Non-neurological organ dysfunction (NNOD) is a prevalent complication and contributes to poor outcome after traumatic brain injury (TBI). Contributing factors to NNOD may include initial TBI severity, but this relationship has not been rigorously studied. The objectives of this study were to describe the frequency and timing of NNOD after TBI, evaluate the association between NNOD and outcome (mortality and Glasgow Outcome Score-Extended [GOSE] at 6 months post-injury), and examine the relationship between multimodal markers of initial TBI severity and NNOD. We performed a secondary analysis of data from participants in both the ProTECT III clinical trial (progesterone vs. placebo in participants with moderate-to-severe TBI) and the embedded Bio-ProTECT blood biomarker study (N = 536 individuals). We reviewed laboratory and clinical data to determine the prevalence of NNOD in renal, hematological, hepatic, cardiovascular, and respiratory systems, based on the sequential organ failure assessment system. TBI severity was assessed using index Glasgow coma scale score (iGCS-first GCS post-primary resuscitation), Rotterdam computed tomography (CT) score, head-region abbreviated injury scale scores, and baseline TBI biomarkers (S100 calcium binding protein B [S100b], glial fibrillary acidic protein [GFAP], ubiquitin C-terminal hydrolase-L1 [UCHL1], and spectrin breakdown products [SBDP]). NNOD frequencies by organ system were 72% (respiratory), 52% (cardiovascular), 45% (hematological), 8% (renal), and 2% (hepatic). All TBI severity markers were positively correlated (using Spearman coefficients) with the number of systems in dysfunction. To examine effects of NNOD on outcome independent of TBI severity, we used logistic regression and adjusted for age, sex, iGCS, Rotterdam CT score, and biomarker load score (mean biomarker quartile), wherein each additional system of dysfunction resulted in a 1.30× higher odds of unfavorable GOSE (95% confidence interval [CI]: [1.01-1.67], p = 0.04). Stratification analyses revealed the relationship between greater NNOD and worse outcome was most pronounced among individuals with more severe Rotterdam CT and lower GCS scores. In conclusion, NNOD occurs frequently after moderate-to-severe TBI, is associated with higher odds of unfavorable GOSE at 6 months, and is positively associated with multimodal biomarkers of baseline TBI severity. This is the first study to demonstrate a relationship between TBI blood biomarker levels and NNOD. Future study is needed to determine mechanisms of NNOD and their relationships to subsequent neurological injury. While TBI research has historically focused on brain-centric measures and outcomes, this study builds on mounting evidence that non-neurological organ systems play an important role in injury response after TBI.

Traumatic brain injury (TBI) and subsequent post-traumatic epilepsy (PTE) often impair daily activities and mental health (MH), which contribute to long-term TBI-related disability. PTE also affects driving capacity, which impacts functional independence, community participation, and satisfaction with life (SWL). However, studies evaluating the collective impact of PTE on multidimensional outcomes are lacking. Thus, we generated a model to investigate how PTE after moderate-to-severe (ms)TBI affects TBI-associated impairments, limits activities and participation, and influences SWL. Of 5108 participants with msTBI enrolled into the National Institute for Disability, Independent Living, and Rehabilitation Research TBI Model Systems between 2010 and 2018 and with seizure-event data available at year-1 post-TBI, 1214 had complete outcome data and 1003 had complete covariate data used for analysis. We constructed a conceptual framework illustrating hypothesized interrelationships between year-1 PTE, driving status, functional independence measure (FIM), depression and anxiety, as well as year-2 participation, and SWL. We performed univariate and multivariable linear and logistic regressions. A covariate-adjusted structural equation model (SEM), using the lavaan package (R), assessed the conceptual framework's suitability in establishing PTE links with outcomes 1-2 years post-injury. Multiple parameters were evaluated to assess SEM fit. Year-1 PTE was correlated with year-1 FIM motor (standardized coefficient, beta(std) = -0.112, p = 0.007) and showed a trend level association with year-1 FIM cognition (beta(std) = -0.070, p = 0.079). Individuals with year-1 PTE were less likely to drive independently at year 1 (beta(std) = -0.148, p < 0.001). In addition, FIM motor (beta(std) = 0.323, p < 0.001), FIM cognition (beta(std) = 0.181, p = 0.012), and anxiety (beta(std) = -0.135, p = 0.024) influenced driving status. FIM cognition was associated with year-1 depression (beta(std) = 0.386, p < 0.001) and year-1 anxiety (beta(std) = 0.396, p < 0.001), whereas year-1 FIM motor (beta(std) = 0.186, p = 0.003), depression (beta(std) = -0.322, p = 0.011), and driving status (beta(std) = 0.233, p < 0.001) directly affected year-2 objective life participation metrics. Moreover, year-1 depression (beta(std) = -0.382, p = 0.001) and year-2 participation (beta(std) = 0.160, p < 0.001) had direct effects on year-2 SWL. SWL was influenced indirectly by year-1 variables, including functional impairment, anxiety, and driving status-factors that impacted year-2 participation directly or indirectly, and consequently year-2 SWL, forming a complex relationship with year-1 PTE. A sensitivity analysis SEM showed that the number of MH disorders was associated with participation and SWL (p < 0.001), and this combined MH variable was directly related to driving status (p < 0.02). Developing PTE during year-1 after msTBI affects multiple aspects of life. PTE effects extend to motor and cognitive abilities, driving capabilities, and indirectly, to life participation and overall SWL. The implications underscore the crucial need for effective PTE management strategies during the first year post-TBI to minimize the adverse impact on factors influencing multidimensional year-2 participation and SWL outcomes. Addressing transportation barriers is warranted to enhance the well-being of those with PTE and msTBI, emphasizing a holistic approach. Further research is recommended for SEM validation studies, including testing causal inference pathways that might inform future prevention and treatment trials.

Ranked set sampling (RSS) is a cost-efficient study design that uses inexpensive baseline ranking to select a more informative subset of individuals for full measurement. While RSS is well known to improve precision over simple random sampling (SRS) for uncensored outcomes, survival analysis under RSS has largely been limited to estimation of the Kaplan-Meier survival curve under random censoring. Consequently, many standard tools routinely used with SRS data, including log-rank and weighted log-rank tests, restricted mean survival time summaries, and window-based mean life measures, are not yet fully developed for RSS settings, particularly when ranking is imperfect and censoring is present. This work develops a unified survival analysis framework for balanced RSS designs that preserves efficiency gains while providing the inferential tools expected in applied practice. We formalize Kaplan-Meier and Nelson-Aalen estimators for right-censored data under both perfect and concomitant-based imperfect ranking and establish their large-sample properties using martingale and empirical process methods adapted to the rank-wise RSS structure. Rank-aware Greenwood-type variance estimators are proposed, and efficiency relative to SRS is evaluated through simulation studies varying set size, number of cycles, censoring proportion, and ranking quality. The framework is further extended to log-rank and Fleming-Harrington weighted tests, as well as restricted and window mean life functionals with asymptotic variance formulas and two-sample comparisons. An implementation plan with real-data illustrations is provided to facilitate practical use.

OBJECTIVE:Although traumatic brain injury (TBI) and post-traumatic epilepsy (PTE) are common, there are no prospective models quantifying individual epilepsy risk after moderate-to-severe TBI (msTBI). We generated parsimonious prediction models to quantify individual epilepsy risk between acute inpatient rehabilitation for individuals 2 years after msTBI. METHODS:We used data from 6089 prospectively enrolled participants (≥16 years) in the TBI Model Systems National Database. Of these, 4126 individuals had complete seizure data collected over a 2-year period post-injury. We performed a case-complete analysis to generate multiple prediction models using least absolute shrinkage and selection operator logistic regression. Baseline predictors were used to assess 2-year seizure risk (Model 1). Then a 2-year seizure risk was assessed excluding the acute care variables (Model 2). In addition, we generated prognostic models predicting new/recurrent seizures during Year 2 post-msTBI (Model 3) and predicting new seizures only during Year 2 (Model 4). We assessed model sensitivity when keeping specificity ≥.60, area under the receiver-operating characteristic curve (AUROC), and AUROC model performance through 5-fold cross-validation (CV). RESULTS:Model 1 (73.8% men, 44.1 ± 19.7 years, 76.1% moderate TBI) had a model sensitivity = 76.00% and average AUROC = .73 ± .02 in 5-fold CV. Model 2 had a model sensitivity = 72.16% and average AUROC = .70 ± .02 in 5-fold CV. Model 3 had a sensitivity = 86.63% and average AUROC = .84 ± .03 in 5-fold CV. Model 4 had a sensitivity = 73.68% and average AUROC = .67 ± .03 in 5-fold CV. Cranial surgeries, acute care seizures, intracranial fragments, and traumatic hemorrhages were consistent predictors across all models. Demographic and mental health variables contributed to some models. Simulated, clinical examples model individual PTE predictions. SIGNIFICANCE:Using information available, acute-care, and year-1 post-injury data, parsimonious quantitative epilepsy prediction models following msTBI may facilitate timely evidence-based PTE prognostication within a 2-year period. We developed interactive web-based tools for testing prediction model external validity among independent cohorts. Individualized PTE risk may inform clinical trial development/design and clinical decision support tools for this population.

The prevalence of low birth weight (LBW) is an important indicator of child health and wellbeing. However, in many countries, decisions regarding care and treatment are often based on mothers’ perceptions of their children’s birth size due to a lack of objective birth weight data. Additionally, birth weight data that is self-reported or recorded often encounters the issue of heaping. This study assesses the concordance between the perceived birth size and the reported or recorded birth weight. We also investigate how the presence of heaped birth weight data affects this concordance, as well as the relationship between concordance and various sociodemographic factors. We examined 4,641 birth records reported in the 2019 Bangladesh Multiple Indicator Cluster Survey. The sensitivity-specificity analysis was performed to assess perceived birth size’s ability to predict LBW, while Cohen’s Kappa statistic assessed reliability. We used the kernel smoothing technique to correct heaping of birth weight data, as well as a multivariable multinomial logistic model to assess factors associated with concordance. Maternally-perceived birth size exhibited a low sensitivity (63.5

Mental health disorders are responsible for 125.3 million disability-adjusted life years (DALYs) lost globally, with depression in adolescents rising faster than in adults. In total, more than 90% of the 1.2 billion adolescents in the world live in low- and middle-income countries (LMICs). Despite the rise in teenage marriage and pregnancy in LMICs, there is a paucity of research on the role of pregnancy as a risk factor for adolescent mental health, and the role of spousal connectedness as a potential protective factor. This study aims to address this gap. A total of 2408 currently married adolescent (aged 15–19 years) women from the Bangladesh Adolescent Health and Wellbeing Survey 2019–2020 were analysed. Multivariable models were used to assess the factors associated with depression symptoms and major depressive disorder (MDD). The prevalence of moderate/severe depressive symptoms or MDD among adolescents was 14.6%, well ahead of south Asian levels. The odds of having moderate/severe depressive symptoms (adjusted odds ratio [AOR]=1.94, 95% confidence interval [CI] 1.37–2.76) or MDD (AOR=1.63, 95%CI 1.18–2.25) were higher in pregnant adolescent women than in non-pregnant adolescent women. A closer relationship with one’s husband reduced the risk of developing moderate/severe depressive symptoms (AOR=0.90, 95% CI 0.84–0.96) or MDD (AOR=0.91, 95% CI 0.86–0.97). Pregnancy and connectedness had no statistically significant interaction effect on depression. There is an urgent need for affordable and scalable interventions to reduce the risk of mental health morbidity in pregnant adolescent women especially in low- and middle-income countries.

Coastal morphology makes Bangladesh vulnerable to environmental hazards and climate change. Therefore, environmental characteristics may shape population health, including child health. The prevalence of stunting among under-five aged (U5) children is high in Bangladesh. However, there is a lack of research on environmental predictors of stunting. This study aimed to assess the association between community-level environmental characteristics and stunting using pooled data from the three latest Bangladesh demographic and health surveys (BDHS). According to the multilevel model, rainfall, distance to protected areas, and vegetation index showed a nonlinear association with stunting. The temperature was inversely, and distance to water bodies was positively related to stunting. Overall, results evidence the environmental characteristics are predictive of stunting, and these characteristics should be taken into account during intervention design to minimise the negative effects of environmental change on child health. Further research is also necessary to comprehend the causal pathways between environmental characteristics and stunting in Bangladesh.

•Several sociodemographic factors predict married women's overweight and obesity.•Particular groups based on these factors are most prone to overweight and obesity.•These may help design and implement targeted interventions.

Infant and young child feeding (IYCF) is crucial for the growth, development, and survival of a child. This study aims to examine the factors associated with IYCF practices among the under-2 aged children in Bangladesh and spatial variability. A total of 2336 children aged 6-23 months were selected from the 2014 Bangladesh Demographic and Health Survey (BDHS). A multi-level logistic regression was used to determine the association between socio-demographic and health-related factors accounting for the cluster effects. An adaptive bandwidth kernel density estimator (KDE) was used to investigate the spatial variability of IYCF practices in Bangladesh. The prevalence of minimum dietary diversity (MDD), minimum meal frequency (MMF), and minimum acceptable diet (MAD) were 29%, 47%, and 19% respectively, where the suboptimal IYCF practice was 96% among them. The study findings show that higher maternal age at childbirth and maternal education level are positively associated with MDD, MMF, and MAD. The likelihood of MMF and MAD was higher for children whose mothers had the decision-making abilities (adjusted odds ratio, [AOR] 1.23 and 1.26, respectively) and had exposure to media (AOR 1.43 and 1.38, respectively). Four or more antenatal care (ANC) visits positively associated with the MDD (AOR 1.47; 95% confidence interval [CI] 1.07-2.02) and ICFI (AOR 3.06; 95% CI 1.24-7.54). The heatmap shows Northern parts of Bangladesh performed better than South-West parts in child feeding practices. Overall, age-appropriate IYCF practices are not satisfactory, and many maternal characteristics contribute significantly to this. Geospatial inequalities are also notable across Bangladesh. The current scenario reinforces the need for programmes and interventions that address the spectrum of IYCF practices and help reduce spatial disparities.

The only accurate snapshot we had of Bangladesh's demographics at the point of the nation's birth was already almost a decade old: the census of 1961. The turmoil of the coming years meant that the next census would not take place until well after the Liberation War, in 1974. The picture in 1974 in the census was grim: Bangladesh was among the poorest nations in the world, with poor capacity to spend where it needed the most, on building its health infrastructure. With per capita income of roughly $US144 (in 1985 dollars, according to World Bank figures) and a population density of around 1400 per square mile (i.e. 2.6 square kilometres) and 90% of the nation's economy propped up by a system of subsistence agriculture, survival rather than health was the question occupying the minds of administrators. Half the nation was undernourished, life expectancy was estimated at 40 for men and 45 for women (World Bank figures), and 15% of Bangladeshi children died in their first five years (Quddus & Becker, 2000). This chapter shows how far Bangladesh has come in just five decades from this grim beginning.

Childhood stunting prevalence varies across small geographic areas, which may reflect social and built environmental characteristics. Though literacy is a known predictor of stunting, small area research on adult literacy shaping childhood stunting prevalence has not been investigated for Bangladesh. This study examines small area variation in childhood stunting prevalence and estimates the association between small area stunting prevalence and adult literacy rate controlling for other covariates. Data on stunting prevalence, adult literacy rates and other covariates were extracted for 478 subdistricts of Bangladesh. Spatial regression models were used to estimate the relationship between adult literacy rates and childhood stunting prevalence at the subdistrict-level. Stunting prevalence substantially varied across subdistricts in Bangladesh. Subdistrict-level stunting prevalence was inversely associated with the subdistrict-level adult literacy rate. Furthermore, the estimated effect for the female literacy rate was slightly more than the male literacy rate. Among other covariates, higher levels of people without access to a sanitary toilet was linked to greater stunting. The findings of small-area variation in stunting prevalence and its associated area-level compositional and contextual features could be useful for planning and targeting the geographic area-specific interventions to alleviate the extent of childhood stunting in Bangladesh. This study may also help policymakers allocate resources in geographical hotspots of higher stunting prevalence. An integrated approach to improving adult literacy and increasing sanitation coverage through multisectoral collaboration can help reduce the childhood stunting burden in Bangladesh.

Objective: Characterize relationships among substance misuse, depression, employment, and suicidal ideation (SI) following moderate to severe traumatic brain injury (TBI). Design: Prospective cohort study. Setting: Inpatient rehabilitation centers with telephone follow-up; level I/II trauma centers in the United States. Participants: Individuals with moderate to severe TBI with data in both the National Trauma Data Bank and the Traumatic Brain Injury Model Systems National Database, aged 18 to 59 years, with SI data at year 1 or year 2 postinjury (N = 1377). Main Outcome Measure: Primary outcome of SI, with secondary employment, substance misuse, and depression outcomes at years 1 and 2 postinjury. Results: Cross-lagged structural equation modeling analysis showed that year 1 unemployment and substance misuse were associated with a higher prevalence of year 1 depression. Depression was associated with concurrent SI at years 1 and 2. Older adults and women had a greater likelihood of year 1 depression. More severe overall injury (injury severity score) was associated with a greater likelihood of year 1 SI, and year 1 SI was associated with a greater likelihood of year 2 SI. Conclusions: Substance misuse, unemployment, depression, and greater extracranial injury burden independently contributed to year 1 SI; in turn, year 1 SI and year 2 depression contributed to year 2 SI. Older age and female sex were associated with year 1 depression. Understanding and mitigating these risk factors are crucial for effectively managing post-TBI SI to prevent postinjury suicide.

Traumatic brain injury (TBI) induces immune dysfunction that can be captured clinically by an increase in the neutrophil-to-lymphocyte ratio (NLR). However, few studies have characterized the temporal dynamics of NLR post-TBI and its relationship with hospital-acquired infections (HAI), resource utilization, or outcome. We assessed NLR and HAI over the first 21 days post-injury in adults with moderate-to-severe TBI (n = 196) using group-based trajectory (TRAJ), changepoint, and mixed-effects multivariable regression analysis to characterize temporal dynamics. We identified two groups with unique NLR profiles: a high (n = 67) versus a low (n = 129) TRAJ group. High NLR TRAJ had higher rates (76.12% vs. 55.04%, p = 0.004) and earlier time to infection (p = 0.003). In changepoint-derived day 0–5 and 6–20 epochs, low lymphocyte TRAJ, early in recovery, resulted in more frequent HAIs (p = 0.042), subsequently increasing later NLR levels (p ≤ 0.0001). Both high NLR TRAJ and HAIs increased hospital length of stay (LOS) and days on ventilation (p ≤ 0.05 all), while only high NLR TRAJ significantly increased odds of unfavorable six-month outcome as measured by the Glasgow Outcome Scale (GOS) (p = 0.046) in multivariable regression. These findings provide insight into the temporal dynamics and interrelatedness of immune factors which collectively impact susceptibility to infection and greater hospital resource utilization, as well as influence recovery.

BACKGROUND:Inflammatory cascades following traumatic brain injury (TBI) can have both beneficial and detrimental effects on recovery. Single biomarker studies do not adequately reflect the major arms of immunity and their relationships to long-term outcomes. Thus, we applied treelet transform (TT) analysis to identify clusters of interrelated inflammatory markers reflecting major components of systemic immune function for which substantial variation exists among individuals with moderate-to-severe TBI. METHODS:Serial blood samples from 221 adults with moderate-to-severe TBI were collected over 1-6 months post-injury (n = 607 samples). Samples were assayed for 33 inflammatory markers using Millipore multiplex technology. TT was applied to standardized mean biomarker values generated to identify latent patterns of correlated markers. Treelet clusters (TC) were characterized by biomarkers related to adaptive immunity (TC1), innate immunity (TC2), soluble molecules (TC3), allergy immunity (TC4), and chemokines (TC5). For each TC, a score was generated as the linear combination of standardized biomarker concentrations and cluster load for each individual in the cohort. Ordinal logistic or linear regression was used to test associations between TC scores and 6- and 12-month Glasgow Outcome Scale (GOS), Disability Rating Scale (DRS), and covariates. RESULTS:When adjusting for clinical covariates, TC5 was significantly associated with 6-month GOS (odds ratio, OR = 1.44; p-value, p = 0.025) and 6-month DRS scores (OR = 1.46; p = 0.013). TC5 relationships were attenuated when including all TC scores in the model (GOS: OR = 1.29, p = 0.163; DRS: OR = 1.33, p = 0.100). When adjusting for all TC scores and covariates, only TC3 was associated with 6- and 12-month GOS (OR = 1.32, p = 0.041; OR = 1.39, p = 0.002) and also 6- and 12-month DRS (OR = 1.38, p = 0.016; OR = 1.58, p = 0.0002). When applying TT to inflammation markers significantly associated with 6-month GOS, multivariate modeling confirmed that TC3 remained significantly associated with GOS. Biomarker cluster membership remained consistent between the GOS-specific dendrogram and overall dendrogram. CONCLUSIONS:TT effectively characterized chronic, systemic immunity among a cohort of individuals with moderate-to-severe TBI. We posit that chronic chemokine levels are effector molecules propagating cellular immune dysfunction, while chronic soluble receptors are inflammatory damage readouts perpetuated, in part, by persistent dysfunctional cellular immunity to impact neuro-recovery.