Synoptic review on existing and potential sources for bias in dental research methodology with methods on their prevention and remedies

The results of years of dental study serve as the foundation for the practise of medicine and,for that matter,dentistry.Doctors may have their own preferences for techniques and materials,but whether directly or indirectly,their decisions are influenced by systematic reviews and meta-analyses.However,due to poorly conducted or presented research,this very basic foundation may not be reliable.Bias in research is one of several factors that might make study results or research itself unreliable.Bias can be introduced into research at many stages,deliberately or unknowingly.Bias can appear at any point during the research process,even before the study itself begins.There are many biases in research,but some of them are more relevant to dentistry research than others.Because it is said that“eyes see what the mind knows”,it is essential to have a complete understanding of the different types of bias,how and when they get entrenched,and what steps may be taken to prevent or lessen them if they do occur.This comprehensive summary of bias in dentistry research is provided by this synoptic review.The goal is to identify gaps and measures that have been taken-or that should have been taken-by providing both descriptive and evaluative summaries,as well as examples from the literature,when needed.

Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach

Background Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected.Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community.However,no standard method exists to effectively analyse and interpret this type of data.In this study,we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type.Methods Two sets of leprosy case detection delay data were evaluated:a cohort of 181 patients from the post exposure prophylaxis for leprosy(PEP4LEP)study in high endemic districts of Ethiopia,Mozambique,and Tanzania;and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review.Bayesian models were fit to each dataset to assess which probability distribution(log-normal,gamma or Weibull)best describes variation in observed case detection delays using leave-one-out cross-validation,and to estimate the effects of individual factors.Results For both datasets,detection delays were best described with a log-normal distribution combined with covariates age,sex and leprosy subtype[expected log predictive density(ELPD)for the joint model:-1123.9].Patients with multibacillary(MB)leprosy experienced longer delays compared to paucibacillary(PB)leprosy,with a relative difference of 1.57[95%Bayesian credible interval(BCI):1.14-2.15].Those in the PEP4LEP cohort had 1.51(95%BCI:1.08-2.13)times longer case detection delay compared to the self-reported patient delays in the systematic review.Conclusions The log-normal model presented here could be used to compare leprosy case detection delay datasets,including PEP4LEP where the primary outcome measure is reduction in case detection delay.We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.

From 30 million to zero malaria cases in China: lessons learned for China-Africa collaboration in malaria elimination

Malaria was once one of the most serious public health problems in China,with more than 30 million malaria cases annually before 1949.However,the disease burden has sharply declined and the epidemic areas has shrunken after the implementation of an integrated malaria control and elimination strategy,especially since 2000.Till now,China has successfully scaled up its efforts to become malaria-free and is currently being evaluated for malaria-free certification by the WHO.In the battle against malaria,China's efforts have spanned generations,reducing from an incidence high of 122.9/10000(6.97 million cases)in 1954 to 0.06/10000(7855 cases)in 2010.In 2017,for the first time,China reached zero indigenous case of malaria,putting the country on track to record three consecutive years of zero transmission by 2020,accoding to the National Malaria Elimination Action Plan(2010-2020).China's efforts to eliminate malaria is impressive,and the country is dedicated to sharing its lessons learned in malaria elimination-including,but not limited to,the application of novel genetics-based approaches-with other nations through new initiatives.China will promote international relationships and establish collaborative platforms on a wide range of topics in roughly 65 countries,including 20 African nations.China's experience in applying innovative genetics-based approaches and tools to characterize malaria parasite populations,including surveillance of markers related to drug resistance,categorization of cases as indigenous or imported,and objective identification of the likely sources of infections to inform efforts towards malaria control and elimination in Africa could offer game-changing results when applied to settings with ongoing transmission.