Frequent inappropriate use of unweighted summary statistics in systematic reviews of pathogen genotypes or genogroups.

OBJECTIVES: Our study aimed to systematically assess and report the methodological quality used in epidemiological systematic reviews (SRs) and meta-analysis (MA) of pathogen genotypes/genogroups. STUDY DESIGN AND SETTING: Nine electronic databases and manual search of reference lists were used to identify relevant studies. The method types were divided into three groups: 1) with weighted pooling analysis (which we call MA), (2) unweighted analysis of the study-level measures (which we call summary statistics), and (3) without any data pooling (which we call SR only). Characteristics were evaluated using Assessment of Multiple Systematic Reviews (AMSTAR), Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), and Risk Of Bias In Systematic reviews (ROBIS) tools. The protocol was registered in PROSPERO with CRD42017078146. RESULTS: Among 36 included articles, 5 (14%) studies conducted SR only, 16 (44%) performed MA, and 15 (42%) used summary statistics. The univariable and multivariable linear regression of AMSTAR and PRISMA scores showed that MA had higher quality compared with those with summary statistics. The SR only and summary statistics groups had approximately equal scores among three scales of AMSTAR, PRISMA, and ROBIS. The methodological quality of epidemiological studies has improved from 1999 to 2017. CONCLUSION: Despite the frequent use of unweighted summary statistics, MA remains the most suitable method for reaching rational conclusions in epidemiological studies of pathogen genotypes/genogroups

Frequent inappropriate use of unweighted summary statistics in systematic reviews of pathogen genotypes or genogroups

Objectives: Our study aimed to systematically assess and report the methodological quality used in epidemiological systematic reviews (SRs) and meta-analysis (MA) of pathogen genotypes/genogroups.Study design and setting: Nine electronic databases and manual search of reference lists were used to identify relevant studies. The method types were divided into three groups: 1) with weighted pooling analysis (which we call MA), (2) unweighted analysis of the study-level measures (which we call summary statistics), and (3) without any data pooling (which we call SR only). Characteristics were evaluated using Assessment of Multiple Systematic Reviews (AMSTAR), Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), and Risk Of Bias In Systematic reviews (ROBIS) tools. The protocol was registered in PROSPERO with CRD42017078146.Results: Among 36 included articles, 5 (14%) studies conducted SR only, 16 (44%) performed MA, and 15 (42%) used summary statistics. The univariable and multivariable linear regression of AMSTAR and PRISMA scores showed that MA had higher quality compared with those with summary statistics. The SR only and summary statistics groups had approximately equal scores among three scales of AMSTAR, PRISMA, and ROBIS. The methodological quality of epidemiological studies has improved from 1999 to 2017.Conclusion: Despite the frequent use of unweighted summary statistics, MA remains the most suitable method for reaching rational conclusions in epidemiological studies of pathogen genotypes/genogroups

Deep learning models for forecasting dengue fever based on climate data in Vietnam

BackgroundDengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam.ObjectiveThis study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change.MethodsConvolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997-2013 were used to train models, which were then evaluated using data from 2014-2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).Results and discussionLSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features.ConclusionThis study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years