Journal of Biostatistics and Epidemiology

Adjustment of Truncation Effect in First Birth Interval using Current Status Data Technique

Sachin Kumar — 2025-06-15

Background: Estimating the First Birth Interval (FBI) from cross-sectional data often presents challenges related to truncation effects. These challenges stem from the data’s inability to capture the enough exposure for a event, resulting in potential biases and inaccuracies in FBI estimates. Recognizing and addressing truncation effects is essential for obtaining more precise and meaningful fertility parameter estimates in a cross-sectional survey.

Objective: This study seeks to mitigate truncation effects in the estimation of the FBI by utilizing the Current Status Data Technique. This approach involves focusing on women with specific marital durations, providing a means to counteract the bias caused by truncation and thereby yielding more accurate and reliable FBI estimates.

Methodology: Data from the National Family Health Survey (NFHS-IV) are employed for this study. The Current Status Data Technique is applied to the dataset, considering exclusively those women with marital durations less than 120 months. This methodology enables the adjustment of truncation effects and facilitates a more precise estimation of the FBI. Statistical analysis is conducted to determine the FBI distribution and ascertain the necessary sample size.

Results: The application of the Current Status Data Technique yields an FBI estimate of 30.70 months. To achieve reliable estimations of the FBI using Current Status Techniques, a minimum sample size exceeding ”5000” observations is required.

Conclusion: Truncation effect in FBI is address and some non parametric adjustment is used for estimating the duration of FBI. The Current Status Data Technique emerges as a valuable tool for mitigating these effects and enhancing the precision of FBI estimates. This research contributes to an improved understanding of fertility dynamics and provides valuable insights for future studies on the First Birth Interval.

Socioeconomic Inequality in Chronic Complications of Type 2 Diabetes Mellitus in Iran: Concentration Index and Decomposition Approach

Sedigheh Mafakheri — 2025-02-09

Background: In Iran, evidence regarding the impact of socioeconomic (SES) inequality on the progression and complications of type 2 diabetes mellitus (T2DM) are sparse and needs growing body of research. SES is a complex construct and its impact on the health outcomes should be evaluated in an efficient and flexible way. The purpose of this study is to examine SES inequality in chronic complications among T2DM patients using methods of decomposing inequality.

Methods: This cross-sectional study included patients with T2DM receiving care at the diabetes clinic in Hamadan City, Iran, between April and September 2023. Demographic, SES, diabetic related factors were obtained from medical records and face-to-face interviews. A binary logistic regression model was utilized to investigate the relationship between diabetes complications and independent variables. The concentration index (CI) and decomposition approach were used to evaluate SES inequality and determine the contribution of each factor to inequality.

Results: A total of 530 patients (61.6% females and 54.9% less than 60 years) were included. In the study population, 22.3%, 9.5%, and 4.7% had retinopathy, kidney failure, and diabetic foot ulcers, respectively. The CI for retinopathy, kidney failure, and foot ulcers were [(CI: -0.273, p<0.001), (CI: -0.089, p<0.001), (CI: -0.122, p<0.001), respectively]. Factors with the greatest contribution to SES inequality for retinopathy were economic status (44.6%), duration of T2DM (24.5%), age (18.7%), and healthy lifestyle (12.5%), for kidney failure were economic status (33.8%), hypertension (22.5%), education level (21.3%), and HbA1c value (13.5%), and for foot ulcers were economic status (78.7%), duration of T2DM (33.6%), HbA1c value (17.2%) hypertension (7.4%).

Conclusion: This study demonstrated that SES inequality in chronic complications of T2DM with greatest contribution for economic status. It is recommended that policymakers and health professionals consider the main causes of SES inequality in the chronic complications of T2DM when developing health strategies.

Analysis of Vertical Ground Reaction Force Data in Predicting Parkinson’s Disease

Varun Jain — 2025-02-09

Background: Parkinson’s disease (PD) is a complex, progressive neurodegenerative disorder known to negatively impair patient gait. Therefore, with gait and vertical ground reaction force (VGRF) data, an association can be made between the data and Parkinson’s disease.

Methods: Data from 146 participants; 93 with Parkinson’s disease and 73 without Parkinson’s disease was obtained from a PhysioNet database for use in this article. A Fourier Analysis and several support vector machine learning models were computed in MATLAB to classify whether an individual had Parkinson’s disease.

Results: From the Fourier analysis, it was determined that a statistically significant difference was present between the VGRF data of individuals with and without Parkinson’s disease. Additionally, it was found that a Minimum Classification Error Optimized SVM machine learning model using Bayesian statistics was able to classify individuals with Parkinson’s disease using VGRF data at an accuracy of 67.1%, and sensitivity of 80.43%.

Conclusion: Therefore, it can be determined that vertical ground reaction force can predict Parkinson’s Disease with considerable accuracy which could be improved with an increased number of participants.

Risk factors related to congenital hypothyroidism: Systematic review and Meta-analysis

Boutaina Boumehdi — 2025-02-09

Introduction: Congenital endocrine disorders have a global impact on the morbidity and mortality of children and are a public health problem that heavily affects society and the daily lives of affected children and their families. The severity and consequences of congenital hypothyroidism (CH) on physical and especially cerebral maturation combined with lifetime mental retardation make CH neonatal screening one of the costliest preventive health programs. Thus, early diagnosis can improve the prognosis of the disease. The objective of the study is to examine CH’s risk factors reported in previous studies.

Methods: Meta-analysis was performed according to the PRISMA checklist. PUBMED, Google Scholar, Scopus, MEDLINE, Web of Science, and Springer were analyzed using R version 4.0.3. For further review, we assessed eligibility analysis to identify influential studies.

Results: Of 63 studies, 22 studies were suitable for synthesis. Based on this review, risk factors related to CH were birth weight, age of pregnancy, female sex, home environment, notion of inbreeding, seasonality, multiple pregnancy, gestational diabetes, parity, advanced maternal age, parental thyroid disease, gestational diabetes, ethnicity, maternal body mass index (BMI), and socio-economic status.

Conclusion: This systematic review and meta-analysis indicates that the risk factors related to CH vary by country and even by inter-region according to geographical, genetic, and socioeconomic specificities.

Using The Causal Attitude Network Model to analyze the factors affecting public attitude and acceptance of COVID-19 and vaccination in Indonesia

Asti Meiza — 2025-02-09

Introduction: Attitudes about COVID-19 relate to cognitions, feelings, and behaviors regarding the pandemic and vaccination, as well as other factors, such as demographic characteristics, and health-related information. This research uses the Causal Attitude Network (CAN) model to measure attitudes and acceptance of COVID-19 vaccination among 1385 Indonesian people from 15 cities.

Methods: Data was obtained from instruments that made in the Netherlands and adapted to Indonesian language and culture. This research integrates psychometrics with network analysis which is an advanced implementation of the field of Statistics to reveal the interaction between psychological factors that shape people's attitudes towards COVID-19 and vaccination in Indonesia. Data analysis used JASP, an open source statistical analysis software.

Results: From this research, it was found that attitude elements regarding trust in vaccine development and awareness of the importance of vaccines in Indonesian society have a high influence on other attitude elements. Attitude elements regarding the habit of wearing masks and awareness about the importance of the COVID-19 vaccine are the attitude elements that have the highest impact on changing other attitude elements,

Conclusion: Two attitude elements, namely trust and awareness, are the attitude elements that most influence other attitude elements. Trust in the development of the COVID-19 vaccine is related to trust in the experts in developing the COVID-19 vaccine. In other words, increasing public confidence in the development of a science-appropriate COVID-19 vaccine will be in line with increasing public trust in COVID-19 vaccine developers, and vice versa.