Resident Population Density Research Articles

Risk factors for Lyme disease among residents of rural, suburban, and urban areas in the United States: A Case-Control Study

Abstract lyme disease (LD) is the most common vector-borne disease in the United States, with 476,000 cases estimated each year. It is unclear how LD risk factors vary by residential setting. We conducted a case-control study on LD risk by rural, urban, and suburban residential settings. Individuals from 15 high-incidence states and the District of Columbia in the Optum Research Database were identified as cases (LD medical claim) or controls (no LD medical claim, matched by county of residence and census block group population density). Participants were surveyed about LD history, outdoor activities, and residential characteristics. The final analytic dataset had 750 LD cases and 965 controls. Residence in a rural setting had increased LD risk (OR 1.41, 95% CI 1.16, 1.72). In multivariable analyses, activities associated with LD were hiking/walking/running or having an occupation in forests, wooded areas, or areas of tall grass (all respondents), and spending time in a yard (rural and urban residents only). Public health interventions can help prevent LD in high-incidence jurisdictions by reinforcing the nearuniversal LD risk for rural residents, and highlighting activities that lead to increased LD risk for those in areas with less ubiquitous tick exposure like in urban and suburban settings.

A dataset of permanent resident population density in Henan Province (2020)

A dataset of permanent resident population density in Henan Province (2020)

Availability of Cranial Prostheses for Black Patients at Comprehensive Cancer Centers.

For patients who seek to camouflage cancer-related hair loss, cranial prostheses such as wigs and hair pieces exist. We sought to determine the availability of yaki-textured, type IV curls, and afro wigs at boutiques in Comprehensive Cancer Centers (CCCs). The 56 CCCs in the United States were surveyed to see whether they had an affiliated wig boutique for patients experiencing hair loss. Boutique workers were then asked a series of seven questions regarding cranial prostheses options for patients seeking yaki-textured, type IV curls, and afro wigs. The availability of wigs was compared with US Census data on population size and density of Black residents. Of the 56 CCCs, 27 (46%) institutions had active affiliated hair boutiques. We were able to reach 19 (70%) of the 27 boutiques, of which 53% (n = 10) offered yaki-textured wigs, 37% (n = 7) offered type IV curls or afro wigs, and 47% (n = 9) offered neither. Two additional boutiques offered in-store catalogs for Black patients who were interested in naturally appearing wigs. Although two institutions offered a wig bank that was free to all patients, neither had yaki-textured or afro wigs in stock. There was no significant relationship between population size or density of Black residents and availability of these wigs. Many Black patients undergoing cancer treatment interested in cranial prosthesis do not have consistent access to wigs with textures comparable with type IV hair at CCCs. With increased inventory of racially inclusive wigs, partnerships with third-party vendors, and support for the previous authorization process, we can better support Black patients experiencing cancer-related hair loss.

Availability of cranial prostheses for Black patients at comprehensive cancer centers.

e13681 Background: For patients who seek to camouflage cancer-related hair loss, cranial prostheses such as wigs and hair pieces exist. We sought to determine the availability of yaki-textured, type IV curls, and afro wigs at boutiques in Comprehensive Cancer Centers (CCCs). Methods: The 56 CCCs in the United States were surveyed to see if they had an affiliated wig boutique or shop for patients experiencing hair loss. Boutique workers were then asked a series of seven questions regarding cranial prostheses options for patients seeking yaki-textured, type IV curls, and afro wigs. The availability of these wigs was compared to US Census data on population size and density of Black residents. Results: Of the 56 CCCs, 26 (46%) institutions had active affiliated hair boutiques, of which 27% (n=7) offered yaki-textured wigs, type IV curls, or afro wigs. Two additional boutiques offered in-store catalogues for Black patients who were interested in naturally appearing wigs. Though two institutions offered a wig bank that was free to all patients, neither had yaki-textured or afro wigs in stock. There was no significant relationship between population or density of Black residents and availability of these wigs. Therefore, most Black patients with cancer who are seeking cranial prostheses will not have access to racially inclusive wigs where they are receiving treatment. Conclusions: Not being able to purchase wigs at CCCs poses potential financial challenges when seeking insurance coverage or reimbursement. Thus, Black patients may disproportionately experience emotional and financial hardships secondary to cancer-related hair loss. With increased inventory of racially inclusive wigs, partnerships with third-party vendors, and support for the prior authorization process, we can better support Black patients experiencing cancer-related hair loss. [Table: see text]

Examining the relationship between social determinants of health, measures of structural racism and county-level overdose deaths from 2017-2020.

Despite being an important determinant of health outcomes, measures of structural racism are lacking in studies examining the relationship between the social determinants of health (SDOH) and overdose deaths. The aim of this study is to examine the association between per capita revenue generated from fines and forfeitures, a novel measure of structural racism, and other SDOH with county-level overdose deaths from 2017-2020. This longitudinal analysis of 2,846 counties from 2017-2020 used bivariate and multivariate Generalized Estimating Equations models to estimate associations between county overdose mortality rates and SDOH characteristics, including the fines and forfeitures measure. In our multivariate model, higher per capita fine and forfeiture revenue (5.76; CI: 4.76, 6.78), households receiving food stamps (1.15; CI: 0.77, 1.53), residents that are veterans (1.07; CI: 0.52, 1.63), substance use treatment availability (4.69; CI: 3.03, 6.33) and lower population density (-0.002; CI: -0.004, -0.001) and percent of Black residents (-0.7`; CI: -1.01, -0.42) were significantly associated with higher overdose death rates. There was a significant additive interaction between the fines and forfeitures measure (0.10; CI: 0.03, 0.17) and the percent of Black residents. Our findings suggest that structural racism, along with other SDOH, is associated with overdose deaths. Future research should focus on connecting individual-level data on fines and forfeitures to overdose deaths and other health outcomes, include measures of justice-related fines, such as court fees, and assess whether interventions aimed at increasing economic vitality in disadvantaged communities impact overdose deaths in a meaningful way.

The Impact of Built Environment on the Commuting Distance of Middle/Low-income Tenant Workers in Mega Cities Based on Nonlinear Analysis in Machine Learning

The issues of housing and traffic in China's mega cities have become increasingly pressing problems, particularly for middle/low-income tenant workers. These tenants are from less advantaged socioeconomic backgrounds, which has resulted in a significant geographical separation between their workplace and their residence. Although a large number of studies have confirmed that built environment factors have a solid impact on residents’ commuting distance, few studies have investigated the mechanism underlying the nonlinear influence on middle/low-income tenants. This paper aims to provide an in-depth analysis of the key factors and nonlinear influencing mechanism of the built environment on middle/low-income tenant workers’ commuting distance by establishing a gradient-boosting decision tree model, using Beijing as an empirical case. The paper reveals three primary findings: (1) An important nonlinear relationship between the surrounding built environment and peoples’ jobs–housing spatial proximity can be observed for those middle/low-income tenant workers who use slow and public modes of commuting. Specifically, the density of public transport stations, road networks, and workplaces, and the land use mix play a dominant role. (2) A limited effect of built environment factors can be found for the same group of tenant workers who choose cars as their mode of commuting. (3) The differences in self-selected commuting modes have a significant mediating effect on the relationship between the built environment and jobs–housing situation among middle/low-income tenant workers. Given this, effective policy guidance for residents’ travel modes is necessary to optimize the built environment indicators to achieve the best effect. In addition, we should consider giving priority to the matching indicators such as land use mix and resident population density. Another possibility is to strengthen the connection to the public transport stations, which in turn can optimize the walkability in residential environments.

Testing Density-Dependent and Path-Dependent Population Dynamics in Greece with Spatial Quantile and Geographically Weighted Regressions

Regional variability in the spatial distribution of resident population and across-country density divides have consolidated heterogeneous demographic patterns at the base of modern urban systems in Europe. Although economic, historical, institutional, and cultural factors have demonstrated to affect the spatial distribution of resident population, density-dependence and path-dependence are mechanisms persistently shaping demographic dynamics at both local and regional scale. Analysis of density-dependent patterns of population growth (and decline) over sufficiently long time intervals allows a refined comprehension of socioeconomic processes underlying demographic divides. Despite a long settlement history, empirical investigation of the role of density-dependence in the long-term evolution of human populations along urban–rural gradients is relatively scarce especially in Mediterranean countries. The present study performs a comparative analysis of population distribution in 1033 Greek municipalities identifying (and testing the significance of) density-dependent and path-dependent mechanisms of population growth between 1961 and 2011, using spatially implicit and explicit econometric approaches. Results highlight a positive impact of density on population growth where settlements are concentrated. Assuming goodness of fit of the tested models as a proxy of density-dependence, the empirical findings clarify how density-dependent mechanisms were not significant all over the study period, being instead associated with specific phases of the city life cycle—basically urbanization with population concentrating in central locations. Density-dependence was less intense with suburbanization and counter-urbanization—when population sprawled over larger areas at medium–low density. An improved understanding of density-dependent and path-dependent mechanisms of population growth contributes to rethink spatial planning, regional development strategies, and socio-demographic policies adapting to heterogeneous (and rapidly changing) local contexts.

Urban Waste Management and Prediction through Socio-Economic Values and Visualizing the Spatiotemporal Relationship on an Advanced GIS-Based Dashboard

Enhancing data-driven decision-making is vital for waste authorities. Although few studies have explored the influence of socio-economic indicators on waste tonnage, comprehensive analysis of urban waste data focusing on geographical information is also scarce. There is a dearth of dashboards for visualizing waste tonnage with spatial relationship maps. This study aims to present a prediction model useful for estimating urban waste by using personal income (I), the number of income earners (E), land values (L), the estimated resident population (P) and population density (D), called the IELPD measures. An innovative approach is developed to identify the correlation between urban household waste data and socio-economic factors and develop an advanced dashboard based on a geographic information system (GIS). To accomplish this, relationship maps and regression analysis are deployed to visualize household waste data spanning six years of waste production in New South Wales, Australia, classified into three categories: recyclable, residual and organic (RRO) wastes. Furthermore, this classification enables accessing the association between these three waste categories and the IELPD metrics. And there are four types of visualization generated, that is, thematic mapping, spatial relationship maps, correlation matrices and dashboard development. The regression analysis shows a substantial association between RRO waste tonnage, population changes and a minor correlation with land values. Overall, this study contributes to urban waste data storytelling and its spatiotemporal associations with socio-economic determinants. This paper offers a valuable prediction model of the IELPD metrics to estimate urban waste and visualize them in a dashboard allowing practitioners and decision-makers to track trends in the RRO waste stream in urban waste generally.

House Price Prediction Model of Zhaoqing City Based on Correlation Analysis and Multiple Linear Regression Analysis

Situated in southern China, Zhaoqing City is a part of Guangdong Province, China. The total administrative area of the city covers 14,891 square kilometers. The data of China’s seventh population census in 2020 showed that the permanent resident population in Zhaoqing City reached up to 4,413,594. Meanwhile, Zhaoqing is one of the cities in the Guangdong-Hong Kong-Macao Greater Bay Area. House price analysis and prediction carried out against Zhaoqing City will have directive significance for relevant policies formulated by the local government, residential investment or purchase of consumers, and prediction of house price trend as well as business decisions made by enterprises. By virtue of machine learning and statistical theory, the house price in Zhaoqing City from 2010 to 2020 will be researched, and the house price prediction model of Zhaoqing City will be constructed in this paper with several variables including GDP, proportion of tertiary industry, income of urban residents, fiscal revenue, land price, investment volume in real estate development, permanent resident population, population density, and proportion of urban population in net migration. First of all, the methods of correlation analysis will be utilized, to select variables that are highly correlated with house price data based on correlation coefficients. Then, the model will be constructed for predicting the house price on the basis of multiple linear regression analysis that is conducted with selected variables. Finally, the prediction model will be adjusted gradually based on data with different correlations selected from available data, to realize better imitative effect and more precise predictive effect and select optimum prediction model. By means of the above model, the house prices of Zhaoqing City in 2021 and beyond will be predicted accurately, with preferable fitting effect and prediction effect.

Empirical Analysis on Influencing Factors of Traffic Casualties in China

In order to further explore the complex correlation between the influencing factors of road traffic accidents in China, this paper takes the road traffic safety-related data of 31 provinces from 2004 to 2018 as the sample data, selects five index data that may affect the occurrence of traffic accidents in various regions of the country, i.e. resident population, urban population, population density, GDP and number of car drivers, and uses Eviews software for stepwise regression analysis to establish a fixed effect model of the influencing factors of traffic accidents and analyze the macro and micro factors that affect the death toll of traffic accidents in China. The model shows that each index has a significant impact on the number of road traffic casualties. Urban population, population density and number of car drivers are positively correlated with the number of road traffic casualties. The resident population and GDP are negatively correlated with the number of road traffic casualties. According to the research conclusions, this paper puts forward some suggestions, such as optimizing the management technology of road traffic construction, strengthening the education system of traffic safety awareness of residents in the new urbanization, increasing the investment in traffic construction, and establishing an institution for differentiated training and management of drivers based on driving experience.

Lights Out? COVID-19 Containment Policies and Economic Activity

This paper estimates how COVID-19 lockdown policies impacted economic activity. Using a difference-in-differences methodology, we establish the divergence of district-level nighttime light intensity, mobility and migration, as well as household-level income and consumption, across different containment zones across India. We find that nighttime light intensity was 9.3% lower in the post period (May to July 2020) for districts with the most severe restrictions, (which could imply between 12.4% and 18.6% lower GDP), and 1.5% lower for districts with intermediate restrictions, compared to districts with the least restrictions. The differences were largest in May during the graded lockdown, and slowly tapered in June and July. Lower household income and consumption are plausible channels for these results. Stricter containment measures had larger impacts in districts with greater population density, older residents, and more services employment. Our findings point to the role of pandemic containment policies on future economic growth and inequality.

Racial and ethnic disparities in SARS-CoV-2 pandemic: analysis of a COVID-19 observational registry for a diverse US metropolitan population

IntroductionData on race and ethnic disparities for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are limited. We analysed sociodemographic factors associated with higher likelihood of SARS-CoV-2 infection and explore...

Evolution and influencing factors of China's rural population distribution patterns since 1990.

Population is a major production factor in rural development in China, which makes the study of rural population distribution patterns at different times and the factors influencing the population distribution an important foundation for understanding the issues in rural China and moving forward with the implementation of rural revitalization strategies. This paper analyzed the spatial evolution of the population in rural China based on population census data for the People’s Republic of China by county in 1990, 2000 and 2010. Applying the geographical detector method, this paper also delved into the contributing factors that influenced the distribution based on the natural, social and economic data, such as the potential crop productivity, the average slope, the urbanization rate and the time cost to reach the nearest cities. The results indicate that the migration of the population from the rural areas into the cities, which was a result of rapid urbanization, did not change the original population distribution in rural China significantly. The rural population was still concentrated in the eastern plains, basins and deltas, and the North China Plain and Sichuan Basin still house the bulk of rural residents, but the population density of rural residents in the North China Plain and Sichuan Basin decreased from 1990 to 2010. The rural population in China tended to be distributed around the cities. Seventy-four percent of the rural population lived in an area within a 60-minute driving distance from the surrounding cities. The areas with dense rural population were basically consistent with the locations of the current major urban agglomerations in China. The current distribution of the rural population in China was a result of natural, social and economic conditions and location factors. Among them, natural factors such as the potential crop productivity and the degree of surface fragmentation had the most significant influence.

Contributions of Resident Populations and Nonresident Activities of Small Mammals to the Results of Censuses Performed Using the Permanent Removal Method

Population demography parameters of common small mammals have been obtained using two different census methods and intercompared. Resident population density and nonresident activity parameters were obtained on live-trap lines using the capture–mark–recapture method. Abundance parameters were obtained on break-back trap lines using the permanent removal (PR) method. Censuses performed using the PR method reflect the population dynamics of small mammals in general, but they may misrepresent the species ratio in the community. Multiple regression analysis shows that results obtained using the PR method are significantly affected both by the resident population density and nonresident activity of the species. Together, these factors explain 88.8% of the variance, while their individual contributions assessed on the basis of semipartial correlations in the multiple correlation model are as follows: r = 0.39 (resident population) and r = 0.46 (nonresident activities).

COMPRIME - COnhecer Mais PaRa Intervir MElhor: Preliminary Mapping of Municipal Level Determinants of COVID-19 Transmission in Portugal at Different Moments of the 1st Epidemic Wave

Background: The role of demographic and socio-economic determinants of COVID-19 transmission is still unclear and is expected to vary in different contexts and epidemic periods. Exploring such determinants may generate a hypothesis about transmission and aid the definition of prevention strategies. Objectives: To identify municipality-level demographic and socio-economic determinants of COVID-19 in Portugal. Methods: We assessed determinants of COVID-19 daily cases at 4 moments of the first COVID-19 epidemic wave in Portugal, related with lockdown and post-lockdown measures. We selected 60 potential determinants from 5 dimensions: population and settlement, disease, economy, social context, and mobility. We conducted a multiple linear regression (MLR) stepwise analysis (p < 0.05) and an artificial neural network (ANN) analysis with the variables to identify predictors of the number of daily cases. Results: For MLR, some of the identified variables were: resident population and population density, exports, overnight stays in touristic facilities, the location quotient of employment in accommodation, catering and similar activities, education, restaurants and lodging, some industries and building construction, the share of the population working outside the municipality, the net migration rate, income, and renting. In ANN, some of the identified variables were: population density and resident population, urbanization, students in higher education, income, exports, social housing buildings, production services employment, and the share of the population working outside the municipality of residence. Conclusions: Several factors were identified as possible determinants of COVID-19 transmission at the municipality level. Despite limitations to the study, we believe that this information should be considered to promote communication and prevention approaches. Further research should be conducted.

Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification

Karst Rocky Desertification (KRD) has become the most serious ecological disaster in Southwest China. We used the data of Thematic Mapper (TM) images from 1990, 1995, 2000, 2004, and 2011 and the 2016 Operational Land Imager (OLI) image. These sensing images were pre-processed by removing non-karst areas based on lithology and cutting away the land types impossibly generating KRD from land use maps. Then, we used a Classification And Regression Tree (CART) to classify the KRD. We want to improve the interpretation accuracy of KRD through the above steps. The results were as follows: (1) The KRD experiences the evolution process of ‘first deterioration and then improvement’. The rate is −4.94 km2.a−1 over a period of 1990 to 2004, and the rate is 36.48 km2.a−1 from 2004 to 2016; (2) The most influential factors causing KRD formation are the lithology and the resident population density, with contribution rates of 30.17% and 25.86%, respectively.

The Consequences of Small Mammal Censuses by Method of Irreversible Removal

Experiments were performed to estimate the effect of removal trapping with Gero trap lines on resident population density and dispersal rate of Clethrionomys voles and Sorex shrews. Parameters of animal density and dispersal were calculated based on the results of live-trap censuses taken before and immediately after the removal trapping. Resident population density in the latter case was accounted for not only by animals that escaped removal but also by those that moved in from surrounding areas during the next few days. Local reduction of animal numbers resulted in increased dispersal. Parameters of dispersal obtained by the method of long-term removal may be overestimated.

Neighborhood Greenness Attenuates the Adverse Effect of PM2.5 on Cardiovascular Mortality in Neighborhoods of Lower Socioeconomic Status.

Features of the environment may modify the effect of particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) on health. Therefore, we investigated how neighborhood sociodemographic and land-use characteristics may modify the association between PM2.5 and cardiovascular mortality. We obtained residence-level geocoded cardiovascular mortality cases from the Massachusetts Department of Public Health (n = 179,986), and PM2.5 predictions from a satellite-based model (2001–2011). We appended census block group-level information on sociodemographic factors and walkability, and calculated neighborhood greenness within a 250 m buffer surrounding each residence. We found a 2.54% (1.34%; 3.74%) increase in cardiovascular mortality associated with a 10 µg/m3 increase in two-day average PM2.5. Walkability or greenness did not modify the association. However, when stratifying by neighborhood sociodemographic characteristics, smaller PM2.5 effects were observed in greener areas only among cases who resided in neighborhoods with a higher population density and lower percentages of white residents or residents with a high school diploma. In conclusion, the PM2.5 effects on cardiovascular mortality were attenuated by higher greenness only in areas with sociodemographic features that are highly correlated with lower socioeconomic status. Previous evidence suggests health benefits linked to neighborhood greenness may be stronger among lower socioeconomic groups. Attenuation of the PM2.5–mortality relationship due to greenness may explain some of this evidence.

Factorial Invariance of the Abbreviated Neighborhood Environment Walkability Scale among Senior Women in the Nurses’ Health Study Cohort

ABSTRACTThe purpose of this study was to examine the factorial invariance of the Abbreviated Neighborhood Environment Walkability Scale (NEWS-A) across subgroups based on demographic, health-related, behavioral, and environmental characteristics among Nurses’ Health Study participants (N = 2,919; age M = 73.0, SD = 6.9 years) living in California, Massachusetts, and Pennsylvania. A series of multigroup confirmatory factor analyses were conducted to evaluate increasingly restrictive hypotheses of factorial invariance. Factorial invariance was supported across age, walking limitations, and neighborhood walking. Only partial scalar invariance was supported across state residence and neighborhood population density. This evidence provides support for using the NEWS-A with older women of different ages, who have different degrees of walking limitations, and who engage in different amounts of neighborhood walking. Partial scalar invariance suggests that researchers should be cautious when using the NEWS-A to compare older adults living in different states and neighborhoods with different levels of population density.

Quality indicators of nutritional care practice in elderly care

ObjectivesThe aim is to explore the effects of antecedent, structural and process quality indicators of nutritional care practice on meal satisfaction and screened nutritional status among older adults in residential care homes.DesignData for this Swedish cross-sectional study regarding older adults living in residential care homes were collected by i) a national questionnaire, ii) records from the quality registry Senior Alert, iii) data from an Open Comparison survey of elderly care in 2013/2014. The data represented 1154 individuals in 117 of 290 Swedish municipalities.MeasurementsMeal satisfaction (%) and adequate nutritional status, screened by the Mini Nutritional Assessment Short Form (MNA-SF), were the two outcome variables assessed through their association with population density of municipalities and residents’ age, together with 12 quality indicators pertaining to structure and process domains in the Donabedian model of care.ResultsMeal satisfaction was associated with rural and urban municipalities, with the structure quality indicators: local food policies, private meal providers, on-site cooking, availability of clinical/community dietitians, food service dietitians, and with the process quality indicators: meal choice, satisfaction surveys, and ‘meal councils’. Adequate nutritional status was positively associated with availability of clinical/community dietitians, and energy and nutrient calculated menus, and negatively associated with chilled food production systems.ConclusionMunicipality characteristics and structure quality indicators had the strongest associations with meal satisfaction, and quality indicators with local characteristics emerge as important for meal satisfaction. Nutritional competence appears vital for residents to be well-nourished.