bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Heterogeneous Effects of Family Structure on Early Child Health
Authors: Mulenga Mulenga Chonzi 
Published: April 2023

Background
Ending preventable deaths of newborns and children under 5 years of age by 2030 is one of the targets of the Sustainable Development Goals. Data from the 2013-14 Zambia Demographic and Health Survey shows that there has been improvements in some indicators of child health over the years and this has coincided with the widespread demographic changes in family formation. It’s hypothesized that children from monogamous households tend to have much better health outcomes compared to single or polygynous households. This study therefore set out to examine the effect of heterogeneous family structures on early child health.
Methods
Using data from the 2013-14 ZDHS, the study employed the Kids Recode (KR) data file which contains information about children under the age of five (5) of interviewed women. The analysis was conducted at three levels: descriptive, bivariate and multivariate.
Results
The study established that family structure had a statistically significant effect on early child health and that there was heterogeneity in the health outcomes of the children from different forms of marriage, with the ever married and polygamous marriage categories showing higher odds of having underweight children. The study also established that mother’s nutrition status, region, sex of child, mother’s age at birth of the child and child vaccination were also among other factors associated with the early child health in Zambia.
Conclusion
There is need for the government to implement policies that will limit the exposure of children to adverse health which would require more costly interventions later in life.
Longitudinal Joint Modelling of Binary and Continuous Outcomes: A Comparison of Bridge and Normal Distributions
Authors: Amini Payam , Verbeke Geert , Zayeri Farid , Mahjub Hossein , Maroufizadeh Saman , Moghimbeigi Abbas 
Published: April 2023

Background: Longitudinal joint models consider the variation caused by repeated measurements over time as well as the association among the response variables. In the case of combining binary and continuous response variables using generalized linear mixed models, integrating over a normally distributed random intercept in the binary logistic regression sub-model does not yield to a closed form. In this paper, we assessed the impact of assuming a Bridge distribution for the random intercept in the binary logistic regression submodel and compared the results to that of normal distribution.
Method: The response variables are combined through correlated random intercepts. The random intercept in the continuous outcome submodel follows a normal distribution. The random intercept in the binary outcome submodel follows a normal or Bridge distribution. The estimations were carried out using a likelihood-based approach in direct and conditional joint modeling approaches. To illustrate the performance of the models, a simulation study was conducted
Results: Based on the simulation results and regardless of the joint modeling approach, the models with a Bridge distribution for the random intercept of the binary outcome resulted in a slightly more accurate estimations and better performance.
Conclusion: In addition to the fact that assuming a bridge distribution for the random intercept in binary logistic regression yields to the same interpretation of parameter estimates in marginal and conditional forms, our study revealed that even if the random intercept of binary logistic regression is normally distributed, assuming a Bridge distribution in the model will result in more accurate results.
A Bayesian Logistic Regression approach in Asthma Persistence Prediction
Authors: Spyroglou Ioannis I. , Spöck Gunter , Chatzimichail Eleni A. , Rigas Alexandros G. , Paraskakis E.N. 
Published: April 2023

Background: A number of models based on clinical parameters have been used for the prediction of asthma persistence in children. The number and significance of factors that are used in a proposed model play a cardinal role in prediction accuracy. Different models may lead to different significant variables. In addition, the accuracy of a model in medicine is really important since an accurate prediction of illness persistence may improve prevention and treatment intervention for the children at risk.
Methods: Data from 147 asthmatic children were analyzed by a new method for predicting asthma outcome using Principal Component Analysis (PCA) in combination with a Bayesian logistic regression approach implemented by the Markov Chain Monte Carlo (MCMC). The use of PCA is required due to multicollinearity among the explanatory variables.
Results: This method using the most appropriate models seems to predict asthma with an accuracy of 84.076% and 86.3673%, a Sensitivity of 84.96% and 87.25% and a Specificity of 83.22% and 85.52%, respectively.
Conclusion: Our approach predicts asthma with high accuracy, gives steadier results in terms of positive and negative patients and provides better information about the influence of each factor (demographic, symptoms etc.) in asthma prediction.
Evaluating Infant Complementary Feeding Pattern and Some Related Factors in Health Care Centers in Khorramabad, 2017 Year
Authors: Anbari Khatereh , Tajabadi Zohreh , Baharvand Parastoo , Bazvand Mostafa , Babak Khodadadi  
Published: April 2023

Objective: Inappropriate starting of complementary feeding is an important factor leading to infant growth delay and malnutrition. This study was aimed to determine the complementary feeding pattern and some related factors in health care centers in Khorramabad.
Methods: This cross-sectional study, included 300 one year old infants who had referred to nine health care centers in Khorramabad by multistage sampling. In present study, we used a questionnaire included individual information about mothers and infants and a checklist for evaluating breastfeeding and complementary feeding. Chi-square test and one way ANOVA were used to analyze the data.
Results: In present study, 50.7% of infants were girls and 60.3% of cases were the first infant in their family. 59.3% of infants were delivered by cesarean. Average age of starting complementary feeding was 0.5 8.5. 75.7% of infants had exclusive breastfeeding up to first six months of their life. Among most cases, the first complementary food was rice flour porridge (29.3%) and almond porridge (15.7%). Most mothers had acquired necessary information about complementary feeding by using booklets (20.7%) and physician guidance (16.7%). There was a statistically significant relationship between age of starting complementary feeding and mother’s education (P=0.043) level and employment status (P=0.001).
Conclusion: Related authorities should begin comprehensive and effective education by using novel training strategies to raise mothers' knowledge about age of starting complementary feeding and how to do it at health care centers.
A multi-sectoral intervention model to scale up family planning services utilization at the primary health care level: evidence from a priority district, Fayoum governorate, Egypt
Authors: Elden Nesreen Mohamed Kamal , Khairy Walaa Ahmed , Amin Tarek Tawfik 
Published: April 2023

Background: Egypt is one of the most populous countries in the Middle East and Africa. Evidence from developing countries revealed that increasing family planning use is associated with substantial declines in fertility and population growth. The objective of this study was to assess the impact of implementing a multisectoral intervention model on the family planning utilization at the Primary Health Care (PHC) level in a priority district, Fayoum governorate, Egypt.
Methods: A quasi-experimental design was conducted from July 2016 to September 2016. The model targeted 10 PHC units out of 23 at Tamia district, with two types of interventions; health sector and non-health sector related interventions carried out in the intervention units and their catchment areas. The family planning utilization of the intervention units (n=10) was compared to the control units (n=13). Additionally, the overall family planning utilization at the district level was measured.
Results: Following the implementation of the interventions, the overall family planning utilization at the district level showed a significant increase in mean ± Standard Error of the Mean (SEM) of new family planning clients (44.4±11.0vs. 63.3±13.8; P= 0.006) recording 43% change. The mean ± SEM of intrauterine devices dispensed from the intervention units significantly increased by 391% (3.5±1.0 vs. 17.2±3.3; P=0.002). Findings from the control units didn't reveal significant increase regarding the dispense of any family planning method.
Conclusion: Mobilizing and optimizing resources use, empowering district authorities and strengthening collaboration across sectors were key drivers of the success of this model in scaling up family planning services utilization.
Obesity: transition from adolescence to adulthood and feedback partial gmm logistic model with time-dependent covariates
Authors: Fang Di , Irimata Kyle M. , Rhodes Rachael N. , Wilson Jeffrey R. 
Published: April 2023

Background: The aim of this study is to investigate the impact of certain covariates on obesity. More importantly, we seek to determine the feedback of obesity on depression, and physical activity as they transition from adolescence to young adulthood.
Methods: Using 15 years of nationally representative data from 6560 adolescents (Add health data), we estimate feedback and associations between depression, and activity scale on obesity while we adjusted for gender, age, race/ethnicity, socioeconomic status through a GMM logistic regression model with time-dependent covariates.
Results: Activity (p<0.001) and depression (p<0.001) have significant impact on Obesity. In early years, alcohol had no impact (p=0.895 and p=0.476) on obesity but in later years it did (p<0.001). In the early years, television hours had an impact but as they got older, it did not.
Conclusion: Our findings suggest that public health researchers can target obesity simultaneously with depression, and activity scale. These findings contribute new insights into the feedback of obesity on depression, and activity. This unique model allows segments of associations to be addressed rather than assuming all associations remain the same over 15 years.
Bayesian model averaging: improved variable selection for matched case-control studies
Authors: Mu Yi , See Isaac , Edwards Jonathan R. 
Published: April 2023

Background: The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field. However, this approach has been criticized for not taking into account the uncertainty of the model selection process itself. This limitation can be addressed by a Bayesian model averaging approach: instead of focusing on a single model and a few factors, Bayesian model averaging considers all the models with non-negligible probabilities to make inference.
Methods: This paper reports on a simulation study designed to emulate a matched case-control study and compares classical versus Bayesian model averaging selection methods. We used Matthews’s correlation coefficient to measure the quality of binary classifications. Both classical and Bayesian model averaging were also applied and compared for the analysis of a matched case-control study of patients with methicillin-resistant Staphylococcus aureus infections after hospital discharge 2011-2013.
Results: Bayesian model averaging outperformed the classical approach with much lower false positive rates and higher Matthew’s correlation scores. Bayesian model averaging also produced more reliable and robust effect estimates.
Conclusion: Bayesian model averaging is a conceptually simple, unified approach that produces robust results. It can be used to replace controversial P-values for case-control study in medical research.
Demographic and Regional Trends of Smoking among Thai Females from 1986 to 2014
Authors: Gautam Nirmal , Lim Apiradee , Ueranantasun Attachai 
Published: April 2023

Background: Smoking is a major public health burden causing substantial morbidity and mortality. The aim of the study was to assess the prevalences and demographic trends of smoking among Thai females.
Methods Data from Thailand’s National Statistics Office for 1986 to 2014 were used. Multiple logistic regression was implemented to identify the prevalences and trends of female smoking by region (Bangkok, Central, Northeast, North, and South) and area (Rural and Urban).
Results: The overall prevalence of smoking was 3.4% among females aged 15 years and above in 2014. Gradually decreasing trends of smoking were observed from 1991 to 2007. Afterwards, the trends of smoking was found higher in 2011 to 2014 among aged 15-39 years. Furthermore, there were substantial differences in female smoking in different regions and areas. Of those, the Northern Thailand and rural areas had the highest prevalence of smokers.
Conclusion: Smoking among Thai females in the past 20 years had a decreasing trend, but it can be seen that since 2007 the trend of smoking had shown an increasing trend and a variation among the regions. This imply that, tobacco control policies are important and in need particularly for young females, living in rural areas and Northern provinces.
Maternal Dietary Pattern with an Emphasis on Child Growth Pattern and Exclusive Breastfeeding Duration
Authors: Borazjani Fatemeh , Hardani Amir Kamal , Bokaee Shadi , Angali Kambiz Ahmadi 
Published: April 2023

Background: maternal nutrition and breast milk nutrient concentration are associated with infant's growth. Enhanced knowledge encourages mothers to breast feed for the sake of optimizing growth and development.
Methods: the current study was performed longitudinally in some rural areas, south of Iran, from birth to six months. Out of 319 candidates with serial anthropometric measurements of an infant, including weight, height, and head circumference at four times (birth, two, four, and six months ), 195 were included in the multivariate regression model (total measurements, n=776) to construct the pattern of growth. Then, information regarding demographic background, lactation status and infant supplementation intake status, feeding practices, maternal anthropometric measurements, and food frequency questionnaire were collected from mothers and their children during six months in two rural health centers.
Results: The most important association between dietary factors and infant anthropometrics (weight, height, and head circumference) involved Factor2 vegetables, including leafy vegetables, tomato, onion and garlic, (weight β=0.35 95%CI 0.27 - 0.44, length β=3.82 95%CI 3.42 - 4.22, head circumference β=2.55 95%CI 2.32 - 2.79) and Factor1 fruit, including orange, kiwi, cherries, watermelon, date, and grapes) (weight β=0.117 95%CI 0.04-0.189, length β=0.59 95%CI 0.257-0.929, head circumference β=0.307 95% CI 0.108-0.507), respectively. Generalized Estimation Equation model revealed that Factor2 dairy (including low fat milk, yogurt and dough) significantly contributed to weight growth velocity (β=-0.009 95% CI -0.016 - 0.001).
Conclusion: a dietary habit of fruit and vegetables, which are rich in bioactive components by breast feed mother showed better growth for infants. As growth is multifactorial, maternal nutrition and breastfeeding duration is a simple modifiable factor which can affect the subsequent growth and development of the child during the first six months.
Key words: exclusive breastfeeding, growth, anthropometric measures, maternal nutrition, and multivariate multiple regression.
Associations between sociodemographic characteristics and tobacco usage in adult cancer survivors: Evidence from a population-based study
Authors: Mann Alexander J , Malcolm Janice C 
Published: April 2023

Background: the risk of developing new cancers persists for 15 million cancer survivors in the United States, yet many continue to engage in high-risk behaviours. This analysis aims to compare tobacco use in cancer-free respondents and cancer survivors, in order to elucidate trends and behavioural patterns associated with increased tobacco use in individuals that have survived cancer.
Methods: the Health Information National Trends Survey data of 2014 and 2017 was analysed for this study. Descriptive statistics were generated, and the likelihood of tobacco use was predicted using weighted logistic regression. Included in the study population were 941 cancer survivors, predominantly white (80%), 60-70 years of age, married (52%), with some level of education past high school (65%).
Results: the current smoking rate for cancer survivors was 12.1% versus 14.3% for those without cancer. Sub-high school education (OR 3.02, 95% CI [1.11-8.19]), separation/divorce (OR 2.71, 95% CI [1.52-4.83]), female gender, and lower household income were associated with an increased likelihood of cigarette use amongst cancer survivors. Cervical cancer (19.2%) and lymphoma (20%) survivors were most likely to smoke cigarettes compared to other cancer survivors.
Conclusions: this study demonstrated certain sociodemographic characteristics increase the likelihood of cigarette smoking in cancer survivors. These outcomes suggest cancer survivors with only high school education or lower, and those with household incomes of less than $35,000 are at greater risk and should be targeted for personalised tobacco cessation interventions in the future. High prevalence of smoking in cervical cancer survivors and an increased risk of tobacco-linked cancers suggests focus must be directed to interventions targeting female cancer survivors. Allocating further resources toward the at-risk populations identified in this study may reduce further morbidities in cancer survivors.
Discovering potential blood-based cytokine biomarkers for Alzheimer’s disease using Firth Logistic Regression
Authors: Abdullah Mohammad Nasir , Wah Yap Bee , Zakaria Yuslina , Majeed Abu Bakar Abdul , Huat Ong Seng 
Published: April 2023

Background: Alzheimer’s disease (AD) is a neurodegenerative disorder where patients suffer from memory loss, cognitive impairment and progressive disability. Individual blood biomarkers have not been successful in defining the disease pathology, progression and diagnosis of AD. There is a need to identify multiplex panels of blood biomarkers for early diagnosis of AD with high sensitivity and specificity. This study focused on identification of cytokine biomarkers. The maximum likelihood estimates of the ordinary logistic regression model cannot be obtained when there is complete separation and the alternative is Firth logistic regression which uses a penalised Maximum Likelihood in parameter estimation.
Methods: This paper reports a Firth logistic regression application in finding potential blood-based cytokine biomarkers for Alzheimer’s disease in a matched case control study. We used a principle component analysis to discriminate the correlated, completely separated covariates.
Results: The Firth logistic regression results showed that nine individual biomarkers IL-1β, IL-6, IL-12, IFN-γ, IL-10, IL-13, IP-10, MCP-1 and MIP-1α had a significant relationshipwith elevated risk for AD as compared to the healthy control (HC). Principal component analysis with varimax rotation for the nine biomarkers revealed four factors (total variance explained=85.5%). The main principal component biomarkers were IL-1β, IL-6, IL-13 and MCP-1 (total variance explained=62.3%). Firth’s logistic regression model with the first principal component had accuracy of 78.2% with sensitivity and specificity of 71.8% and 75% respectively.
Conclusion: Firth’s logistic regression is a useful technique in identification of significant biomarkers when there is an issue of data separation.
Obstetric and perinatal outcomes of teenage pregnant women: a retrospective study
Authors: Derme Martina , Leoncini Emanuele , Vetrano Giuseppe , Carlomagno Lisa , Aleandri Vincenzo 
Published: April 2023

Background: teenage pregnancy is a worldwide social problem. The aim of this study is to provide more data for a better understanding of the possible maternal and foetal risks associated with teenage pregnancies.
Methods: the hospital records of all pregnant women, aged between 14 and 19, from the obstetric registers of the Policlinico Umberto I Hospital in Rome, between 2000 and 2010, have been completely reviewed (n=184). For each pregnant woman socio-demographic characteristics, obstetric history, pregnancy and birth outcomes were also determined. Our results were compared with a control group composed of 150 primigravida adult women aged 20-29 years who delivered at the Policlinico Umberto I Hospital in Rome in the same period.
Results: the mean age ± SD of the study group was 17.9 ± 1.2, while that of the control group was 25.4 ± 2.4. The control group had a significantly lower risk of preterm delivery (p=0.000). The rate of low birth weight babies born to the young mothers was significantly higher than that of babies born to the adult mothers (p=0.036). The study group had a lower risk of instrumental delivery and a higher proportion of spontaneous delivery (p=0.000). Finally, we observed a statistically significant difference of the APGAR score at the fifth minute between the two groups (p=0.004).
Conclusions: our results seemed to confirm the outcomes of previous studies for adolescent pregnant women, mainly regarding the increased risks of preterm deliveries and low birth weight babies, the higher incidence of spontaneous vaginal delivery and the lower incidence of instrumental delivery.
Comparison of multi-state Markov models for cancer progression with different procedures for parameters estimation. An application to breast cancer
Authors: Ventura Leonardo , Carreras Giulia , Puliti Donatella , Paci Eugenio , Zappa Marco , Miccinesi Guido 
Published: April 2023

Background: the knowledge of sojourn time (the duration of the preclinical screen-detectable period) and screening test sensitivity is crucial for understanding the disease progression and the effectiveness of screening programmes. For this purpose a model of the natural history of the disease is needed. The aim of this work is to provide an illustration of the application of multistate Markov models for breast cancer progression to the data of the Florentine screening programme, in order to estimate the sojourn time and sensitivity for breast cancer screening.
Methods: three different multi-state Markov models of increasing complexity were used with three different estimation procedures based on non-linear least squares, maximum likelihood, and on a Bayesian approach. All the models produced estimates for screening sensitivity and mean sojourn time. The data used in our application seem to lead to a non-identifiability problem, since the estimation procedures for both the Maximum Likelihood and Non-Linear Least Squares gave estimates that changed with the parameters’ initial values or difficultly converged. In order to take this problem into account we used the Bayesian Approach by incorporating prior information on all the parameters.
Results: the mean sojourn time varied between 2-7 years and 3-5 years for women aged 50-59 and 60-69, respectively. When the model complexity was increased a higher variability in estimates was observed among the estimation procedures. The results of the screening sensitivity estimates were highly variable, both among estimation techniques and models - varying between 63% and 100%, and between 77% and 100% for women aged 50-59 and 60-69, respectively.
Conclusions: results are in accord with the literature; those obtained through the Bayesian Approach seem to be more reliable.