte la santé des chiens de compagnie. Les auteurs décrivent l'infrastructure des données mise au point pour intégrer et analyser des données environnementales multi-niveaux, afin de mieux comprendre les phénomènes de comorbidité et de vieillissement chez le chien.

La exposición a factores ambientales puede tener muchas e importantes repercusiones en los resultados sanitarios. Si bien se han dedicado cuantiosos recursos a aprehender la influencia del entorno en las personas, poco se ha hecho para estudiar el modo en que las características del medio, tanto natural como artificial, repercuten en la salud de los animales. El proyecto sobre Envejecimiento canino [Dog Aging Project: DAP] es un estudio longitudinal de ciencia ciudadana centrado en el envejecimiento de los perros de compañía. Combinando la información de encuestas realizadas a propietarios y de fuentes secundarias y vinculando los datos a coordenadas geográficas codificadas, el DAP ha permitido reunir información de variables ligadas al hogar, el jardín y el barrio de más de 40 000 perros. El conjunto de datos ambientales del DAP cubre cuatro grandes ámbitos: medio físico y urbanizado; condiciones químicas del entorno y exposición a sustancias químicas; régimen alimentario y ejercicio; y medio e interacciones sociales. Pasando por el uso combinado de datos biométricos, historias clínicas y mediciones de la función cognitiva y el comportamiento, el DAP apunta ahora a emplear técnicas de trabajo con macrodatos para hacer evolucionar nuestras ideas sobre la influencia del mundo que nos rodea en la salud de los perros de compañía. Los autores describen la infraestructura de datos establecida para integrar y analizar datos ambientales multiestratificados que nos ayuden a conocer mejor los procesos de comorbilidad y envejecimiento en el perro.

Disparities in arthroplasty utilization for rotator cuff tear arthropathy.

BACKGROUND: Rotator cuff tear arthropathy (CTA) carries a significant symptomatic burden for patients. Reverse shoulder arthroplasty (RSA) is an effective treatment intervention for CTA. Disparities in musculoskeletal medicine are well documented; however, there is a paucity of literature on how social determinants of health affect utilization rates. The purpose of this study is to determine how social determinants of health affect the utilization rates of RSA. METHODS: A single-center retrospective review was conducted for adult patients diagnosed with CTA between 2015 and 2020. Patients were divided by those who underwent RSA and those who were offered RSA but did not undergo surgery. Each patient's zip code was used to determine the most specific median household income in the US Census Bureau database and compared to the multistate metropolitan statistical area median income. Income levels were defined by the US Department of Housing and Urban Development's (HUD's) 2022 Income Limits Documentation System and the Federal Reserve's (FED's) Community Reinvestment Act. Because of numeric restrictions, patients were grouped into racial cohorts of Black, White, and all other races. RESULTS: Patients of other races had significantly lower odds of continuing to surgery compared with White patients in models controlled for median household income (odds ratio [OR] 0.38, 95% confidence interval [CI] 0.18-0.81, P = .01), HUD's 3 income levels (OR 0.36, 95% CI 0.18-0.74, P = .01), and FED's income levels (OR 0.37, 95% CI 0.17-0.79, P = .01). There was no significantly different odds of going on to surgery between FED income levels and median household income levels, but when compared with those with low HUD income, those below median had significantly lower odds of going on to surgery (OR 0.43, 95% CI 0.23-0.80, P = .01). CONCLUSION: Although contradictory to reported health care utilization for Black patients, our study supports reported disparities in utilization for other ethnic minorities. These findings may suggest that improvements in utilization efforts targeted Black-identifying patients but not necessarily other ethnic minorities. The findings of this study can help providers understand how social determinants of health play a role in the utilization of care for CTA and direct mitigation efforts to reduce disparities in access to adequate orthopedic care.

High-Risk Neighborhoods and Neurodevelopmental Outcomes in Infants Born Preterm.

OBJECTIVE: To study the association between neighborhood risk and moderate to severe neurodevelopmental impairment (NDI) at 22-26 months corrected age in children born at <34 weeks of gestation. We hypothesized that infants born preterm living in high-risk neighborhoods would have a greater risk of NDI and cognitive, motor, and language delays. STUDY DESIGN: We studied a retrospective cohort of 1291 infants born preterm between 2005 and 2016, excluding infants with congenital anomalies. NDI was defined as any one of the following: a Bayley Scales of Infant and Toddler Development-III Cognitive or Motor composite score <85, bilateral blindness, bilateral hearing impairment, or moderate-severe cerebral palsy. Maternal addresses were geocoded to identify census block groups and create high-risk versus low-risk neighborhood groups. Bivariate and regression analyses were run to assess the impact of neighborhood risk on outcomes. RESULTS: Infants from high-risk (n = 538; 42%) and low-risk (n = 753; 58%) neighborhoods were compared. In bivariate analyses, the risk of NDI and cognitive, motor, and language delays was greater in high-risk neighborhoods. In adjusted regression models, the risks of NDI (OR, 1.43; 95% CI, 1.04-1.98), cognitive delay (OR, 1.62; 95% CI, 1.15-2.28), and language delay (OR, 1.58; 95% CI, 1.15-2.16) were greater in high-risk neighborhoods. Breast milk at discharge was more common in low-risk neighborhoods and was protective of NDI in regression analysis. CONCLUSIONS: High neighborhood risk provides an independent contribution to preterm adverse NDI, cognitive, and language outcomes. In addition, breast milk at discharge was protective. Knowledge of neighborhood risk may inform the targeted implementation of programs for socially disadvantaged infants.

The impact of socioeconomic status on telemedicine utilization during the COVID-19 pandemic among surgical clinics at an academic tertiary care center.

BACKGROUND: The COVID-19 pandemic caused many surgical providers to conduct outpatient evaluations using remote audiovisual conferencing technology (i.e., telemedicine) for the first time in 2020. We describe our year-long institutional experience with telemedicine in several general surgery clinics at an academic tertiary care center and examine the relationship between area-based socioeconomic measures and the likelihood of telemedicine participation. METHODS: We performed a retrospective review of our outpatient telemedicine utilization among four subspecialty clinics (including two acute care and two elective surgery clinics). Geocoding was used to link patient visit data to area-based socioeconomic measures and a multivariable analysis was performed to examine the relationship between socioeconomic indicators and patient participation in telemedicine. RESULTS: While total outpatient visits per month reached a nadir in April 2020 (65% decrease in patient visits when compared to January 2020), there was a sharp increase in telemedicine utilization during the same month (38% of all visits compared to 0.8% of all visits in the month prior). Higher rates of telemedicine utilization were observed in the two elective surgery clinics (61% and 54%) compared to the two acute care surgery clinics (14% and 9%). A multivariable analysis demonstrated a borderline-significant linear trend (p = 0.07) between decreasing socioeconomic status and decreasing odds of telemedicine participation among elective surgery visits. A sensitivity analysis to examine the reliability of this trend showed similar results. CONCLUSION: Telemedicine has many patient-centered benefits, and this study demonstrates that for certain elective subspecialty clinics, telemedicine may be utilized as the preferred method for surgical consultations. However, to ensure the equitable adoption and advancement of telemedicine services, healthcare providers will need to focus on mitigating the socioeconomic barriers to telemedicine participation.

Location, Location, Location: The Role of Objective Neighborhood Characteristics for Perceptions of Control.

INTRODUCTION: Control beliefs can protect against age-related declines in functioning. It is unclear whether neighborhood characteristics shape how much control people perceive over their life. This article studies associations of neighborhood characteristics with control beliefs of residents of a diverse metropolitan area (Berlin, Germany). METHODS: We combine self-report data about perceptions of control obtained from participants in the Berlin Aging Study II (N = 507, 60-87 years, 51% women) with multisource geo-referenced indicators of neighborhood characteristics using linear regression models. RESULTS: Findings indicate that objective neighborhood characteristics (i.e., unemployment rate) are indeed tied to perceptions of control, in particular, how much control participants feel others have over their lives. Including neighborhood characteristics in part doubled the amount of explained variance compared with a reference model covarying for demographic characteristics only (from R2 = 0.017 to R2 = 0.030 for internal control beliefs; R2 = 0.056 to R2 = 0.102 for external control beliefs in chance; R2 = 0.006 to R2 = 0.030 for external control beliefs in powerful others). DISCUSSION/CONCLUSION: Findings highlight the importance of access to neighborhood resources for control beliefs across old age and can inform interventions to build up neighborhood characteristics which might be especially helpful in residential areas with high unemployment.

[Geocoding one million of addresses using API: a semiautomatic multistep procedure]. / Geocodificare un milione di indirizzi mediante l'utilizzo di API: una procedura semiautomatica a più step.

OBJECTIVES: to geocode all residence addresses from Lazio Health Information System in order to obtain a geographical regional database. DESIGN: a semiautomatic and multistep geocoding procedure using several tools and software. SETTING AND PARTICIPANTS: all residence addresses of resident population of Lazio Region (Central Italy) in 2020. MAIN OUTCOME MEASURES: geographic coordinates at residence addresses and accuracy level of geocoding procedure for more than 1 million of addresses. RESULTS: the 99% of residence addresses in the Lazio Region have been geocoded thanks to the purposed procedure; almost 94% of the addresses have been geocoded with a good level of accuracy (more than 56% at civic number level). In the province of Rome, the percentage of addresses geocoded with a good level of accuracy is higher (97.1%), while in the province of Rieti and Frosinone is lower (82.7% and 84.2%, respectively). CONCLUSIONS: this method is useful to obtain accurate geographic coordinates of residences of the entire regional population. This database will be useful for several epidemiological studies in the Region.

Marked Reduction of Socioeconomic and Racial Disparities in Invasive Pneumococcal Disease Associated With Conjugate Pneumococcal Vaccines.

BACKGROUND: It is not known whether reductions in socioeconomic and racial disparities in incidence of invasive pneumococcal disease (defined as the isolation of Streptococcus pneumoniae from a normally sterile body site) noted after pneumococcal conjugate vaccine (PCV) introduction have been sustained. METHODS: Individual-level data collected from 20 Tennessee counties participating in Active Bacterial Core surveillance over 19 years were linked to neighborhood-level socioeconomic factors. Incidence rates were analyzed across 3 periods-pre-7-valent PCV (pre-PCV7; 1998-1999), pre-13-valent PCV (pre-PCV13; 2001-2009), and post-PCV13 (2011-2016)-by socioeconomic factors. RESULTS: A total of 8491 cases of invasive pneumococcal disease were identified. Incidence for invasive pneumococcal disease decreased from 22.9 (1998-1999) to 17.9 (2001-2009) to 12.7 (2011-2016) cases per 100 000 person-years. Post-PCV13 incidence (95% confidence interval [CI]) of PCV13-serotype disease in high- and low-poverty neighborhoods was 3.1 (2.7-3.5) and 1.4 (1.0-1.8), respectively, compared with pre-PCV7 incidence of 17.8 (15.7-19.9) and 6.4 (4.9-7.9). Before PCV introduction, incidence (95% CI) of PCV13-serotype disease was higher in blacks than whites (17.3 [15.1-19.5] vs 11.8 [10.6-13.0], respectively); after introduction, PCV13-type disease incidence was greatly reduced in both groups (white: 2.7 [2.4-3.0]; black: 2.2 [1.8-2.6]). CONCLUSIONS: Introduction of PCV13 was associated with substantial reductions in overall incidence and socioeconomic and racial disparities in PCV13-serotype incidence.

Web Data Mining: Validity of Data from Google Earth for Food Retail Evaluation.

To overcome the challenge of obtaining accurate data on community food retail, we developed an innovative tool to automatically capture food retail data from Google Earth (GE). The proposed method is relevant to non-commercial use or scholarly purposes. We aimed to test the validity of web sources data for the assessment of community food retail environment by comparison to ground-truth observations (gold standard). A secondary aim was to test whether validity differs by type of food outlet and socioeconomic status (SES). The study area included a sample of 300 census tracts stratified by SES in two of the largest cities in Brazil, Rio de Janeiro and Belo Horizonte. The GE web service was used to develop a tool for automatic acquisition of food retail data through the generation of a regular grid of points. To test its validity, this data was compared with the ground-truth data. Compared to the 856 outlets identified in 285 census tracts by the ground-truth method, the GE interface identified 731 outlets. In both cities, the GE interface scored moderate to excellent compared to the ground-truth data across all of the validity measures: sensitivity, specificity, positive predictive value, negative predictive value and accuracy (ranging from 66.3 to 100%). The validity did not differ by SES strata. Supermarkets, convenience stores and restaurants yielded better results than other store types. To our knowledge, this research is the first to investigate using GE as a tool to capture community food retail data. Our results suggest that the GE interface could be used to measure the community food environment. Validity was satisfactory for different SES areas and types of outlets.

A pragmatic guide to geoparsing evaluation: Toponyms, Named Entity Recognition and pragmatics.

Empirical methods in geoparsing have thus far lacked a standard evaluation framework describing the task, metrics and data used to compare state-of-the-art systems. Evaluation is further made inconsistent, even unrepresentative of real world usage by the lack of distinction between the different types of toponyms, which necessitates new guidelines, a consolidation of metrics and a detailed toponym taxonomy with implications for Named Entity Recognition (NER) and beyond. To address these deficiencies, our manuscript introduces a new framework in three parts. (Part 1) Task Definition: clarified via corpus linguistic analysis proposing a fine-grained Pragmatic Taxonomy of Toponyms. (Part 2) Metrics: discussed and reviewed for a rigorous evaluation including recommendations for NER/Geoparsing practitioners. (Part 3) Evaluation data: shared via a new dataset called GeoWebNews to provide test/train examples and enable immediate use of our contributions. In addition to fine-grained Geotagging and Toponym Resolution (Geocoding), this dataset is also suitable for prototyping and evaluating machine learning NLP models.

Neighborhood socioeconomic disadvantage is associated with multimorbidity in a geographically-defined community.

BACKGROUND: Persons with low socioeconomic status may be disproportionately at risk for multimorbidity. METHODS: Adults aged ≥20 years on 4/1/2015 from 7 counties in Minnesota were identified using the Rochester Epidemiology Project (population-based sample). A composite measure of neighborhood socioeconomic disadvantage, the area deprivation index (ADI), was estimated at the census block group level (n = 251). The prevalence of 21 chronic conditions was obtained to calculate the proportion of persons with multimorbidity (≥2 chronic conditions) and severe multimorbidity (≥5 chronic conditions). Hierarchical logistic regression was used to estimate the association of ADI with multimorbidity and severe multimorbidity using odds ratios (OR). RESULTS: Among 198,941 persons (46.7% male, 30.6% aged ≥60 years), the age- and sex-standardized (to the United States 2010 census) median prevalence (Q1, Q3) was 23.4% (21.3%, 25.9%) for multimorbidity and 4.8% (4.0%, 5.7%) for severe multimorbidity. Compared with persons in the lowest quintile of ADI, persons in the highest quintile had a 50% increased risk of multimorbidity (OR 1.50, 95% CI 1.39-1.62) and a 67% increased risk of severe multimorbidity (OR 1.67, 95% CI 1.51-1.86) after adjusting for age, sex, race, and ethnicity. Associations were stronger after further adjustment for individual level of education; persons in the highest quintile had a 78% increased risk of multimorbidity (OR 1.78, 95% CI 1.62-1.96) and a 92% increased risk of severe multimorbidity (OR 1.92, 95% CI 1.72-2.13). There was evidence of interactions between ADI and age, between ADI and sex, and between ADI and education. After age 70 years, no difference in the risk of multimorbidity was observed across quintiles of ADI. The pattern of increasing multimorbidity with increasing ADI was more pronounced in women. Finally, there was less variability across quintiles of ADI for the most highly educated group. CONCLUSIONS: Higher ADI was associated with increased risk of multimorbidity, and the associations were strengthened after adjustment for individual level of education, suggesting that neighborhood context plays a role in health above and beyond individual measures of socioeconomic status. Furthermore, associations were more pronounced in younger persons and women, highlighting the importance of interventions to prevent chronic conditions in younger women, in particular.

Accuracy of matching residential postal codes to census geography.

BACKGROUND: Postal codes are often the only geographic identifier available to match subjects in a health dataset to census geography. This paper describes the characteristics of postal codes reported by the Canadian population on the census and, as an indicator of geocoding accuracy, the proportion that are linked to a single dissemination area (DA). DATA AND METHODS: Postal codes reported on the 2016 Census questionnaire were matched to a combination of the Postal Code Conversion File (PCCF) and the Postal Code Conversion File Plus (PCCF+ version 7B) (reference date November 2018) to calculate population-weighted counts and the number of matches to DAs by province or territory, delivery mode type (DMT), population centre or rural area size, and census metropolitan area. The number of single matches to census tracts (CTs), census subdivisions (CSDs) and census divisions (CDs) was also calculated. RESULTS: In Canada, 72.6% of the population reported postal codes that matched to a single DA. This proportion was higher in urban cores (87.1%) and among postal codes for an urban street address (DMT=A) (85.3%) or apartment building (DMT=B) (95.3%), and was lower in rural areas (26.2% to 38.1%) and among rural postal codes (13.9%). In comparison, 89.3% and 95.4% of the population reported postal codes matching to a single CSD or CD, respectively, while 92.1% of the population that live within CT boundaries were matched to a single CT. DISCUSSION: Matching postal codes to census geography is relatively accurate and frequently one to one in urban centres. In rural areas and for some types of postal code DMTs, alternative approaches to using the PCCF and PCCF+ for attaching census geography might be explored.

The use of geographical analysis in assessing the impact of patients' home addresses on their participation in outpatient cardiac rehabilitation: a prospective cohort study.

PURPOSE: Geographical analysis is becoming a powerful tool for evaluating the quality of medical services and acquiring fundamental data for medical decision-making. Using geographical analysis, we evaluated the impact of the distance from patients' homes to the hospital on their participation in outpatient cardiac rehabilitation (OCR). METHODS: All patients hospitalized for percutaneous coronary intervention, coronary artery bypass grafting, valvular surgery, congestive heart failure, and aortic diseases were advised to participate in an OCR program after discharge. Using the dataset of our cohort study of OCR from 2004 to 2015 (n = 9,019), we used geographical analysis to investigate the impact of the distance from patients' homes to hospital on their participation in our OCR program. RESULTS: Patients whose road distance from home to hospital was 0-10 km, 10-20 km, and 20-30 km participated more in OCR than those whose road distance was ≧ 30 km (OR 4.34, 95% CI 3.80-4.96; OR 2.98, 95% CI 2.61-3.40; and OR 1.90, 95% CI 1.61-2.23, respectively). Especially in patients with heart failure, the longer the distance, the lesser the participation rate (P < .001). CONCLUSIONS: Using geographical analysis, we successfully evaluated the factors influencing patients' participation in OCR. This illustrates the importance of using geographical analysis in future epidemiological and clinical studies. TRIAL REGISTRATION: UMIN000028435.

Disparities in genetic services utilization in a random sample of young breast cancer survivors.

PURPOSE: Increasing use of genetic services (counseling/testing) among young breast cancer survivors (YBCS) can help decrease breast cancer incidence and mortality. The study examined use of genetic services between Black and White/Other YBCS, attitudes and knowledge of breast cancer risk factors, and reasons for disparities in using genetic services. METHODS: We used baseline data from a randomized control trial including a population-based, stratified random sample of 3000 potentially eligible YBCS, with oversampling of Black YBCS. RESULTS: Among 883 YBCS (353 Black, 530 White/Other) were significant disparities between the two racial groups. More White/Other YBCS had received genetic counseling and had genetic testing than Blacks. Although White/Other YBCS resided farther away from board-certified genetic counseling centers, they had fewer barriers to access these services. Black race, high out-of-pocket costs, older age, and more years since diagnosis were negatively associated with use of genetic services. Black YBCS had lower knowledge of breast cancer risk factors. Higher education and genetic counseling were associated with higher genetic knowledge. CONCLUSION: Racial inequalities of cost-related access to care and education create disparities in genetic services utilization. System-based interventions that reduce socioeconomic disparities and empower YBCS with genetic knowledge, as well as physician referrals, can increase access to genetic services.

Utilization of Census Tract-Based Neighborhood Poverty Rates to Predict Non-adherence to Screening Colonoscopy.

OBJECTIVES: Efforts to improve colorectal cancer (CRC) screening rates include recognizing predictors of colonoscopy non-adherence and identifying these high-risk patient populations. Past studies have focused on individual-level factors but few have evaluated the influence of neighborhood-level predictors. We sought to assess the effect of census tract-based neighborhood poverty rates on scheduled screening colonoscopy non-adherence. METHODS: In this prospective observational cohort study, data from electronic medical records and appointment tracking software were collected in 599 patients scheduled to undergo outpatient CRC screening colonoscopy at two academic endoscopy centers between January 2011 and December 2012. Non-adherence was defined as failure to attend a colonoscopy appointment within 1 year of the date it was electronically scheduled. Neighborhood poverty rate was determined by matching patients' self-reported home address with their corresponding US census tract. Individual factors including medical comorbidities and prior appointment adherence behavior were also collected. RESULTS: Overall, 17% (65/383) of patients were non-adherent to scheduled colonoscopy at 1-year follow-up. Neighborhood poverty rate was a significant predictor of non-adherence to scheduled screening colonoscopy in multivariate modeling (OR 1.53 per 10% increase in neighborhood poverty rate, 95% CI 1.21-1.95, p < 0.001). By incorporating the neighborhood poverty rate, screening colonoscopy non-adherence was 31% at the highest quartile compared to 14% at the lowest quartile of neighborhood poverty rates (p = 0.006). CONCLUSIONS: Census tract-based neighborhood poverty rates can be used to predict non-adherence to scheduled screening colonoscopy. Targeted efforts to increase CRC screening efficiency and completion among patients living in high-poverty geographic regions could reduce screening disparities and improve utilization of endoscopy unit resources.

Using data from online geocoding services for the assessment of environmental obesogenic factors: a feasibility study.

BACKGROUND: The increasing prevalence of obesity is a major public health problem in many countries. Built environment factors are known to be associated with obesity, which is an important risk factor for type 2 diabetes. Online geocoding services could be used to identify regions with a high concentration of obesogenic factors. The aim of our study was to examine the feasibility of integrating information from online geocoding services for the assessment of obesogenic environments. METHODS: We identified environmental factors associated with obesity from the literature and translated these factors into variables from the online geocoding services Google Maps and OpenStreetMap (OSM). We tested whether spatial data points can be downloaded from these services and processed and visualized on maps. True- and false-positive values, false-negative values, sensitivities and positive predictive values of the processed data were determined using search engines and in-field inspections within four pilot areas in Bavaria, Germany. RESULTS: Several environmental factors could be identified from the literature that were either positively or negatively correlated with weight outcomes in previous studies. The diversity of query variables was higher in OSM compared with Google Maps. In each pilot area, query results from Google showed a higher absolute number of true-positive hits and of false-positive hits, but a lower number of false-negative hits during the validation process. The positive predictive value of database hits was higher in OSM and ranged between 81 and 100% compared with a range of 63-89% for Google Maps. In contrast, sensitivities were higher in Google Maps (between 59 and 98%) than in OSM (between 20 and 64%). CONCLUSIONS: It was possible to operationalize obesogenic factors identified from the literature with data and variables available from geocoding services. The validity of Google Maps and OSM was reasonable. The assessment of environmental obesogenic factors via geocoding services could potentially be applied in diabetes surveillance.

Twins Research Australia: A New Paradigm for Driving Twin Research.

Twins Research Australia (TRA) is a community of twins and researchers working on health research to benefit everyone, including twins. TRA leads multidisciplinary research through the application of twin and family study designs, with the aim of sustaining long-term twin research that, both now and in the future, gives back to the community. This article summarizes TRA's recent achievements and future directions, including new methodologies addressing causation, linkage to health, economic and educational administrative datasets and to geospatial data to provide insight into health and disease. We also explain how TRA's knowledge translation and exchange activities are key to communicating the impact of twin studies to twins and the wider community. Building researcher capability, providing registry resources and partnering with all key stakeholders, particularly the participants, are important for how TRA is advancing twin research to improve health outcomes for society. TRA provides researchers with open access to its vibrant volunteer membership of twins, higher order multiples (multiples) and families who are willing to consider participation in research. Established four decades ago, this resource facilitates and supports research across multiple stages and a breadth of health domains.

Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation.

In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico's 2017 Earthquake is presented, and the data extracted during and after the event are reported.

An approach to the asthma-protective farm effect by geocoding: Good farms and better farms.

BACKGROUND: The highly consistent association of growing up on a farm with a reduced asthma risk has so far been attributed to direct farm exposure. In contrast, geographic determinants of the larger environment have never been assessed. In this study, the effects of proximity to farms and environmental variables in relation to the residential address on asthma and atopy were assessed. METHODS: Addresses of 2265 children of the Bavarian arm of the GABRIELA study were converted into geocodes. Proximity to the nearest cow farm was calculated, and environmental characteristics were derived from satellite data or terrestrial monitoring. Bacterial diversity in mattress dust samples was assessed in 501 children by sequencing of the 16S rRNA amplicons. Logistic regression models were used to calculate associations between outcomes and exposure variables. RESULTS: Asthma and atopy were inversely associated with the presence of a farm within a radius of maximum 100 m. The environmental variables greenness, tree cover, soil sealing, altitude, air pollution differed not only between farm and non-farm children but also between farm children with and without another farm nearby. The latter distinction revealed strong associations with characteristics of traditional farms including a broader diversity of microbial exposure, which mainly contributed to the protective effect on asthma. In non-farm children, the protective effect of a farm nearby was completely explained by consumption of farm milk. CONCLUSIONS: Clustering of farms within a neighborhood of 100 m is strongly associated with the protective effect on asthma and may represent a more traditional style of farming with broader microbial exposure.

Calculating census tract-based life expectancy in New York state: a generalizable approach.

BACKGROUND: Life expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities. METHODS: Our study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008-2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years. RESULTS: Aggregation resulted in a 9.9% reduction in the number of areas. Tracts with < 2% of population living below the poverty level had a LE of 82.8 years, while tracts with a poverty level ≥ 25% had a LE of 75.5. We observed differences in LE in border areas, of up to 10.4 years, when excluding or including deaths of study area residents that occurred outside the study area. The range and standard deviation at the county level (77.5-82.8, SD = 1.2 years) were smaller than our final sub-county areas (64.7-92.0, SD = 3.3 years). The correlation between LE and poverty were similar and statistically significant (p < 0.0001) at the county (r = - 0.58) and sub-county level (r = - 0.58). The correlations between LE and percent African-American at the county level were (r = 0.11, p = 0.43) and at the sub-county level (r = - 0.25, p < 0.0001). CONCLUSION: The proposed approach for geocoding and aggregation of mortality and population data provides a solution for health departments to produce stable empirically-derived LE estimates using data coded to the tract. Reliable estimates within sub-county areas are needed to aid public health officials in focusing preventive health programs in areas where health disparities would be masked by county level estimates.