scholarly journals Comparing spatio-temporal methods of non-communicable disease surveillance.

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Mico Hamlyn ◽  
Frederic B. Piel
Keyword(s):  
Bayesian Methods ◽  
Disease Surveillance ◽  
Small Area ◽  
Roc Analysis ◽  
Communicable Disease ◽  
Cluster Detection ◽  
Simulation Framework ◽  
Disease Clusters ◽  
Spatio Temporal ◽  
Non Communicable Disease

ObjectiveTo determine the merits of different surveillance methods for cluster detection, in particular when used in conjuction with small area data. This will be investigated using a simulated framework. This is with a view to support further surviellance work using real small area data.IntroductionHealth surveillance is well established for infectious diseases, but less so for non-communicable diseases. When spatio-temporal methods are used, selection often appears to be driven by arbitrary criteria, rather than optimal detection capabilities. Our aim is to use a theoretical simulation framework with known spatio-temporal clusters to investigate the sensitivity and specificity of several traditional (e.g. SatScan and Cusum) and Bayesian (incl. BaySTDetect and Dcluster) statistical methods for spatio-temporal cluster detection of non-communicable disease.MethodsCount data were generated using various random effects (RE). A subset of areas was randomly given an increased relative risk (RR) to simulate disease clusters. Simulations were conducted in R using a grid of 625 areas. We used 12 times= nteps within a hierarchical Poisson model. Multiple values of model parameters, including REs and the RR within clusters, were then tested. The range of RE (values) was derived from real-world data from England on common and rare diseases. RR ranging between 1.2 and 1.8 were tested to reflect both low and high exposures to pollutants and other risk factors. ROC analysis, based on 50 simulations, was used to assess the performance of each statistical method for each combination of parameter values.ResultsOur ROC analysis suggested that SaTScan usually had the highest specificity at low sensitivities (<0.5), although its maximum sensitivity was often lower than when using the Bayesian methods. In scenarios where the RR within clusters was lower, all methods had less sensitivity at a given specificity. Cusum usually performed quite similarly to SatScan, while the two Bayesian methods considered often misidentified a high proportion of disease clusters. P-values generated by SaTScan need to be considered with caution as they did not relate closely with the sensitivity or specificity of the ROC curves from our simulations.ConclusionsReal-world investigations of spatio-temporal signals (e.g. disease clusters) are often complex and time consuming. Identifying the best method to reduce the risks of identifying false positives and of missing real clusters is therefore essential. Despite the inherent constraints of theoretical simulations, such a framework allows to objectively assess the performance of different methods. Overall, our simulation framework suggested that SatScan would usually be the easiest, most user-friendly and best performing space-time methods for non-communicable disease surveillance.

DETERMINANTS OF LEISURE-TIME PHYSICAL ACTIVITY: EVIDENCE FROM MALAYSIA

2014 ◽  
Vol 59 (02) ◽  
pp. 1450017 ◽  
Author(s):  
YONG KANG CHEAH ◽  
ANDREW K. G. TAN
Keyword(s):  
Physical Activity ◽  
Disease Surveillance ◽  
Leisure Time ◽  
Leisure Time Physical Activity ◽  
Communicable Disease ◽  
Smoking Status ◽  
Sample Selection ◽  
Health Lifestyle ◽  
Non Communicable Disease ◽  
Heckman’S Sample Selection Model

This paper examines how socio-demographic and health-lifestyle factors determine participation and duration of leisure-time physical activity in Malaysia. Based on the Malaysia Non-Communicable Disease Surveillance-1 data, Heckman's sample selection model is employed to estimate the probability to participate and duration on physical activity. Results indicate that gender, age, years of education and family illness history are significant in explaining participation probability in leisure-time physical activity. Gender, income level, smoking-status and years of education are significant in explaining the weekly duration conditional on participation, whereas smoking-status and years of education are significant in determining the unconditional level of leisure-time physical activity.

scholarly journals Non-communicable Disease Surveillance in Malaysia: An Overview of Existing Systems and Priorities Going Forward

2021 ◽  
Vol 9 ◽  
Author(s):  
Arunah Chandran ◽  
Shurendar Selva Kumar ◽  
Noran Naqiah Hairi ◽  
Wah Yun Low ◽  
Feisul Idzwan Mustapha
Keyword(s):  
Health System ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Health Indicators ◽  
System Capacity ◽  
World Health ◽  
Multiple Sources ◽  
Monitoring Framework ◽  
Health Organization ◽  
Non Communicable Disease

In 2012, the World Health Organization (WHO) set a comprehensive set of nine global voluntary targets, including the landmark “25 by 25” mortality reduction target, and 25 indicators. WHO has also highlighted the importance of Non-Communicable Disease (NCD) surveillance as a key action by Member States in addressing NCDs. This study aimed to examine the current national NCD surveillance tools, activities and performance in Malaysia based on the WHO Global Monitoring Framework for NCDs and to highlight gaps and priorities moving forward. A desk review was conducted from August to October in 2020, to examine the current national NCD surveillance activities in Malaysia from multiple sources. Policy and program documents relating to NCD surveillance in Malaysia from 2010 to 2020 were identified and analyzed. The findings of this review are presented according to the three major themes of the Global Monitoring Framework: monitoring of exposure/risk factor, monitoring of outcomes and health system capacity/response. Currently, there is a robust monitoring system for NCD Surveillance in Malaysia for indicators that are monitored by the WHO NCD Global Monitoring Framework, particularly for outcome and exposure monitoring. However, Malaysia still lacks data for the surveillance of the health system indicators of the framework. Although Malaysia has an NCD surveillance in place that is adequate for the WHO NCD Global Monitoring Framework, there are areas that require strengthening. The country must also look beyond these set of indicators in view of the increasing burden and impact of the COVID-19 pandemic. This includes incorporating mental health indicators and leveraging on alternate sources of data relating to behaviors.

scholarly journals Spatio-Temporal Cluster Detection for Legionellosis using Multiple Patient Addresses

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Eric R. Peterson ◽  
Sharon K. Greene
Keyword(s):  
New York ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Onset Date ◽  
Preliminary Evaluation ◽  
Case File ◽  
Temporal Cluster ◽  
Address Data ◽  
Scanning Window ◽  
Spatio Temporal

ObjectiveTo improve timeliness and sensitivity of legionellosis clusterdetection in New York City (NYC) by using all addresses availablefor each patient in one analysis.IntroductionThe Bureau of Communicable Disease (BCD) at the NYCDepartment of Health and Mental Hygiene performs daily automatedanalyses using SaTScan to detect spatio-temporal clusters for37 reportable diseases.1Initially, we analyzed one address per patient,prioritizing home address if available. On September 25, 2015, aBCD investigator noticed two legionellosis cases with similar workaddresses. A third case was identified in a nearby residential facility,and an investigation was initiated to identify a common exposuresource. Four days later, after additional cases living nearby werereported, the SaTScan analysis detected a corresponding cluster.In response to this signaling delay, we implemented a multiple address(MA) analysis to improve upon single address (SA) analyses by usingall location data available on possible exposure sites.2MethodsPositiveLegionellatest results for NYC residents are reported toBCD with patient demographic and address data. BCD interviews allcases to elicit additional locations of potential exposure and enters theaddresses into a disease surveillance database (Maven). Addressesare assigned X/Y coordinates in near real-time via integration with ageocoding webservice.We used the prospective space-time permutation scan statistic inSaTScan,3enabling the advanced input feature on the spatial neighborstab to “include location ID in the scanning window if at least one setof coordinates is included.” This option considered a case as includedin a given cluster ifanyof the case’s addresses were within the cluster.The case file included: unique case ID (as the location ID), number ofcases, onset date, and day of week. The coordinate file included: caseID and X/Y coordinates for each address per case, resulting in one ormore rows per case. We searched for alive clusters with a temporalrange of 2 to 30 days and a maximum spatial size of 50% of observedcases. The study period was 1 year. Monte Carlo simulations (N=999)were used to determine statistical significance.We mimicked prospective surveillance to determine when theSeptember 2015 cluster would have been detected had this analysisbeen in place, by performing daily SA and MA analyses fromSeptember 21 (when the first outbreak-linked case was reported)to September 29 (when the initial SaTScan analysis signaled). Anycluster with a recurrence interval (RI)≥100 days was summarized ina map and linelist. Prospective, automated analyses were launchedin April 2016 and run daily using Microsoft Task Scheduler, SAS9.4, and SaTScan 9.4.1. Signals through July 2016 were summarized.ResultsIn mimicked prospective analysis, the SA and MA SaTScananalyses identified clusters of 13 and 11 cases, respectively, startingSeptember 27, 2015. The MA cluster was more spatially focused(2.11 km vs. 5.42 km) and more unlikely to occur by chance alone(RI of 16,256 days vs. 8,758 days). In prospective analyses, a MAcluster of 6 cases was identified on July 5, 2016 with a radius of1.69 km (RI=100 days). On July 6, the MA cluster case countincreased to 7 and maintained the same radius (RI=685 days), whilea cluster of the same 7 cases was identified by the SA analysis witha larger radius (1.97 km) and lower RI (292 days). The RI for bothclusters peaked on July 7 (MA: 2348 days, SA: 713 days).ConclusionsIn preliminary evaluation, the MA analysis facilitated clusterdetection using non-residential possible exposure sites, such asworkplaces. Timeliness was slightly improved, but the larger practicalbenefit was identifying more spatially focused clusters. Smallerclusters are useful for more precisely targeting legionellosis infectionsource identification and remediation activities, especially in urbanenvironments with high population and building densities.

scholarly journals Advances in spatiotemporal models for non-communicable disease surveillance

2020 ◽  
Vol 49 (Supplement_1) ◽  
pp. i26-i37
Author(s):  
Marta Blangiardo ◽  
Areti Boulieri ◽  
Peter Diggle ◽  
Frédéric B Piel ◽  
Gavin Shaddick ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Communicable Disease ◽  
Surveillance Systems ◽  
Complex Data ◽  
Bayesian Hierarchical ◽  
Complex Models ◽  
Intervention Policy ◽  
Spatiotemporal Models ◽  
Modelling Process ◽  
Non Communicable Disease

Abstract Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.

scholarly journals Biomolecules and Electrochemical Tools in Chronic Non-Communicable Disease Surveillance: A Systematic Review

Biosensors ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 121
Author(s):  
Ana Lúcia Morais ◽  
Patrícia Rijo ◽  
María Belén Batanero Hernán ◽  
Marisa Nicolai
Keyword(s):  
Disease Surveillance ◽  
Molecular Detection ◽  
Communicable Disease ◽  
Biological Fluids ◽  
Electrochemical Techniques ◽  
Disease Diagnosis ◽  
Electrochemical Analysis ◽  
Detection Techniques ◽  
Biological Identification ◽  
Non Communicable Disease

Over recent three decades, the electrochemical techniques have become widely used in biological identification and detection, because it presents optimum features for efficient and sensitive molecular detection of organic compounds, being able to trace quantities with a minimum of reagents and sample manipulation. Given these special features, electrochemical techniques are regularly exploited in disease diagnosis and monitoring. Specifically, amperometric electrochemical analysis has proven to be quite suitable for the detection of physiological biomarkers in monitoring health conditions, as well as toward the control of reactive oxygen species released in the course of oxidative burst during inflammatory events. Besides, electrochemical detection techniques involve a simple and swift assessment that provides a low detection-limit for most of the molecules enclosed biological fluids and related to non-transmittable morbidities.

scholarly journals Potential use of telephone-based survey for non-communicable disease surveillance in Sri Lanka

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
H. M. M. Herath ◽  
N. P. Weerasinghe ◽  
T. P. Weerarathna ◽  
A. Hemantha ◽  
A. Amarathunga
Keyword(s):  
Sri Lanka ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Potential Use ◽  
Non Communicable Disease
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