Higher Percentage Of Households Research Articles

Indicator–based vulnerability assessment of households to typhoons and inundation in a ward in Da Nang City, Vietnam

Abstract The indicator–based vulnerability assessment of households to typhoons and inundation was conducted in a ward of Da Nang city after the historical typhoon Son Ca 2022. A set of 18 indicators, encompassing three components of exposure, sensitivity, and adaptive capacity, was used for vulnerability assessment. In–depth and semi–structured interviews revealed that 42% and 89% of the interviewed households were affected by typhoons and inundation, respectively. The study noted that nearly 87% of households experienced inundation–induced damage at varying intensities in October 2022. The surveyed results also demonstrated that 88% of the interviewed households had at least 01 child under 11 years old, 22% had at 1–2 elderly above 65 years old in the family, and 16% were categorized as poor or near–poor households. 73% of the households resided in single–story houses (Type IV), 5% were living in temporary accommodations, and 8% lacked a stable livelihood. Although a high percentage of households were equipped with basic disaster mitigation tools (100%) and respondents received warning information about inundation (85%), only 16% engaged in disaster prevention training and propaganda courses. The assessment of exposure, sensitivity, and adaptive capacity to typhoons and inundation was 0.58, 0.39, and 0.34, respectively (0 to 1 scale). Although the survey area was considered a highly adaptable and proactive area to disasters, the present study revealed that the vulnerability to historical disasters in the survey area was 0.63 (–1 to 2 scale). The results indicate that residents and authorities are insufficiently prepared to manage historical disasters effectively. The results of this study offer valuable insights for policymakers and stakeholders in improving adaptive capacity and disaster preparedness effectiveness.

Abstract 78: Mobile Stroke Units Address Disparities in Stroke Care as Demonstrated by Zip Code Level Socioeconomic Status

Introduction: Acute ischemic stroke (AIS) disproportionately affects minority populations with evidence showing black and Hispanic patients utilize traditional EMS less, have greater delay in arrival to the emergency department, are less likely to receive thrombolysis, and have lower odds for good functional outcomes. 1–3 Mobile Stroke Units (MSUs) bring diagnostic and treatment tools for AIS directly to patients, achieving faster thrombolytic administration and better outcomes. 4,5 Objective: This study compared demographic data and used Census data to characterize the socioeconomics of patient zip codes served and transported by our MSU based in Rochester, NY relative to patients that arrived at our institution via traditional EMS and private vehicle. Methods: Data was extracted from the 2018 American Community Survey (ACS), a 5-year a rolling survey of the US population. Zip code level socioeconomic status (zSES) was calculated based on 5 key factors by assigning percentile ranks to each across all US zip codes and combined to create one index variable measuring zSES. zSES, its factors, and demographics were compared amongst MSU and non-MSU patients. Results: 468 patients transported by our MSUs to our institution between October 2018 to January 2022 were compared to the 2,296 non-MSU patients for the same time-period. Patients transported by MSU lived in zip codes with significantly lower SES Index (0.482 versus 0.527, p=0.013). Component analysis showed that MSU patients came from zip codes with a higher percentage of households with single parents, higher percentage of individuals below the federal poverty line, higher percentage of unemployed individuals, and higher percentage of adults with less than a 12 th grade education. Per capita income did not differ. In addition, patients transported by our MSU’s were significantly more female, black, and Hispanic. Age did not differ. Conclusion: Mobile Stroke Units (MSU) help address disparities in stroke care, by bringing care to patients who live in zip codes that have significantly lower SES and are of historically marginalized communities, compared to patients transported by EMS and private vehicle.

Neighborhood-Level Risk Factors for Severe Hyperglycemia among Emergency Department Patients without a Prior Diabetes Diagnosis.

A person's place of residence is a strong risk factor for important diagnosed chronic diseases such as diabetes. It is unclear whether neighborhood-level risk factors also predict the probability of undiagnosed disease. The objective of this study was to identify neighborhood-level variables associated with severe hyperglycemia among emergency department (ED) patients without a history of diabetes. We analyzed patients without previously diagnosed diabetes for whom a random serum glucose value was obtained in the ED. We defined random glucose values ≥ 200mg/dL as severe hyperglycemia, indicating probable undiagnosed diabetes. Patient addresses were geocoded and matched with neighborhood-level socioeconomic measures from the American Community Survey and claims-based surveillance estimates of diabetes prevalence. Neighborhood-level exposure variables were standardized based on z-scores, and a series of logistic regression models were used to assess the association of selected exposures and hyperglycemia adjusting for biological and social individual-level risk factors for diabetes. Of 77,882 ED patients without a history of diabetes presenting in 2021, 1,715 (2.2%) had severe hyperglycemia. Many geospatial exposures were associated with uncontrolled hyperglycemia, even after controlling for individual-level risk factors. The most strongly associated neighborhood-level variables included lower markers of educational attainment, higher percentage of households where limited English is spoken, lower rates of white-collar employment, and higher rates of Medicaid insurance. Including these geospatial factors in risk assessment models may help identify important subgroups of patients with undiagnosed disease.

Spatial-temporal distribution of maxillofacial injuries resulting from intimate partner violence against women.

Maxillofacial trauma resulting from intimate partner violence (IPV) represents an important oral health problem. In this sense, the aim of this study was to investigate the spatial-temporal distribution of maxillofacial trauma resulting from IPV against women, using a geostatistical approach. An ecological study was carried out including the analysis of confirmed IPV cases against women treated at a Center for Forensic Medicine and Dentistry over a four-year observation period, as well as the evaluation of population data from the victims' places of residence extracted from the last demographic census of the Brazilian Institute of Geography and Statistics. Statistical analysis included: (i) finite mixture modeling to establish incidence trajectory patterns; (ii) Getis-Ord indicator (Gi*) for spatial autocorrelation; (iii) spatial regression analysis (p < .05). Two distinct trajectory patterns (TP1 and TP2) related to IPV incidence were identified using finite mixture modeling, suggesting spatial-temporal disparities at theregional level. In TP1, it was observed that IPV incidence was relatively low and remained stable over time, covering almost two-thirds (62.0%) of investigated spatial units. TP2 was characterized by higher IPV incidence witha tendency to increase in the last year, including more than one-third of neighborhoods (38.0%). Autocorrelation analysis showeda predominance of hot areas (hotspots) in the Eastern zone (p < .05) and in the Western zone (p < .05); and cold areas (coldspots) in the Northern zone (p < .05). In addition, statistically significant association was observed among neighborhoods with higher percentage of households with family householder without income and higher incidence of maxillofacial trauma resulting from IPV against women (β=5.305; SE=1.741; p=.002). The findings indicate anassociation between higher IPV incidence against women, maxillofacial trauma, and socio-spatial vulnerability.

Home as the first site for suicide prevention: a Hong Kong experience

IntroductionThere has been little research into at-home suicide cases globally, and particularly in Asian regions. This study aimed to investigate the differences in characteristics between suicide cases in Hong Kong...

Patterns in COVID-19 Vaccination Coverage, by Social Vulnerability and Urbanicity - United States, December 14, 2020-May 1, 2021.

Disparities in vaccination coverage by social vulnerability, defined as social and structural factors associated with adverse health outcomes, were noted during the first 2.5 months of the U.S. COVID-19 vaccination campaign, which began during mid-December 2020 (1). As vaccine eligibility and availability continue to expand, assuring equitable coverage for disproportionately affected communities remains a priority. CDC examined COVID-19 vaccine administration and 2018 CDC social vulnerability index (SVI) data to ascertain whether inequities in COVID-19 vaccination coverage with respect to county-level SVI have persisted, overall and by urbanicity. Vaccination coverage was defined as the number of persons aged ≥18 years (adults) who had received ≥1 dose of any Food and Drug Administration (FDA)-authorized COVID-19 vaccine divided by the total adult population in a specified SVI category.† SVI was examined overall and by its four themes (socioeconomic status, household composition and disability, racial/ethnic minority status and language, and housing type and transportation). Counties were categorized into SVI quartiles, in which quartile 1 (Q1) represented the lowest level of vulnerability and quartile 4 (Q4), the highest. Trends in vaccination coverage were assessed by SVI quartile and urbanicity, which was categorized as large central metropolitan, large fringe metropolitan (areas surrounding large cities, e.g., suburban), medium and small metropolitan, and nonmetropolitan counties.§ During December 14, 2020-May 1, 2021, disparities in vaccination coverage by SVI increased, especially in large fringe metropolitan (e.g., suburban) and nonmetropolitan counties. By May 1, 2021, vaccination coverage was lower among adults living in counties with the highest overall SVI; differences were most pronounced in large fringe metropolitan (Q4 coverage = 45.0% versus Q1 coverage = 61.7%) and nonmetropolitan (Q4 = 40.6% versus Q1 = 52.9%) counties. Vaccination coverage disparities were largest for two SVI themes: socioeconomic status (Q4 = 44.3% versus Q1 = 61.0%) and household composition and disability (Q4 = 42.0% versus Q1 = 60.1%). Outreach efforts, including expanding public health messaging tailored to local populations and increasing vaccination access, could help increase vaccination coverage in high-SVI counties.

Understanding Variation In Nonurgent Pediatric Emergency Department Use In Communities With Concentrated Disadvantage.

Children in communities with concentrated socioeconomic and structural disadvantage tend to have elevated rates of nonurgent visits to emergency departments (EDs). Using a spatial regression model of 264 census block groups in Pittsburgh, Pennsylvania, we investigated sociodemographic and structural factors associated with lower-than-expected ("low utilization") versus higher-than-expected ("high utilization") nonurgent ED visit rates among children in block groups with concentrated disadvantage. Compared with high-utilization block groups, low-utilization block groups had higher percentages of households with two adults, high school graduates, access to vehicles, sound housing quality, and owner-occupied housing. Notably, low-utilization block groups did not differ significantly from high-utilization block groups either in the percentage of households located within very close proximity to public transit or primary care or in children's health insurance coverage rates. Stakeholders wishing to reduce pediatric nonurgent ED visits among families in communities of concentrated disadvantage should consider strategies to mitigate financial, time, transportation, and health literacy constraints that may affect families' access to primary care.

Transportation, access to safe sanitation, and number of midwives and economic growth in districts in Indonesia

Transportation is the main driver of all economic sectors in a country. It boosts economic activity and promotes a commodity and geographical area. As a maritime country with more than 17,000 islands Indonesia needs to connect through transportation so that economy can grow and commodities across regions can be traded and commercialized. Other factors that affect economic growth are health personnel and infrastructure development. This study is aimed to investigate the association between transportation, access to sanitation, and health personnel availability and economic growth in districts (kabupaten/kota) in Indonesia. The data used came from the Indonesia Database for Policy and Economic Research (INDO-DAPOER) of the World Bank that covered 331 districts in Indonesia in 2005, 2008, and 2011. The dependent variable was the total GDP excluding oil and gas, while the independent variables were the villages with asphalt road, household access to safe sanitation, and the number of midwives. The results of the study indicate that in districts in Indonesia higher GDP was associated with higher percentage of village with asphalt road, higher percentage of households with access to safe sanitation, and higher number of midwives.

The Lost Tradition of Economic Equality in America, 1600–1870 by Daniel R. Mandell

Reviewed by: The Lost Tradition of Economic Equality in America, 1600–1870 by Daniel R. Mandell John Majewski (bio) Keywords Inequality, Economic equality, Capitalism, Egalitarianism The Lost Tradition of Economic Equality in America, 1600–1870. By Daniel R. Mandell. (Baltimore: Johns Hopkins University Press, 2020. Pp. 314. Cloth, $49.95.) Historians seeking to more explicitly link the past to current economic and political debates can choose between two basic strategies. One strategy—which the 1619 Project used to such great effect—is to expose [End Page 681] the injustices of the past and trace their impact to the present. The other strategy—which Daniel R. Mandell employs in this thought-provoking synthesis—is to recover ideas and movements from the past that can inspire change and reform in the present. Mandell argues that from its colonial origins, a series of thinkers and activists believed that economic equality—whether achieved through universal education, land reform, or government regulation of market activity—was an important value that profoundly influenced government policy. Mandell seamlessly analyzes a number of different egalitarian traditions with nuance and sophistication. He begins in early modern England, where groups such as the Levelers called for the end of great estates and the redistribution of property. Powerful landlords violently crushed the Levelers, but echoes of the movement survived in a republican commonwealth tradition that that linked economic inequality with political inequality and corruption. That tradition took root in colonial North America, where far higher percentage of households owned land. North American Indians—often portrayed in Enlightenment discourse as noble savages— came to symbolize full economic and social equality. As inequality grew in the second half of the eighteenth century, a revolutionary tradition developed that linked economic inequality with British corruption and aristocracy that influenced the anti-Federalists as well as the opponents of Hamiltonian economic policies. Mandell proceeds to show how diff erent movements in the early republic grappled with inequality through the expansion of schooling, the removal of property requirements for voting, and the rise of workingmen parties that supported higher pay for journeymen workers and a ten-hour work day. While these reform movements achieved some significant political successes, they did not halt the advance of capitalism. The movement to expand education, which received strong support from farmers and workers, often resulted in a curriculum that tacitly endorsed inequality and social hierarchies. Political reformers successfully eliminated property requirements in a number of states, but universal white male suffrage did little to remove the influence of the wealthy and well connected. The workingmen parties sometimes put forth socialist ideas, but the Democratic Party successfully incorporated their attacks on wealth and privilege without “fundamental restructuring of the American economy” (119). In the aftermath of financial panics and economic depression in the late 1830s and early 1840s, more radical critiques found significant traction. [End Page 682] Some focused on developing communal alternatives to capitalism. Albert Brisbane found initial success in organizing cooperative communities founded on the ideas of the French socialists Charles Fourier. The communal enterprises ultimately failed, but managed to attract significant support from political and intellectual elites such as Horace Greeley, giving hope that a message of cooperation over competition might find traction. Founded in 1844, the National Reform Association (NRA) fused land reform, anti-rent rhetoric, and support for a ten-hour workday to put forward a political platform explicitly critical of great concentrations of wealth. The NRA could not find political traction as an independent movement, but its central idea that all government land should become free and open to ordinary farm families eventually found a home in the Republican Party. The failure to redistribute land to formerly enslaved families during Reconstruction marked the limits of the land reform movement. Liberal conceptions of private property triumphed throughout the Gilded Age. Mandell successfully demonstrates that a number of vibrant egalitarian traditions offered important critiques of economic inequality in the eighteenth and nineteenth centuries. The book represents a deep engagement with a broad array of thinkers, all of whom are put into specific political, cultural, and social contexts. Specialists and researchers will find Man-dell’s sophisticated and insightful analysis deeply engaging, yet the book’s writing is so...

Adapting to Climate Extreme Events Based on Livelihood Strategies: Evidence from Rural Areas in Thua Thien Hue Province, Vietnam

The key farming communities in Vietnam are generally poor and lack resources to adapt to and mitigate the impacts of climate extreme events (CEEs), but the extent of their adaptation strategies is not well understood. This study aims to analyze the impacts of CEEs, current barriers, and adaptation strategies based on three categories of livelihood strategies. The classification method is first used to divide the livelihood strategy into these three categories, and a multinomial logistic model (MLS) is then applied to determine the set of parameters that affect adaptation options. CEEs result in significant damage in terms of both financial and health dimensions. Various barriers remain, such as the low capacity of relevant staff, lack of local budgets, and outdated methods being used to estimate and mitigate the impacts of CEEs. Notably, there were over 44%, and 28% conducted reactive and proactive adaptations, respectively, while a high percentage of households did not implement at least one adaptation method, around 27%. The MLS model is able to explain about 51.2% of the driving factors that influence adaptation strategies. In addition, the behavior of households in choosing adaptation methods shows the difference between perceptions of CEEs’ impacts and livelihood strategy profiles. There is a need for a package of adaptive solutions to address the impacts of CEEs that cover the many different household perspectives and involve stakeholders at multiple levels.

Association of poor housing conditions with COVID-19 incidence and mortality across US counties.

Poor housing conditions have been linked with worse health outcomes and infectious disease spread. Since the relationship of poor housing conditions with incidence and mortality of COVID-19 is unknown, we investigated the association between poor housing condition and COVID-19 incidence and mortality in US counties. We conducted cross-sectional analysis of county-level data from the US Centers for Disease Control, US Census Bureau and John Hopkins Coronavirus Resource Center for 3135 US counties. The exposure of interest was percentage of households with poor housing conditions (one or greater of: overcrowding, high housing cost, incomplete kitchen facilities, or incomplete plumbing facilities). Outcomes were incidence rate ratios (IRR) and mortality rate ratios (MRR) of COVID-19 across US counties through 4/21/2020. Multilevel generalized linear modeling (with total population of each county as a denominator) was utilized to estimate relative risk of incidence and mortality related to poor housing conditions with adjustment for population density and county characteristics including demographics, income, education, prevalence of medical comorbidities, access to healthcare insurance and emergency rooms, and state-level COVID-19 test density. We report incidence rate ratios (IRRs) and mortality ratios (MRRs) for a 5% increase in prevalence in households with poor housing conditions. Across 3135 US counties, the mean percentage of households with poor housing conditions was 14.2% (range 2.7% to 60.2%). On April 21st, the mean (SD) number of cases and deaths of COVID-19 were 255.68 (2877.03) cases and 13.90 (272.22) deaths per county, respectively. In the adjusted models standardized by county population, with each 5% increase in percent households with poor housing conditions, there was a 50% higher risk of COVID-19 incidence (IRR 1.50, 95% CI: 1.38-1.62) and a 42% higher risk of COVID-19 mortality (MRR 1.42, 95% CI: 1.25-1.61). Results remained similar using earlier timepoints (3/31/2020 and 4/10/2020). Counties with a higher percentage of households with poor housing had higher incidence of, and mortality associated with, COVID-19. These findings suggest targeted health policies to support individuals living in poor housing conditions should be considered in further efforts to mitigate adverse outcomes associated with COVID-19.

Incidence and neighborhood-level determinants of child welfare involvement

BackgroundChild maltreatment is a global public health issue that has been linked with multiple negative health and life outcomes. ObjectiveThis study evaluates the association between children placed in out-of-home care and neighborhood-level factors using eight years of administrative data. Participants and settingBetween 2011–2018, 33,890 unique instances of child welfare involvement were captured in a department of child and family services database in a southern state in the United States. MethodsRemoval addresses were geocoded and linked to the U.S. Census Bureau’s American Community Survey to obtain census tract socioeconomic factors. Incidence overall and stratified by individual and neighborhood-level factors was computed. Rate ratios, relative indexes of inequality, and concentration curves quantified disparities in incidence of child welfare involvement by neighborhood-level factors. ResultsIncidence of children less than 19 years old placed into out-of-home care was 255 per 100,000 person-years (95 % CI: 252, 258). At the individual level, incidence was highest among children <5 and 15–17 years old, comparable between male and female children, and higher among Black children. At the neighborhood level, incidence was highest in census tracts with lower median household incomes, higher percentages of households below poverty or of female-headed or single-parent households, higher unemployment rates, and fewer residents with some college education or health insurance. ConclusionsIncidence of children placed into out-of-home care is disproportionally higher for those who live in disadvantaged communities. Understanding neighborhood-level risk factors that may be linked to child welfare involvement can help inform policy and target prevention efforts.

Modeling determinants of ridesourcing usage: A census tract-level analysis of Chicago

Ridesourcing services provided by companies like Uber, Lyft, and Didi have grown rapidly over the past decade and now serve a sizable portion of trips in many metropolitan areas. An understanding of these services (e.g. to whom, where, when, and for what purposes do they provide service?) is critical for regulating, planning, and managing urban multi-modal transportation systems effectively. Unfortunately, little is known about ridesourcing travel because private companies providing ridesourcing services were not previously subject to data sharing requirements. Fortunately, the city of Chicago recently collected and released spatially (census tract) and temporally (15-minute interval) aggregated data on ridesourcing trips collected from private companies. This study analyzes the Chicago ridesourcing data to examine factors influencing ridesourcing usage. The study employs a random-effects negative binomial (RENB) regression approach to model ridesourcing usage. Determinants considered in the model include weekend vs. weekday and weather variables as well as census tract socio-demographics and commute characteristics, land-use variables, places of interest, transit supply, parking features, and crime. The model results indicate ridesourcing demand is higher on days when temperatures are lower, there is less precipitation, and on the weekend, as well as in census tracts with (i) higher household incomes, (ii) a higher percentage of workers who carpool or take transit to work, (iii) a higher percentage of households with zero vehicles, (iv) higher population and employment density, (v) higher land-use diversity, (vi) fewer parking spots and higher parking rates, (vii) more restaurants, and (viii) more homicides. The results also demonstrate a non-linear (and insightful) relationship between ridesourcing demand and transit supply variables. The paper discusses the implications of these model results to inform transportation planning and policymaking as well as future research.

The Effect of State Gun Laws on the Supply of Guns and Gun Crimes: A Multilevel Modeling Analysis

Gun homicides alone cause 11 000 deaths each year. Numerous shootings in recent years, terrorist and otherwise, have sparked heated debates about gun control policy, particularly with regard to Second Amendment issues. Such discussions are more often than not driven by political and philosophical ideology, rather than empirical data. The self‐imposed ban on the dissemination of gun‐related statistics on the part of the Centers for Disease Control has created difficulty in obtaining data, particularly at the sub‐state level. Furthermore, the majority of previous research has not examined the effect on crimes that are gun specific, that is, homicides or robberies committed using guns versus all homicides or robberies. The objective of this research is to examine the multiple relationships between gun ownership and gun crimes at the sub‐state level and to then examine the effect of state‐level gun laws on the sub‐state relationships via multilevel modeling.This research involves two separate units of analysis, counties and states. Initial multivariate regression was conducted on a subset of counties in the United States examining the relationship between the percentage of all households with at least one gun, and both the gun homicide rate as well as the gun robbery rate. Data on gun ownership were obtained from the CDC's BRFSS SMART County‐level data files from the mid‐2000s. These data are sampled and weighted to be representative of the entire county. A total of 218 weighted, aggregated counties were used from two separate non‐overlapping years combined. Gun crime data were obtained from the FBI Uniform Crime Reports, which provides total universe data on crimes, including the number committed with a gun. State gun law rankings were obtained from the Law Center to Prevent Gun Violence, which quantifies state gun laws into a numeric index. Standard county‐level control variables such as race, income, poverty, region of the country, level of education, and population density were employed. Multilevel models were then run overlaying state gun laws with county‐level data.The population studied included adults age 18 and older who responded to the Centers for Disease Control's BRFSS survey questions, as well as all gun crimes known to the police.In traditional multivariate analysis, a higher percentage of households with guns was associated with lower gun robbery rates as well as lower gun homicide rates. Multilevel modeling using state gun laws indicated that more restrictive gun laws were also associated with lower gun robbery and homicide rates. The percentage of households with guns remained negative and significant in the multilevel models. Moreover, restrictive state gun laws were associated with lower gun ownership rates at the county level.Both legal gun ownership and more restrictive state gun laws are associated with lower rates of violent gun crimes (robbery and homicide).The results unexpectedly provide support for both pro‐ and anti‐gun positions. This suggests that causal patterns are more complex than either position might indicate, and public health policies on gun supply and restriction would be better informed by empirical analysis.

1393-P: Residential Inequality and Birth Outcomes of Gestational Diabetes

Introduction: Clinical studies have found disparities in gestational diabetes mellitus (GDM) outcomes due to individual racial and economic differences. In contrast, this pilot study examines the association of structures variations with GDM outcomes by race. Methods: Data were obtained from a healthcare system for a study population of 2,189 women with GDM, 18 years and above, singleton births, who delivered between January 1, 2014 and December 31, 2018. Clinical data were merged with structural measures of mothers’ census tracts of residence from the U.S. Census Bureau. Outcome variables are mother and infant birth complications. Data are analyzed using zero correlations by race. Results: Mother’s: white mothers tended to have fewer complications (r=-0.07, p&amp;lt;.05); not other races. For African American women, living in a tract with a higher % of households below the poverty level and higher % of less than $35K earners increased the likelihood of their experiencing Hemorrhage (r=0.11, p&amp;lt;0.03; r=0.12, p&amp;lt;0.05). For women of other races, living in a tract with higher percentage of household below the poverty level increased the likelihood of Hemorrhage (r=0.11, p&amp;lt;.05). For baby’s complications, white infants and infants from other races whose mothers resided in areas with a higher percentage of households below the poverty level had a higher risk of shoulder dystocia while infants of African American mothers who resided in census tracts with a higher percentage of earners below $35K were at higher risk for hospital revisit in &amp;lt;30 days (r=0.12, p&amp;lt;0.05). African American infants from neighborhoods with higher % of household below poverty level and no household vehicle had a higher likelihood of revisit. Conclusion: Selected results show variations in complications among different races by neighborhood characteristics. The influence of race on GDM outcomes is linked to income and socioeconomic status. Income may be more important to GDM outcomes than race. Disclosure L. Thomas: None. M.R. Fawcett: None. C. Jurkovitz: None. J. Lenhard: Speaker’s Bureau; Self; Janssen Pharmaceuticals, Inc. Funding National Institutes of Health (U54GM104941)

Association of Poor Housing Conditions with COVID-19 Incidence and Mortality Across US Counties

Background: The association between poor housing conditions, which can compromise physical distancing, and COVID-19 incidence and mortality across US counties remains unknown. Methods: We conducted cross-sectional analysis of county-level data from the US Centers for Disease Control, US Census Bureau and John Hopkins Coronavirus Resource Center for 3141 US counties. The exposure of interest was percentage of households with poor housing conditions (one or more of: overcrowding, high housing cost, incomplete kitchen facilities, or incomplete plumbing facilities). Outcomes were incidence rate ratios (IRR) and mortality rate ratios (MRR) of COVID-19 across US counties through 4/21/2020. Multilevel generalized linear modeling was utilized with adjustment for population density and county characteristics including demographics, income, education, prevalence of medical comorbidities, access to healthcare insurance and emergency rooms, and state-level COVID-19 test density. Findings: Across 3135 US counties, the mean percentage of households with poor housing conditions was 14.2% (range 2.7% to 60.2%). In the fully adjusted models, with each 5% increase in percent households with poor housing conditions, there was a 50% higher risk of COVID-19 incidence (IRR 1.50, 95% CI: 1.38 – 1.62) and a 42% higher risk of COVID-19 mortality (MRR 1.42, 95% CI: 1.25 – 1.61). Results remained similar using earlier timepoints (3/31/2020 and 4/10/2020). Interpretation: Counties with a higher percentage of households with poor housing had higher incidence of, and mortality associated with, COVID-19. These findings suggest targeted health policies to support individuals living in poor housing conditions should be considered to mitigate adverse outcomes associated with COVID-19. Funding Statement: None. Declaration of Interests: None. Ethics Approval Statement: Since there is no individual identifying information, the data were in aggregate by county, and publicly available from the Centers for Disease Control (CDC), US census Bureau and John Hopkins Coronavirus Resource Center, the protocol received exemption from the Providence Veterans Affairs Medical Center Institutional Review Board.

Modeling Ridesourcing Trip Generation: Chicago Case Study

Ridesourcing services provided by companies like Uber, Lyft, and Didi have grown rapidly over the past decade and now serve a sizable portion of trips in many metropolitan areas. An understanding of these services (e.g. to whom, where, when, and for what purposes do they provide service?) is critical for regulating, planning, and managing urban multi-modal transportation systems effectively. Unfortunately, little is known about ridesourcing travel because private companies providing ridesourcing services were not previously subject to data sharing requirements. Fortunately, the city of Chicago recently collected and released spatially (census tract) and temporally (15-minute interval) aggregated data on ridesourcing trips collected from private companies. This study analyzes the Chicago ridesourcing data to examine factors influencing ridesourcing usage. The study employs a random-effects negative binomial (RENB) regression approach to model ridesourcing usage. Determinants considered in the model include weekend vs. weekday and weather variables as well as census tract socio-demographics and commute characteristics, land-use variables, places of interest, transit supply, parking features, and crime. The model results indicate ridesourcing demand is higher on days when temperatures are lower, there is less precipitation, and on the weekend, as well as in census tracts with (i) higher household incomes, (ii) a higher percentage of workers who carpool or take transit to work, (iii) a higher percentage of households with zero vehicles, (iv) higher population and employment density, (v) higher land-use diversity, (vi) fewer parking spots and higher parking rates, (vii) more restaurants, and (viii) more homicides. The results also demonstrate a non-linear (and insightful) relationship between ridesourcing demand and transit supply variables. The paper discusses the implications of these model results to inform transportation planning and policymaking as well as future research.

Dealing with energy poverty in Cyprus – an overview

ABSTRACTEnergy poverty is the inability of a household to maintain basic energy services in the household, such as heating and cooling, due to unaffordability. This issue has been gaining public awareness since the financial crisis of 2008. Based on European-wide indicators, Cyprus demonstrates a high percentage of households with poor energy affordability. However, an in-depth investigation on energy poverty manifestations and measures has not been carried out. This paper aims to address this gap in knowledge, by outlining current measures associated with vulnerable consumers in Cyprus. Recommendations are made for the adoption of additional and/or complimentary action to effectively address energy poverty in Cyprus. Furthermore, a web-based survey collected demographic and economic information of dwellers, as well as technical characteristics of dwellings and perception of thermal comfort. Preliminary results indicate that approximately 40% of respondents reported spending up to a quarter of their net income towards energy bills.

Spatial analysis and mapping of child malnutrition in Burkina Faso

Abstract Background Approximately 45% of all child deaths are associated with malnutrition in sub-Saharan Africa. However, information on the geographical variations of child malnutrition in Burkina Faso is limited. This study examined the geographical distribution and the community characteristics associated with child malnutrition in Burkina Faso using spatial regression analysis. Methods Data from the 2011 Burkina Faso Demographic Health Survey were analyzed. A representative sample of 572 communities and 6,120 children under age of five were in the survey. A general Kriging interpolation method was used to generate spatial malnutrition patterns. The Local Indicator of Spatial Autocorrelation was used to identify particular communities clustering with high and low child malnutrition scores. In the spatial regression analysis, we used a two-step procedure combining generalized estimating equation models (GEE) and spatial lag modeling techniques. Results The average rates of stunting and wasting were 32.48%, and 15.05%, respectively. Stunting hotspots were observed to be in the eastern and northeastern part of Burkina Faso (i.e. Oudolan, Séno), while high rates of wasting were observed in the central-north part. The results of the spatial regression analysis revealed lower stunting rates in communities with a higher percentage of households with improved sanitation. Communities with higher rates of professionally assisted births were associated with low wasting rates while communities with higher rates of households with low wealth index reported higher rates of wasting. Spatial lag models appeared to estimate the relationship between community-level risk factors and stunting and wasting more accurately than the ordinary least squares models. Conclusions This study revealed significant geographical patterns and community factors associated with childhood malnutrition. These factors should be considered for future programs aiming at reducing child malnutrition in Burkina Faso. Key messages The study identifies spatial trends and contextual factors associated with child malnutrition in Burkina Faso. Public health programs should focus on the regions identified as hot spots of child malnutrition in Burkina Faso.

Contextual Factors and Spatial Patterns of Childhood Malnutrition in Provinces of Burkina Faso.

Approximately 45% of all children's deaths are associated with malnutrition, and sub-Saharan Africa is hardest hit by this phenomenon. However, information on geographical variations of malnutrition in developing countries is limited. This study examined the geographical distribution and community characteristics associated with child malnutrition in Burkina Faso. Data from the 2011 Burkina Faso Demographic Health Survey were analyzed. A general Kriging interpolation method was used to generate spatial malnutrition patterns. The global Moran's I test was used to identify significant malnutrition spatial patterns. Generalized estimating equations (GEEs) were fitted to examine the association between community level factors and malnutrition. Average rates of stunting and wasting in the communities were 32.48% and 15.05%, respectively. Stunting hotspots were observed in the eastern and northeastern parts of Burkina Faso (i.e. Oudolan, Séno and Yagha, among others), while high rates of wasting were observed in the north-central part. The GEE results revealed lower stunting rates in communities with a higher percentage of households with improved sanitation. Communities with higher rates of professionally assisted births were associated with low wasting rates, while communities with higher rates of households with a low wealth index reported higher rates of wasting. Spatial statistical models of malnutrition prevalence are useful for indicating hotspots over wide areas and hence, for guiding intervention strategies. This study revealed significant geographical patterns and community factors associated with childhood malnutrition. These factors should be considered in future programs aimed at reducing malnutrition in Burkina Faso.