scholarly journals Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Theophilus O. Ogunyemi ◽  
Mohammad-Reza Siadat ◽  
Suzan Arslanturk ◽  
Kim A. Killinger ◽  
Ananias C. Diokno
Keyword(s):  
Risk Factors ◽  
Urinary Incontinence ◽  
Longitudinal Data ◽  
Statistical Methods ◽  
Repeated Measures ◽  
Significant Risk ◽  
Large Data ◽  
Data Sets ◽  
Predictive Index ◽  
Urine Loss

Longitudinal data for studying urinary incontinence (UI) risk factors are rare. Data from one study, the hallmark Medical, Epidemiological, and Social Aspects of Aging (MESA), have been analyzed in the past; however, repeated measures analyses that are crucial for analyzing longitudinal data have not been applied. We tested a novel application of statistical methods to identify UI risk factors in older women. MESA data were collected at baseline and yearly from a sample of 1955 men and women in the community. Only women responding to the 762 baseline and 559 follow-up questions at one year in each respective survey were examined. To test their utility in mining large data sets, and as a preliminary step to creating a predictive index for developing UI, logistic regression, generalized estimating equations (GEEs), and proportional hazard regression (PHREG) methods were used on the existing MESA data. The GEE and PHREG combination identified 15 significant risk factors associated with developing UI out of which six of them, namely, urinary frequency, urgency, any urine loss, urine loss after emptying, subject’s anticipation, and doctor’s proactivity, are found most highly significant by both methods. These six factors are potential candidates for constructing a future UI predictive index.

Prevalence and risk factors of root resorption of adjacent teeth in maxillary canine impaction, among untreated children and adolescents

2018 ◽  
Vol 41 (5) ◽  
pp. 447-453 ◽  
Author(s):  
Frédéric Rafflenbeul ◽  
Catherine-Isabelle Gros ◽  
François Lefebvre ◽  
Sophie Bahi-Gross ◽  
Raphaëlle Maizeray ◽  
...  
Keyword(s):  
Risk Factors ◽  
Children And Adolescents ◽  
Root Resorption ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Data Sets ◽  
Maxillary Canine ◽  
3D Data ◽  
Potential Risk Factors ◽  
Associated Risk Factors

Summary Objectives The aim of this retrospective study was to assess in maxillary canine impaction cases both the prevalence of root resorption of adjacent teeth among untreated children and adolescents, and its associated risk factors. Subjects and methods Sixty subjects (mean age 12.2 years; SD 1.9; range 8–17 years) with 83 displaced maxillary canines and without any past or ongoing orthodontic treatment were included in this study. The presence of root resorption was evaluated on images from a single cone beam computed tomography (CBCT) unit. Potential risk factors were measured on the CBCT images and on panoramic reconstructions of the 3D data sets. The sample was characterized by descriptive statistics and multiple logistic regressions were performed to predict root resorption. Results Root resorption of at least one adjacent tooth was detected in 67.5 per cent of the affected quadrants. It was found that 55.7 per cent of the lateral incisors, 8.4 per cent of the central incisors, and 19.5 per cent of first premolars were resorbed. Of the detected resorptions, 71.7 per cent were considered slight, 14.9 per cent moderate, and 13.4 per cent severe. Contact between the displaced canine(s) and the adjacent teeth roots was the only identified statistically significant risk factor, all teeth being considered (odds ratio [OR] = 18.7, 95% confidence interval: 2.26–756, P < 0.01). An enlarged canine dental follicle, a peg upper lateral, or an upper lateral agenesis were not significantly associated with root resorption of adjacent teeth, nor were age nor gender. Conclusions Root resorption of adjacent teeth was detected in more than two-thirds of a sample of sixty untreated children and adolescents.

scholarly journals Urinary Incontinence Among Omani Women: Prevalence, risk factors and impact on quality of life

2020 ◽  
Vol 20 (1) ◽  
pp. 45 ◽  
Author(s):  
Maisa H. Al Kiyumi ◽  
Zaleikha I. Al Belushi ◽  
Sanjay Jaju ◽  
Abdulaziz M. Al Mahrezi
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Urinary Incontinence ◽  
Primary Healthcare ◽  
Short Form ◽  
Significant Risk ◽  
Medical Attention ◽  
Negative Consequences ◽  
International Consultation

Objectives: Urinary incontinence (UI) in women is a common health problem which can have a negative impact on quality of life (QOL). This study aimed to determine the prevalence, risk factors and impact of UI on the QOL of Omani women attending primary healthcare centres in Muscat, Oman. Methods: This crosssectional study was conducted at three primary healthcare centres in Muscat from April to August 2018. Women who were 20–50 years of age, not pregnant, not in the six-month postnatal period, not seriously ill and not diagnosed with pelvic organ prolapse were included. A self-administered questionnaire using the International Consultation Incontinence Questionnaire-Short Form was used to evaluate the frequency, severity and impact of UI on QOL. Results: A total of 1,070 women were included in this study (response rate = 92.5%). The mean age was 31.39 ± 7.64 years. UI was reported by 369 (34.5%) women, of whom 182 (49.3%) had stress UI, 97 (26.3%) had urgency UI, 88 (23.8%) had mixed UI and two (0.5%) had other UI. Age, body mass index (BMI), having been married or employed, hypertension, cough, constipation and vaginal or assisted vaginal delivery during the previous birth were significant risk factors for UI. Only 41 (11.1%) out of the 369 women with UI had ever sought medical advice despite the fact that more than two thirds of the women with UI reported negative effects on their QOL. Conclusion: UI is a common medical problem in Oman. Several risk factors, including age and BMI, were identified. Despite its negative consequences for QOL, women were found to be reluctant to seek medical attention for the condition.Keywords: Urinary Incontinence; Prevalence; Risk Factors; Quality of Life; Oman.

scholarly journals Alzheimer's disease progression and risk factors: A standardized comparison between six large data sets

2019 ◽  
Vol 5 (1) ◽  
pp. 515-523
Author(s):  
Stephanie Evans ◽  
Kevin McRae‐McKee ◽  
Christoforos Hadjichrysanthou ◽  
Mei Mei Wong ◽  
David Ames ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets

scholarly journals Determinants of Urinary Incontinence and Subtypes Among the Elderly in Nursing Homes

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongyan Tai ◽  
Shunying Liu ◽  
Haiqin Wang ◽  
Hongzhuan Tan
Keyword(s):  
Risk Factors ◽  
Urinary Incontinence ◽  
Nursing Home ◽  
Nursing Homes ◽  
Spinal Surgery ◽  
Short Form ◽  
Significant Risk ◽  
Nursing Home Residents ◽  
International Consultation ◽  
History Of

Urinary incontinence (UI) is a common problem among older adults. This study investigated the prevalence of UI in nursing home residents aged ≥75 years in China and examined potential risk factors associated with UI and its subtypes. Data were collected during face-to-face interviews using a general questionnaire, the International Consultation Incontinence Questionnaire Short-Form, and the Barthel Index. A total of 551 participants aged ≥75 years residing in Changsha city were enrolled from June to December 2018. The UI prevalence rate among nursing home residents aged ≥75 years was 24.3%. The most frequent subtype was mixed (M) UI (38.1%), followed by urge (U) UI (35.1%), stress (S) UI (11.9%), and other types (14.9%). In terms of severity, 57.5% had moderate UI, while 35.1% had mild and 7.5% had severe UI. Constipation, immobility, wheelchair use, cardiovascular disease (CVD), and pelvic or spinal surgery were significant risk factors for UI. Participants with a history of surgery had higher risks of SUI (odds ratio [OR] = 4.87, 95% confidence interval [CI]: 1.55–15.30) and UUI (OR = 1.97, 95% CI: 1.05–3.71), those who were immobile or used a wheelchair had higher rates of MUI (OR = 11.07, 95% CI: 4.19–29.28; OR = 3.36, 95% CI: 1.16–9.78) and other UI types (OR = 7.89, 95% CI: 1.99–31.30; OR = 14.90, 95% CI: 4.88–45.50), those with CVD had a higher rate of UUI (OR = 2.25, 95% CI: 1.17–4.34), and those with diabetes had a higher risk of UUI (OR = 2.250, 95% CI: 1.14–4.44). Use of oral antithrombotic agents increased UUI risk (OR = 4.98, 95% CI: 2.10–11.85) whereas sedative hypnotic drug use was associated with a higher risk of MUI (OR = 3.62, 95% CI: 1.25–10.45). Each UI subtype has distinct risk factors, and elderly residents of nursing homes with a history of CVD and pelvic or spinal surgery who experience constipation should be closely monitored. Reducing time spent in bed and engaging in active rehabilitation including walking and muscle strengthening may aid in UI prevention and treatment.

scholarly journals Bone cement implantation syndrome in cemented hip hemiarthroplasty—a persistent risk

2021 ◽  
Author(s):  
Karoline Weingärtner ◽  
Philipp Störmann ◽  
David Schramm ◽  
Sebastian Wutzler ◽  
Kai Zacharowski ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Outcome ◽  
Bone Cement ◽  
Significant Risk ◽  
Cardiovascular Complications ◽  
Proximal Femur Fracture ◽  
Data Sets ◽  
Clinical Progression ◽  
The Mean ◽  
Bone Cement Implantation Syndrome

Abstract Background Every year, ~ 210,000 initial implantations of hip endoprostheses are carried out in Germany alone. The “bone cement implantation syndrome” (BCIS) is considered a severe peri- and early-postoperative complication when implanting cemented prostheses. The origin of the BCIS and its impact on the clinical outcome are still uncertain. This study investigates the clinical progression after BCIS cases in patients with cemented hemiarthroplasty. Risk factors for the occurrence of BCIS are evaluated. Material and methods Clinical data of all patients with a proximal femur fracture and which received a cemented hemiarthroplasty within a period of 9.5 years have been collected. BCIS (+) patients and BCIS (−) patients were compared with respect to their demographics and clinical outcome. Risk factors for the development of BCIS were identified. Results A total of 208 patients could be included with complete data sets. The mean age was 81.1 ± 10.0 years. Overall, 37% of the patients showed symptoms of BCIS. In comparison to BCIS (−) patients there was a significantly higher rate of cardiovascular complications (27.3% vs. 13.7%, p = 0.016) and a higher in-hospital mortality rate (15.6% vs. 4.6%, p = 0.006) in BCIS (+) patients. Age, absence of a femoral borehole and ASA status were identified as statistically significant risk factors of BCIS. Conclusion BCIS is frequently observed and in some cases severe complication. The therapy is exclusively symptomatic; identifying preventional measures might reduce the occurrence of BCIS.

scholarly journals Two Approaches to Classifying and Quantifying Physical Resilience in Longitudinal Data

2019 ◽  
Vol 75 (4) ◽  
pp. 731-738 ◽  
Author(s):  
Cathleen Colón-Emeric ◽  
Carl F Pieper ◽  
Kenneth E Schmader ◽  
Richard Sloane ◽  
Allison Bloom ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Statistical Methods ◽  
Latent Class ◽  
Prediction Models ◽  
Profile Analysis ◽  
Weighted Kappa ◽  
Data Sets ◽  
Multiple Outcomes ◽  
Differential Approach ◽  
Fracture Cohort

Abstract Background Approaches for quantifying physical resilience in older adults have not been described. Methods We apply two conceptual approaches to defining physical resilience to existing longitudinal data sets in which outcomes are measured after an acute physical stressor. A “recovery phenotype” approach uses statistical methods to describe how quickly and completely a patient recovers. Statistical methods using a recovery phenotype approach can consider multiple outcomes simultaneously in a composite score (eg, factor analysis and principal components analysis) or identify groups of patients with similar recovery trajectories across multiple outcomes (eg, latent class profile analysis). An “expected recovery differential” approach quantifies how patients’ actual outcomes are compared to their predicted outcome based on a population-derived model and their individual clinical characteristics at the time of the stressor. Results Application of the approaches identified different participants as being the most or least physically resilient. In the viral respiratory cohort (n = 186) weighted kappa for agreement across resilience quartiles was 0.37 (0.27–0.47). The expected recovery differential approach identified a group with more comorbidities and lower baseline function as highly resilient. In the hip fracture cohort (n = 541), comparison of the expected recovery differentials across 10 outcome measures within individuals provided preliminary support for the hypothesis that there is a latent resilience trait at the whole-person level. Conclusions We posit that recovery phenotypes may be useful in clinical applications such as prediction models because they summarize the observed outcomes across multiple measures. Expected recovery differentials offer insight into mechanisms behind physical resilience not captured by age and other comorbidities.

scholarly journals Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data

2018 ◽  
Vol 25 (12) ◽  
pp. 1669-1674 ◽  
Author(s):  
Ian Barnett ◽  
John Torous ◽  
Patrick Staples ◽  
Matcheri Keshavan ◽  
Jukka-Pekka Onnela
Keyword(s):  
Longitudinal Study ◽  
Longitudinal Data ◽  
Statistical Methods ◽  
Statistical Models ◽  
Repeated Measures ◽  
Extended Period ◽  
Multiple Comparisons ◽  
Digital Data ◽  
Digital Phenotyping ◽  
Improper Use

AbstractObjectivesAs smartphones and sensors become more prominently used in mobile health, the methods used to analyze the resulting data must also be carefully considered. The advantages of smartphone-based studies, including large quantities of temporally dense longitudinally captured data, must be matched with the appropriate statistical methods in order draw valid conclusions. In this paper, we review and provide recommendations in 3 critical domains of analysis for these types of temporally dense longitudinal data and highlight how misleading results can arise from improper use of these methods.Target AudienceClinicians, biostatisticians, and data analysts who have digital phenotyping data or are interested in performing a digital phenotyping study or any other type of longitudinal study with frequent measurements taken over an extended period of time.ScopeWe cover the following topics: 1) statistical models using longitudinal repeated measures, 2) multiple comparisons of correlated tests, and 3) dimension reduction for correlated behavioral covariates. While these 3 classes of methods are frequently used in digital phenotyping data analysis, we demonstrate via actual clinical studies data that they may sometimes not perform as expected when applied to novel digital data.

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