Local Demographic Characteristics Research Articles

Zoning’s treatment of housing: Analysis from a nationwide sample of places

ABSTRACT Advocates of increased housing supply and affordability in the United States have targeted zoning regulations as drivers of housing cost growth, but they lack an understanding of the prevalence and geographical distribution of different types of zoning regulations because few efforts exist to compile zoning data across localities. This study measures the amount of land allocated to different housing types in a random, stratified sample of 90 places of over 10,000 residents across the U.S. Despite our exclusion of smaller communities, we find that variations of single-family zoning are more than three times as common as zoning for three or more units per lot. We also analyze socio-demographic, built environment, and political characteristics of the sampled places in relation to their zoning practices, finding few relationships between place-level characteristics and the shares of land dedicated to different housing types. Our results from a national sample suggest significant variation in zoning practices relative to local demographic characteristics and point to the importance of understanding zoning practices and paths for reform in their local and regional contexts.

Identifying trusted local sources and predicting behavior change pathways according to COVID-19 vaccination status: Results of a 2022 statewide survey of Alaskan adults

BackgroundLow rates of COVID-19 vaccination remain a substantial public health challenge. Despite early successes, vaccinations of Alaskans trail the US average, drawing attention to the need for better-designed and targeted vaccine confidence interventions. Our objective was to assess levels of community trust and theory-driven predictors of vaccination status to inform the design of future programs. MethodsWe conducted a cross-sectional, telephone-based survey of 940 Alaskan adults between May and June 2022. Data were collected on vaccination status (including initial vaccination and receipt of booster shots), trust in local community members, demographic characteristics, and thematic questions designed using the Capability, Opportunity, Motivation - Behavior (COM-B) model to examine possible predictors (barriers/facilitators) of vaccination status. FindingsAmong those who are not fully vaccinated and boosted, we observe significantly lower trust placed in many immediate community members, especially health workers (e.g., doctors, nurses, specialty care physicians, health administrators). Firefighters and emergency medical technicians enjoy the most community trust, followed by medical professionals. Among those who received only a primary vaccine series, we find that perceptions of whether close friends are vaccinated, a sense of professional responsibility, and age were the strongest predictors vaccination status. Among the unvaccinated, we find significant predictive power from the same variables, as well as perceptions of whether family members are vaccinated, perceived risks from non-vaccination and whether vaccination is a healthy choice. ConclusionsThese findings will help inform the design and targeting of future vaccine promotion interventions to adult populations in Alaska. Interventions that leverage reflective motivation and social opportunity domains of the COM-B framework may be most effective. Local community members including firefighters and emergency medical technicians, as well as medical professionals may be perceived as the most trustworthy and influential messengers among those who are not fully vaccinated and boosted.

Quantifying Household Water Use and Its Determinants in Low-Income, Water-Scarce Households in Karachi

Water system investments in megacities in South Asia are driven by a perceived ‘shortage’ of water supply compared to water demand. However, water demand estimates for such cities often do not reflect local socioeconomic and demographic characteristics nor account for seasonal variability. In this study, using a mixed-methods approach, we quantify household water use and its determinants and assess the seasonality of access to piped water in the low-income, water-scarce township of Lyari in Karachi. Results from over 600 household surveys show that the reported per capita water usage at the household level is lower than the assumed water demand by the city’s water utility. Water use is found to differ by gender, season, and economic status. More affluent households are able to “purchase” water security and sustain higher water use even in situations of extreme water scarcity. The perceived sufficiency of water supply improves markedly in the winter despite no increase in supply, indicating reduced demand. These findings can inform more accurate water demand projections for Karachi and highlight the importance of accounting for local socioeconomic and environmental contexts in water demand projections.

Spatiotemporal evolution of COVID-19 in Portugal’s Mainland with self-organizing maps

BackgroundSelf-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood.MethodsWe use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution.ResultsThe results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant.ConclusionsThe results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.

Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea.

The Seoul metropolitan area is one of the most populated metropolitan areas in the world; hence, Seoul’s COVID-19 cases are highly concentrated. This study identified local demographic and socio-economic characteristics that affected SARS-CoV-2 transmission to provide locally targeted intervention policies. For the effective control of outbreaks, locally targeted intervention policies are required since the SARS-CoV-2 transmission process is heterogeneous over space. To identify the local COVID-19 characteristics, this study applied the geographically weighted lasso (GWL). GWL provides local regression coefficients, which were used to account for the spatial heterogeneity of SARS-CoV-2 outbreaks. In particular, the GWL pinpoints statistically significant regions with specific local characteristics. The applied explanatory variables involving demographic and socio-economic characteristics that were associated with higher SARS-CoV-2 transmission in the Seoul metropolitan area were as follows: young adults (19~34 years), older population, Christian population, foreign-born population, low-income households, and subway commuters. The COVID-19 case data were classified into three periods: the first period (from January 2020 to July 2021), the second period (from August to November 2020), and the third period (from December 2020 to February 2021), and the GWL was fitted for the entire period (from January 2020 to February 2021). The result showed that young adults, the Christian population, and subway commuters were the most significant local characteristics that influenced SARS-CoV-2 transmissions in the Seoul metropolitan area.

Are School Reopening Decisions Related to Union Influence?

ObjectiveThe COVID‐19 pandemic led to widespread school closures affecting millions of K‐12 students in the United States in the spring of 2020. Groups representing teachers have pushed to reopen public schools virtually in the fall because of concerns about the health risks associated with reopening in person. In theory, stronger teachers’ unions may more successfully influence public school districts to reopen without in‐person instruction.MethodsWe examine the relationship between teachers’ union strength and the reopening decisions of 835 public school districts in the United States using regression analyses.ResultsWe find that school districts in locations with stronger teachers’ unions are less likely to reopen in person even after we control semiparametrically for differences in local demographic characteristics. These results are robust to four measures of union strength, various potential confounding characteristics, a further disaggregation to the county level, and various analytic techniques and datasets. We do not find evidence that measures of COVID‐19 risk are correlated with school reopening decisions.ConclusionOur findings that school closures are uncorrelated with the actual incidence of the virus, but are rather strongly associated with unionization, implies that the decision to close schools has been a political—not scientific—decision.

Family Life of Poles in Western Siberia in the Second Half of the 19th – Early 20th Century

The article is devoted to the family life of the Poles in Siberia in the second half of the 19th – early 20th century. The relevance of the research topic is due to the weak study of the history of family life of the Poles in pre-revolutionary Siberia, as well as the importance of the family institution in society. The Poles in Siberia were a specific national-confessional group. Their family life was greatly influenced by the formation of the Polish community in the region, as well as local social and demographic characteristics. The aim of this work is to examine the features of formation of family building and family life of poles in Western Siberia in the second half of the 19th – early of the 20th century, given the social circumstances in the region. The methodological basis of the research is the concept of frontier existence of cultures and the theory of modernization. Exiles who came to Siberia for many years tried to live a full life: they got married, children were born and brought up. The difficulties of life in the harsh region, especially significant for exiles, forced them to look for support, first, in family members and relatives, since family cooperation helped them survive. The demographic characteristics of Polish families during this period were significantly specific in contrast to other national groups. The prevalence of mixed marriages of the Poles with representatives of other Christian denominations in post-reform Siberia indicates the intensity of ethno-cultural interactions. In the resulting ethnic-mixed families, in most cases, a combination of elements of the spiritual culture of various peoples of the region was found. At the same time, religious issues usually receded into the background. Children raised in such families perceived a respectful attitude to their parents’ past, to their origin, and national and religious tolerance was developed in family life. Keywords: Siberia, family, the Poles, community, diaspora, demography, everyday life

Are School Reopening Decisions Related to Union Influence?

The COVID-19 pandemic led to widespread school closures affecting millions of K-12 students in the United States in the spring of 2020. Groups representing teachers have pushed to reopen public schools virtually in the fall because of concerns about the health risks associated with reopening in person. In theory, stronger teachers’ unions may more successfully influence public school districts to reopen without in-person instruction. Using data on the reopening decisions of 835 public school districts in the United States, we find that school districts in locations with stronger teachers’ unions are less likely to reopen in person even after we control semi-parametrically for differences in local demographic characteristics. These results are robust to four measures of union strength, various potential confounding characteristics, and a further disaggregation to the county level. We also do not find evidence to suggest that measures of COVID-19 risk are correlated with school reopening decisions.

Children’s unscheduled primary and emergency care in Ireland: a multimethod approach to understanding decision making, trends, outcomes and parental perspectives (CUPID): project protocol

IntroductionThe aim of this project is to determine the patterns, decision-making processes and parental preferences associated with unscheduled paediatric healthcare utilisation in Ireland. Unscheduled paediatric healthcare is outpatient care provided...

Distributional disparities in residential rooftop solar potential and penetration in four cities in the United States

Single-family residential rooftop solar adoption in the United States has not occurred equitably across the country, nor across socioeconomic and demographic groups. In response, state and local governments have developed solar equity programs, primarily focused on increasing adoption by low- and moderate-income (LMI) households. This study merged national datasets that estimate rooftop solar potential, the distribution of rooftop solar systems, and census tract- level socioeconomic and demographic characteristics to answer three questions. First, how are spatial distributions of rooftop potential and penetration similar and different across cities? Second, how is rooftop penetration distributed across non-LMI and LMI communities in different cities? Third, how do the relationships between rooftop penetration and local socioeconomic and demographic characteristics, identified as barriers to solar adoption, differ? Using GIS, bivariate and multivariate analyses, these questions were examined in four US cities – Riverside and San Bernardino, California, Washington, DC, and Chicago, Illinois – to understand both universal and distinct local manifestations. Findings include: higher rooftop potential existed in some LMI communities; higher rooftop potential did not necessarily translate to higher rooftop penetration, especially if higher potential was in LMI communities; and beyond income, other socioeconomic and demographic characteristics such as race/ethnicity, limited English proficiency, age of housing stock, and internet access were associated with rooftop penetration. While there remains great potential for expanding rooftop solar to LMI households and communities, understanding the local dynamics of solar potential and penetration may inform better policy development and implementation.

The drivers of West Nile virus human illness in the Chicago, Illinois, USA area: Fine scale dynamic effects of weather, mosquito infection, social, and biological conditions.

West Nile virus (WNV) has consistently been reported to be associated with human cases of illness in the region near Chicago, Illinois. However, the number of reported cases of human illness varies across years, with intermittent outbreaks. Several dynamic factors, including temperature, rainfall, and infection status of vector mosquito populations, are responsible for much of these observed variations. However, local landscape structure and human demographic characteristics also play a key role. The geographic and temporal scales used to analyze such complex data affect the observed associations. Here, we used spatial and statistical modeling approaches to investigate the factors that drive the outcome of WNV human illness on fine temporal and spatial scales. Our approach included multi-level modeling of long-term weekly data from 2005 to 2016, with weekly measures of mosquito infection, human illness and weather combined with more stable landscape and demographic factors on the geographical scale of 1000m hexagons. We found that hot weather conditions, warm winters, and higher MIR in earlier weeks increased the probability of an area of having a WNV human case. Higher population and the proportion of urban light intensity in an area also increased the probability of observing a WNV human case. A higher proportion of open water sources, percentage of grass land, deciduous forests, and housing built post 1990 decreased the probability of having a WNV case. Additionally, we found that cumulative positive mosquito pools up to 31 weeks can strongly predict the total annual human WNV cases in the Chicago region. This study helped us to improve our understanding of the fine-scale drivers of spatiotemporal variability of human WNV cases.

Obesity and bankruptcy: Evidence from US counties

Using annual county-level data on nonbusiness bankruptcy for the period 2005–2017 and obesity for the period 2004–2016, this paper finds that higher obesity rates are associated with higher bankruptcy rates beyond what can be explained by local economic conditions and demographic characteristics, state-specific economic shocks, and county-specific time trends. The magnitude suggests that a one-percentage point increase in the obesity rate is associated with a 0.02−0.03 increase (or a 1.0 percent increase) in Chapter 7 bankruptcy rates per 1000 residents and a 0.02−0.04 increase (or a 3–4 percent increase) in Chapter 13 bankruptcy rates per 1000 residents.

Geospatial and seasonal variation of bronchiolitis in England: a cohort study using hospital episode statistics

BackgroundRates of hospital admissions for bronchiolitis vary seasonally and geographically across England; however, seasonal differences by area remain unexplored. We sought to describe spatial variation in the seasonality of hospital...

Innovative startup creation: the effect of local factors and demographic characteristics of entrepreneurs

The paper investigates factors affecting the creation of innovative startups—that is, new enterprises that produce, develop, and commercialize innovative goods or services of high technological value. Analyzing data on Italian provinces, the paper studies whether and how the birth rate of innovative startups is affected by the following characteristics of the local context: local density of universities and research centers, the education level of the local population, and the number of incubators in the area. In addition, the study tries to identify whether the impact of these contextual factors varies when firms are created by entrepreneurs that have peculiar demographic characteristics—i.e., that are female, young, or foreign-born. Hypotheses are tested through a multiple regression analysis that uses as the dependent variable innovative startup birth rates at provincial level, calculated using data regarding the 8264 innovative startups created in Italy between 2015 and 2018 in 105 Italian provinces. Results regarding the overall sample of innovative startups show that the innovative startups birth rate is positively related to education level and to the number of incubators in the area. However, the study highlights that differences in the demographic characteristics of the entrepreneurs correspond to a different relevance of the local contextual factors investigated in fostering innovative startup creation.

BLP-2LASSO for aggregate discrete choice models with rich covariates

Summary We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers’ aggregate behaviour across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls for market-specific preference heterogeneity. Given rich demographic data, implementing these models requires specifying which variables to include in the analysis, an ad hoc process typically guided primarily by a researcher’s intuition. We propose a data-driven approach to estimate these models, applying penalized estimation algorithms from the recent literature in high-dimensional econometrics. Our application explores the effect of campaign spending on vote shares in data from Mexican elections.

Understanding the potential of state-based public health genomics programs to mitigate disparities in access to clinical genetic services

PurposeState health agencies (SHAs) have developed public health genomics (PHG) programs that play an instrumental role in advancing precision public health, but there is limited research on their approaches. This study examines how PHG programs attempt to mitigate or forestall health disparities and inequities in the utilization of genomic medicine. MethodsWe compared PHG programs in three states: Connecticut, Michigan, and Utah. We analyzed 85 in-depth interviews with SHA internal and external collaborators and program documents. We employed a qualitative coding process to capture themes relating to health disparities and inequities. ResultsEach SHA implemented population-level approaches to identify individuals who carry genetic variants that increase risk of hereditary cancers. However, each SHA developed a unique strategy—which we label public health action repertoires—to reach specific subgroups who faced barriers in accessing genetic services. These strategies varied across states given demographics of the state population, state-level partnerships, and availability of healthcare services. ConclusionOur findings illustrate the imperative of tailoring PHG programs to local demographic characteristics and existing community resources. Furthermore, our study highlights how integrating genomics into precision public health will require multilevel, multisector collaboration to optimize efficacy and equity.

Using synthetic populations to understand geospatial patterns in opioid related overdose and predicted opioid misuse

Ohio is leading the nation in an epidemic of overdose deaths, most of which are caused by opioids. Through this study we estimate associations between opioid drug overdoses measured as EMS calls and model-predicted drug misuse. The RTI-developed synthetic population statistically represents every household in Cincinnati and allows one to develop a geographically explicit model that links Cincinnati EMS data, and other datasets. From the publicly available National Survey on Drug Use and Health (NSDUH), we developed a model of opioid misuse and assigned probability of misuse to each synthetic individual. We then analyzed EMS overdose data in the context of local level misuse and demographic characteristics. The main results show locations where there is a dramatic variation in ratio values between overdose events and the number of misusers. We concluded that, for optimal efficacy, intervention strategies should consider the existence of exceptional geographic locations with extremely high or low values of this ratio.

Extreme weather, public transport ridership and moderating effect of bus stop shelters

This paper examines how extreme weather conditions influence urban public transport ridership with a particular focus on the role of bus stop shelters. Using bus ridership data from the Salt Lake City metropolitan area, we find that extreme weather such as very high and low temperatures, and heavy rainfall reduces public transport ridership, while bus stop shelters have a modest effect on mitigating ridership losses resulting from these adverse weather conditions. The moderating effect of shelters is more pronounced on weekdays, and for bus stops with lower service frequency and fewer transfers. Our research also shows that the installation of bus shelters correlates with a variety of factors including service frequency, land use types, and local socioeconomic and demographic characteristics. Overall, our findings suggest that public transport amenities with weather-proof attributes have the potential to retain and attract more ridership on extreme weather days.

Linking Opioid-Overdose Data to Human Services and Criminal Justice Data: Opportunities for Intervention.

In Allegheny County, Pennsylvania, the incidence of opioid-related overdose deaths increased from 17.4 per 100 000 population in 2008 to 23.9 per 100 000 population in 2014. Our objectives were to describe local demographic characteristics of this epidemic, identify public human services targets for intervention, determine temporal relationships between use of public human services and overdose mortality, and provide recommendations about potentially beneficial interventions. We used autopsy data from the Allegheny County Medical Examiner to link people who died of overdoses from 2008 through 2014 to their premortem incarcerations and use of mental health services and substance use disorder services. We calculated the frequency of use of public human services by decedents and the interval between the last use of these services and overdose death. Of the 1399 decedents, 957 (68.4%) had a public human service encounter before overdose death. Of these 957 decedents, 531 (55.5%) had ever been incarcerated, 616 (64.4%) had ever used a mental health service, and 702 (73.4%) had ever used a substance use disorder service. Of 211 decedents incarcerated in the year before their overdose death, 54 (25.6%) overdosed within 30 days of their last release from jail. Of 510 decedents using mental health services in the year before death, 231 (45.3%) overdosed within 30 days of their last use of the services. Of 350 decedents using substance use disorder services in the year before their overdose death, 134 (38.3%) overdosed within 30 days of their last use of the services. Merging data on overdose mortality with data on use of public human services can be a useful strategy to identify trends in, and factors contributing to, the opioid epidemic; to target interventions; and to stimulate collaboration to address the epidemic.

Quantifying the influence of temperature on hand, foot and mouth disease incidence in Wuhan, Central China

Hand, foot and mouth disease (HFMD) is a substantial burden throughout Asia, but the effects of temperature pattern on HFMD risk are inconsistent. To quantify the effect of temperature on HFMD incidence, Wuhan was chosen as the study site because of its high temperature variability and high HFMD incidence. Daily series of HFMD counts and meteorological variables during 2010–2015 were obtained. Distributed lag non-linear models were applied to characterize the temperature-HFMD relationship and to assess its variability across different ages, genders, and types of child care. Totally, 80,219 patients of 0–5 years experienced HFMD in 2010–2015 in Wuhan. The cumulative relative risk of HFMD increased linearly with temperature over 7 days (lag0–7), while it presented as an approximately inverted V-shape over 14 days (lag0–14). The cumulative relative risk at lag0–14 peaked at 26.4 °C with value of 2.78 (95%CI: 2.08–3.72) compared with the 5th percentile temperature (1.7 °C). Subgroup analyses revealed that children attended daycare were more vulnerable to temperature variation than those cared for at home. This study suggests that public health actions should take into consideration local weather conditions and demographic characteristics.