Developmental Coordination Disorder (DCD) is a chronic neurodevelopmental disorder that results in difficulty in motor coordination observed in school-going children that interferes with classroom ...performance. Suspected DCD (S-DCD) children may show poor motor, as well as academic performance at school, and hence the present study aimed to find out the prevalence of S-DCD in children of age 5–10 years in central India and to find its association with preterm and/or low birth weight (LBW).
A total of 716 normal school-going children of age 5–10 years (both genders) were included in the study from four schools of the city by stratified sampling method. Children with any diagnosed neurological, orthopedic, rheumatologic, metabolic, cardiopulmonary, or psychological disorders were excluded. Data was collected using the parent-administered Developmental Coordination Disorder Questionnaire-2007 (DCDQ′07) and a parent/caregiver proforma. Children were sorted into three age subgroups (5–7.11 years, 8–9.11 years and 9–9.11 years).
Prevalence of S-DCD in 5–7.11 years (21.5%), 8–9.11years (23.9%) and is highest in 10–10.11 years (30.6%). Preterm children showed a higher prevalence of S-DCD (preterm: 29.54%, term: 23.10%). Children with LBW also showed a higher prevalence of S-DCD (30.15%) and among normal birth weight (21.43%). In children with both preterm and LBW history, the prevalence of suspected DCD was found to be 51.72%.
Prevalence of suspected DCD was found to be 23.9% in the 5–10 years age group. It was also observed that S-DCD is strongly associated with preterm, as well as low birth weight in children of age 5–10 years.
Club foot is most commonly observed congenital deformity among newborns which needs regular concern from medical and healthcare providers (1 in 1000 births). Club foot deformity adversely affects ...child's daily functions and may cause dependency for basic activities like standing, walking, running, stairs up and down etc.
Search engines were used to collect randomized controlled trails (from 2015 to 2022) such as Pubmed, Pubmed Central, Google, Google Scholar, ResearchGate etc. Total 328 articles found on basis of key words, club foot management, treatment, foot deformity, scales etc, among those 98 articles were retrieved on basis of selection criteria of club foot management out of which only 10 articles fulfill the inclusion criteria.
In review, found that there was many management approaches were present out of which step stretch, wall stretch, active stretch, towel stretch, passive stretch, common Indian relaxing posture, new novel device to stretch calf i.e. tension bar tendon stretch (TBTS) found effective for club foot management.
Among various management techniques foot muscle stretching method, strengthening methods, devices and splints for club foot among children found effective.
•Source of data collection: Pubmed, Pubmed Central, Google, Google Scholar, ResearchGate.•This review article provides information about different techniques of correcting club foot deformity in rehabilitation.•CTEV or clubfoot is a common pediatric congenital foot deformity (1/1,000 live births).•Considering variation treatment, the soft tissues should be subjected to different stretching strategies.•Splinting provides sustained stretching to muscles and tendons, improving the correction of equines deformities/club foot.
Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an ...unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation.
This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak.
We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user's understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation.
The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak.
AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.
Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a ...reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.
Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This ...practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence.
Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence?
We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies.
Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence.
To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.
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