scholarly journals A Geospatial Analysis of salmonellosis and its with socioeconomic status in Texas

2021 ◽  
Author(s):  
Anand Gourishankar
Keyword(s):  
Risk Factors ◽  
Socioeconomic Status ◽  
Negative Binomial ◽  
Hot Spot ◽  
College Education ◽  
Cold Spot ◽  
Local Health ◽  
Association Rate ◽  
Housing Problem ◽  
Social Association

Background: Social, behavioral, and environmental factors affect salmonellosis. The study's objective was to find the association between salmonellosis and socioeconomic status (SES) in hot spot areas and statewide counties. Methods: Retrospective county-level data on salmonellosis in 2017 were obtained from the Texas surveillance database. A statistically significant hot spot analysis identified high infection rates. We compared the socioeconomic status factors between hot and cold spot counties. We modeled zero-inflation negative binomial regression, and the final model's residual was tested for spatial clustering. Results: There were a total of 5113 salmonelloses from 254 counties with an unadjusted crude rate of 18 per 100,000 Person-year. Nine SES risk factors in the hot spot counties were as follows: low values of the severe housing problem, unemployment, African American, and high values of college education, social association rate, fast food/full-service restaurant use, Hispanic, and senior low access-to-store (P < 0.05). A 12% difference existed between local health departments in hot (25%) and cold spot (37%) counties (P = 0.81). Statewide independent risk factors were severe housing problem (IRR = 1.1; CI:1.05-1.14), social association rate (IRR = 0.89; CI:0.87-0.92), college education (IRR = 1.05; CI: 1.04-1.07), and non-Hispanic senior local access-to-store (IRR = 1.98; CI: 1.26-3.11). The severe housing problem predicted zero occurrences of infection in a county (OR = 0.51; CI: 0.28-0.95). Conclusions: Disparity exists in salmonellosis and socioeconomic status. Attention to unmet needs will decrease salmonellosis. A severe housing problem is a notable risk.

scholarly journals Geospatial analysis of salmonellosis and its association with socioeconomic status in Texas

2021 ◽  
Vol 9 (4) ◽  
pp. e001214
Author(s):  
Anand Gourishankar
Keyword(s):  
Risk Factors ◽  
Socioeconomic Status ◽  
Fast Food ◽  
Hot Spot ◽  
College Education ◽  
Cold Spot ◽  
Local Health ◽  
Association Rate ◽  
Housing Problem ◽  
Social Association

ObjectiveThe study’s objective was to find the association between salmonellosis and socioeconomic status (SES) in hot spot areas and statewide counties.DesignA retrospective cohort study.SettingThe data were recorded regarding salmonellosis in 2017 from the Texas surveillance database. It included assessment of hot spot analysis and SES association with salmonellosis at the county level.ParticipantsPatients with salmonellosis of all age groups in Texas.ResultsThere were a total of 5113 salmonellosis from 254 counties with an unadjusted crude rate of 18 per 100 000 person-years. Seven SES risk factors in the hot spot counties were as follows: low values of the severe housing problem, unemployment, African American and high values of social association rate, fast food/full-service restaurant use, Hispanic and Hispanic senior low access-to-store (p<0.05). A 12% difference existed between local health departments in hot (25%) and cold spot (37%) counties (χ2 (1, n=108)=0.5, p=0.81).Statewide independent risk factors were severe housing problem (incidence rate ratio (IRR)=1.1; 95% CI: 1.05 to 1.14), social association rate (IRR=0.89; 95% CI: 0.87 to 0.92), college education (IRR=1.05; 95% CI: 1.04 to 1.07) and non-Hispanic senior local access-to-store (IRR=1.98; 95% CI: 1.26 to 3.11). The severe housing problem predicted zero occurrences of infection in a county (OR=0.51; 95% CI: 0.28 to 0.95).ConclusionsDisparity exists in salmonellosis and SES. Attention to unmet needs will decrease salmonellosis. Severe housing problem is a notable risk.

scholarly journals Tick trails: the role of online recreational trail reviews in identifying risk factors and behavioral recommendations associated with tick encounters in Indiana

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kristina R. Anderson ◽  
Jordan Blekking ◽  
Oghenekaro Omodior
Keyword(s):  
Risk Factors ◽  
Land Management ◽  
Negative Binomial ◽  
Hot Spot ◽  
Negative Binomial Regression ◽  
Online Reviews ◽  
User Reviews ◽  
Binomial Regression ◽  
State Region ◽  
Recreational Trails

Abstract Background Recreational trails abound across the United States and represent high risk areas for tick exposure. Although online reviews represent a rich source of user information, they have rarely been used in determining the risk of tick exposure during recreational trail use. Based on online user reviews and comments, the purpose of this study was to determine risk factors and behavioral recommendations associated with tick encounters (Tick Presence) on recreational trails in the state of Indiana, U.S. Methods We reviewed 26,016 user comments left on AllTrails.com for 697 Indiana trails. Reviews were evaluated to determine Tick Presence/Absence, the total number of Tick Presence Reviews per trail, and multiple trail and user behavioral characteristics. We used hot spot (Getis-Ord Gi*) analysis to test the hypothesis of whether there are clusters in the number of Tick Presence Reviews. Pearson chi-square tests of independence evaluated whether tick presence was associated with several trail characteristics. Finally, negative binomial regression evaluated the strength of the association between the number of Tick Presence Reviews and several trail characteristics. Results Tick Presence was recorded at 10% (n = 65) of trails and occurred most frequently in May. Hot spot analysis revealed statistically significant clusters of Tick Presence Reviews on trails in the Southern Indiana State Region. Results of χ2 tests indicated significant associations between Tick Presence Reviews and (a) State Region and (b) Land Management Type; Mann-Whitney U tests detected significant differences in Tick Presence Reviews based on Trail Length and Elevation Gain. Subsequent results of a negative binomial regression model indicated that Southern Indiana State Region, Federal and Private Land Management Type, and Elevation Gain were factors significantly associated with Tick Presence Reviews. Content of user reviews indicated several behaviors employed to prevent tick encounters, particularly Repellent Application and Recreational Deterrence; 25% included a behavior Recommendation to others. Conclusions Online, user-generated trail reviews have the potential to serve as rich data sources for identifying recreational trails, where 1) the risk of tick exposure is great, 2) more robust active tick and tick-borne pathogen surveillance may be warranted, and 3) tailored prevention interventions are needed.

scholarly journals Risk factors associated with mortality of COVID-19 in 3125 counties of the United States

2020 ◽  
Author(s):  
Ting Tian ◽  
Jingwen Zhang ◽  
Liyuan Hu ◽  
Yukang Jiang ◽  
Congyuan Duan ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Negative Binomial ◽  
The United States ◽  
Outdoor Environment ◽  
Local Health ◽  
County Level ◽  
Factors Associated ◽  
High Prevalence ◽  
The Impact

Background The number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. Methods To find out the risk factors associated with county-level mortality of COVID-19 with various levels of prevalence, a negative binomial design was applied to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Results 3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P<0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median and high prevalence counties . The segregation between non-Whites and Whites and higher Hispanic population had higher likelihood of risk of the deaths in all infected counties. Conclusions The mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may lead to the reduction in the mortality of COVID-19.

scholarly journals Risk factors associated with mortality of COVID-19 in 3125 counties of the United States

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Ting Tian ◽  
Jingwen Zhang ◽  
Liyuan Hu ◽  
Yukang Jiang ◽  
Congyuan Duan ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Clustering Analysis ◽  
Negative Binomial ◽  
The United States ◽  
Outdoor Environment ◽  
Local Health ◽  
County Level ◽  
Factors Associated ◽  
The Impact

Abstract Background The number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. The risk factors associated with county-level mortality of COVID-19 with various levels of prevalence are not well understood. Methods Using the data obtained from the County Health Rankings and Roadmaps program, this study applied a negative binomial design to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Clustering analysis and Kruskal–Wallis tests were used in our statistical analysis. Results A total of 3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P < 0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median (0.049, P < 0.001) and high (0.114, P < 0.001) prevalence counties. The segregation between non-Whites and Whites (low: 0.015, P < 0.001; median:0.025, P < 0.001; high: 0.019, P = 0.005) and higher Hispanic population (low and median: 0.020, P < 0.001; high: 0.014, P = 0.009) had higher likelihood of risk of the deaths in all infected counties. Conclusions The mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may help decision makers, the public health officials, and the general public better control the risk of pandemic, particularly in the reduction in the mortality of COVID-19. Graphic abstract

scholarly journals The malaria burden of Amerindian groups of three Venezuelan states: a descriptive study based on programmatic data

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Juan C. Gabaldón-Figueira ◽  
Carlos Chaccour ◽  
Jorge Moreno ◽  
Maria Villegas ◽  
Leopoldo Villegas
Keyword(s):  
Risk Factors ◽  
Plasmodium Falciparum ◽  
Malaria Incidence ◽  
Hot Spot ◽  
Public Health Problem ◽  
Epidemiological Data ◽  
Descriptive Study ◽  
Malaria Burden ◽  
Indigenous Groups ◽  
The Impact

Abstract Background Fifty-three percent of all cases of malaria in the Americas in 2019 came from Venezuela, where the epidemic is heavily focused south of the Orinoco river, and where most of the country’s Amerindian groups live. Although the disease is known to represent a significant public health problem among these populations, little epidemiological data exists on the subject. This study aims to provide information on malaria incidence, geospatial clustering, and risk factors associated to Plasmodium falciparum infection among these groups. Methods This is a descriptive study based on the analysis of published and unpublished programmatic data collected by Venezuelan health authorities and non-government organizations between 2014 and 2018. The Annual Parasite Index among indigenous groups (API-i) in municipalities of three states (Amazonas, Bolivar, and Sucre) were calculated and compared using the Kruskal Wallis test, risk factors for Plasmodium falciparum infection were identified via binomial logistic regression and maps were constructed to identify clusters of malaria cases among indigenous patients via Moran’s I and Getis-Ord’s hot spot analysis. Results 116,097 cases of malaria in Amerindian groups were registered during the study period. An increasing trend was observed between 2014 and 2016 but reverted in 2018. Malaria incidence remains higher than in 2014 and hot spots were identified in the three states, although more importantly in the south of Bolivar. Most cases (73.3%) were caused by Plasmodium vivax, but the Hoti, Yanomami, and Eñepa indigenous groups presented higher odds for infection with Plasmodium falciparum. Conclusion Malaria cases among Amerindian populations increased between 2014 and 2018 and seem to have a different geographic distribution than those among the general population. These findings suggest that tailored interventions will be necessary to curb the impact of malaria transmission in these groups.

610 Self-Reported Sleep and Circadian Characteristics Predict Future Substance Use: A Longitudinal Analysis from the NCANDA Study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A240-A240
Author(s):  
Brant Hasler ◽  
Jessica Graves ◽  
Meredith Wallace ◽  
Stephanie Claudatos ◽  
Fiona Baker ◽  
...  
Keyword(s):  
Risk Factors ◽  
Substance Use ◽  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Parental Education ◽  
Negative Binomial ◽  
Longitudinal Design ◽  
Random Effect ◽  
Cannabis Use ◽  
Increased Risk

Abstract Introduction Growing evidence indicates that sleep characteristics predict later substance use and related problems during adolescence and young adulthood. However, most prior studies have assessed a limited range of sleep characteristics, studied only a narrow age span, and included relatively few follow-up assessments. Here, we used multiple years of data from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study, which spans the adolescent period with an accelerated longitudinal design, to examine whether multiple sleep characteristics in any year predict substance use the following year. Methods The sample included 831 participants (423 females; age 12–21 years at baseline) from NCANDA. Sleep variables included the previous year’s circadian preference, sleep quality, daytime sleepiness, timing of midsleep (weekday and weekend), and sleep duration (weekday and weekend). Each sleep variable’s association with the subsequent year’s substance use (cannabis use or alcohol binge severity) across years 1–5 was tested separately using generalized linear mixed models (zero-inflated Negative Binomial for cannabis; ordinal for binge severity) with age, sex, race, visit, parental education, previous year’s substance use (yes/no) as covariates and subject as a random effect. Results With regard to cannabis use, greater eveningness and shorter weekday sleep duration predicted an increased risk for additional days of cannabis use the following year, while greater eveningness and later weekend midsleep predicted a greater likelihood of any cannabis use the following year. With regard to alcohol binge severity, greater eveningness, greater daytime sleepiness, and shorter sleep duration (weekday and weekend) all predicted an increased risk for more severe alcohol bingeing the following year. Post-hoc stratified analyses indicated that some of these associations may differ between high school-age and college-age participants. Conclusion Our findings extend prior work, indicating that eveningness and later sleep timing, as well as shorter sleep duration, especially on weekdays, are risk factors for future cannabis use and alcohol misuse. These results underscore a need for greater attention to sleep characteristics as potential risk factors for substance use in adolescents and young adults and may inform future areas of intervention. Support (if any) Grants from NIH: R01AA025626 (Hasler) and U01AA021690 (Clark) and UO1 AA021696 (Baker & Colrain)

Prevalence of Mental Disorders by Socioeconomic Status in Australia: A Cross-Sectional Epidemiological Study

2020 ◽  
pp. 089011712096865
Author(s):  
Rubayyat Hashmi ◽  
Khorshed Alam ◽  
Jeff Gow ◽  
Sonja March
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Socioeconomic Status ◽  
Mental Disorders ◽  
National Health Survey ◽  
Cross Sectional ◽  
Lower Odds ◽  
Socioeconomic Groups ◽  
Income Groups ◽  
Weighted Prevalence

Purpose: To present the prevalence of 3 broad categories of mental disorder (anxiety-related, affective and other disorders) by socioeconomic status and examine the associated socioeconomic risk factors of mental disorders in Australia. Design: A population-based, cross-sectional national health survey on mental health and its risk factors across Australia. Setting: National Health Survey (NHS), 2017-2018 conducted by the Australian Bureau of Statistics (ABS) Participants: Under aged: 4,945 persons, Adult: 16,370 persons and total: 21,315 persons Measures: Patient-reported mental disorder outcomes Analysis: Weighted prevalence rates by socioeconomic status (equivalised household income, education qualifications, Socio-Economic Index for Areas (SEIFA) scores, labor force status and industry sector where the adult respondent had their main job) were estimated using cross-tabulation. Logistic regression utilizing subsamples of underage and adult age groups were analyzed to test the association between socioeconomic status and mental disorders. Results: Anxiety-related disorders were the most common type of disorders with a weighted prevalence rate of 20.04% (95% CI: 18.49-21.69) for the poorest, 13.85% (95% CI: 12.48-15.35) for the richest and 16.34% (95% CI: 15.7-17) overall. The weighted prevalence rate for mood/affective disorders were 20.19% (95% CI: 18.63-21.84) for the poorest, 9.96% (95% CI: 8.79-11.27) for the richest, and 13.57% (95% CI: 12.99-14.17) overall. Other mental disorders prevalence were for the poorest: 9.07% (95% CI: 7.91-10.39), the richest: 3.83% (95% CI: 3.14-4.66), and overall: 5.93% (95% CI: 5.53-6.36). These patterns are also reflected if all mental disorders were aggregated with the poorest: 30.97% (95% CI: 29.15-32.86), the richest: 19.59% (95% CI: 18.02-21.26), and overall: 23.93% (95% CI: 23.19-24.69). The underage logistic regression model showed significant lower odds for the middle (AOR: 0.75, 95% CI: 0.53 -1.04, p < 0.1), rich (AOR: 0.71, 95% CI: 0.5-0.99, p < 0.05) and richest (AOR: 0.6, 95% CI: 0.41-0.89, p < 0.01) income groups. Similarly, in the adult logistic model, there were significant lower odds for middle (AOR: 0.84, 95% CI: 0.72-0.98, p < 0.05), rich (AOR: 0.73, 95% CI: 0.62-0.86, p < 0.01) and richest (AOR: 0.76, 95% CI: 0.63-0.91, p < 0.01) income groups. Conclusion: The prevalence of mental disorders in Australia varied significantly across socioeconomic groups. Knowledge of different mental health needs in different socioeconomic groups can assist in framing evidence-based health promotion and improve the targeting of health resource allocation strategies.

scholarly journals The effect of self-rated health, subjective socioeconomic status, social capital, and physical activity on life satisfaction: a cross-sectional study in urban western Iran

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nader Rajabi Gilan ◽  
Mehdi khezeli ◽  
Shirin Zardoshtian
Keyword(s):  
Physical Activity ◽  
Social Capital ◽  
Socioeconomic Status ◽  
Life Satisfaction ◽  
College Education ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Subjective Socioeconomic Status ◽  
Self Rated Health

Abstract Background Life satisfaction is an important component in designing strategies to improve health outcomes in different groups of society. This study aimed to investigate the effect of subjective socioeconomic status (SSS), social capital (SC), self-rated health (SRH), and physical activity (PA) on life satisfaction (LS) in Iran. Methods This cross-sectional study was conducted on 1187 people (643 men and 544 women) lived in five western cities in Iran. The sampling method was multistage clustering. Data collection tool was a five part questionnaire including demographic characteristics, socioeconomic status ladder, social capital scale, a question to measure physical activity, and the life satisfaction scale. Data were analyzed using independent t-test, one way ANOVA, and Ordinal Logistic Regression. Result Life satisfaction was higher in married men and women compared to single and widows (p < 0.05). Among the variables included in the main model, the significant predictors were college education (− 0.500), marriage (coefficient = 0.422), age 25–34 years (coefficient = − 0.384), SRH (coefficient = 0.477), male sex (coefficient = 0.425), SSS (coefficient = 0.373), trust (coefficient = 0.115), and belonging and empathy (coefficient = 0.064). Conclusion SRH and SSS were significant predictors of life satisfaction in west Iranian society. Being married was associated with higher LS, but college education affects LS adversely.

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