HIV Risk Factors Among People With Severe Mental Illness in Urban and Rural Areas

1999 ◽  
Vol 50 (4) ◽  
pp. 556-558 ◽  
Author(s):  
Mary F. Brunette ◽  
Stanley D. Rosenberg ◽  
Lisa A. Goodman ◽  
Kim T. Mueser ◽  
Fred C. Osher ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mental Illness ◽  
Severe Mental Illness ◽  
Hiv Risk ◽  
Rural Areas ◽  
Urban And Rural Areas ◽  
Hiv Risk Factors

HIV risk factors for persons with serious mental illness

1994 ◽  
Vol 30 (6) ◽  
pp. 551-563 ◽  
Author(s):  
Michael D. Knox ◽  
Timothy L. Boaz ◽  
Martha A. Friedrich ◽  
Michael G. Dow
Keyword(s):  
Risk Factors ◽  
Mental Illness ◽  
Serious Mental Illness ◽  
Hiv Risk ◽  
Hiv Risk Factors

HIV Risk Factors for Persons with Serious Mental Illness

1995 ◽  
Vol 9 (4) ◽  
pp. 192-198
Author(s):  
Michael D. Knox ◽  
Timothy L. Boaz ◽  
Martha A. Friedrich ◽  
Michael G. Dow
Keyword(s):  
Risk Factors ◽  
Mental Illness ◽  
Serious Mental Illness ◽  
Hiv Risk ◽  
Hiv Risk Factors

Gender Differences in HIV Risk-Factors in Main-Partner Relationships

2011 ◽  
Author(s):  
Danielle Uding ◽  
Kanisha Collie ◽  
Kevin Wells ◽  
Zoe Peterson ◽  
Akshay Iyengar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gender Differences ◽  
Hiv Risk ◽  
Main Partner ◽  
Partner Relationships ◽  
Hiv Risk Factors

scholarly journals The Prevalence and Awareness of Cardiovascular Diseases Risk Factors among the Lebanese Population: A Prospective Study Comparing Urban to Rural Populations

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Iqbal Fahs ◽  
Zainab Khalife ◽  
Diana Malaeb ◽  
Mohammad Iskandarani ◽  
Pascale Salameh
Keyword(s):  
Risk Factors ◽  
Rural Areas ◽  
Overweight And Obesity ◽  
Undiagnosed Diabetes ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Undiagnosed Hypertension ◽  
Lebanese Population ◽  
Urban And Rural Areas ◽  
A Prospective Study

Introduction. CVDs are largely driven by modifiable risk factors. This study sought to determine the awareness and prevalence of the modifiable CVDs risk factors among the Lebanese population.Methods. In a cross-sectional survey, 1000 participants aged ≥ 45 years were randomly selected from pharmacies and interviewed. The data was analyzed with SPSS version 21.0 software.Results. Differences between urban and rural areas include alcohol consumption (2.8% versus 1.7%;p=0.0001), cardioprotective vegetable servings (6.1% versus 2.3%;p=0.016), sedentary hours per day (18.6% versus 15.1%;p=0.002), and hypertension (38.5% versus 25.4%;p=0.001). The prevalence of overweight and obesity (77.3% versus 75.2%;p=0.468), smoking (39.3% versus 43.3%;p=0.232), diabetes (25.4% versus 21.4%;p=0.173), and dyslipidemia (25 versus 21.2%) was reported. Measurements revealed 19.3% of undiagnosed hypertension (12.4% versus 22.4%,p=0.001), 61.7% of hypertension (59.8% versus 62.6%;p=0.203), and 7.9% of undiagnosed diabetes (6.6% versus 8.6%;p=0.323). The declared awareness of CVDs risk factors was highest for smoking (91.5% versus 89.7%;p=0.339) and lowest for diabetes (54.4 versus 55.7%;p=0.692).Conclusion. This study has shown a high prevalence of modifiable CVDs risk factors in the Lebanese population ≥ 45 years, among which hypertension is the most prominent.

scholarly journals Impact of HIV risk factors on survival in Iranian HIV-infected patients: A Bayesian approach to retrospective cohort

2017 ◽  
Vol 2 ◽  
pp. 100-106 ◽  
Author(s):  
Mohammad Reza Maracy ◽  
Shayan Mostafaei ◽  
Mohsen Moghoofei ◽  
Marjan Mansourian
Keyword(s):  
Risk Factors ◽  
Hiv Risk ◽  
Bayesian Approach ◽  
Retrospective Cohort ◽  
Hiv Risk Factors

scholarly journals Oral health inequality in people with severe mental illness: a cross-sectional study using National Health and Nutrition Examination Survey 1999-2016

2021 ◽  
Author(s):  
Jing Kang ◽  
Jianhua Wu ◽  
Vishal Aggarwal ◽  
David Shiers ◽  
Tim Doran ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mental Illness ◽  
Oral Health ◽  
General Population ◽  
Health Outcomes ◽  
Severe Mental Illness ◽  
Tooth Loss ◽  
List Type ◽  
Cross Sectional ◽  
Oral Health Outcomes

AbstractOBJECTIVETo explore whether people with severe mental illness (SMI) experience worse oral health compared to the general population, and the risk factors for poor oral health in people with SMI.METHODThis study used cross-sectional data from the National Health and Nutrition Examination Survey (1999-2016) including on self-rated oral health, ache in mouth, tooth loss, periodontitis stage, and number of decayed, missing, and filled teeth. Candidate risk factors for poor oral health included demographic characteristics, lifestyle factors, physical health comorbidities, and dental hygiene behaviours. The authors used ordinal logistic regression and zero-inflated negative binomial models to explore predictors of oral health outcomes.RESULTS53,348 cases were included in the analysis, including 718 people with SMI. In the fully adjusted model, people with SMI were more likely to suffer from tooth loss (OR 1.40, 95% CI: 1.12-1.75). In people with SMI, the risk factors identified for poor oral health outcomes were older age, white ethnicity, lower income, smoking history, and diabetes. Engaging in physical activity and daily use of dental floss were associated with better oral health outcomes.CONCLUSIONSPeople with SMI experience higher rates of tooth loss than the general population, and certain subgroups are particularly at risk. Having a healthy lifestyle such as performing regular physical exercise and flossing may lower the risk of poor oral health. These findings suggest opportunities for targeted prevention and early intervention strategies to mitigate adverse oral health outcomes.Significant outcomes (x3)People with severe mental illness were at 40% higher risk of tooth loss when compared to the general population.Older adults, smokers and people with diabetes were at particularly high risk of poor oral health.Physical exercise and daily use of dental floss were associated with better oral health outcomes.Limitations (x3)The number of cases with data on periodontal disease was limited.The study was cross-sectional so causation could not be inferred.The analysis used prescriptions of antipsychotic and mood stabilising medication as a proxy measure of severe mental illness, as clinical diagnoses were not available in the dataset.Data availability statementThe NHANES 1999-2016 data is available at CDC website: https://www.cdc.gov/nchs/nhanes/index.htm, and is accessible and free to download for everyone.

scholarly journals Comparison of The Risk Factors For Falls Among Older Adults Between Urban and Rural Areas of Shantou City, China

2020 ◽  
Author(s):  
Xiaodong Chen ◽  
Zeting Lin ◽  
Ran Gao ◽  
Yijian Yang ◽  
Liping Li
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Fall Prevention ◽  
Rural Areas ◽  
Urban Areas ◽  
Multivariable Analysis ◽  
Living Environment ◽  
Balance Function ◽  
Urban And Rural Areas ◽  
Risk Factors For Falls

Abstract Background: To investigate the prevalence of falls and risk factors among older adults in urban and rural areas and to facilitate the design of fall prevention interventions.Methods: We used cluster random sampling to investigate the sociodemographic information, living habits, medical history, and falls among 649 older adult participants. Univariable and multivariable logistic regression was used to examine fall risk factors in urban and rural areas.Results: The fall rate and rate of injury from falls among older adults in urban areas were 27.3% and 18.6%, respectively, which were higher than those in rural areas (17.0% and 12.2%; P<0.05). Multivariable analysis showed that the risk factors for falls among urban older adults included a high school or below education level (OR=3.737, 95% CI: 1.503~9.291); diabetes medicine use (OR=4.518, 95% CI: 1.228~16.626); incontinence (OR=8.792, 95% CI: 1.894~40.824); lack of fall prevention education (OR = 11.907, 95% CI: 1.321～107.354); and reduced balance function (OR = 3.901, 95% CI: 1.894~7.815). The risk factors among rural older adults included a previous nonfarming occupation (OR=2.496, 95% CI: 1.416~4.398); incontinence (OR =11.396, 95% CI: 1.901~68.327); poor living environment (OR=3.457, 95% CI: 1.488~8.033); and reduced balance function (OR =4.260, 95% CI: 2.361~7.688).Discussion: The rate of falls among older adults in urban areas is higher than that in rural areas of Shantou City. Fall prevention in urban areas should target older adults with low education and modify the diabetes medication use. Interventions should focus on improving the home environment of older adults in rural areas.

Comparisons of the prevalence of and risk factors for elderly depression between urban and rural populations in Japan

2012 ◽  
Vol 24 (8) ◽  
pp. 1235-1241 ◽  
Author(s):  
Y. Abe ◽  
N. Fujise ◽  
R. Fukunaga ◽  
Y. Nakagawa ◽  
M. Ikeda
Keyword(s):  
Risk Factors ◽  
Social Support ◽  
Sleep Disturbance ◽  
Rural Areas ◽  
Geriatric Depression Scale ◽  
Depression Scale ◽  
Sampling Procedure ◽  
Financial Strain ◽  
Poor Social Support ◽  
Urban And Rural Areas

ABSTRACTBackground: The aim of the present study was to investigate the differences in the prevalence of and risk factors for elderly depression between urban and rural areas in Japan and to further understanding of the features of elderly depression.Methods: A multistage, random sampling procedure and mailing method were used in urban and rural areas in Kumamoto Prefecture. A total of 2,152 participants aged 65 years and older were evaluated for depression using the Geriatric Depression Scale (GDS). Factors associated with depression were also examined. In order to assess the relationship between risk factors and subjective happiness, the Philadelphia Geriatric Center Morale Scale (PGC-MS) was used.Results: Depressive symptoms were associated with living alone, being unemployed, chronic illness, sleep disturbance, suicidal ideation, financial strain, and poor social support; the risk factors for elderly depression were almost the same in the two areas. Although three factors (financial strain, work status, and PGC-MS) were significantly associated with depression in both areas on logistic regression analysis, sleep disturbance was significant only for the urban area, and poor social support was significant only for the rural area.Conclusions: Although factors related to depression did not differ markedly between urban and rural elderly people, some risk factors differed between the two areas. Effective intervention programs for elderly depression should pay more attention to regional differences.

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