The Prevalence and Pattern of Admission Mortality among Acute Stroke Patients Managed at a Tertiary Hospital in Abakaliki, Nigeria: A Retrospective Study.

BACKGROUND: Stroke ranks as the second leading cause of mortality worldwide, following ischemic heart disease, and is expected to maintain this position through 2030. This neurological ailment is profoundly impactful, imposing a significant burden on health and the economy. In 2019 alone, it was responsible for 6.6 million fatalities and the loss of 143 million disability-adjusted life years (DALYs) across the globe. OBJECTIVES: This study highlighted the prevalence and pattern of admission mortality among acute stroke patients managed over 9 years in a private tertiary hospital in Abakaliki, Nigeria. METHODOLOGY: This was a retrospective hospital-based study conducted at a tertiary hospital in Abakaliki, Nigeria from January 2014 to December 2022. Relevant data were extracted from the patients' case notes and the sociodemographic, clinical and laboratory parameters of acute stroke survivors were compared with those of their dead counterparts. RESULTS: Out of the 172 (males - 57%; females - 43%) patients that fulfilled the inclusion criteria, 53 (30.81%) had haemorrhagic stroke while 119 (69.19%) had ischaemic stroke. The overall admission mortality rate was 15.12%, and it was more common in patients with haemorrhagic stroke, advancing age, severe hypertension, severe stroke, impairment of consciousness, renal dysfunction, hypernatremia, neutrophilic leucocytosis, and short admission duration. CONCLUSIONS: High mortality rates are linked to acute stroke admissions, particularly in cases involving haemorrhagic stroke, increasing age, severe hypertension, substantial stroke severity, impaired consciousness, renal dysfunction, hypernatremia, neutrophilic leukocytosis, and brief admission duration.

CONTEXTE: L'accident vasculaire cérébral (AVC) est la deuxième cause de mortalité dans le monde, après la cardiopathie ischémique, et devrait conserver cette position jusqu'en 2030. Cette affection neurologique a un impact profond, imposant une charge significative sur la santé et l'économie. En 2019 seulement, elle a été responsable de 6,6 millions de décès et de la perte de 143 millions d'années de vie ajustées sur l'incapacité (DALYs) dans le monde. OBJECTIFS: Cette étude a mis en évidence la prévalence et le modèle de la mortalité à l'admission chez les patients victimes d'AVC aigu gérés pendant 9 ans dans un hôpital privé tertiaire à Abakaliki, Nigéria. MÉTHODOLOGIE: Il s'agissait d'une étude rétrospective en milieu hospitalier menée dans un hôpital tertiaire à Abakaliki, Nigéria, de janvier 2014 à décembre 2022. Les données pertinentes ont été extraites des dossiers des patients et les paramètres sociodémographiques, cliniques et de laboratoire des survivants d'un AVC aigu ont été comparés à ceux de leurs homologues décédés. RÉSULTATS: Parmi les 172 patients (hommes - 57 % ; femmes - 43 %) qui remplissaient les critères d'inclusion, 53 (30,81 %) avaient un AVC hémorragique tandis que 119 (69,19 %) avaient un AVC ischémique. Le taux global de mortalité à l'admission était de 15,12 % et était plus fréquent chez les patients ayant un AVC hémorragique, un âge avancé, une hypertension sévère, un AVC sévère, une altération de la conscience, une dysfoncti on rénal e, une hyper natrémie, une l eucocytose neutrophilique et une courte durée d'admission. CONCLUSIONS: Les taux de mortalité élevés sont liés aux admissions pour AVC aigu, en particulier dans les cas d'AVC hémorragique, d'âge avancé, d'hypertension sévère, de sévérité importante de l'AVC, d'altération de la conscience, de dysfonction rénale, d'hypernatrémie, de leucocytose neutrophilique et de courte durée d'admission. MOTS-CLÉS: Facteurs de risque, Mortalité à l'admission, AVC aigu, Étude rétrospective.

Mapping complex public health problems with causal loop diagrams.

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

Multiple forms of perceived job discrimination and hypertension risk among employed women: Findings from the Sister Study.

BACKGROUND: Hypertension has been linked to socially patterned stressors, including discrimination. Few studies have quantified the risk of hypertension associated with exposure to perceived job discrimination. METHODS: We used prospective cohort data from the Sister Study (enrollment from 2003-2009) to estimate self-reported incident hypertension associated with perceived job discrimination based on race, gender, age, sexual orientation, or health status. Job discrimination in the prior 5 years was assessed in 2008-2012, and incident doctor-diagnosed hypertension was ascertained in previously hypertension-free participants. RESULTS: Among the 16,770 eligible participants aged 37-78 years at the start of follow-up, 10.5% reported job discrimination in the past 5 years, and 19.2% (n = 3226) reported incident hypertension during a median follow-up of 9.7 years (interquartile range 8.2-11.0 years). Self-reported poor health or inclusion in minoritized groups based on race/ethnicity or sexual orientation were more frequent among those reporting job discrimination. In a Cox proportional hazards model adjusting for covariates, report of at least one type of job discrimination (compared to none) was associated with a 14% (hazard ratio = 1.14 [95% confidence: 1.02-1.27]) higher hypertension risk. Results from sensitivity analyses reinforced the findings. CONCLUSIONS: Results suggest that interventions addressing job discrimination could have workplace equity and health benefits.

Effects of hypertension and use of antihypertensive drugs in pregnancy on the risks of childhood cancers in Taiwan.

BACKGROUND: Childhood cancers are associated with high mortality and morbidity, and some maternal prescription drug use during pregnancy has been implicated in cancer risk. There are few studies on the effects of hypertension, preeclampsia, and the use of antihypertensives in pregnancy on children's cancer risks. OBJECTIVE: This population-based cohort study analyzed the relationship between hypertension, preeclampsia, and antihypertensives taken during pregnancy and the risks of childhood cancers in the offspring. METHODS: Data on all children born in Taiwan between 2004 and 2015 (N = 2,294,292) were obtained from the Maternal and Child Health Database. This registry was linked with the National Health Insurance Database and Cancer Registry to get the records of maternal use of diuretics or other antihypertensives in pregnancy and records of children with cancer diagnosed before 13 years. We used Cox proportional hazard modeling to estimate the influence of maternal health conditions and antihypertensive drug exposure on the risks of developing childhood cancers. RESULTS: Offspring of mothers with hypertension (chronic or gestational) had a higher risk of acute lymphocytic lymphoma [hazard ratio (HR) = 1.87, 95% Confidence Interval (CI) 1.32 - 2.65] and non-Hodgkin's lymphoma (HR = 1.96, 95% CI 1.34 - 2.86). We estimated only a weak increased cancer risk in children whose mothers used diuretics (HR = 1.16, 95% CI 0.77 - 1.74) or used antihypertensives other than diuretics (HR = 1.15, 95% CI 0.86 - 1.54) before birth. CONCLUSIONS: In this cohort study, children whose mothers had chronic and gestational hypertension had an increased risk of developing childhood cancer.

Associations of U.S. state-level COVID-19 policies intensity with cannabis sharing behaviors in 2020.

BACKGROUND: Cannabis use before the COVID-19 pandemic for many involved sharing prepared cannabis for inhalation, practices that were less prevalent during the pandemic. State-level COVID-19 containment policies may have influenced this decrease. This study examined the extent to which the intensity of state-level COVID-19 policies were associated with individual-level cannabis sharing. Findings have the potential to guide harm reduction policies for future respiratory pandemics and seasonal respiratory virus waves. METHODS: This study used cross-sectional individual-level data from the COVID-19 Cannabis Study, an anonymous U.S.-based web survey on cannabis use disseminated during the early phase of the pandemic (Full sample N = 1,883). We combined individual-level data with state-level policy data from Kaiser Family Foundation's State COVID-19 Data and Policy Actions for three time-points from June to August 2020 that overlapped with the survey period. Cannabis sharing was dichotomized as any versus no sharing. We adapted a previously published coding framework to score the intensity of COVID-19 policies implemented in each U.S. state and averaged the policy score across the time period. We then used Poisson regression models to quantify the associations of the average state-level COVID-19 policy score with cannabis sharing during the pandemic. RESULTS: Participants (n = 925) reporting using inhalation as a mode for cannabis use were included in this analysis. Most respondents were male (64.1%), non-Hispanic White (54.3%), with a mean age of 33.7 years (SD 8.8). A large proportion (74.9%) reported sharing cannabis during the pandemic. Those who shared cannabis more commonly lived in states with a lower average policy score (16.7, IQR 12.3-21.5) compared to those who did not share (18.6, IQR 15.3-25.3). In adjusted models, the prevalence ratio of any cannabis sharing per every 5-unit increase in the average COVID-19 policy score was 0.97 (95% CI 0.93, 1.01). CONCLUSIONS: Fewer individuals shared cannabis in states with more intense COVID-19 containment policies compared to those in states with less intense policies. Individuals who use cannabis may be willing to make changes to their behavior and may further benefit from specific and directed public health messaging to avoid sharing during respiratory infection outbreaks.

Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study.

BACKGROUND: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. OBJECTIVE: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. METHODS: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. RESULTS: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths. CONCLUSIONS: The study's results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups.

Pregnancy Outcome Following Treatment of Premalignant Lesion of the Cervix in Southeast Nigeria; A Retrospective Case-Control Study.

BACKGROUND: Treatment of cervical pre-cancers involves ablative and excisional therapies, and these have the potential to affect future pregnancy outcomes of women. Understanding the impact of the various treatment modalities on the outcome of pregnancies can motivate the development of interventions to improve pregnancy outcomes in women who had cervical pre-cancer treatment. AIM: We aimed to the effect of cervical pre-cancer treatment on second-trimester miscarriages and preterm births. MATERIALS AND METHODS: Matched case-control study in which 373 women who had a pregnancy after cervical pre-cancer treatment were matched with 373 controls. McNemar Chi-square was used to compare the prevalence of second-trimester miscarriage and preterm birth between the study group and the matched controls. Conditional logistic regression analysis was done to determine the risk factors for second-trimester miscarriage and preterm birth. RESULTS: Second-trimester miscarriages and preterm births were higher in women who had cervical pre-cancer treatment (AOR: 2.05, 95% CI: 1.174 - 3.693, p: 0.01) and (AOR: 2.74, 95% CI: 1.591 - 4.902, p: 0.0001) respectively. In addition, large loop excision of the transformation zone (LLETZ) of the cervix increased the odds of second-trimester miscarriage (AOR: 1.22, 95% CI: 1.034 - 1.441, p: 0.019) and preterm birth (AOR: 2.98, 95% CI: 1.793 - 3.965, p: 0.001). Cryotherapy and thermocoagulation were not associated with increased miscarriage and preterm birth. Treatment to pregnancy interval of ≥ 12 months decreased the odds of second-trimester miscarriage (AOR: 0.605, 95% CI: 0.502 - 0.808, p: 0.031) and preterm birth (AOR: 0.484, 95% CI: 0.317 - 0.738, p: 0.001). CONCLUSION: There is an increased odds of second-trimester miscarriage and preterm birth in women treated with LLETZ. A treatment-to-pregnancy interval of ≥ 12 months reduces this odd. Ablative therapies do not increase the odds of miscarriages and preterm births. This information could guide decisions on the choice of method of treatment for cervical pre-cancer in women of childbearing age.

The impact of maternal pertussis vaccination recommendation on infant pertussis incidence and mortality in the USA: an interrupted time series analysis.

BACKGROUND: Pertussis is a contagious respiratory disease. Maternal tetanus-diphtheria-acellular pertussis vaccination during pregnancy has been recommended by the United States Centres for Disease Control (US CDC) Advisory Committee on Immunization Practices (ACIP) for unvaccinated pregnant women since October 2011 to prevent infection among infants; in 2012, ACIP extended this recommendation to every pregnancy, regardless of previous vaccination status. The population-level effect of these recommendations on infant pertussis is unknown. This study aimed to examine the impact of the 2011/2012 ACIP pertussis recommendation on pertussis incidence and mortality among US infants. METHODS: We used monthly data on pertussis deaths among infants aged <1 year between January 2005 and December 2017 in the CDC Death Data and yearly infant pertussis incidence data from the CDC National Notifiable Disease Surveillance System to perform an interrupted time series analysis, accounting for the passage of the Affordable Care Act. RESULTS: This study included 156 months of data. A potential decline in trend in infant pertussis incidence was noted during the post-recommendations period. No appreciable differences in trend were found in population-level infant pertussis mortality after the guideline changes in both adjusted and unadjusted models. Results were similar for all mortality sensitivity analyses. CONCLUSIONS: The 2011/2012 ACIP maternal pertussis vaccination recommendations were not associated with a population-level change in the trend in mortality, but were potentially associated with a decrease in incidence in the USA between 2005 and 2017.

A prediction model for classifying maternal pregnancy smoking using California state birth certificate information.

BACKGROUND: Systematically recorded smoking data are not always available in vital statistics records, and even when available it can underestimate true smoking rates. OBJECTIVE: To develop a prediction model for maternal tobacco smoking in late pregnancy based on birth certificate information using a combination of self- or provider-reported smoking and biomarkers (smoking metabolites) in neonatal blood spots as the alloyed gold standard. METHODS: We designed a case-control study where childhood cancer cases were identified from the California Cancer Registry and controls were from the California birth rolls between 1983 and 2011 who were cancer-free by the age of six. In this analysis, we included 894 control participants and performed high-resolution metabolomics analyses in their neonatal dried blood spots, where we extracted cotinine [mass-to-charge ratio (m/z) = 177.1023] and hydroxycotinine (m/z = 193.0973). Potential predictors of smoking were selected from California birth certificates. Logistic regression with stepwise backward selection was used to build a prediction model. Model performance was evaluated in a training sample, a bootstrapped sample, and an external validation sample. RESULTS: Out of seven predictor variables entered into the logistic model, five were selected by the stepwise procedure: maternal race/ethnicity, maternal education, child's birth year, parity, and child's birth weight. We calculated an overall discrimination accuracy of 0.72 and an area under the receiver operating characteristic curve (AUC) of 0.81 (95% confidence interval [CI] 0.77, 0.84) in the training set. Similar accuracies were achieved in the internal (AUC 0.81, 95% CI 0.77, 0.84) and external (AUC 0.69, 95% CI 0.64, 0.74) validation sets. CONCLUSIONS: This easy-to-apply model may benefit future birth registry-based studies when there is missing maternal smoking information; however, some smoking status misclassification remains a concern when only variables from the birth certificate are used to predict maternal smoking.

Risk of Severe Maternal Morbidity Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection During Pregnancy.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy increases the risk of adverse fetal and neonatal outcomes, but the contribution to severe maternal morbidity (SMM) has been less frequently documented. Methods: We conducted a national cohort study of 93 624 deliveries occurring between 11 March 2020 and 1 July 2021 using medical claims information from the OptumLabs Data Warehouse. SARS-CoV-2 infection was identified from diagnostic and laboratory testing claims records. We identified 21 SMM conditions using International Classification of Diseases, Tenth Revision, Clinical Modification and procedure codes and compared SMM conditions by SARS-CoV-2 status using Poisson regression with robust variance, adjusting for maternal sociodemographic and health factors, onset of labor, and week of conception. Results: Approximately 5% of deliveries had a record of SARS-CoV-2 infection: 27.0% <7 days before delivery, 13.5% within 7-30 days of delivery, and 59.5% earlier in pregnancy. Compared to uninfected pregnancies, the adjusted risk of SMM was 2.22 times higher (95% confidence interval [CI], 1.97-2.48) among those infected <7 days before delivery and 1.66 times higher (95% CI, 1.23-2.08) among those infected 7-30 days before delivery. The highest risks were observed for acute respiratory distress syndrome (adjusted risk ratio [aRR], 13.24 [95% CI, 12.86-13.61]) and acute renal failure (aRR, 3.91 [95% CI, 3.32-4.50]). Conclusions: COVID-19 is associated with increased rates of SMM.

Investigation of the structure and magnitude of time-varying uncontrolled confounding in simulated cohort data analyzed using g-computation.

BACKGROUND: When estimating the effect of time-varying exposures on longer-term outcomes, the assumption of conditional exchangeability or no uncontrolled confounding extends beyond baseline confounding to include time-varying confounding. We illustrate the structures and magnitude of uncontrolled time-varying confounding in exposure effect estimates obtained from g-computation when sequential conditional exchangeability is violated. METHODS: We used directed acyclic graphs (DAGs) to depict time-varying uncontrolled confounding. We performed simulations and used g-computation to quantify the effects of each time-varying exposure for each DAG type. Models adjusting all time-varying confounders were considered the true (bias-adjusted) estimate. The exclusion of time-varying uncontrolled confounders represented the biased effect estimate and an unmet 'no uncontrolled confounding' assumption. True and biased estimates were compared across DAGs, with different magnitudes of uncontrolled confounding. RESULTS: Time-varying uncontrolled confounding can present in several scenarios, including relationships into subsequently measured exposure(s), outcome, unmeasured confounder(s) and other measured confounder(s). In simulations, effect estimates obtained from g-computation were more biased in DAGs when the uncontrolled confounders were directly related to the outcome. Complex DAGs that included relationships between uncontrolled confounders and other variables and relationships where exposures caused uncontrolled confounders at the next time point resulted in the most biased effect estimates. In these complex DAGs, excluding uncontrolled confounders affected the multiple effect estimates. CONCLUSIONS: Time-varying uncontrolled confounding has the potential to substantially impact observed effect estimates. Given the importance of longitudinal studies in advising public health, the impact of time-varying uncontrolled confounding warrants more recognition and evaluation using quantitative bias analysis.

Associations of U.S. state-level COVID-19 policies intensity with cannabis sharing behaviors in 2020.

Background: Cannabis use before the COVID-19 pandemic for many involved sharing prepared cannabis for inhalation, practices that were less prevalent during the pandemic. State-level COVID-19 containment policies may have influenced this decrease. This study examined the extent to which the intensity of state-level COVID-19 policies were associated with individual-level cannabis sharing. Findings have the potential to guide harm reduction policies for future respiratory pandemics and seasonal respiratory virus waves. Methods: This study used cross-sectional individual-level data from the COVID-19 Cannabis Study, an anonymous U.S.-based web survey on cannabis use disseminated during the early phase of the pandemic (Full sample N = 1,883). We combined individual-level data with state-level policy data from Kaiser Family Foundation's State COVID-19 Data and Policy Actions for three time-points from June to August 2020 that overlapped with the survey period. Cannabis sharing was dichotomized as any versus no sharing. We adapted a previously published coding framework to score the intensity of COVID-19 policies implemented in each U.S. state and averaged the policy score across the time period. We then used logistic regression models to quantify the associations of the average state-level COVID-19 policy score with cannabis sharing during the pandemic. Results: Participants (n = 975) reporting using inhalation as a mode for cannabis use were included in this analysis. Most respondents were male (64.1%), non-Hispanic White (54.3%), with a mean age of 33.7 years (SD 8.8). A large proportion (75.1%) reported sharing cannabis during the pandemic. Those who shared cannabis more commonly lived in states with a lower average policy score (15.3, IQR 11.3-19.0) compared to those who did not share (16.3, IQR 13.7-22.7). In adjusted models, the odds of any cannabis sharing per every 5-unit increase in the average COVID-19 policy score were 0.78 (95% CI 0.58, 1.04). Conclusions: Fewer individuals shared cannabis in states with more intense COVID-19 containment policies compared to those in states with less intense policies. Individuals who use cannabis may be willing to make changes to their behavior and may further benefit from specific and directed public health messaging to avoid sharing during respiratory infection outbreaks.

Development and validation of a prediction equation for body fat percentage from measured BMI: a supervised machine learning approach.

Body mass index is a widely used but poor predictor of adiposity in populations with excessive fat-free mass. Rigorous predictive models validated specifically in a nationally representative sample of the US population and that could be used for calibration purposes are needed. The objective of this study was to develop and validate prediction equations of body fat percentage obtained from Dual Energy X-ray Absorptiometry using body mass index (BMI) and socio-demographics. We used the National Health and Nutrition Examination Survey (NHANES) data from 5931 and 2340 adults aged 20 to 69 in 1999-2002 and 2003-2006, respectively. A supervised machine learning using ordinary least squares and a validation set approach were used to develop and select best models based on R2 and root mean square error. We compared our findings with other published models and utilized our best models to assess the amount of bias in the association between predicted body fat and elevated low-density lipoprotein (LDL). Three models included BMI, BMI2, age, gender, education, income, and interaction terms and produced R-squared values of 0.87 and yielded the smallest standard errors of estimation. The amount of bias in the association between predicted BF% and elevated LDL from our best model was -0.005. Our models provided strong predictive abilities and low bias compared to most published models. Its strengths rely on its simplicity and its ease of use in low-resource settings.

The estimated effect of season and vitamin D in the first trimester on pubertal timing in girls and boys: a cohort study and an instrumental variable analysis.

BACKGROUND: Season of birth has been associated with age at menarche. Maternal vitamin D levels in pregnancy may explain this effect. We investigated whether the season of first trimester or maternal 25-hydroxyvitamin D3 [25(OH)D3] levels were associated with pubertal timing in children. METHODS: We conducted a follow-up study of 15 819 children born in 2000-03 from the Puberty Cohort, nested in the Danish National Birth Cohort (DNBC). Mean differences in attaining numerous pubertal markers, including a combined estimate for the average age at attaining all pubertal markers, were estimated for low (November-April) relative to high (May-October) sunshine exposure season in the first trimester using multivariable interval-censored regression models. Moreover, we conducted a two-sample instrumental variable analysis using season as an instrument for maternal first-trimester 25(OH)D3 plasma levels obtained from a non-overlapping subset (n = 827) in the DNBC. RESULTS: For the combined estimate, girls and boys of mothers who had their first trimester during November-April had earlier pubertal timing than girls and boys of mothers whose first trimester occurred during May-October: -1.0 months (95% CI: -1.7 to -0.3) and -0.7 months (95% CI: -1.4 to -0.1), respectively. In the instrumental variable analysis, girls and boys also had earlier pubertal timing: respectively, -1.3 months (95% CI: -2.1 to -0.4) and -1.0 months (95% CI: -1.8 to -0.2) per SD (22 nmol/L) decrease in 25(OH)D3. CONCLUSIONS: Both first pregnancy trimester during November-April and lower 25(OH)D3 were associated with earlier pubertal timing in girls and boys.

Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data.

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

Maternal vitamin D levels and male reproductive health: a population-based follow-up study.

Maternal vitamin D levels during pregnancy may be important for reproductive health in male offspring by regulating cell proliferation and differentiation during development. We conducted a follow-up study of 827 young men from the Fetal Programming of Semen Quality (FEPOS) cohort, nested in the Danish National Birth Cohort to investigate if maternal vitamin D levels were associated with measures of reproductive health in adult sons. These included semen characteristics, testes volume, and reproductive hormone levels and were analysed according to maternal vitamin D (25(OH)D3) levels during pregnancy. In addition, an instrumental variable analysis using seasonality in sun exposure as an instrument for maternal vitamin D levels was conducted. We found that sons of mothers with vitamin D levels < 25 nmol/L had 11% (95% CI - 19 to - 2) lower testes volume and a 1.4 (95% CI 1.0 to 1.9) times higher risk of having low testes volume (< 15 mL), in addition to 20% (95% CI - 40 to 9) lower total sperm count and a 1.6 (95% CI 0.9 to 2.9) times higher risk of having a low total sperm count (< 39 million) compared with sons of mothers with vitamin D levels > 75 nmol/L. Continuous models, spline plots and an instrumental variable analysis supported these findings. Low maternal vitamin D levels were associated with lower testes volume and lower total sperm count with indications of dose-dependency. Maternal vitamin D level above 75 nmol/L during pregnancy may be beneficial for testes function in adult sons.

Puff, Puff, Don't Pass: harm reduction for cannabis use during a viral respiratory pandemic.

BACKGROUND: Prior to the COVID-19 pandemic, cannabis use social practices often involved sharing prepared cannabis (joints/blunts/cigarettes) and cannabis-related paraphernalia. Previous studies have demonstrated that sharing paraphernalia for cannabis, tobacco, and crack cocaine is a risk factor for respiratory viral and bacterial infections. Although COVID-19 is a respiratory viral infection that spreads through droplets and airborne transmission, it is unclear if many individuals adopted harm reduction practices around sharing cannabis. This study: quantifies the prevalence of sharing prepared non-medical cannabis and cannabis-related paraphernalia reported before and during the pandemic; assesses changes in sharing of non-medical cannabis from before to during the pandemic; assess the association between frequency of non-medical cannabis use and sharing of cannabis during the pandemic; and describes how respondents obtained their cannabis and the reasons for changing their cannabis use during the pandemic to explain differences in sharing patterns. METHODS: This cross-sectional study used data collected from an anonymous, US-based web survey on cannabis-related behaviors from August to September 2020 (n = 1833). Participants were included if they reported using a mode of inhalation for non-medical cannabis consumption. We calculated proportional changes in sharing cannabis before/during the COVID-19 pandemic. Associations between frequency of cannabis use and cannabis sharing during the COVID-19 pandemic were assessed using logistic regression analysis. RESULTS: Overall, 1,112 participants reported non-medical cannabis use; 925 (83.2%) reported a mode of cannabis inhalation. More respondents reported no sharing during (24.9%) than before the pandemic (12.4%; p < 0.01); less respondents shared most of the time (19.5% before; 11.2% during; p < 0.01) and always during the pandemic (5.2% before; 3.1% during; p < 0.01). After adjusting for covariates, the odds of any sharing during the pandemic for those who reported ≥ weekly cannabis use was 0.53 (95% CI 0.38, 0.75) compared to those who reported ≤ monthly. CONCLUSIONS: Sharing of prepared cannabis and cannabis-related paraphernalia decreased during the COVID-19 pandemic compared to before the pandemic. This finding suggests potential risk mitigation strategies taken by participants for COVID-19 prevention either directly through behavior change or indirectly through adherence to COVID-19 prevention recommendations. Harm reduction messaging around sharing of cannabis during surges of COVID-19 or other respiratory infections may provide benefit in reducing infection among those who use cannabis, especially as cannabis use in the USA continues to increase.

Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction.

Background: U.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe. Methods: We developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions. Results: The model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education. Conclusions: Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

Maternal anemia and the risk of childhood cancer: A population-based cohort study in Taiwan.

BACKGROUND: Childhood cancer may be related to maternal health in pregnancy. Maternal anemia is a common condition in pregnancy, especially in low-income countries, but the association between maternal anemia and childhood cancer has not been widely studied. OBJECTIVE: To examine the potential relation between maternal anemia during pregnancy and childhood cancers in a population-based cohort study in Taiwan. METHODS: We examined the relationship between maternal anemia and childhood cancer in Taiwan (N = 2160 cancer cases, 2,076,877 noncases). Cases were taken from the National Cancer Registry, and noncases were selected from birth records. Using national health registries, we obtained maternal anemia diagnoses. We estimated the risks for childhood cancers using Cox proportional hazard analysis. RESULTS: There was an increased risk of cancers in children born to mothers with nutritional anemia (hazard ratio (HR): 1.32, 95% CI 0.99, 1.76). Iron deficiency anemia (HR: 1.30, 95% CI 0.97-1.75) carried an increased risk, while non-nutritional anemias were not associated with childhood cancer risk. CONCLUSION: Our results provide additional support for screening for anemia during pregnancy. Adequate nutrition and vitamin supplementation may help to prevent some childhood cancer.

Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling.

BACKGROUND: Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel, alternative approach to multiple-bias adjustment involves the simultaneous adjustment of all biases via imputation and/or regression weighting. The imputed value or weight corresponds to the probability of the missing data and serves to 'reconstruct' the unbiased data that would be observed based on the provided assumptions of the degree of bias. METHODS: We motivate and describe the steps necessary to implement this method. We also demonstrate the validity of this method through a simulation study with an exposure-outcome relationship that is biased by uncontrolled confounding, exposure misclassification, and selection bias. RESULTS: The study revealed that a non-biased effect estimate can be obtained when correct bias parameters are applied. It also found that incorrect specification of every bias parameter by +/-25% still produced an effect estimate with less bias than the observed, biased effect. CONCLUSIONS: Simultaneous multi-bias analysis is a useful way of investigating and understanding how multiple sources of bias may affect naive effect estimates. This new method can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies.