Minmaxing of Bayesian Improved Surname Geocoding and Geography Level Ups in Predicting Race

2021 ◽  
pp. 1-7
Author(s):  
Jesse T. Clark ◽  
John A. Curiel ◽  
Tyler S. Steelman
Keyword(s):  
Demographic Data ◽  
Critical Factor ◽  
County Level ◽  
Racial Identification ◽  
Substantial Portion ◽  
Ecological Data ◽  
Trade Offs ◽  
Hispanic Voters ◽  
Black Voters ◽  
Zip Code

Abstract Racial identification is a critical factor in understanding a multitude of important outcomes in many fields. However, inferring an individual’s race from ecological data is prone to bias and error. This process was only recently improved via Bayesian improved surname geocoding (BISG). With surname and geographic-based demographic data, it is possible to more accurately estimate individual racial identification than ever before. However, the level of geography used in this process varies widely. Whereas some existing work makes use of geocoding to place individuals in precise census blocks, a substantial portion either skips geocoding altogether or relies on estimation using surname or county-level analyses. Presently, the trade-offs of such variation are unknown. In this letter, we quantify those trade-offs through a validation of BISG on Georgia’s voter file using both geocoded and nongeocoded processes and introduce a new level of geography—ZIP codes—to this method. We find that when estimating the racial identification of White and Black voters, nongeocoded ZIP code-based estimates are acceptable alternatives. However, census blocks provide the most accurate estimations when imputing racial identification for Asian and Hispanic voters. Our results document the most efficient means to sequentially conduct BISG analysis to maximize racial identification estimation while simultaneously minimizing data missingness and bias.

Clinical and Demographic Risk Factors for COVID-19 during Delivery Hospitalizations in New York City

2021 ◽  
Author(s):  
Desmond Sutton ◽  
Timothy Wen ◽  
Anna P. Staniczenko ◽  
Yongmei Huang ◽  
Maria Andrikopoulou ◽  
...  
Keyword(s):  
Risk Factors ◽  
New York ◽  
New York City ◽  
Logistic Regression ◽  
York City ◽  
Demographic Data ◽  
Outcome Data ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Zip Code

Objective This study was aimed to review 4 weeks of universal novel coronavirus disease 2019 (COVID-19) screening among delivery hospitalizations, at two hospitals in March and April 2020 in New York City, to compare outcomes between patients based on COVID-19 status and to determine whether demographic risk factors and symptoms predicted screening positive for COVID-19. Study Design This retrospective cohort study evaluated all patients admitted for delivery from March 22 to April 18, 2020, at two New York City hospitals. Obstetrical and neonatal outcomes were collected. The relationship between COVID-19 and demographic, clinical, and maternal and neonatal outcome data was evaluated. Demographic data included the number of COVID-19 cases ascertained by ZIP code of residence. Adjusted logistic regression models were performed to determine predictability of demographic risk factors for COVID-19. Results Of 454 women delivered, 79 (17%) had COVID-19. Of those, 27.9% (n = 22) had symptoms such as cough (13.9%), fever (10.1%), chest pain (5.1%), and myalgia (5.1%). While women with COVID-19 were more likely to live in the ZIP codes quartile with the most cases (47 vs. 41%) and less likely to live in the ZIP code quartile with the fewest cases (6 vs. 14%), these comparisons were not statistically significant (p = 0.18). Women with COVID-19 were less likely to have a vaginal delivery (55.2 vs. 51.9%, p = 0.04) and had a significantly longer postpartum length of stay with cesarean (2.00 vs. 2.67days, p < 0.01). COVID-19 was associated with higher risk for diagnoses of chorioamnionitis and pneumonia and fevers without a focal diagnosis. In adjusted analyses, including demographic factors, logistic regression demonstrated a c-statistic of 0.71 (95% confidence interval [CI]: 0.69, 0.80). Conclusion COVID-19 symptoms were present in a minority of COVID-19-positive women admitted for delivery. Significant differences in obstetrical outcomes were found. While demographic risk factors demonstrated acceptable discrimination, risk prediction does not capture a significant portion of COVID-19-positive patients. Key Points

scholarly journals A Vulnerability Assessment for a Future HIV Outbreak Associated With Injection Drug Use in Illinois, 2017–2018

2021 ◽  
Vol 6 ◽  
Author(s):  
Cara Jane Bergo ◽  
Jennifer R. Epstein ◽  
Stacey Hoferka ◽  
Marynia Aniela Kolak ◽  
Mai T. Pho
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Drug Use ◽  
Injection Drug Use ◽  
Negative Binomial ◽  
Cook County ◽  
Injection Drug ◽  
County Level ◽  
Opioid Use ◽  
Zip Code

The current opioid crisis and the increase in injection drug use (IDU) have led to outbreaks of HIV in communities across the country. These outbreaks have prompted country and statewide examination into identifying factors to determine areas at risk of a future HIV outbreak. Based on methodology used in a prior nationwide county-level analysis by the US Centers for Disease Control and Prevention (CDC), we examined Illinois at the ZIP code level (n = 1,383). Combined acute and chronic hepatitis C virus (HCV) infection among persons &lt;40 years of age was used as an outcome proxy measure for IDU. Local and statewide data sources were used to identify variables that are potentially predictive of high risk for HIV/HCV transmission that fell within three main groups: health outcomes, access/resources, and the social/economic/physical environment. A multivariable negative binomial regression was performed with population as an offset. The vulnerability score for each ZIP code was created using the final regression model that consisted of 11 factors, six risk factors, and five protective factors. ZIP codes identified with the highest vulnerability ranking (top 10%) were distributed across the state yet focused in the rural southern region. The most populous county, Cook County, had only one vulnerable ZIP code. This analysis reveals more areas vulnerable to future outbreaks compared to past national analyses and provides more precise indications of vulnerability at the ZIP code level. The ability to assess the risk at sub-county level allows local jurisdictions to more finely tune surveillance and preventive measures and target activities in these high-risk areas. The final model contained a mix of protective and risk factors revealing a heightened level of complexity underlying the relationship between characteristics that impact HCV risk. Following this analysis, Illinois prioritized recommendations to include increasing access to harm reduction services, specifically sterile syringe services, naloxone access, infectious disease screening and increased linkage to care for HCV and opioid use disorder.

scholarly journals Optimizing Screening for HIV

2020 ◽  
Author(s):  
Antoine Chaillon ◽  
Martin Hoenigl ◽  
Lorri Freitas ◽  
Haruna Feldman ◽  
Winston Tilghman ◽  
...  
Keyword(s):  
At Risk ◽  
Screening Program ◽  
Demographic Data ◽  
The United States ◽  
Hiv Prevalence ◽  
Screening Tests ◽  
Transmitted Infection ◽  
Sexually Transmitted ◽  
At Risk Populations ◽  
Zip Code

Abstract Background The HIV epidemic is unevenly distributed throughout the United States, even within neighborhoods. This study evaluated how effectively current testing approaches reached persons at risk for HIV infection across San Diego (SD) County, California. Methods HIV case and testing data, sexually transmitted infection (STI) and socio-demographic data for SD County were collected from the SD Health and Human Services Agency and the ‘Early Test’ community-based HIV screening program between 1998 and 2016. Relationships between HIV diagnoses, HIV prevalence, and STI diagnoses with screening at zip code level were evaluated. Results Overall, 379,074 HIV tests were performed. The numbers of HIV tests performed on persons residing in a zip code or region overall strongly correlated with prevalent HIV cases (R2=0.714), new HIV diagnoses (R2=0.798), and STI diagnoses (R2=0.768 [chlamydia],0.836 [gonorrhea], 0.655 [syphilis]) in those regions. Zip codes with the highest HIV prevalence had the highest number of tests per resident and fewest number of tests per diagnosis. Even though most screening tests occurred at fixed venues located in high prevalence areas, screening of residents from lower prevalence areas was mostly proportional to the prevalence of HIV and rates of new HIV and STI diagnoses in those locales. Conclusion This study supported the ability of a small number of standalone testing centers to reach at-risk populations dispersed across SD County. These methods can also be used to highlight geographic areas, or demographic segments that may benefit from more intensive screening.

Public Interest in Veterans’ Lottery Tickets

2017 ◽  
Vol 44 (3) ◽  
pp. 408-435
Author(s):  
David M. Yaskewich
Keyword(s):  
Public Interest ◽  
College Graduates ◽  
Military Veterans ◽  
Lottery Ticket ◽  
County Level ◽  
Ticket Sales ◽  
Trade Offs ◽  
Level Data ◽  
Explained Variation ◽  
Election Results

While many states earmark lottery proceeds for education programs, a few states have started allocating portions of lottery revenues toward state-provided services for military veterans. The decision to shift funds toward veterans’ services and away from other programs creates an opportunity to study society’s willingness to help veterans when faced with real trade-offs. Using county-level data from three states, I examined public interest in veterans’ lottery tickets by analyzing consumer and voter behaviors. In two states that sold veterans’ tickets, IA and TX, a similar set of county-level variables explained variation in both veterans’ and nonveterans’ ticket sales. However, there were a few cases in which sales patterns differed across ticket categories. Election results from a statewide referendum in MO to create a veterans’ lottery ticket suggested that opposition came from counties with a large proportion of college graduates and high population densities.

scholarly journals Usage of a Digital Health Workplace Intervention Based on Socioeconomic Environment and Race: Retrospective Secondary Cross-Sectional Study (Preprint)

2017 ◽  
Author(s):  
Conor Senecal ◽  
R Jay Widmer ◽  
Kent Bailey ◽  
Lilach O Lerman ◽  
Amir Lerman
Keyword(s):  
Socioeconomic Status ◽  
High Risk ◽  
Digital Health ◽  
Demographic Data ◽  
Health Intervention ◽  
Median Income ◽  
Cross Sectional ◽  
Socioeconomic Environment ◽  
One Year ◽  
Zip Code

BACKGROUND Digital health tools have been associated with improvement of cardiovascular disease (CVD) risk factors and outcomes; however, the differential use of these technologies among various ethnic and economic classes is not well known. OBJECTIVE To identify the effect of socioeconomic environment on usage of a digital health intervention. METHODS A retrospective secondary cross-sectional analysis of a workplace digital health tool use, in association with a change in intermediate markers of CVD, was undertaken over the course of one year in 26,188 participants in a work health program across 81 organizations in 42 American states between 2011 and 2014. Baseline demographic data for participants included age, sex, race, home zip code, weight, height, blood pressure, glucose, lipids, and hemoglobin A1c. Follow-up data was then obtained in 90-day increments for up to one year. Using publicly available data from the American Community Survey, we obtained the median income for each zip code as a marker for socioeconomic status via median household income. Digital health intervention usage was analyzed based on socioeconomic status as well as age, gender, and race. RESULTS The cohort was found to represent a wide sample of socioeconomic environments from a median income of US $11,000 to $171,000. As a whole, doubling of income was associated with 7.6% increase in log-in frequency. However, there were marked differences between races. Black participants showed a 40.5% increase and Hispanic participants showed a 57.8% increase in use with a doubling of income, compared to 3% for Caucasian participants. CONCLUSIONS The current study demonstrated that socioeconomic data confirms no relevant relationship between socioeconomic environment and digital health intervention usage for Caucasian users. However, a strong relationship is present for black and Hispanic users. Thus, socioeconomic environment plays a prominent role only in minority groups that represent a high-risk group for CVD. This finding identifies a need for digital health apps that are effective in these high-risk groups.

scholarly journals Measuring Race and Ancestry in the Age of Genetic Testing

Demography ◽  
2021 ◽  
Author(s):  
Sasha Shen Johfre ◽  
Aliya Saperstein ◽  
Jill A. Hollenbach
Keyword(s):  
Genetic Testing ◽  
Demographic Data ◽  
Racial Identification ◽  
Ancestral Origin ◽  
Multiple Regions ◽  
Asking Questions ◽  
Middle Aged Adults ◽  
Unique Study ◽  
Present Experimental Evidence ◽  
Demographic Data Collection

Abstract Will the rise of genetic ancestry tests (GATs) change how Americans respond to questions about race and ancestry on censuses and surveys? To provide an answer, we draw on a unique study of more than 100,000 U.S. adults that inquired about respondents' race, ancestry, and genealogical knowledge. We find that people in our sample who have taken a GAT, compared with those who have not, are more likely to self-identify as multiracial and are particularly likely to select three or more races. This difference in multiple-race reporting stems from three factors: (1) people who identify as multiracial are more likely to take GATs; (2) GAT takers are more likely to report multiple regions of ancestral origin; and (3) GAT takers more frequently translate reported ancestral diversity into multiracial self-identification. Our results imply that Americans will select three or more races at higher rates in future demographic data collection, with marked increases in multiple-race reporting among middle-aged adults. We also present experimental evidence that asking questions about ancestry before racial identification moderates some of these GAT-linked reporting differences. Demographers should consider how the meaning of U.S. race data may be changing as more Americans are exposed to information from GATs.

scholarly journals Water, Energy, Food, Waste Nexus: Between Synergy and Trade-offs in Romania Based on Entrepreneurship and Economic Performance

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5172
Author(s):  
Radu Petrariu ◽  
Marius Constantin ◽  
Mihai Dinu ◽  
Simona Roxana Pătărlăgeanu ◽  
Mădălina Elena Deaconu
Keyword(s):  
Climate Change ◽  
Public Finance ◽  
Economic Performance ◽  
Decision Makers ◽  
Entrepreneurial Activity ◽  
County Level ◽  
Waste Production ◽  
Trade Offs ◽  
Research Gap

Boosting the externalities across the water, energy, food, and waste (WEFW) sectors is challenging, especially considering tightening constraints such as population growth, climate change, resource-intensive lifestyles, increased waste production, sanitary crises and many others. The nexus approach supports the transition to a more sustainable future because intersectoral trade-offs can be reduced and externalities exploited, making imperative for decision makers, entrepreneurs, and civil society to simultaneously engage, with respect to all the components of the nexus. This research addressed intersectoral synergies and trade-offs in the case of the WEFW nexus in Romania, judging from the perspectives of entrepreneurial activity and economic results. The objective of this research was to explore the nexus in-depth by statistically analyzing the financial and economic indicators reported by active enterprises at county-level, based on the Romanian Ministry of Public Finance data. Research results describe the effects of the policies implemented in the fields of WEFW sectors. At the same time, attention was paid to the quality of the entrepreneurial activity, analyzed from the perspective of economic performance. This paper fills a research gap regarding the WEFW nexus by resorting to an economic and entrepreneurial performance assessment in order to find sectoral pathways toward policy cohesion in Romania. Findings suggested the existence of major trade-offs among sectors, owing to the fact that each county has a different development degree.

Using GIS with Publicly Available Data for Hospital Market Share Analysis

2011 ◽  
pp. 280-289
Author(s):  
Evan R. Wolarsky
Keyword(s):  
Market Share ◽  
Complex Analysis ◽  
Demographic Data ◽  
Hospital Charges ◽  
Hospital Discharges ◽  
Hospital Choice ◽  
Quantitative Evidence ◽  
System A ◽  
Zip Code ◽  
Case Based

Publicly available data of all hospital discharges has been available since Medicare changed to a case-based reimbursement system. A non-confidential version of this dataset contains a Zip Code identifier for each discharge, in addition to diagnoses, procedures, payer information, hospital charges and basic demographic data. The method for converting the raw data into a useful marketing database is described. An application of this database in conjunction with GIS is presented here. In this application, the market share of a community hospital is analyzed. A series of maps shows that geography plays an important role in hospital choice, and a linear regression model provides quantitative evidence of this pattern. Finally, bivariate maps are used for more complex analysis.

scholarly journals Certificate-Of-Need Regulation and Healthcare Service Quality: Evidence from the Nursing Home Industry

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 423
Author(s):  
Bichaka Fayissa ◽  
Saleh Alsaif ◽  
Fady Mansour ◽  
Tesa E. Leonce ◽  
Franklin G. Mixon
Keyword(s):  
Nursing Home ◽  
Service Quality ◽  
Demographic Data ◽  
County Level ◽  
Nursing Assistant ◽  
Certificate Of Need ◽  
Licensed Practical Nurse ◽  
Certified Nursing Assistant ◽  
Level Data ◽  
Nursing Home Industry

This quantitative study investigates the effect of certificate-of-need (CON) regulation on the quality of care in the nursing home industry. It uses county-level demographic data from the 48 contiguous US states that are extracted from the American Community Survey (ACS) and cover the years 2012, 2013, and 2014. In doing so, it employs a new set of service quality variables captured from a variety of county-level data sources. Instrumental variables results indicate that health survey scores for nursing homes that are computed by healthcare professionals are about 18–24% lower, depending on the type of nursing home under consideration, in states with CON regulation. We also find that the presence of CON regulation leads to a substitution of lower-quality certified nursing assistant care for higher-quality licensed practical nurse care, regardless of the type of nursing home under consideration.

The Spatial Distribution of Depressive Illness in Plymouth

1980 ◽  
Vol 136 (2) ◽  
pp. 167-180 ◽  
Author(s):  
K. G. Dean ◽  
H. D. James
Keyword(s):  
Hospital Admissions ◽  
Economic Status ◽  
Demographic Data ◽  
Depressive Illness ◽  
Housing Tenure ◽  
Diagnostic Process ◽  
Ecological Data ◽  
Private Households ◽  
Geographic Structure ◽  
Lower Socioeconomic

SummaryDiagnostic and demographic data were collected from all 2,298 psychiatric hospital admissions for affective illnesses from private households in the City of Plymouth for the six-year period 1970–1975 inclusive. Intercorrelations of diagnostic subtypes were performed, together with a multiple regression analysis against spatial and ecological data from the 1971 Census. Rate differences were related to the geographic structure of Plymouth. With psychotic illnesses, ecological correlations were low for male and female first admissions and for male readmissions. However, important correlations relating to socio-economic status, housing tenure and structure, population instability, and other sociodemographic features emerged in varying degrees of specificity for reactive and neurotic illness in males, and for all readmissions in females, largely irrespective of diagnostic subtype. Explanations for the processes underlying these patterns are offered in terms of population structure, particularly the differing vulnerability of age and marital status groups, the referral and diagnostic process, social and physical stresses in the lower socioeconomic groups, and urban drift.

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