Resting-State Functional Connectivity Predicts Attention Problems in Children: Evidence from the ABCD Study.

Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder, and numerous functional and structural differences have been identified in the brains of individuals with ADHD compared to controls. This study uses data from the baseline sample of the large, epidemiologically informed Adolescent Brain Cognitive Development Study of children aged 9-10 years old (N = 7979). Cross-validated Poisson elastic net regression models were used to predict a dimensional measure of ADHD symptomatology from within- and between-network resting-state correlations and several known risk factors, such as biological sex, socioeconomic status, and parental history of problematic alcohol and drug use. We found parental history of drug use and biological sex to be the most important predictors of attention problems. The connection between the default mode network and the dorsal attention network was the only brain network identified as important for predicting attention problems. Specifically, we found that reduced magnitudes of the anticorrelation between the default mode and dorsal attention networks relate to increased attention problems in children. Our findings complement and extend recent studies that have connected individual differences in structural and task-based fMRI to ADHD symptomatology and individual differences in resting-state fMRI to ADHD diagnoses.

Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.

Healthcare-seeking behavior for diarrhea in under-five children and associated factors in sub-Saharan Africa: a multilevel robust Poisson regression model.

Background: Diarrhea is one of the leading causes of child death in sub-Saharan Africa (SSA). Children with diarrhea who do not receive medical advice or treatment are at high risk of poor health outcomes and increased mortality. Prompt and adequate treatment is essential to mitigate these risks. However, studies that have been conducted on the factors influencing healthcare-seeking behavior (HSB) for diarrhea in under-five children in SSA are scarce. Therefore, the purpose of this research was to determine the variables related to HSB for diarrhea in children under the age of five. Methods: A secondary data analysis was conducted on the most recent data from the Demographic and Health Surveys in 35 SSA countries. The study included a total weighted sample of 51,791 children under the age of five with diarrhea. We presented the adjusted prevalence ratio and the 95% confidence interval in the multivariable multilevel robust Poisson regression analysis to show the statistical significance and strength of the association between HSB and its determinants. Results: The pooled prevalence of HSB for diarrhea in under-five children was 58.71% (95%CI: 55.39 to 62.04). Factors found to be associated with HSB included maternal age, education and working status, antenatal care visits, postnatal checkups for the child, wasting, distance to a health facility, SSA region, and country income level. Conclusion: More than 40% of under-five children with diarrhea in SSA did not receive medical advice or treatment. To improve healthcare-seeking behavior, effective health policy interventions are necessary. These include enhancing the education and employment status of mothers, promoting regular antenatal and postnatal care visits, building health facilities in close proximity, and raising awareness in the community about the importance of seeking healthcare services for malnourished children.

Quantifying the magnitude of the general contextual effect in a multilevel study of SARS-CoV-2 infection in Ontario, Canada: application of the median rate ratio in population health research.

BACKGROUND: Regional variations in SARS-CoV-2 infection were observed in Canada and other countries. Studies have used multilevel analyses to examine how a context, such as a neighbourhood, can affect the SARS-CoV-2 infection rates of the people within it. However, few multilevel studies have quantified the magnitude of the general contextual effect (GCE) in SARS-CoV-2 infection rates and assessed how it may be associated with individual- and area-level characteristics. To address this gap, we will illustrate the application of the median rate ratio (MRR) in a multilevel Poisson analysis for quantifying the GCE in SARS-CoV-2 infection rates in Ontario, Canada. METHODS: We conducted a population-based, two-level multilevel observational study where individuals were nested into regions (i.e., forward sortation areas [FSAs]). The study population included community-dwelling adults in Ontario, Canada, between March 1, 2020, and May 1, 2021. The model included seven individual-level variables (age, sex, asthma, diabetes, hypertension, congestive heart failure, and chronic obstructive pulmonary disease) and four FSA census-based variables (household size, household income, employment, and driving to work). The MRR is a median value of the rate ratios comparing two patients with identical characteristics randomly selected from two different regions ordered by rate. We examined the attenuation of the MRR after including individual-level and FSA census-based variables to assess their role in explaining the variation in rates between regions. RESULTS: Of the 11 789 128 Ontario adult community-dwelling residents, 343 787 had at least one SARS-CoV-2 infection during the study period. After adjusting for individual-level and FSA census-based variables, the MRR was attenuated to 1.67 (39% reduction from unadjusted MRR). The strongest FSA census-based associations were household size (RR = 1.88, 95% CI: 1.71-1.97) and driving to work (RR = 0.68, 95% CI: 0.65-0.71). CONCLUSIONS: The individual- and area-level characteristics in our study accounted for approximately 40% of the between-region variation in SARS-CoV-2 infection rates measured by MRR in Ontario, Canada. These findings suggest that population-based policies to address social determinants of health that attenuate the MRR may reduce the observed between-region heterogeneity in SARS-CoV-2 infection rates.

Association of hypoglycemia problem-solving abilities with severe hypoglycemia in adults with type 1 diabetes: a Poisson regression analysis.

Background: Severe hypoglycemia (SH) poses a significant challenge in the management of type 1 diabetes (T1D); however, the factors that offer protection other than diabetes technologies are under-studied. The primary objective of this study was to examine the association between hypoglycemia problem-solving (HPS) abilities and severe hypoglycemic events in adults with T1D using Poisson regression analysis. Methods: In this cross-sectional study, 287 adults with T1D (mean age: 50.3 ± 14.5 years, male: 36.2%, diabetes duration: 17.5 ± 11.2 years, mean HbA1c: 7.7 ± 0.9%) were included and categorized into two groups: non-SH (n = 262) and SH (n = 25). Data on diabetic complications, the hypoglycemia problem-solving scale (HPSS), and treatment details were collected. Impaired awareness of hypoglycemia (IAH) was evaluated using Gold's method. Univariate and multivariable Poisson regression models were used for the analysis, and the findings were presented as incidence rate ratios (IRRs) at 95% confidence interval (CI). Results: The incidence of SH was 16.7 (95% CI 7.5-26.0) per 100 person-years. In the univariate Poisson regression analysis, findings revealed associations between IAH, diabetic peripheral neuropathy (DPN), and HPSS1. On the other hand, the multivariate Poisson regression analysis, utilizing stepwise variable selection, identified DPN (IRR: 4.65, 95% CI 1.96-11.04; P < 0.001) and HPSS1 score (IRR: 0.51, 95% CI 0.34, 0.76; P = 0.001) as factors significantly associated with SH. Conclusion: We identified HPS abilities, in addition to DPN, were associated with SH in adults with T1D. Trial registration: University Hospital Medical Information Network (UMIN) Center: UMIN000039475), approval date: February 13, 2020.

Investigating the impact of climatic and environmental factors on HFRS prevalence in Anhui Province, China, using satellite and reanalysis data.

Introduction: Hemorrhagic Fever with Renal Syndrome (HFRS) is the most commonly diagnosed zoonosis in Asia. Despite taking various preventive measures, HFRS remains prevalent across multiple regions in China. This study aims to investigate the impact of climatic and environmental factors on the prevalence of HFRS in Anhui Province, China, utilizing satellite and reanalysis data. Methods: We collect monthly HFRS data from Anhui Province spanning 2005 to 2019 and integrated MODIS satellite datasets and ERA5 reanalysis data, including variables such as precipitation, temperature, humidity, solar radiation, aerosol optical depth (AOD), and Normalized Difference Vegetation Index (NDVI). Continuous wavelet transform, Spearman correlation analysis, and Poisson regression analysis are employed to assess the association between climatic and environmental factors and HFRS cases. Results: Our findings reveal that HFRS cases predominantly occur during the spring and winter seasons, with the highest peak intensity observed in a 9-year cycle. Notably, the monthly average relative humidity exhibits a Spearman correlation coefficient of 0.404 at a 4-month lag, taking precedence over other contributing factors. Poisson regression analysis elucidates that NDVI at a 2-month lag, mean temperature (T) and solar radiation (SR) at a 4-month lag, precipitation (P), relative humidity (RH), and AOD at a 5-month lag exhibit the most robust explanatory power for HFRS occurrence. Moreover, the developed predictive model exhibiting commendable accuracy. Discussion: This study provides key evidence for understanding how climatic and environmental factors influence the transmission of HFRS at the provincial scale. Insights from this research are critical for formulating effective preventive strategies and serving as a resource for HFRS prevention and control efforts.

Application of longitudinal multilevel zero inflated Poisson regression in modeling of infectious diseases among infants in Ethiopia.

BACKGROUND: In sub-Saharan African countries, preventable and manageable diseases such as diarrhea and acute respiratory infections still claim the lives of children. Hence, this study aims to estimate the rate of change in the log expected number of days a child suffers from Diarrhea (NOD) and flu/common cold (NOF) among children aged 6 to 11 months at the baseline of the study. METHODOLOGY: This study used secondary data which exhibit a longitudinal and multilevel structure. Based on the results of exploratory analysis, a multilevel zero-inflated Poisson regression model with a rate of change in the log expected NOD and NOF described by a quadratic trend was proposed to efficiently analyze both outcomes accounting for correlation between observations and individuals through random effects. Furthermore, residual plots were used to assess the goodness of fit of the model. RESULTS: Considering subject and cluster-specific random effects, the results revealed a quadratic trend in the rate of change of the log expected NOD. Initially, low dose iron Micronutrient Powder (MNP) users exhibited a higher rate of change compared to non-users, but this trend reversed over time. Similarly, the log expected NOF decreased for children who used MNP and exclusively breastfed for six months, in comparison to their counterparts. In addition, the odds of not having flu decreased with each two-week increment for MNP users, as compared to non-MNP users. Furthermore, an increase in NOD resulted in an increase in the log expected NOF. Region and exclusive breastfeeding also have a significant relationships with both NOD and NOF. CONCLUSION: The findings of this study underscore the importance of commencing analysis of data generated from a study with exploratory analysis. The study highlights the critical role of promoting EBF for the first six months and supporting children with additional food after six months to reduce the burden of infectious diseases.

Exploring the Impact of a Structured Educational Approach on Peristomal Skin Complications: An Interim Analysis.

This study, employing an interim analysis, investigates the effects of the Dermamecum protocol, a structured educational and tailored approach that stratifies ostomy patients into risk paths (green, yellow, red) based on pre-operative and post-operative characteristics. The green path indicates a low risk of peristomal skin complications (PSCs), focusing on sustaining healthy behaviours and basic stoma care. The yellow path represents a moderate risk, emphasizing the need for patients to self-monitor and recognize early signs of complications. The red path corresponds to high risk, requiring stringent monitoring and immediate access to healthcare support. The study aims to reduce PSCs and improve patient outcomes. Methods include the stratification of 226 patients, with significant differences in gender distribution, BMI categories, and stoma types across the paths. Results show an occurrence rate of PSCs of 5.9% in all risk paths (5.7% green path, 4.7% yellow path, and 7.9% red path, p = 0.685), significantly lower than the median rate of 35% reported in the literature. Multiple correspondence analysis validated the stratification, with distinct clusters for each path. Poisson regression models in the exploratory framework of an interim analysis identified male gender as the only significant predictor of PSCs, indicating the need for gender-specific interventions. The findings suggest that the Dermamecum protocol effectively reduces early PSCs, providing a foundation for further research.

A Poisson regression approach for assessing morbidity risk and determinants among under five children in Nigeria.

In this paper, we have provided more insights on the relationship between under five morbidity in Nigeria and some background characteristics using a Poisson regression model and the most recent 2018 NDHS data on Acute Respiratory Infection (ARI), diarrhoea and fever. Some of our results are that children 36-47 months old have the highest risk of ARI [OR = 1.45; CI (1.31,1.60)] while children less than 6 months old have the lowest risk of ARI [OR = 0.14; CI (0.11,0.17)]. The prevalence of diarrhoea is generally high among children under 48-59 months old but highest among children 6-11 months old [OR = 4.34; CI (3.69,5.09)]. Compared to children 48-59 months old, children in all other age categories except 24-34 months old have a high risk of fever [OR = 0.95; CI (0.73,1.24)]. ARI is more prevalent among female children [OR = 8.88; CI (8.02,9.82)] while diarrhoea [OR = 21.75; (19.10,24.76)] and fever [OR = 4.78; CI (4.31,5.32)] are more prevalent among male children. Children in urban areas are more likely to suffer ARI [OR = 9.49; CI (8.31,10.85)] while children in rural areas are more likely to suffer both diarrhoea [OR = 21.75; CI (19.10,24.76)] and fever [OR = 4.90; CI (4.26,5.63)]. Children in the South-South have the highest risk of ARI [OR = 4.03; CI (3.65,4.454)] while children in the North Central have the lowest risk of ARI [OR = 1.55; CI (1.38,1.74)] and highest risk of diarrhoea [OR = 3.34; CI (2.30,5.11)]. Children in the Northeast have the highest risk of fever [OR = 1.30; CI (1.14,1.48)]. In the Northcentral region, Kogi state has the highest prevalence of fever [OR = 2.27; CI (1.62,3.17)], while Benue state has the lowest [OR = 0.35; CI (0.20,0.60)]. Children in Abuja state face similar risks of fever and diarrhoea [OR = 0.84; CI (0.55,1.27)], with the risk of diarrhoea in Abuja being comparable to that in Plateau state [OR = 1.57; CI (0.92,2.70)]. Nasarawa state records the highest incidence of diarrhoea in the Northcentral [OR = 5.12; CI (3.03,8.65)], whereas Kogi state reports the lowest [OR = 0.29; CI (0.16,0.53)]. In the Northeast, Borno state has the highest rate of fever [OR = 3.28; CI (2.80,3.84)], and Bauchi state the lowest [OR = 0.38; CI (0.29,0.50)]. In Adamawa state, the risks of fever and diarrhoea are nearly equivalent [OR = 1.17; CI (0.97,1.41)], and the risk of fever there is similar to that in Taraba state [OR = 0.92; CI (0.75,1.12)]. Diarrhoea is most prevalent in Yobe state [OR = 3.17; CI (2.37,4.23)] and least prevalent in Borno state [OR = 0.26; CI (0.20,0.33)]. In the Northwest, the risk of fever is similarly high in Zamfara and Kebbi states [OR = 1.04; CI (0.93,1.17)], with Kastina state showing the lowest risk [OR = 0.39; CI (0.34,0.46)]. Children in Zamfara state experience notably different risks of fever and diarrhoea [OR = 0.07; CI (0.05,0.10)]. Kaduna state reports the highest incidence of diarrhoea [OR = 21.88; CI (15.54,30.82)], while Kano state has the lowest [OR = 2.50; CI (1.73,3.63)]. In the Southeast, Imo state leads in fever incidence [OR = 8.20; CI (5.61,11.98)], while Anambra state has the lowest [OR = 0.40; CI (0.21,0.78)]. In Abia state, the risk of fever is comparable to that in Enugu state [OR = 1.03; CI (0.63,1.71)], but the risks of fever and diarrhoea in Abia differ significantly [OR = 2.67; CI (1.75,4.06)]. Abia state also has the highest diarrhoea rate in the Southeast [OR = 2.67; CI (1.75,4.06)], with Ebonyi state having the lowest [OR = 0.05; CI (0.03,0.09)]. In the South-South region, Bayelsa and Edo states have similar risks of fever [OR = 1.28; CI (0.84,1.95)], with Akwa Ibom state reporting the highest fever rate [OR = 4.62; CI (3.27,6.52)] and Delta state the lowest [OR = 0.08; CI (0.02,0.25)]. Children in Bayelsa state face distinctly different risks of fever and diarrhoea [OR = 0.56; CI (0.34,0.95)]. Rivers state shows the highest incidence of diarrhoea in the South-South [OR = 10.50; CI (4.78,23.06)], while Akwa Ibom state has the lowest [OR = 0.30; CI (0.15,0.57)]. In the Southwest, Lagos and Osun states have similar risks of fever [OR = 1.00; CI (0.59,1.69)], with Ogun state experiencing the highest incidence [OR = 3.47; CI (2.28,5.28)] and Oyo state the lowest [OR = 0.18; CI (0.07,0.46)]. In Lagos state, the risks of fever and diarrhoea are comparable [OR = 0.96; CI (0.57,1.64)], and the risk of diarrhoea is similar to those in Ekiti, Ogun, and Ondo states. Oyo state has the highest diarrhoea rate in the Southwest [OR = 10.99; CI (3.81,31.67)], with Ogun state reporting the lowest [OR = 0.77; CI (0.42,1.42)]. Children of mothers with more than secondary education are significantly less likely to suffer ARI [OR = 0.35; CI (0.29,0.42)], whereas children of mothers without any education run a higher risk of diarrhoea [OR = 2.12; CI (1.89,2.38)] and fever [OR = 2.61; CI (2.34,2.91)]. Our analysis also indicated that household wealth quintile is a significant determinant of morbidity. The results in this paper could help the government and non-governmental agencies to focus and target intervention programs for ARI, diarrhoea and fever on the most vulnerable and risky under five groups and populations in Nigeria.

Chemsex Session Typologies and Associated Sociodemographic Factors in Sexual Minority Men: Latent Class Analysis From a Cultural Perspective Using a Cross-Sectional Survey.

Background: Chemsex prevalence is still not well known, and both the lack of homogeneity and cultural component of chemsex practices are usually overlooked. Objective: This study aims to estimate the proportion of sexual minority men (SMM) engaging in chemsex sessions, while understanding the cultural dimension of chemsex, and to analyze distinct session typologies with potential risk differences and the sociodemographic factors associated with engaging in them. Methods: A total of 5711 SMM residing throughout Spain participated in an anonymous web-based survey that assessed chemsex session engagement and characteristics, drug use, and sociodemographic variables. We measured the association of sociodemographic factors with engaging in chemsex sessions by calculating adjusted prevalence ratios, using multivariate Poisson regression analysis. Chemsex typologies were analyzed using latent class analysis, and sociodemographic factors were associated with the different risk classes. Results: Our results determined that 21.1% (1205/5711; 95% CI 20.0%-22.1%) of SMM engaged in chemsex sessions during their lifetime. Participating in sessions was significantly associated with being a migrant, not having a comfortable financial situation, openly living their sexuality, residing in bigger municipalities, older age, using steroids, and living with HIV (adjusted prevalence ratio: range 1.17-2.01; all P values <.05). Three typologies of sessions with different risks were identified with latent class analysis, with 23.2% of SMM engaging in sessions taking part in higher-risk ones, which was associated with younger age, using steroids, living in bigger municipalities, openly living their sexuality, and living with HIV, compared to SMM engaging in lower-risk sessions (odds ratio: range 2.75-4.99). Conclusions: Chemsex is relatively common among SMM in Spain, but it is important to differentiate typologies of sessions with varying risks, and the proportion of SMM engaging in high-risk sessions is low. Chemsex is highly associated with sociodemographic factors. Chemsex should be prioritized in public health programs, which should consider the different forms of sessions with their varying risks and prevalence, while also considering the cultural dimension inherent to chemsex.

Interactions between latent variables in count regression models.

In psychology and the social sciences, researchers often model count outcome variables accounting for latent predictors and their interactions. Even though neglecting measurement error in such count regression models (e.g., Poisson or negative binomial regression) can have unfavorable consequences like attenuation bias, such analyses are often carried out in the generalized linear model (GLM) framework using fallible covariates such as sum scores. An alternative is count regression models based on structural equation modeling, which allow to specify latent covariates and thereby account for measurement error. However, the issue of how and when to include interactions between latent covariates or between latent and manifest covariates is rarely discussed for count regression models. In this paper, we present a latent variable count regression model (LV-CRM) allowing for latent covariates as well as interactions among both latent and manifest covariates. We conducted three simulation studies, investigating the estimation accuracy of the LV-CRM and comparing it to GLM-based count regression models. Interestingly, we found that even in scenarios with high reliabilities, the regression coefficients from a GLM-based model can be severely biased. In contrast, even for moderate sample sizes, the LV-CRM provided virtually unbiased regression coefficients. Additionally, statistical inferences yielded mixed results for the GLM-based models (i.e., low coverage rates, but acceptable empirical detection rates), but were generally acceptable using the LV-CRM. We provide an applied example from clinical psychology illustrating how the LV-CRM framework can be used to model count regressions with latent interactions.

Quality Assessment of TikTok as a Source of Information About Mitral Valve Regurgitation in China: Cross-Sectional Study.

BACKGROUND: In China, mitral valve regurgitation (MR) is the most common cardiovascular valve disease. However, patients in China typically experience a high incidence of this condition, coupled with a low level of health knowledge and a relatively low rate of surgical treatment. TikTok hosts a vast amount of content related to diseases and health knowledge, providing viewers with access to relevant information. However, there has been no investigation or evaluation of the quality of videos specifically addressing MR. OBJECTIVE: This study aims to assess the quality of videos about MR on TikTok in China. METHODS: A cross-sectional study was conducted on the Chinese version of TikTok on September 9, 2023. The top 100 videos on MR were included and evaluated using quantitative scoring tools such as the modified DISCERN (mDISCERN), the Journal of the American Medical Association (JAMA) benchmark criteria, the Global Quality Score (GQS), and the Patient Education Materials Assessment Tool for Audio-Visual Content (PEMAT-A/V). Correlation and stepwise regression analyses were performed to examine the relationships between video quality and various characteristics. RESULTS: We obtained 88 valid video files, of which most (n=81, 92%) were uploaded by certified physicians, primarily cardiac surgeons, and cardiologists. News agencies/organizations and physicians had higher GQS scores compared with individuals (news agencies/organizations vs individuals, P=.001; physicians vs individuals, P=.03). Additionally, news agencies/organizations had higher PEMAT understandability scores than individuals (P=.01). Videos focused on disease knowledge scored higher in GQS (P<.001), PEMAT understandability (P<.001), and PEMAT actionability (P<.001) compared with videos covering surgical cases. PEMAT actionability scores were higher for outpatient cases compared with surgical cases (P<.001). Additionally, videos focused on surgical techniques had lower PEMAT actionability scores than those about disease knowledge (P=.04). The strongest correlations observed were between thumbs up and comments (r=0.92, P<.001), thumbs up and favorites (r=0.89, P<.001), thumbs up and shares (r=0.87, P<.001), comments and favorites (r=0.81, P<.001), comments and shares (r=0.87, P<.001), and favorites and shares (r=0.83, P<.001). Stepwise regression analysis identified "length (P<.001)," "content (P<.001)," and "physicians (P=.004)" as significant predictors of GQS. The final model (model 3) explained 50.1% of the variance in GQSs. The predictive equation for GQS is as follows: GQS = 3.230 - 0.294 × content - 0.274 × physicians + 0.005 × length. This model was statistically significant (P=.004) and showed no issues with multicollinearity or autocorrelation. CONCLUSIONS: Our study reveals that while most MR-related videos on TikTok were uploaded by certified physicians, ensuring professional and scientific content, the overall quality scores were suboptimal. Despite the educational value of these videos, the guidance provided was often insufficient. The predictive equation for GQS developed from our analysis offers valuable insights but should be applied with caution beyond the study context. It suggests that creators should focus on improving both the content and presentation of their videos to enhance the quality of health information shared on social media.

Exploring the relationship between blood platelet and other components utilizing count regression: A cross-sectional study in Bangladesh.

Background and Aims: Blood, vital for transporting nutrients and maintaining balance, comprises red blood cells, white blood cells, and platelets, each pivotal. Imbalances lead to issues-low red cells cause fatigue (anemia), high white cells hint at infection, low counts raise infection risks. Using trendy statistical approaches, investigating the complex link between platelet counts and numerous blood components. Our investigation, leveraging count regression approaches, revealed deep insights into the interaction between platelet counts and other important hematological markers. Methods: A cross-sectional study utilized data from 3120 individuals, including both male and female participants, who visited these hospitals between June 16, 2022 and December 17, 2022, to assess their blood samples through testing by using convenience non-parametric sampling framework. Platelet count was taken into account as a measure of outcome in this research. This specific study region was chosen for its easy accessibility, which helped the seamless execution of the data-gathering technique. Count regression, negative binomial regression, and quasi-Poisson regression techniques have been employed for examining relationship of the data sets. Results: Three different count regression models were utilized to assess the proper association between the response and the relevant covariates and we found negative binomial count regression model (Akaike information criterion = 76.55, Bayesian information criterion = 76.59, and deviance = 3.14) was providing comparatively better performance than others. Based on the chosen model we found white blood cell, erythrocyte sedimentation rate, and eosinophils are significant but neutrophil, monocyte, and lymphocyte are not significant. We have also gone through proper model adequacy checking for our selected model and we found enough evidence to justify our model. Conclusion: From the result, we found insightful remarks into the mechanisms involved in platelet production and regulation, which can aid in developing increased effective treatments and interventions to maintain optimal platelet levels and prevent health problems related to abnormal platelet counts.

Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi.

Recent research has established existence of a correlation between women's education and fertility, suggesting that they share similar risk factors. However, in many studies, the two variables were analysed separately, which could bias the conclusions by undermining the apparent correlations of such paired outcomes. In this article, the univariate and bivariate Poisson regression models were applied to nationally representative sample of 24,562 women from the 2015-16 Malawi demographic and health survey to examine the risk factors of women's education levels and fertility. The R software version 4.1.2 was used for the analyses. The results showed that estimates from the bivariate Poisson model were consistent with those obtained from the separate univariate Poisson models. The sizes of estimates of coefficients, their standard errors, p-values, and directions were comparable in both bivariate and univariate Poisson models. Using either the univariate or bivariate Poisson model, it was found that the age of a woman at first sexual experience, her current age, household wealth index, and contraceptive usage were significantly associated with both the woman's schooling and fertility. The study further revealed that ethnicity, religion, and region of residence impacted education level only and not fertility. Similarly, marital status and occupation impacted fertility only and not education. The study also found that higher education levels were linked to a lower number of children, with a strong negative correlation of -0.62 between the two variables. The study recommends using bivariate Poisson regression for analysing paired count response data, when there is an apparent covariance between the outcome variables. The results suggest that efforts by policymakers to achieve the desired women's sexual and reproductive health in sub-Saharan Africa should be intertwined with improving women's and girls' education attainment in the region.

Beyond Averages: Unpacking Disparities in School-Based Vaccination Coverage in Eastern Sydney: An Ecological Analysis.

School vaccination programs are crucial for achieving high immunisation coverage among adolescents, but substantial disparities exist across schools and regions. This ecological study aimed to determine associations between school characteristics and vaccination coverage for diphtheria-tetanus-acellular pertussis (dTpa) and human papillomavirus (HPV) vaccines among year 7 students in southeastern Sydney. An analysis of data from 70 mainstream schools participating in the 2019 South Eastern Sydney Local Health District School Vaccination Program utilised quasi-Poisson regression models to assess associations between vaccination coverage and school attendance, socio-educational status, Aboriginal enrolments, language background other than English (LBOTE), school sector (government, Catholic, or independent), and coeducation status. Median school coverage was 88% for dTpa, 88% for HPV-girls, and 86% for HPV-boys, with interquartile ranges of 82-93%, 84-92%, and 78-91%, respectively. Higher school attendance was associated with increased dTpa vaccination coverage (PR 1.14, 95% CI 1.02-1.27). Single-sex schools showed higher HPV vaccination coverage compared to coeducational schools for both girls (PR 2.24, 95% CI 2.04-2.46) and boys (PR 1.89, 95% CI 1.72-2.08). No significant associations were found for ICSEA, Aboriginal enrolments, LBOTE, or school sector. School attendance and coeducational status significantly influenced vaccination coverage, with differential impacts on dTpa and HPV vaccines. These findings highlight the need for targeted strategies to address disparities in school-based vaccination programs. Research using qualitative methods could be useful to understand the beliefs and attitudes contributing to these disparities in vaccine uptake so that programs can be tailored to maximise participation.

A simple Cox approach to estimating risk ratios without sharing individual-level data in multi-site studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

Use of Statistics in Plant Cell Tissue Culture.

Statistics and experimental design are important tools for plant cell and tissue culture researchers and should be used when planning and conducting experiments as well as during the analysis and interpretation of experimental results. The chapter provides basic concepts important to the statistical analysis of data obtained from plant tissue culture experiments and illustrates the application of common statistical procedures to analyze binomial, count, and continuous data for experiments with different treatment factors as well as identifying trends of dosage treatment factors.

Association between precipitation and mortality due to diarrheal diseases by climate zone: A multi-country modeling study.

Background: Precipitation could affect the transmission of diarrheal diseases. The diverse precipitation patterns across different climates might influence the degree of diarrheal risk from precipitation. This study determined the associations between precipitation and diarrheal mortality in tropical, temperate, and arid climate regions. Methods: Daily counts of diarrheal mortality and 28-day cumulative precipitation from 1997 to 2019 were analyzed across 29 locations in eight middle-income countries (Argentina, Brazil, Costa Rica, India, Peru, the Philippines, South Africa, and Thailand). A two-stage approach was employed: the first stage is conditional Poisson regression models for each location, and the second stage is meta-analysis for pooling location-specific coefficients by climate zone. Results: In tropical climates, higher precipitation increases the risk of diarrheal mortality. Under extremely wet conditions (95th percentile of 28-day cumulative precipitation), diarrheal mortality increased by 17.8% (95% confidence interval [CI] = 10.4%, 25.7%) compared with minimum-risk precipitation. For temperate and arid climates, diarrheal mortality increases in both dry and wet conditions. In extremely dry conditions (fifth percentile of 28-day cumulative precipitation), diarrheal mortality risk increases by 3.8% (95% CI = 1.2%, 6.5%) for temperate and 5.5% (95% CI = 1.0%, 10.2%) for arid climates. Similarly, under extremely wet conditions, diarrheal mortality risk increases by 2.5% (95% CI = -0.1%, 5.1%) for temperate and 4.1% (95% CI = 1.1%, 7.3%) for arid climates. Conclusions: Associations between precipitation and diarrheal mortality exhibit variations across different climate zones. It is crucial to consider climate-specific variations when generating global projections of future precipitation-related diarrheal mortality.

Estimated Incidence of Hospitalizations Attributable to RSV Infection Among Adults in Ontario, Canada, Between 2013 and 2019.

INTRODUCTION: Adult respiratory syncytial virus (RSV) burden is underestimated due to non-specific symptoms, limited standard-of-care and delayed testing, reduced diagnostic test sensitivity-particularly when using single diagnostic specimen-when compared to children, and variable test sensitivity based on the upper airway specimen source. We estimated RSV-attributable hospitalization incidence among adults aged ≥ 18 years in Ontario, Canada, using a retrospective time-series model-based approach. METHODS: The Institute for Clinical Evaluative Sciences data repository provided weekly numbers of hospitalizations (from 2013 to 2019) for respiratory, cardiovascular, and cardiorespiratory disorders. The number of hospitalizations attributable to RSV was estimated using a quasi-Poisson regression model that considered probable overdispersion and was based on periodic and aperiodic time trends and viral activity. As proxies for viral activity, weekly counts of RSV and influenza hospitalizations in children under 2 years and adults aged 60 years and over, respectively, were employed. Models were stratified by age and risk group. RESULTS: In patients ≥ 60 years, RSV-attributable incidence rates were high for cardiorespiratory hospitalizations (range [mean] in 2013-2019: 186-246 [215] per 100,000 person-years, 3‒4% of all cardiorespiratory hospitalizations), and subgroups including respiratory hospitalizations (144-192 [167] per 100,000 person-years, 5‒7% of all respiratory hospitalizations) and cardiovascular hospitalizations (95-126 [110] per 100,000 person-years, 2‒3% of all cardiovascular hospitalizations). RSV-attributable cardiorespiratory hospitalization incidence increased with age, from 14-18 [17] hospitalizations per 100,000 person-years (18-49 years) to 317-411 [362] per 100,000 person-years (≥ 75 years). CONCLUSIONS: Estimated RSV-attributable respiratory hospitalization incidence among people ≥ 60 years in Ontario, Canada, is comparable to other incidence estimates from high-income countries, including model-based and pooled prospective estimates. Recently introduced RSV vaccines could have a substantial public health impact.

Association of adverse childhood experiences with poor health condition among middle-aged and elderly adults in the United States: A nationally retrospective cohort study.

BACKGROUND: The specific effects of adverse childhood experiences (ACEs) in adulthood and senectitude were less known. We aim to examine the relationship between early ACEs and overall health condition as well as specific dimensions in the middle-aged and elderly population. METHODS: In the 2019-2021 Behavioral Risk Factor Surveillance System Study, robust Poisson regression models were used to estimate the relationship between ACE exposure and current health status among adults aged 45 ≥ years. RESULTS: Of the 195,472 participants, 53.8 % were female and the mean age was 65.0 years. Compared to populations without ACE, ACE exposures were more significantly associated with depression (PR: 2.03, 95 %CI: 1.94-2.21), frequent mental health (PR: 1.85, 95 %CI: 1.74-1.97) and subject cognitive decline (PR: 1.99, 95 %CI:1.85-2.14) than with physical health (PR: 1.37, 95 %CI: 1.32-1.44), with dose-response patterns. The association with mental disorder was especially significant among the elderly population. CONCLUSION: Early ACEs are associated with adverse health outcomes that persist into later life, particularly mental disorders and cognitive decline. Poor mental health may indirectly influence associations with ACEs and cognitive decline as well as physical health. Our findings emphasize the importance of lifelong psychological screening and support for the ACE-exposed middle-aged and elderly population.