A systematic review of dengue outbreak prediction models: Current scenario and future directions.

Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.

Effect of maternal dietary niacin intake on congenital anomalies: a systematic review and meta-analysis.

PURPOSE: The significance of niacin in embryonic development has clinical implications in the counseling of pregnant women and may be used to inform nutrition recommendations. This study, therefore, aims to review the associations between maternal periconceptional niacin intake and congenital anomalies. METHODS: A systematic search of Ovid MEDLINE, ClinicalTrials.gov, AMED, CENTRAL, Emcare, EMBASE, Maternity & Infant Care and Google Scholar was conducted between inception and 30 September 2020. Medical subject heading terms included "nicotinic acids" and related metabolites, "congenital anomalies" and specific types of congenital anomalies. Included studies reported the association between maternal niacin intake and congenital anomalies in their offspring and reported the measure of association. Studies involved solely the women with co-morbidities, animal, in vitro and qualitative studies were excluded. The risk of bias of included studies was assessed using the Newcastle-Ottawa Scale (NOS). A random effects-restricted maximum likelihood model was used to obtain summary estimates, and multivariable meta-regression model was used to adjust study-level covariates. RESULTS: Of 21,908 retrieved citations, 14 case-control studies including 35,743 women met the inclusion criteria. Ten studies were conducted in the U.S, three in Netherlands and one in South Africa. The meta-analysis showed that expectant mothers with an insufficient niacin intake were significantly more likely to have babies with congenital abnormalities (odds ratio 1.13, 95% confidence interval 1.02-1.24) compared to mothers with adequate niacin intake. A similar association between niacin deficiency and congenital anomalies was observed (OR 1.15, 95% CI 1.03-1.26) when sensitivity analysis was conducted by quality of the included studies. Meta-regression showed neither statistically significant impact of study size (p = 0.859) nor time of niacin assessment (p = 0.127). The overall quality of evidence used is high-thirteen studies achieved a rating of six or seven stars out of a possible nine based on the NOS. CONCLUSION: Inadequate maternal niacin intake is associated with an increased risk of congenital anomalies in the offspring. These findings may have implications in dietary counseling and use of niacin supplementation during pregnancy.