E-healthy diet literacy scale and its relationship with behaviors and health outcomes in Taiwan

Abstract The study was to develop the e-healthy diet literacy (e-HDL) questionnaire based on the comprehensive health literacy (HL) conceptual framework, to examine the association among HL, e-HDL, health behaviors and outcomes. A nationwide study was conducted on 1342 adults aged 18 years and above, between April and September 2017. Multi-stage random sampling was used to recruit the participants from four regions and 19 cities and counties in Taiwan. HL and e-HDL were measured by HLS-SF12 and the e-healthy diet literacy questionnaire (e-HDLQ), respectively. Socio-demographics, behaviors (e.g. smoking, drinking and exercising) and health outcomes were also measured. Principal component analysis (PCA), linear regression models and logistic regression models were used. The mean age was 33.9 ± 11.4 years. The e-HDLQ was constructed with 11 items. A positive association between HL and e-HDL was found. In the multivariate analysis, HL and e-HDL were significantly lower in men and higher in those who used Facebook for searching information. HL was positively associated with the ability to pay for medication, and social status. The e-HDL was lower in older participants, and people who searched for healthy cooking, healthy food places or weight control, as compared with ones searched for nutritional therapies, while positively associated with education. Both HL and e-HDL were positively associated with health status and physical activities. In conclusion, the valid e-HDL survey tool was developed for general public use. The e-HDL strongly associated with HL, while both were determined by gender, online searching means and linked to health behaviors and outcomes.

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Individual and Combined Associations of Glucose Metabolic Components With Cognitive Function Modified by Obesity

Frontiers in Endocrinology ◽

10.3389/fendo.2021.769120 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ruixin He ◽

Ruizhi Zheng ◽

Jie Li ◽

Qiuyu Cao ◽

Tianzhichao Hou ◽

...

Keyword(s):

Insulin Resistance ◽

Cognitive Function ◽

Regression Models ◽

Positive Association ◽

Digit Symbol Substitution Test ◽

Metabolic Risk ◽

Model Assessment ◽

Linear Regression Models ◽

Logistic Regression Models ◽

The Individual

AimWe aimed to detect the individual and combined effect of glucose metabolic components on cognitive function in particular domains among older adults.MethodsData of 2,925 adults aged over 60 years from the 2011 to 2014 National Health and Nutrition Examination Survey were analyzed. Individuals’ cognitive function was evaluated using the Digit Symbol Substitution Test (DSST), the Animal Fluency Test (AF), the Consortium to Establish a Registry for Alzheimer’s Disease Immediate Recall (CERAD-IR), and CERAD Delayed Recall (CERAD-DR). Participants’ glucose metabolic health status was determined based on fasting plasma glucose, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), glycated hemoglobin (HbA1c), and 2-h postload glucose. Linear regression models were used to delineate the associations of cognitive function with individual glucose metabolic component and with metformin use. Logistic regression models were performed to evaluate the associations of cognition with the number of glucose metabolic risk components.ResultsCERAD-IR was significantly associated with HOMA-IR and insulin. HbA1c was related to all the cognitive tests except AF. Among participants without obesity, HOMA-IR and insulin were both negatively associated with CERAD-IR and CERAD-DR. Odds of scoring low in DSST increased with the number of glucose metabolic risk components (odds ratio 1.94, 95% confidence interval [CI] 1.26 to 2.98). Metformin use was associated with better performance in DSST among diabetes patients (β = 4.184, 95% CI 1.655 to 6.713).ConclusionsOur findings support the associations of insulin resistance and glycemic level with cognitive function in key domains, especially among adults without obesity. There is a positive association between metformin use and cognition.

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Challenging the Link Between Early Childhood Television Exposure and Later Attention Problems: A Multiverse Approach

Psychological Science ◽

10.1177/0956797620971650 ◽

2021 ◽

pp. 095679762097165

Author(s):

Matthew T. McBee ◽

Rebecca J. Brand ◽

Wallace E. Dixon

Keyword(s):

Early Childhood ◽

Regression Models ◽

Propensity Score Analysis ◽

Harmful Effect ◽

Attention Problems ◽

Linear Regression Models ◽

Data Set ◽

Logistic Regression Models ◽

Score Analysis ◽

Television Exposure

In 2004, Christakis and colleagues published an article in which they claimed that early childhood television exposure causes later attention problems, a claim that continues to be frequently promoted by the popular media. Using the same National Longitudinal Survey of Youth 1979 data set ( N = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis and colleagues was robust to different analytic choices. We evaluated 848 models, including logistic regression models, linear regression models, and two forms of propensity-score analysis. If the claim were true, we would expect most of the justifiable analyses to produce significant results in the predicted direction. However, only 166 models (19.6%) yielded a statistically significant relationship, and most of these employed questionable analytic choices. We concluded that these data do not provide compelling evidence of a harmful effect of TV exposure on attention.

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An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression

Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation ◽

10.4018/978-1-4666-3628-6.ch017 ◽

2013 ◽

pp. 273-289

Author(s):

Jiansheng Wu

Keyword(s):

Nonlinear Regression ◽

Regression Models ◽

Principal Component ◽

Linear Regression Models ◽

Rainfall Forecasting ◽

Forecasting Accuracy ◽

Parametric Regression ◽

Nonlinear Regression Models ◽

Linear And Nonlinear ◽

Artificial Neural Network Ann

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a nonlinear mapping between inputs and outputs, has played a crucial role in forecasting rainfall data. In this paper, an effective hybrid semi-parametric regression ensemble (SRE) model is presented for rainfall forecasting. In this model, three linear regression models are used to capture rainfall linear characteristics and three nonlinear regression models based on ANN are able to capture rainfall nonlinear characteristics. The semi-parametric regression is used for ensemble model based on the principal component analysis technique. Empirical results reveal that the prediction using the SRE model is generally better than those obtained using other models in terms of the same evaluation measurements. The SRE model proposed in this paper can be used as a promising alternative forecasting tool for rainfall to achieve greater forecasting accuracy and improve prediction quality.

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Maternal Health Behaviors and Infant Health Outcomes Among Arab American and Non-Hispanic White Mothers in Massachusetts, 2012-2016

Public Health Reports ◽

10.1177/0033354920941146 ◽

2020 ◽

Vol 135 (5) ◽

pp. 658-667 ◽

Cited By ~ 2

Author(s):

Nadia N. Abuelezam ◽

Adolfo G. Cuevas ◽

Sandro Galea ◽

Summer Sherburne Hawkins

Keyword(s):

Maternal Health ◽

Health Behaviors ◽

Health Outcomes ◽

Infant Health ◽

Arab American ◽

Linear Regression Models ◽

Effect Modifier ◽

White Mothers ◽

Maternal Health Outcomes ◽

Hispanic White

Objectives The health profile of Arab American mothers and infants may differ from that of non–Arab American mothers and infants in the United States as a result of social stigma experienced in the historical and current sociopolitical climate. The objective of our study was to compare maternal health behaviors, maternal health outcomes, and infant health outcomes of Arab American mothers and non-Hispanic white mothers in Massachusetts and to assess the role of nativity as an effect modifier. Methods Using data from Massachusetts birth certificates (2012-2016), we conducted adjusted logistic and linear regression models for maternal health behaviors, maternal health outcomes, and infant health outcomes. We used Arab ethnicity as the exposure of interest and nativity as an effect modifier. Results Arab American mothers had higher odds than non-Hispanic white mothers of initiating breastfeeding (adjusted odds ratio [aOR] = 2.61; 95% CI, 2.39-2.86), giving birth to small-for-gestational-age infants (aOR = 1.28; 95% CI, 1.18-1.39), and having gestational diabetes (aOR = 1.31; 95% CI, 1.20-1.44). Among Arab American mothers, non–US-born mothers had higher odds than US-born mothers of having gestational diabetes (aOR = 1.80; 95% CI, 1.33-2.44) and lower odds of initiating prenatal care in the first trimester (aOR = 0.41; 95% CI, 0.33-0.50). In linear regression models, infants born to non–US-born Arab American mothers weighed 42.1 g (95% CI, −75.8 to −8.4 g) less than infants born to US-born Arab American mothers. Conclusion Although Arab American mothers engage in positive health behaviors, non–US-born mothers had poorer maternal health outcomes and access to prenatal care than US-born mothers, suggesting the need for targeted interventions for non–US-born Arab American mothers.

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The probabilistic reduction approach to specifying multinomial logistic regression models in health outcomes research

Journal of Applied Statistics ◽

10.1080/02664763.2014.909785 ◽

2014 ◽

Vol 41 (10) ◽

pp. 2206-2221 ◽

Cited By ~ 1

Author(s):

Jason S. Bergtold ◽

Eberechukwu Onukwugha

Keyword(s):

Logistic Regression ◽

Health Outcomes ◽

Outcomes Research ◽

Regression Models ◽

Multinomial Logistic Regression ◽

Logistic Regression Models ◽

Health Outcomes Research ◽

Reduction Approach

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Prevalence and adverse impact of extended antibiotic prophylaxis in urologic oncology surgery.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.4_suppl.294 ◽

2014 ◽

Vol 32 (4_suppl) ◽

pp. 294-294

Author(s):

Matthew Mossanen ◽

Josh Calvert ◽

Sarah Holt ◽

Andrew Callaway James ◽

Jonathan L. Wright ◽

...

Keyword(s):

Antibiotic Prophylaxis ◽

Regression Models ◽

Linear Regression Models ◽

Urologic Oncology ◽

Logistic Regression Models ◽

Factors Associated ◽

Provider Characteristics ◽

Diagnosis Codes ◽

Postoperative Length ◽

Hospital Acquired

294 Background: Providers exhibit variation in the selection of the class, dose, and duration of prescribed antibiotic prophylaxis (ABP) to prevent postsurgical infections. We sought to evaluate ABP practice patterns for common inpatient urologic oncology surgeries and ascertain the association between extended ABP and hospital-acquired Clostridium difficile (C. diff) infections. Methods: From the PREMIER database for 2007–2012, we identified patients who underwent radical prostatectomy (RP), radical or partial nephrectomy (Nephx), or radical cystectomy (RC). We defined extended ABP from charges for antibiotics ≥ 2 days after surgery; exclusive of patients with a switch in antibiotic class within 2 postoperative days for presumption of infection. We identified postoperative C. diff infections using ICD-9 diagnosis codes. Hierarchical linear regression models were constructed by procedure to identify patient and provider factors associated with extended ABP. Logistic regression models evaluated the association between extended ABP and postoperative C. diff infection, adjusting for patient and provider characteristics. Results: We identified 59,184 RP patients, 27,921 Nephx patients, and 5,425 RC patients. RC patients were more likely to receive extended ABP (56%) than RP (18%) or Nephx (29%) patients (p<0.001). Other factors associated with extended ABP included prolonged postoperative length of stay (OR ≥ 1.69, p<0.001 for all procedures), and surgical volume (p<0.001 for highest vs. lowest volume quartiles). Hospital identity explained 35% of the variability in ABP after RP, 23% after Nephx, and 20% after RC. Among Nephx and RC patients, extended ABP was associated with significantly higher odds of postoperative C. diff infection (OR 3.79, 95% CI 2.46–5.84, and OR 1.64, 95% CI 1.12–2.39, respectively). Conclusions: We identified marked hospital-level variability in extended ABP following RP, Nephx, and RC, which was associated with significantly increased odds of hospital-acquired C. diff infections. Efforts to increase provider compliance with national ABP guidelines may decrease preventable hospital-acquired infections after urologic cancer surgery.

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Use of principal component scores in multiple linear regression models for simulation of chlorophyll-a and phytoplankton abundance at a karst deep reservoir, southwest of China

Acta Ecologica Sinica ◽

10.1016/j.chnaes.2013.11.009 ◽

2014 ◽

Vol 34 (1) ◽

pp. 72-78 ◽

Cited By ~ 3

Author(s):

Li Qiuhua ◽

Shang Lihai ◽

Gao Tingjing ◽

Zhang Lei ◽

Ou Teng ◽

...

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Chlorophyll A ◽

Regression Models ◽

Principal Component ◽

Linear Regression Models ◽

Phytoplankton Abundance ◽

Deep Reservoir ◽

Component Scores ◽

Multiple Linear Regression Models

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Forgiveness of dolutegravir-based triple therapy compared to older antiretroviral regimens: a prospective multicenter cohort of adherence patterns and HIV-RNA replication

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab316 ◽

2021 ◽

Author(s):

Jean-Jacques Parienti ◽

Anna L Fournier ◽

Laurent Cotte ◽

Marie-Paule Schneider ◽

Manuel Etienne ◽

...

Keyword(s):

Regression Models ◽

Treatment Interruption ◽

People Living With Hiv ◽

Linear Regression Models ◽

Logistic Regression Models ◽

Treatment Groups ◽

Hiv Rna ◽

Adherence Pattern ◽

Living With Hiv ◽

Rna Levels

Abstract Background For many people living with HIV (PLWH), taking antiretroviral therapy (ARV) every day is difficult. Methods Average adherence (Av-Adh) and log-transformed treatment interruption (TI) to ARV were prospectively measured over 6 months using electronic drug monitoring (EDM) in several cohorts of PLWH. Multivariate linear regression models including baseline confounders explored the influence of EDM-defined adherence (R 2) on 6-month Log10 HIV-RNA. Multivariate logistic regression models were used to compare the risk of HIV-RNA detection within subgroups stratified by lower (≤95%) and higher (>95%) Av-Adh. Results Three hundred ninety nine PLWH were analyzed with different ARV: dolutegravir (n=102), raltegravir (n=90), boosted PI (bPI; n=107), and NNRTI (n=100). In the dolutegravir group, the influence of adherence pattern measures on R 2 for HIV-RNA levels was marginal (+2%). Av-Adh, TI and Av-Adh x TI increased the R 2 for HIV-RNA levels by 54% and 40% in the raltegravir and bPI treatment groups, respectively. TI increased the R 2 for HIV-RNA levels by 36% in the NNRTI treatment group. Compared to dolutegravir-based regimen, the risk of VR was significantly increased for: raltegravir (adjusted OR (aOR), 45.6; 95% confidence interval (CI) [4.5 - 462.1], p=0.001); NNRTIs (aOR, 24.8; 95% CI [2.7 - 228.4], p=0.005) and bPIs (aOR, 28.3; 95%CI [3.4 - 239.4], p=0.002) in PLWH with Av-Adh ≤95%. Among PLWH with >95% Av-Adh, there were no significant differences on the risk of VR among the different ARV. Conclusion These findings support the concept that dolutegravir in combination with two other active ARVs achieves a greater virological suppression than older ARV, including raltegravir, NNRTI and bPI among PLWH with lower adherence.

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Does healthcare inequity reflect variations in peoples’ abilities to access healthcare? Results from a multi-jurisdictional interventional study in two high-income countries

International Journal for Equity in Health ◽

10.1186/s12939-020-01281-6 ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Jeannie Haggerty ◽

Jean-Frederic Levesque ◽

Mark Harris ◽

Catherine Scott ◽

Simone Dahrouge ◽

...

Keyword(s):

Logistic Regression ◽

Primary Healthcare ◽

Social Vulnerability ◽

Regression Models ◽

Social Systems ◽

Social Characteristics ◽

Linear Regression Models ◽

Logistic Regression Models ◽

Wide Range ◽

Social Vulnerabilities

Abstract Background Primary healthcare services must respond to the healthcare-seeking needs of persons with a wide range of personal and social characteristics. In this study, examined whether socially vulnerable persons exhibit lower abilities to access healthcare. First, we examined how personal and social characteristics are associated with the abilities to access healthcare described in the patient-centered accessibility framework and with the likelihood of reporting problematic access. We then examined whether higher abilities to access healthcare are protective against problematic access. Finally, we explored whether social vulnerabilities predict problematic access after accounting for abilities to access healthcare. Methods This is an exploratory analysis of pooled data collected in the Innovative Models Promoting Access-To-Care Transformation (IMPACT) study, a Canadian-Australian research program that aimed to improve access to primary healthcare for vulnerable populations. This specific analysis is based on 284 participants in four study regions who completed a baseline access survey. Hierarchical linear regression models were used to explore the effects of personal or social characteristics on the abilities to access care; logistic regression models, to determine the increased or decreased likelihood of problematic access. Results The likelihood of problematic access varies by personal and social characteristics. Those reporting at least two social vulnerabilities are more likely to experience all indicators of problematic access except hospitalizations. Perceived financial status and accumulated vulnerabilities were also associated with lower abilities to access care. Higher scores on abilities to access healthcare are protective against most indicators of problematic access except hospitalizations. Logistic regression models showed that ability to access is more predictive of problematic access than social vulnerability. Conclusions We showed that those at higher risk of social vulnerability are more likely to report problematic access and also have low scores on ability to seek, reach, pay, and engage with healthcare. Equity-oriented healthcare interventions should pay particular attention to enhancing people’s abilities to access care in addition to modifying organizational processes and structures that reinforce social systems of discrimination or exclusion.

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