Clinical prediction models in children that use repeated measurements with time-varying covariates: a scoping review

BACKGROUND: Emerging evidence suggests that clinical prediction models that use repeated (time-varying) measurements within each patient may have higher predictive accuracy than models that use patient information from a single measurement. OBJECTIVE: To determine the breadth of the published literature reporting the development of clinical prediction models in children that use time-varying predictors. DATA SOURCES: MEDLINE, EMBASE and Cochrane databases. ELIGIBILITY CRITERIA: We included studies reporting the development of a multivariable clinical prediction model in children, with or without validation, to predict a repeatedly measured binary or time-to-event outcome and utilizing at least one repeatedly measured predictor. SYNTHESIS METHODS: We categorized included studies by the method used to model time-varying predictors. RESULTS: Of 99 clinical prediction model studies that had a repeated measurements data structure, only 27 (27%) used methods that incorporated the repeated measurements as time-varying predictors in a single model. Among these 27 time-varying prediction model studies, we grouped model types into nine categories: time-dependent Cox regression, generalized estimating equations, random effects model, landmark model, joint model, neural network, K-nearest neighbor, support vector machine and tree-based algorithms. Where there was comparison of time-varying models to single measurement models, using time-varying predictors improved predictive accuracy. CONCLUSIONS: Various methods have been used to develop time-varying prediction models in children, but there is a paucity of pediatric time-varying models in the literature. Incorporating time-varying covariates in pediatric prediction models may improve predictive accuracy. Future research in pediatric prediction model development should further investigate whether incorporation of time-varying covariates improves predictive accuracy.

Probiotics, Prebiotics, Lactoferrin, and Combination Products for Prevention of Mortality and Morbidity in Preterm Infants: A Systematic Review and Network Meta-Analysis.

Importance: Modulation of intestinal microbiome by administering probiotics, prebiotics, or both may prevent morbidity and mortality in premature infants. Objective: To assess the comparative effectiveness of alternative prophylactic strategies through a network meta-analysis (NMA) of randomized clinical trials. Data Sources: MEDLINE, EMBASE, Science Citation Index Expanded, CINAHL, Scopus, Cochrane CENTRAL, and Google Scholar from inception until May 10, 2023. Study Selection: Eligible trials tested probiotics, prebiotics, lactoferrin, and combination products for prevention of morbidity or mortality in preterm infants. Data Extraction and Synthesis: A frequentist random-effects model was used for the NMA, and the certainty of evidence and inferences regarding relative effectiveness were assessed using the GRADE approach. Main Outcomes and Measures: All-cause mortality, severe necrotizing enterocolitis, culture-proven sepsis, feeding intolerance, time to reach full enteral feeding, and duration of hospitalization. Results: A total of 106 trials involving 25 840 preterm infants were included. Only multiple-strain probiotics were associated with reduced all-cause mortality compared with placebo (risk ratio [RR], 0.69; 95% CI, 0.56 to 0.86; risk difference [RD], -1.7%; 95% CI, -2.4% to -0.8%). Multiple-strain probiotics alone (vs placebo: RR, 0.38; 95% CI, 0.30 to 0.50; RD, -3.7%; 95% CI, -4.1% to -2.9%) or in combination with oligosaccharides (vs placebo: RR, 0.13; 95% CI, 0.05 to 0.37; RD, -5.1%; 95% CI, -5.6% to -3.7%) were among the most effective interventions reducing severe necrotizing enterocolitis. Single-strain probiotics in combination with lactoferrin (vs placebo RR, 0.33; 95% CI, 0.14 to 0.78; RD, -10.7%; 95% CI, -13.7% to -3.5%) were the most effective intervention for reducing sepsis. Multiple-strain probiotics alone (RR, 0.61; 95% CI, 0.46 to 0.80; RD, -10.0%; 95% CI, -13.9% to -5.1%) or in combination with oligosaccharides (RR, 0.45; 95% CI, 0.29 to 0.67; RD, -14.1%; 95% CI, -18.3% to -8.5%) and single-strain probiotics (RR, 0.61; 95% CI, 0.51 to 0.72; RD, -10.0%; 95% CI, -12.6% to -7.2%) proved of best effectiveness in reduction of feeding intolerance vs placebo. Single-strain probiotics (MD, -1.94 days; 95% CI, -2.96 to -0.92) and multistrain probiotics (MD, -2.03 days; 95% CI, -3.04 to -1.02) proved the most effective in reducing the time to reach full enteral feeding compared with placebo. Only single-strain and multistrain probiotics were associated with greater effectiveness compared with placebo in reducing duration of hospitalization (MD, -3.31 days; 95% CI, -5.05 to -1.58; and MD, -2.20 days; 95% CI, -4.08 to -0.31, respectively). Conclusions and Relevance: In this systematic review and NMA, moderate- to high-certainty evidence demonstrated an association between multistrain probiotics and reduction in all-cause mortality; these interventions were also associated with the best effectiveness for other key outcomes. Combination products, including single- and multiple-strain probiotics combined with prebiotics or lactoferrin, were associated with the largest reduction in morbidity and mortality.

Protocol for a living evidence synthesis on variants of concern and COVID-19 vaccine effectiveness.

BACKGROUND: It is evident that COVID-19 will remain a public health concern in the coming years, largely driven by variants of concern (VOC). It is critical to continuously monitor vaccine effectiveness as new variants emerge and new vaccines and/or boosters are developed. Systematic surveillance of the scientific evidence base is necessary to inform public health action and identify key uncertainties. Evidence syntheses may also be used to populate models to fill in research gaps and help to prepare for future public health crises. This protocol outlines the rationale and methods for a living evidence synthesis of the effectiveness of COVID-19 vaccines in reducing the morbidity and mortality associated with, and transmission of, VOC of SARS-CoV-2. METHODS: Living evidence syntheses of vaccine effectiveness will be carried out over one year for (1) a range of potential outcomes in the index individual associated with VOC (pathogenesis); and (2) transmission of VOC. The literature search will be conducted up to May 2023. Observational and database-linkage primary studies will be included, as well as RCTs. Information sources include electronic databases (MEDLINE; Embase; Cochrane, L*OVE; the CNKI and Wangfang platforms), pre-print servers (medRxiv, BiorXiv), and online repositories of grey literature. Title and abstract and full-text screening will be performed by two reviewers using a liberal accelerated method. Data extraction and risk of bias assessment will be completed by one reviewer with verification of the assessment by a second reviewer. Results from included studies will be pooled via random effects meta-analysis when appropriate, or otherwise summarized narratively. DISCUSSION: Evidence generated from our living evidence synthesis will be used to inform policy making, modelling, and prioritization of future research on the effectiveness of COVID-19 vaccines against VOC.

Resistance training prescription for muscle strength and hypertrophy in healthy adults: a systematic review and Bayesian network meta-analysis.

OBJECTIVE: To determine how distinct combinations of resistance training prescription (RTx) variables (load, sets and frequency) affect muscle strength and hypertrophy. DATA SOURCES: MEDLINE, Embase, Emcare, SPORTDiscus, CINAHL, and Web of Science were searched until February 2022. ELIGIBILITY CRITERIA: Randomised trials that included healthy adults, compared at least 2 predefined conditions (non-exercise control (CTRL) and 12 RTx, differentiated by load, sets and/or weekly frequency), and reported muscle strength and/or hypertrophy were included. ANALYSES: Systematic review and Bayesian network meta-analysis methodology was used to compare RTxs and CTRL. Surface under the cumulative ranking curve values were used to rank conditions. Confidence was assessed with threshold analysis. RESULTS: The strength network included 178 studies (n=5097; women=45%). The hypertrophy network included 119 studies (n=3364; women=47%). All RTxs were superior to CTRL for muscle strength and hypertrophy. Higher-load (>80% of single repetition maximum) prescriptions maximised strength gains, and all prescriptions comparably promoted muscle hypertrophy. While the calculated effects of many prescriptions were similar, higher-load, multiset, thrice-weekly training (standardised mean difference (95% credible interval); 1.60 (1.38 to 1.82) vs CTRL) was the highest-ranked RTx for strength, and higher-load, multiset, twice-weekly training (0.66 (0.47 to 0.85) vs CTRL) was the highest-ranked RTx for hypertrophy. Threshold analysis demonstrated these results were extremely robust. CONCLUSION: All RTx promoted strength and hypertrophy compared with no exercise. The highest-ranked prescriptions for strength involved higher loads, whereas the highest-ranked prescriptions for hypertrophy included multiple sets. PROSPERO REGISTRATION NUMBER: CRD42021259663 and CRD42021258902.

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.

Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.

Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity.

Evidence supports a complex interplay of gut microbiome and host metabolism as regulators of obesity. The metabolic phenotype and microbial metabolism of host diet may also contribute to greater obesity risk in children early in life. This study aimed to identify features that discriminated overweight/obese from normal weight infants by integrating gut microbiome and serum metabolome profiles. This prospective analysis included 50 South Asian children living in Canada, selected from the SouTh Asian biRth cohorT (START). Serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry and the relative abundance of bacterial 16S rRNA gene amplicon sequence variant was evaluated at 1 year. Cumulative body mass index (BMIAUC) and skinfold thickness (SSFAUC) scores were calculated from birth to 3 years as the total area under the growth curve (AUC). BMIAUC and/or SSFAUC >85th percentile was used to define overweight/obesity. Data Integration Analysis for Biomarker discovery using Latent cOmponent (DIABLO) was used to identify discriminant features associated with childhood overweight/obesity. The associations between identified features and anthropometric measures were examined using logistic regression. Circulating metabolites including glutamic acid, acetylcarnitine, carnitine, and threonine were positively, whereas γ-aminobutyric acid (GABA), symmetric dimethylarginine (SDMA), and asymmetric dimethylarginine (ADMA) were negatively associated with childhood overweight/obesity. The abundance of the Pseudobutyrivibrio and Lactobacillus genera were positively, and Clostridium sensu stricto 1 and Akkermansia were negatively associated with childhood overweight/obesity. Integrative analysis revealed that Akkermansia was positively whereas Lactobacillus was inversely correlated with GABA and SDMA, and Pseudobutyrivibrio was inversely correlated with GABA. This study provides insights into metabolic and microbial signatures which may regulate satiety, energy metabolism, inflammatory processes, and/or gut barrier function, and therefore, obesity trajectories in childhood. Understanding the functional capacity of these molecular features and potentially modifiable risk factors such as dietary exposures early in life may offer a novel approach for preventing childhood obesity.

Do Psychological and Behavioural Factors Change Over Pregnancy?

OBJECTIVES: To investigate how psychological and behavioural factors change from the first to the last half of pregnancy. METHODS: In this prospective cohort study, we assessed the changes in psychological and behavioural factors across 10 domains among 445 women (mean age = 30.9 years) in Ontario, Canada. We collected data using 2 standardized questionnaires administered at <21 and 32-36 weeks of gestation. We computed intraclass correlation coefficients, percentages of no change, decrease, and increase, and mean differences between the 2 surveys. RESULTS: Most psychological and behavioural factors had intraclass correlation coefficients < 0.50 between the first and the second half of pregnancy, suggesting remarkable changes over the course of pregnancy. We observed significant decreases in self-efficacy, compensatory health beliefs, guilt regarding binge eating, emotional eating, dietary restriction, pregnancy-related nausea and food cravings, sleep duration, and physical activity. We also found increases in anxious and depressive symptoms and the tendency to accept friends' and family's beliefs regarding pregnancy. CONCLUSIONS: In the first prospective analysis, we found that many psychological and behavioural factors changed significantly over pregnancy.

Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis.

Background: Knowing the prevalence of multimorbidity among adults across continents is a crucial piece of information for achieving Sustainable Development Goal 3.4, which calls for reducing premature death due to non-communicable diseases. A high prevalence of multimorbidity indicates high mortality and increased healthcare utilization. We aimed to understand the prevalence of multimorbidity across WHO geographic regions among adults. Methods: We performed a systematic review and meta-analysis of surveys designed to estimate the prevalence of multimorbidity among adults in community settings. We searched PubMed, ScienceDirect, Embase and Google Scholar databases for studies published between January 1, 2000, and December 31, 2021. The random-effects model estimated the pooled proportion of multimorbidity in adults. Heterogeneity was quantified using I2 statistics. We performed subgroup analyses and sensitivity analyses based on continents, age, gender, multimorbidity definition, study periods and sample size. The study protocol was registered with PROSPERO (CRD42020150945). Findings: We analyzed data from 126 peer-reviewed studies that included nearly 15.4 million people (32.1% were male) with a weighted mean age of 56.94 years (standard deviation of 10.84 years) from 54 countries around the world. The overall global prevalence of multimorbidity was 37.2% (95% CI = 34.9-39.4%). South America (45.7%, 95% CI = 39.0-52.5) had the highest prevalence of multimorbidity, followed by North America (43.1%, 95% CI = 32.3-53.8%), Europe (39.2%, 95% CI = 33.2-45.2%), and Asia (35%, 95% CI = 31.4-38.5%). The subgroup study highlights that multimorbidity is more prevalent in females (39.4%, 95% CI = 36.4-42.4%) than males (32.8%, 95% CI = 30.0-35.6%). More than half of the adult population worldwide above 60 years of age had multimorbid conditions (51.0%, 95% CI = 44.1-58.0%). Multimorbidity has become increasingly prevalent in the last two decades, while the prevalence appears to have stayed stable in the recent decade among adults globally. Interpretation: The multimorbidity patterns by geographic regions, time, age, and gender suggest noticeable demographic and regional differences in the burden of multimorbidity. According to insights about prevalence among adults, priority is required for effective and integrative interventions for older adults from South America, Europe, and North America. A high prevalence of multimorbidity among adults from South America suggests immediate interventions are needed to reduce the burden of morbidity. Furthermore, the high prevalence trend in the last two decades indicates that the global burden of multimorbidity continues at the same pace. The low prevalence in Africa suggests that there may be many undiagnosed chronic illness patients in Africa. Funding: None.

Associations of abdominal obesity with different types of bone fractures in adults: A systematic review and dose-response meta-analysis of prospective cohort studies.

Findings on the association between abdominal obesity and hip fracture were summarized in a meta-analysis in 2017; however, no study has examined the dose-response association between abdominal fat indices and hip fracture. Also, we found no meta-analysis investigating other types of bone fractures including any vertebral fractures in relation to abdominal obesity. Therefore, the present systematic review and dose-response meta-analysis of prospective cohort studies were conducted to examine the association between abdominal obesity and different types of bone fractures. A comprehensive literature search was done by searching PubMed, Scopus, Web of Science, and Google Scholar until October 2021. In total, 23 articles from prospective cohort studies with a total sample size of 3,456,631 participants were included. During the follow-up periods ranging between 4 and 26 years, 137,989 cases of bone fracture were recorded. After comparing the highest and lowest categories of abdominal fat indices, the summary relative risks (RRs) of any, hip, and vertebral fractures were 0.99 (95% CI: 0.81-1.20), 1.09 (95% CI: 0.82-1.43), and 1.18 (95% CI: 1.05-1.33), respectively, indicating a significant positive association between abdominal obesity and risk of vertebral fracture. In the non-linear dose-response analysis, abdominal obesity based on the waist-to-hip ratio (WHR) was positively associated with an increased risk of hip fracture from 0.7 to 1.1 units of WHR. In the linear analysis, a 10 cm increase in waist circumference (WC) was associated with a 3% higher risk of vertebral fracture. We found no other dose-response association for other types of bone fractures. In conclusion, abdominal obesity may be associated with a higher risk of hip and vertebral fractures.

Assessment of Concordance between Chairside Ultrasonography and Digital Palpation in Detecting Myofascial Trigger Points in Masticatory Myofascial Pain Syndrome.

INTRODUCTION: Masticatory myofascial pain is a musculoligamentous syndrome that can mimic odontogenic pain. Pain referral to odontogenic structures can be traced to hyperirritated myofascial trigger points (MTrPs). This pragmatic study evaluated the concordance between ultrasonography and palpation in detecting MTrPs in the masseter and temporalis muscles. METHODS: Fifty-seven patients suspected to have temporomandibular disorder were included. MTrPs were palpated manually by expert clinicians. Ultrasonography was then performed by a blind sonographer. The quantity of MTrPs and the involved muscle sections, the pain occurrence, and the location of the MTrPs within the muscle sections were compared using the mean difference (MD) and concordance statistics (Cohen κ and the interclass correlation coefficient [ICC]) as applicable. RESULTS: Ultrasonography located MTrPs as 2.1 ± 1.3 mm2 hypoechoic nodules at a depth of 7 ± 3.3 mm. Ultrasonography moderately agreed with palpation on the quantity of MTrPs per patient (MD = 1; 95% confidence interval [CI], 0.06-1.9; ICC = 0.56; 95% CI, 0.32-0.72). Palpation detected marginally more involved muscle sections per patient (MD = 0.7; 95% CI, 0.06-1.34.05; ICC = 0.64; 95% CI, 0.44-0.77) with more pain occurrence per patient (MD = 1.4; 95% CI, 0.56-2.28; ICC = 0.13; 95% CI, -0.26 to 0.41). There was a discordance in the location of the MTrPs within the muscle sections per patient (κ = -0.46; 95% CI, -0.77 to -0.14). CONCLUSIONS: Ultrasonography and palpation concurred moderately to substantially on the quantity of MTrPs and the involved muscle sections but disagreed on the location of the MTrPs within the muscle sections. Ultrasonography has the potential as a chairside diagnostic aid to help clinicians determine an accurate diagnosis, enhance patient experience during examination, and avoid unnecessary treatments that can mitigate the risk of iatrogenic damage.

Mapping and determinants of consumption of egg and/or flesh foods and zero vegetables or fruits among young children in SSA.

Zero vegetable or fruit and egg and/or flesh foods are the latest indicators for assessing infant and young child feeding practices. Understanding national and subnational heterogeneity and regional clustering in children with SSA is becoming increasingly essential for geographic targeting and policy prioritization. Geographical case identification, determinants, and impacts were all investigated. SSA children's consumption of vegetable or fruit, egg and/or flesh food, and both were low. In SSA, some portions of the Southern, South direction of the Western and Central regions have a lower weight of all bad conditions than others, although children continue to suffer in considerable numbers in all disadvantage circumstances. Children under the age of 1 year, from rural areas, uneducated families, and low income were all disadvantaged by both feeding techniques. To improve child nutrition status, multisectoral collaboration is essential. This framework allows for the tracking, planning, and implementation of nutritional treatments.

Egg and Dietary Cholesterol Intake and Risk of All-Cause, Cardiovascular, and Cancer Mortality: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies.

Objective: This systematic review and meta-analysis of prospective cohort studies examined the associations between egg and dietary cholesterol intake and the risk of mortality from all causes, including cardiovascular disease (CVD) and cancer. Methods: We searched PubMed, Scopus, ISI Web of Knowledge, and Google Scholar until April 2021, as well as references to the relevant articles retrieved. Random-effects models were used to calculate summary relative risk (RR) and 95% confidence intervals (CIs) for the highest vs. lowest categories of egg and dietary cholesterol intake. Also, linear and non-linear dose-response analyses were conducted to examine the dose-response relationships. Results: We included 55 studies, comprising data from 2,772,486 individuals with 228,425, 71,745, and 67,211 cases of all-cause, CVD, and cancer mortality, respectively. Intake of each additional egg per day was associated with a 7% higher risk of all-cause (1.07, 95% CI: 1.02-1.12, I2 = 84.8%) and a 13% higher risk of cancer mortality (1.13, 95% CI: 1.06-1.20, I2 = 54.2%), but was not associated with CVD mortality (1.00, 95% CI: 0.92-1.09, I2 = 81.5%). Non-linear analyses showed increased risks for egg consumption of more than 1.5 and 0.5 eggs/day, respectively. Each 100 mg/day increment in dietary cholesterol intake was associated with a 6% higher risk of all-cause mortality (1.06, 95% CI: 1.03-1.08, I2 = 34.5%) and a 6% higher risk of cancer mortality (1.06, 95% CI: 1.05-1.07, I2 = 0%), but was not associated with CVD mortality (1.04, 95% CI: 0.99-1.10, I2 = 85.9%). Non-linear analyses demonstrated elevated risks of CVD and cancer mortality for intakes more than 450 and 250 mg/day, respectively. Conclusions and Relevance: High-dietary intake of eggs and cholesterol was associated with all-cause and cancer mortality. Little evidence for elevated risks was seen for intakes below 0.5 egg/day or 250 mg/day of dietary cholesterol. Our findings should be considered with caution because of small risk estimates and moderate between-study heterogeneity. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=252564, PROSPERO, identifier: CRD42021252564.

An improved method for analysis of interrupted time series (ITS) data: accounting for patient heterogeneity using weighted analysis.

Interrupted time series (ITS) design is commonly used to evaluate the impact of interventions in healthcare settings. Segmented regression (SR) is the most commonly used statistical method and has been shown to be useful in practical applications involving ITS designs. Nevertheless, SR is prone to aggregation bias, which leads to imprecision and loss of power to detect clinically meaningful differences. The objective of this article is to present a weighted SR method, where variability across patients within the healthcare facility and across time points is incorporated through weights. We present the methodological framework, provide optimal weights associated with data at each time point and discuss relevant statistical inference. We conduct extensive simulations to evaluate performance of our method and provide comparative analysis with the traditional SR using established performance criteria such as bias, mean square error and statistical power. Illustrations using real data is also provided. In most simulation scenarios considered, the weighted SR method produced estimators that are uniformly more precise and relatively less biased compared to the traditional SR. The weighted approach also associated with higher statistical power in the scenarios considered. The performance difference is much larger for data with high variability across patients within healthcare facilities. The weighted method proposed here allows us to account for the heterogeneity in the patient population, leading to increased accuracy and power across all scenarios. We recommend researchers to carefully design their studies and determine their sample size by incorporating heterogeneity in the patient population.

Dietary intake and biomarkers of alpha linolenic acid and risk of all cause, cardiovascular, and cancer mortality: systematic review and dose-response meta-analysis of cohort studies.

OBJECTIVE: To examine the associations between dietary intake and tissue biomarkers of alpha linolenic acid (ALA) and risk of mortality from all causes, cardiovascular disease (CVD), and cancer. DESIGN: Systematic review and meta-analysis of prospective cohort studies. DATA SOURCES: PubMed, Scopus, ISI Web of Science, and Google Scholar to 30 April 2021. STUDY SELECTION: Prospective cohort studies that reported the risk estimates for death from all causes, CVD, and cancer. DATA SYNTHESIS: Summary relative risks and 95% confidence intervals were calculated for the highest versus lowest categories of ALA intake using random effects and fixed effects models. Linear and non-linear dose-response analyses were conducted to assess the dose-response associations between ALA intake and mortality. RESULTS: 41 articles from prospective cohort studies were included in this systematic review and meta-analysis, totalling 1 197 564 participants. During follow-up ranging from two to 32 years, 198 113 deaths from all causes, 62 773 from CVD, and 65 954 from cancer were recorded. High intake of ALA compared with low intake was significantly associated with a lower risk of deaths from all causes (pooled relative risk 0.90, 95% confidence interval 0.83 to 0.97, I2=77.8%, 15 studies), CVD (0.92, 0.86 to 0.99, I2=48.2%, n=16), and coronary heart disease (CHD) (0.89, 0.81 to 0.97, I2=5.6%, n=9), and a slightly higher risk of cancer mortality (1.06, 1.02 to 1.11, I2=3.8%, n=10). In the dose-response analysis, a 1 g/day increase in ALA intake (equivalent to one tablespoon of canola oil or 0.5 ounces of walnut) was associated with a 5% lower risk of all cause (0.95, 0.91 to 0.99, I2=76.2%, n=12) and CVD mortality (0.95, 0.91 to 0.98, I2=30.7%, n=14). The pooled relative risks for the highest compared with lowest tissue levels of ALA indicated a significant inverse association with all cause mortality (0.95, 0.90 to 0.99, I2=8.2%, n=26). Also, based on the dose-response analysis, each 1 standard deviation increment in blood concentrations of ALA was associated with a lower risk of CHD mortality (0.92, 0.86 to 0.98, I2=37.1%, n=14). CONCLUSIONS: The findings show that dietary ALA intake is associated with a reduced risk of mortality from all causes, CVD, and CHD, and a slightly higher risk of cancer mortality, whereas higher blood levels of ALA are associated with a reduced risk of all cause and CHD mortality only. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021229487.

Mapping of mothers' suffering and child mortality in Sub-Saharan Africa.

Child death and mothers who suffer from child death are a public health concern in Sub-Saharan Africa. The location and associated factors of child death and mothers who suffer child death were not identified. To monitor and prioritize effective interventions, it is important to identify hotspots areas and associated factors. Data from nationally representative demographic and health survey and Multiple Indicator Cluster administrated in 42 Sub-Sahara Africa countries, which comprised a total of 398,574 mothers with 1,521,312 children. Spatial heterogeneity conducted hotspot regions identified. A mixed-effect regression model was run, and the adjusted ratio with corresponding 95% confidence intervals was estimated. The prevalence of mothers who suffer child death 27% and 45-49 year of age mother 48%. In Niger, 47% of mothers were suffering child death. Women being without HIV knowledge, stunted, wasted, uneducated, not household head, poor, from rural, and from subtropical significantly increased the odds of the case (P < 0.05). The spatial analysis can support the design and prioritization of interventions. Multispectral interventions for mothers who suffer from child death are urgently needed, improve maternal health and it will reduce the future risk of cases.

Subnational mapping for targeting anaemia prevention in women of reproductive age in Ethiopia: A coverage-equity paradox.

Anaemia in women of reproductive age (WRA) can be effectively addressed if supported by a better understanding of the spatial variations, magnitude, severity and distribution of anaemia. This study aimed to map the subnational spatial distribution of anaemia (any, moderate and severe forms) among WRA in Ethiopia. We identified and mapped (any, moderate and severe) anaemia hotspots in WRA (n = 14,923) at the subnational level and identified risk factors using multilevel logistic regression. Kulldorff scan statistics were used to identify hotspot regions. Ordinary kringing was used to predict the anaemia prevalence in unmeasured areas. The overall anaemia prevalence increased from 16.6% in 2011 to 23.6% in 2016, a rise that was mostly related to the widening of existing hotspot areas. The primary clusters of (any) anaemia were in Somali and Afar regions. The horn of the Somali region represented a cluster of 330 km where 10% of WRA were severely anaemic. The Oromia-Somali border represented a significant cluster covering 247 km, with 9% severe anaemia. Population-dense areas with low anaemia prevalence had high absolute number of cases. Women education, taking iron-folic-acid tablets during pregnancy and birth-delivery in health facilities reduced the risk of any anaemia (P < 0.05). The local-level mapping of anaemia helped identify clusters that require attention but also highlighted the urgent need to study the aetiology of anaemia to improve the effectiveness and safety of interventions. Both relative and absolute anaemia estimates are critical to determine where additional attention is needed.

A Roadmap for Building Data Science Capacity for Health Discovery and Innovation in Africa.

Technological advances now make it possible to generate diverse, complex and varying sizes of data in a wide range of applications from business to engineering to medicine. In the health sciences, in particular, data are being produced at an unprecedented rate across the full spectrum of scientific inquiry spanning basic biology, clinical medicine, public health and health care systems. Leveraging these data can accelerate scientific advances, health discovery and innovations. However, data are just the raw material required to generate new knowledge, not knowledge on its own, as a pile of bricks would not be mistaken for a building. In order to solve complex scientific problems, appropriate methods, tools and technologies must be integrated with domain knowledge expertise to generate and analyze big data. This integrated interdisciplinary approach is what has become to be widely known as data science. Although the discipline of data science has been rapidly evolving over the past couple of decades in resource-rich countries, the situation is bleak in resource-limited settings such as most countries in Africa primarily due to lack of well-trained data scientists. In this paper, we highlight a roadmap for building capacity in health data science in Africa to help spur health discovery and innovation, and propose a sustainable potential solution consisting of three key activities: a graduate-level training, faculty development, and stakeholder engagement. We also outline potential challenges and mitigating strategies.

Prediction of preterm birth in nulliparous women using logistic regression and machine learning.

OBJECTIVE: To predict preterm birth in nulliparous women using logistic regression and machine learning. DESIGN: Population-based retrospective cohort. PARTICIPANTS: Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. METHODS: We used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample. RESULTS: During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters. CONCLUSION: Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.

Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort.

OBJECTIVE: To develop risk prediction models for singleton preterm birth (PTB) < 28 weeks and <32 weeks. METHODS: Using a retrospective cohort of 267,226 singleton births in Ontario hospitals, we included variables from the first and second trimester in multivariable logistic regression models to predict overall and spontaneous PTB < 28 weeks and <32 weeks. RESULTS: During the first trimester, the area under the curve (AUC) for prediction of PTB < 28 weeks for nulliparous and multiparous women was 68.5% (95% CI: 63.5-73.6%) and 73.4% (68.6-78.2%), respectively, while for PTB < 32 weeks it was 68.9% (65.5-72.3%) and 75.5% (72.3-78.7%), respectively. AUCs for second-trimester models were 72.4% (95% CI: 69.7-75.1%) and 78.2% (95% CI: 75.8-80.5%), respectively, in nulliparous and multiparous women. Predicted probabilities were well-calibrated within a wide range around expected base prevalence for the study outcomes. CONCLUSIONS: Our prediction models generated acceptable AUCs for PTB < 28 weeks and <32 weeks with good calibration during the first and second trimester.

Characteristics and quality of systematic reviews and meta-analyses of observational nutritional epidemiology: a cross-sectional study.

BACKGROUND: Dietary recommendations and policies should be guided by rigorous systematic reviews. Reviews that are of poor methodological quality may be ineffective or misleading. Most of the evidence in nutrition comes from nonrandomized studies of nutritional exposures (usually referred to as nutritional epidemiology studies), but to date methodological evaluations of the quality of systematic reviews of such studies have been sparse and inconsistent. OBJECTIVES: We aimed to investigate the quality of recently published systematic reviews and meta-analyses of nutritional epidemiology studies and to propose guidance addressing major limitations. METHODS: We searched MEDLINE (January 2018-August 2019), EMBASE (January 2018-August 2019), and the Cochrane Database of Systematic Reviews (January 2018-February 2019) for systematic reviews of nutritional epidemiology studies. We included a random sample of 150 reviews. RESULTS: Most reviews were published by authors from Asia (n = 49; 32.7%) or Europe (n = 43; 28.7%) and investigated foods or beverages (n = 60; 40.0%) and cancer morbidity and mortality (n = 54; 36%). Reviews often had important limitations: less than one-quarter (n = 30; 20.0%) reported preregistration of a protocol and almost one-third (n = 42; 28.0%) did not report a replicable search strategy. Suboptimal practices and errors in the synthesis of results were common: one-quarter of meta-analyses (n = 30; 26.1%) selected the meta-analytic model based on statistical indicators of heterogeneity and almost half of meta-analyses (n = 50; 43.5%) did not consider dose-response associations even when it was appropriate to do so. Only 16 (10.7%) reviews used an established system to evaluate the certainty of evidence. CONCLUSIONS: Systematic reviews of nutritional epidemiology studies often have serious limitations. Authors can improve future reviews by involving statisticians, methodologists, and researchers with substantive knowledge in the specific area of nutrition being studied and using a rigorous and transparent system to evaluate the certainty of evidence.