Multiple Logistic Regression Analysis of Risk Factors for the Development of Steroid-Dependent Asthma in the Elderly: A Comparison with Younger Asthmatics

Respiration ◽  
2001 ◽  
Vol 68 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Shinichi Ishioka ◽  
Michikazu Terada ◽  
Yoshinori Haruta ◽  
Keiko Hiyama ◽  
Soichiro Hozawa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Multiple Logistic Regression Analysis ◽  
The Elderly ◽  
Multiple Logistic Regression ◽  
Steroid Dependent

scholarly journals 447 - Risk factors associated with sleep disturbance in a general elderly Japanese population: The Arao Cohort Study

2020 ◽  
Vol 32 (S1) ◽  
pp. 167-167
Author(s):  
Kazuki Honda ◽  
Tomohisa Ishikawa ◽  
Ryuji Fukuhara ◽  
Seiji Yuki ◽  
Yusuke Miyagawa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cohort Study ◽  
Sleep Disturbance ◽  
Logistic Regression Analysis ◽  
Barthel Index ◽  
Multiple Logistic Regression Analysis ◽  
Multiple Logistic Regression ◽  
Factors Associated

[Background]Sleep disturbance is a common symptom in elderly people. However, the associated risk factors have not been completely clarified. We examined possible risk factors associated with sleep disturbance in a community-based Japanese cohort study.[Methods]1521 community-dwelling individuals aged 65 years or older were selected from a consecutive series at a cohort study from 2016 to 2018 in Arao city, where located at south part of Japan. In this survey, the clinical valuables were collected as follows: age, sex, occupational status, education, lifestyle information, medical history, EuroQoL(EQ)-5D (a score of health-related quality of life [QOL]), Barthel index (a score of performance in activities of daily living), a score of Geriatric Depression Scale (GDS) and a score of Mini-Mental State Examination (MMSE). Sleep disturbance was assessed by the Pittsburgh Sleep Quality Index (when the global score was 6 or over, sleep disturbance was determined to be present). Multiple logistic regression analysis was used to examine the association between clinical valuables and sleep disturbance. This research was supported by AMED (Japan Agency for Medical Research and Development) under Grant Number JP18dk0207025h0003 and has been approved by the research ethics committee of Kumamoto University. Informed consent was obtained from all participants and their family members.[Results]Multiple logistic regression analysis revealed that Parkinson disease (Odds ratio[OR]=5.59), living alone (OR=1.93), liver disease (OR=1.89), hyperlipidemia (OR=1.36), higher score of GDS (OR=1.14), lower scores of both EQ-5D index (OR=1.11) and Barthel index (OR=1.03) were significantly associated as risk factors with sleep disturbance. Unexpectedly, lower score of MMSE was not a significant risk factor.[Conclusion]These results suggest that several physical illnesses, solitude, depressive symptoms and lower QOL, but not cognitive impairment, might be crucial risk factors associated with sleep disturbance in elderly population.

scholarly journals Stunting Among Under 5-Year-Olds in Nepal: Trends and Risk Factors

2019 ◽  
Vol 24 (S1) ◽  
pp. 39-47 ◽  
Author(s):  
Shyam Sundar Budhathoki ◽  
Amit Bhandari ◽  
Rejina Gurung ◽  
Abhishek Gurung ◽  
Ashish KC
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Nutritional Status ◽  
Logistic Regression Analysis ◽  
Health Surveys ◽  
Multiple Logistic Regression Analysis ◽  
Multiple Logistic Regression ◽  
Multiple Factors ◽  
Children Under 5

Abstract Introduction The nutritional status in the first 5 years of life has lifelong and inter-generational impacts on individual’s potential and development. This study described the trend of stunting and its risk factors in children under 5 years of age between 2001 and 2016 in Nepal. Methods The study used datasets from the 2001, 2006, 2011 and 2016 Nepal Demographic Health Surveys to describe the trend of stunting in under 5-year children. Multiple logistic regression analysis was carried out to assess the risk factors for stunting at the time of the four surveys. Results The nutritional status of under 5-year children improved between 2001 and 2016. Babies born into poorer families had a higher risk of stunting than those born into wealthier families (AOR 1.51, CI 95% 1.23–1.87). Families residing in hill districts had less risk of stunting than those in the Terai plains (AOR 0.75, CI 95% 0.61–0.94). Babies born to uneducated women had a higher risk of stunting than those born to educated women (AOR 1.57, CI 95% 1.28–1.92). Discussion Stunting among under-5-year children decreased in the years spanning 2001–2016. This study demonstrated multiple factors that can be addressed to decrease the risk of stunting, which has important implications for neurodevelopment later in life. We add literature on risk factors for stunting in under-5-year children.

scholarly journals RISK FACTORS OF PREECLAMPSIA WITH SEVERE FEATURES AND ITS COMPLICATIONS

2021 ◽  
Vol 5 (1) ◽  
pp. 29
Author(s):  
Eka Suci Wulandari ◽  
Ernawati Ernawati ◽  
Djohar Nuswantoro
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Gestational Age ◽  
Multiple Logistic Regression Analysis ◽  
Severe Preeclampsia ◽  
Control Group ◽  
P Value ◽  
Multiple Logistic Regression

 Abstract.Background : The maternal mortality rate in Indonesia is dominated by preeclampsia as the second highest cause after bleeding. Several predictors can be categorized as risk factors that can be used to increase alertness in the care of pregnant women with severe preeclampsia to avoid complications. The purpose of this study was to study the relationship between risk factors for severe preeclampsia and its complications. Methods: This study used an observational analytic method with a case control design. The sampling technique is simple random sampling. The number of samples in this study were 80 samples, with 40 samples in each group. The case group was severe preeclampsia with one complication and the control group was severe preeclampsia without complications. Statistical analysis used was chi square and multiple logistic regression analysis. Results: The results showed that there was a significant relationship between age variables (p value = 0.001 < 0.05; OR = 5.318; CI = 2.118 – 13,356), BMI (p value = 0.002 < 0.05; OR = 6.000; CI = 1.958 – 18.384 ) and gestational age (p value = 0.039 < 0.05; OR = 2.636; CI = 1.040 – 6.685). Multiple logistic regression analysis showed that BMI (p = 0.003), age (p = 0.001) and gestational age (p = 0.048) were variables included in the final modeling of the regression analysis with the incidence of severe preeclampsia complications. Conclusion: BMI is the variable that has the strongest relationship with the incidence of severe preeclampsia complications.  

Risk Factors for Developing Peritonitis Caused by Micro-Organisms of Enteral Origin in Peritoneal Dialysis Patients

1998 ◽  
Vol 18 (1) ◽  
pp. 41-45 ◽  
Author(s):  
Francisco Caravaca ◽  
Rosa Ruiz-Calero ◽  
Carmen Dominguez
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Peritoneal Dialysis ◽  
Gastric Acid ◽  
Logistic Regression Analysis ◽  
Odds Ratio ◽  
Multiple Logistic Regression Analysis ◽  
Multiple Logistic Regression ◽  
Dialysis Patients

Objective To investigate the risk factors associated with the development of peritonitis caused by enteral bacteria in peritoneal dialysis patients, including the prescription of gastric acid inhibitors as a potential risk factor. Design Retrospective single-center study. Setting Tertiary university hospital. Patients an d Main Outcome Measures Fifty-five patients who entered into our continuous ambulatory peritoneal dialysis (CAPD) program during the last 6 years were included. Multiple logistic regression analysis was used to establish the best determinants over the development of at least one episode of enteric peritonitis. The predictive variables included in the model were: age, gender, diabetic versus nondiabetic, polycystic versus nonpolycystic kidney diseases, history of constipation, presence or absence of moderate/severe malnutrition, peritoneal transport characteristics, peritoneal protein losses, rate of exit-site infections, rate of total peritonitis, intestinal abnormalities, and treatment with inhibitors of gastric acid secretion. Results The total number of peritonitis episodes during the studied period was 88, which clustered in 34 of 55 patients. Fourteen (16%) were caused by enteric microorganisms in 10 patients: Escherichia coli (6), Klebsiella sp (2), Enterobacter sp (1), and Enterococcus sp (5). Nine of 10 patients who developed enteric peritonitis were on gastric acid inhibitors (3 patients on omeprazole and 6 patients on H2-antagonists), while 15 of 45 patients who did not develop enteric peritonitis were on gastric acid inhibitors (all of them on H2-blockers). There were temporal relationships between the start of gastric acid inhibitors and the development of enteric peritonitis in 6 of 9 patients who were on this medication. Four of 10 patients who developed enteric peritonitis had diverticulosis. Ten of 45 patients who did not develop enteric peritonitis had been diagnosed with diverticulosis of the colon or sigmoid prior to entry to CAPD. The unique patient who was not on gastric acid inhibitors and developed enteric peritonitis, had been diagnosed with chronic atrophic gastritis with achlorhydria. By multiple logistic regression analysis, the treatment with gastric acid inhibitors was the only independent variable that entered into the best predictive equation over the development of enteric peritonitis (Iog likelihood ratio = -26.077, odds ratio = 18; 95% CI odds ratio: 2 - 155). Conclusion Gastric acid inhibitors may increase the risk for developing enteric peritonitis in peritoneal dialysis patients.

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