Household crowding index: a correlate of socioeconomic status and inter-pregnancy spacing in an urban setting

Abstract Mosquito community dynamics in urban areas are influenced by an array of both social and ecological factors. Human socioeconomic factors (SEF) can be related to mosquito abundance and diversity as urban mosquito development sites are modified by varying human activity, e.g., level of abandoned structures or amount of accumulated trash. The goal of this study was to investigate the relationships among mosquito diversity, populations of Aedes aegypti, and SEF in a tropical urban setting. Mosquitoes were collected using BG Sentinel 2 traps and CDC light traps during three periods between late 2018 and early 2019 in San Juan, Puerto Rico, and were identified to species. SEFs (i.e. median household income, population density, college-level educational attainment, unemployment, health insurance coverage, percentage of households below the poverty line, amount of trash and level of abandoned homes) were measured using foot surveys and U.S. Census data. We found 19 species with the two most abundant species being Culex quinquefasciatus (n = 10 641, 87.6%) and Ae. aegypti (n = 1558, 12.8%). We found a positive association between Ae. aegypti abundance and mosquito diversity, which were both negatively related to SES and ecological factors. Specifically, lower socioeconomic status neighborhoods had both more Ae. aegypti and more diverse communities, due to more favorable development habitat, indicating that control efforts should be focused in these areas.

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Assessing disparities in engagement in a post-discharge virtual visit follow-up program.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.29_suppl.100 ◽

2020 ◽

Vol 38 (29_suppl) ◽

pp. 100-100

Author(s):

Abdullateef Abdulkareem ◽

Nathan Handley ◽

Samantha Burdette ◽

Adam Binder

Keyword(s):

Socioeconomic Status ◽

Language Proficiency ◽

English Language ◽

Urban Setting ◽

Transitions Of Care ◽

Area Deprivation ◽

Patient Portal ◽

Post Discharge ◽

The Impact

100 Background: Transitions of care are a frequent focus of quality improvement initiatives. In attempt to improve upon the transitions of care for oncology patients, our institution implemented a post discharge virtual visit follow-up program. Previous studies have suggested that socioeconomic status impacts engagement in technology based interventions. Herein, we report the impact of socio-economic status based on area deprivation index (ADI) on engagement with the program. Methods: All patients admitted to the elective chemotherapy service were included. Retrospective analysis of characteristics of each participant was conducted. Data included eligibility (access to the internet, appropriate device, English language proficiency, ability to set up video visit and a patient portal account) for video visit, interest in participating, completion of the visit and any interventions performed during the visit. In addition, ADI was calculated for each individual. Patients were classified into quartiles based on ADI (quartiles increased with ADI). Chi squared testing was performed to assess whether socioeconomic status affected enrollment in video visits. Simple descriptive analysis was also performed. Results: One hundred seven unique patients were included for review. Of these, 33 (31%), 39 (36%), 16 (15%) and 19 (18%) were in quartile(Q) 1, 2, 3 and 4 respectively. Eligibility per quartile was 29 (88%), 34(87%), 13(81%), and 15(83%). ADI quartile did not significantly affect virtual visit eligibility (p = .50). A total of 91 patients (85%) were eligible for video visits; of these, 46 patients declined. Of the 46 patients that declined 9 (19%), 20 (43%), 8 (17%), and 9 (19%) were in Q1, Q2, Q3 and Q4 respectively. Fifteen patients cited technology issues as reasons for declining telehealth visits - 10 (67%) from Q1 and Q2 and 5 (33%) from Q3 and Q4. The vast majority cited lack of interest as reason for declining. Conclusions: ADI as a measure of socioeconomic status did not significantly affect eligibility for or enrollment in video visits. This may be explained by more ubiquitous access to internet services in a large urban setting. Current research is currently being conducted to understand patient barriers to engagement in virtual visits.

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Social Distancing Associations with COVID-19 Infection and Mortality Are Modified by Crowding and Socioeconomic Status

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18094680 ◽

2021 ◽

Vol 18 (9) ◽

pp. 4680

Author(s):

Trang VoPham ◽

Matthew D. Weaver ◽

Gary Adamkiewicz ◽

Jaime E. Hart

Keyword(s):

Socioeconomic Status ◽

Disease Transmission ◽

Census Data ◽

Minority Population ◽

County Level ◽

Social Distancing ◽

Interaction Terms ◽

Incidence And Mortality ◽

Black Residents ◽

Household Crowding

The SARS-CoV-2 virus is a public health emergency. Social distancing is a key approach to slowing disease transmission. However, more evidence is needed on its efficacy, and little is known on the types of areas where it is more or less effective. We obtained county-level data on COVID-19 incidence and mortality during the first wave, smartphone-based average social distancing (0–5, where higher numbers indicate more social distancing), and census data on demographics and socioeconomic status. Using generalized linear mixed models with a Poisson distribution, we modeled associations between social distancing and COVID-19 incidence and mortality, and multiplicative interaction terms to assess effect modification. In multivariable models, each unit increase in social distancing was associated with a 26% decrease (p < 0.0001) in COVID-19 incidence and a 31% decrease (p < 0.0001) in COVID-19 mortality. Percent crowding, minority population, and median household income were all statistically significant effect modifiers. County-level increases in social distancing led to reductions in COVID-19 incidence and mortality but were most effective in counties with lower percentages of black residents, higher median household incomes, and with lower levels of household crowding.

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Socioeconomic status and the utilization of diagnostic imaging in an urban setting

Canadian Medical Association Journal ◽

10.1503/cmaj.050609 ◽

2005 ◽

Vol 173 (10) ◽

pp. 1173-1177 ◽

Cited By ~ 49

Author(s):

S. Demeter

Keyword(s):

Socioeconomic Status ◽

Diagnostic Imaging ◽

Urban Setting

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Do ethnic inequalities in multimorbidity reflect ethnic differences in socioeconomic status? The HELIUS study

European Journal of Public Health ◽

10.1093/eurpub/ckz012 ◽

2019 ◽

Vol 29 (4) ◽

pp. 687-693 ◽

Cited By ~ 1

Author(s):

Wim J G M Verest ◽

Henrike Galenkamp ◽

Bea Spek ◽

Marieke B Snijder ◽

Karien Stronks ◽

...

Keyword(s):

Socioeconomic Status ◽

Ethnic Minority ◽

Minority Groups ◽

Urban Setting ◽

Ethnic Minority Groups ◽

Ethnic Inequalities ◽

Minority Men ◽

Study Participants ◽

Simultaneous Adjustment ◽

Adjusted Model

Abstract Background The burden of multimorbidity is likely higher in ethnic minority populations, as most individual diseases are more prevalent in minority groups. However, information is scarce. We examined ethnic inequalities in multimorbidity, and investigated to what extent they reflect differences in socioeconomic status (SES). Methods We included Healthy Life in an Urban Setting study participants of Dutch (N = 4582), South-Asian Surinamese (N = 3258), African Surinamese (N = 4267), Ghanaian (N = 2282), Turkish (N = 3879) and Moroccan (N = 4094) origin (aged 18–70 years). Educational level, employment status, income situation and multimorbidity were defined based on questionnaires. We described the prevalence and examined age-adjusted ethnic inequalities in multimorbidity with logistic regression analyses. To assess the contribution of SES, we added SES indicators to the age-adjusted model. Results The prevalence of multimorbidity ranged from 27.1 to 53.4% in men and from 38.5 to 69.6% in women. The prevalence of multimorbidity in most ethnic minority groups was comparable to the prevalence among Dutch participants who were 1–3 decades older. After adjustment for SES, the odds of multimorbidity remained significantly higher in ethnic minority groups. For instance, age-adjusted OR for multimorbidity for the Turkish compared to the Dutch changed from 4.43 (3.84–5.13) to 2.34 (1.99–2.75) in men and from 5.35 (4.69–6.10) to 2.94 (2.54–3.41) in women after simultaneous adjustment for all SES indicators. Conclusions We found a significantly higher prevalence of multimorbidity in ethnic minority men and women compared to Dutch, and results pointed to an earlier onset of multimorbidity in ethnic minority groups. These inequalities in multimorbidity were not fully accounted for by differences in SES.

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Differences in Chlamydia trachomatis seroprevalence between ethnic groups cannot be fully explained by socioeconomic status, sexual healthcare seeking behavior or sexual risk behavior: a cross-sectional analysis in the HEalthy LIfe in an Urban Setting (HELIUS) study

BMC Infectious Diseases ◽

10.1186/s12879-018-3533-7 ◽

2018 ◽

Vol 18 (1) ◽

Cited By ~ 9

Author(s):

Sebastiaan H. Hulstein ◽

Amy Matser ◽

Catharina J. Alberts ◽

Marieke B. Snijder ◽

Martina Willhauck-Fleckenstein ◽

...

Keyword(s):

Socioeconomic Status ◽

Chlamydia Trachomatis ◽

Urban Setting ◽

Cross Sectional ◽

Healthy Life ◽

Helius Study ◽

Seeking Behavior ◽

Healthcare Seeking Behavior ◽

Sectional Analysis ◽

Sexual Healthcare

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Gender Differences in the Relationship between Housing, Socioeconomic Status, and Self-Reported Health Status

Reviews on Environmental Health ◽

10.1515/reveh-2004-19-3-403 ◽

2004 ◽

Vol 19 (3-4) ◽

pp. 177-196 ◽

Cited By ~ 3

Author(s):

James R. Dunn ◽

Jennifer D. Walker ◽

Jennifer Graham ◽

Christina B. Weiss

Keyword(s):

Gender Differences ◽

Socioeconomic Status ◽

Health Status ◽

Poor Health ◽

Future Research ◽

Self Rated Health ◽

Reported Health ◽

Self Reported Health Status ◽

Household Crowding ◽

Fair Poor

Abstract This study investigates gender differences in housing, socioeconomic status, and self-reported health status. The analysis focuses on the social and economic dimensions of housing, such as demand, control, material aspects (affordability, type of dwelling) and meaningful aspects (pride in dwelling, home as a refuge) of everyday life in the domestic environment. A random sample, crosssectional telephone survey was administered in the city of Vancouver, Canada in June 1999 (n = 650). Survey items included measures of material and meaningful dimensions of housing, housing satisfaction, and standard measures of socioeconomic status and social support. The main outcome measure was self-reported health (excellent/very good/good vs. fair/poor). A three-stage analysis provides an overall picture of the sample characteristics for male and female respondents, detects significant relations between individual and housing characteristics and self-rated health status, and investigates male-female differences in the factors associated with fair/poor self-rated health. In multivariate analyses, a small number of socioeconomic dimensions of housing were associated with self-rated health status for women. For men, only one attribute of housing was associated with self-rated health: crowding was positively related to poor health, contradicting expectations and the findings for women. The self-reported strain of housework was unrelated to self-rated health for men, bot strongly related to poor health for women. For men and women, satisfaction with social activities increased the likelihood of reporting better health. Future research should focus on the health effects of geodered differences in domestic and paid work, and on home and family roles and the interaction among gender, household crowding, and health.

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