Real-time public health surveillance for emergency preparedness

Decisions and Decisional Needs of Canadians From all Provinces and Territories During the COVID-19 Pandemic: Population-Based Cross-sectional Surveys

BACKGROUND

Never before COVID-19 had Canadians faced making health-related decisions in a context of significant uncertainty. However, little is known about which type of decisions and the types of difficulties that they faced.

OBJECTIVE

We sought to identify the health-related decisions and decisional needs of Canadians.

METHODS

Our study was codesigned by researchers and knowledge users (eg, patients, clinicians). Informed by the CHERRIES (the Checklist for Reporting Results of Internet E-Surveys) reporting guideline, we conducted 2 online surveys of random samples drawn from the Leger consumer panel of 400,000 Canadians. Eligible participants were adults (≥18 years) who received or were receiving any health services in the past 12 months for themselves (adults) or for their child (parent) or senior with cognitive impairment (caregiver). We assessed decisions and decisional needs using questions informed by the Ottawa Decision Support Framework, including decisional conflict and decision regret using the Decision Conflict Scale (DCS) and the Decision Regret Scale (DRS), respectively. Descriptive statistics were conducted for adults who had decided for themselves or on behalf of someone else. Significant decisional conflict (SDC) was defined as a total DCS score of >37.5 out of 100, and significant decision regret was defined as a total DRS score of >25 out of 100.

RESULTS

From May 18 to June 4, 2021, 14,459 adults and 6542 parents/caregivers were invited to participate. The invitation view rate was 15.5% (2236/14,459) and 28.3% (1850/6542); participation rate, 69.3% (1549/2236) and 28.7% (531/1850); and completion rate, 97.3% (1507/1549) and 95.1% (505/531), respectively. The survey was completed by 1454 (97.3%) adults and 438 (95.1%) parents/caregivers in English (1598/1892, 84.5%) or French (294/1892, 15.5%). Respondents from all 10 Canadian provinces and the northern territories represented a range of ages, education levels, civil statuses, ethnicities, and annual household income. Of 1892 respondents, 541 (28.6%) self-identified as members of marginalized groups. The most frequent decisions were (adults vs parents/caregivers) as follows: COVID-19 vaccination (490/1454, 33.7%, vs 87/438, 19.9%), managing a health condition (253/1454, 17.4%, vs 47/438, 10.7%), other COVID-19 decisions (158/1454, 10.9%, vs 85/438, 19.4%), mental health care (128/1454, 8.8%, vs 27/438, 6.2%), and medication treatments (115/1454, 7.9%, vs 23/438, 5.3%). Caregivers also reported decisions about moving family members to/from nursing or retirement homes (48/438, 11.0%). Adults (323/1454, 22.2%) and parents/caregivers (95/438, 21.7%) had SDC. Factors making decisions difficult were worrying about choosing the wrong option (557/1454, 38.3%, vs 184/438, 42.0%), worrying about getting COVID-19 (506/1454, 34.8%, vs 173/438, 39.5%), public health restrictions (427/1454, 29.4%, vs 158/438, 36.1%), information overload (300/1454, 20.6%, vs 77/438, 17.6%), difficulty separating misinformation from scientific evidence (297/1454, 20.4%, vs 77/438, 17.6%), and difficulty discussing decisions with clinicians (224/1454, 15.4%, vs 51/438, 11.6%). For 1318 (90.6%) adults and 366 (83.6%) parents/caregivers who had decided, 353 (26.8%) and 125 (34.2%) had significant decision regret, respectively. In addition, 1028 (50%) respondents made their decision alone without considering the opinions of clinicians.

CONCLUSIONS

During COVID-19, Canadians who responded to the survey faced several new health-related decisions. Many reported unmet decision-making needs, resulting in SDC and decision regret. Interventions can be designed to address their decisional needs and support patients facing new health-related decisions.

Improving the management and security of COVID 19 diagnostic test data with a digital platform in resource-limited settings: The case of PlaCARD in Cameroon

During the COVID 19 pandemic, round-the-clock demand for COVID -19 laboratory tests exceeded capacity, placing a significant burden on laboratory staff and infrastructure. The use of laboratory information management systems (LIMS) to streamline all phases of laboratory testing (preanalytical, analytical, and postanalytical) has become inevitable. The objective of this study is to describe the architecture, implementation, and requirements of PlaCARD, a software platform for managing patient registration, medical specimens, and diagnostic data flow, as well as reporting and authentication of diagnostic results during the 2019 coronavirus pandemic (COVID -19) in Cameroon. Building on its experience with biosurveillance, CPC developed an open-source, real-time digital health platform with web and mobile applications called PlaCARD to improve the efficiency and timing of disease-related interventions. PlaCARD was quickly adapted to the decentralization strategy of the COVID 19 testing in Cameroon and, after specific user training, was deployed in all COVID 19 diagnostic laboratories and the regional emergency operations center. Overall, 71% of samples tested for COVID 19 by molecular diagnostics in Cameroon from 05 March 2020 to 31 October 2021 were entered into PlaCARD. The median turnaround time for providing results was 2 days [0-2.3] before April 2021 and decreased to 1 day [1- 1] after the introduction of SMS result notification in PlaCARD. The integration of LIMS and workflow management into a single comprehensive software platform (PlaCARD) has strengthened COVID 19 surveillance capabilities in Cameroon. PlaCARD has demonstrated that it can be used as a LIMS for managing and securing test data during an outbreak.

Workplace violence during the COVID-19 pandemic: March-October, 2020, United States

PROBLEM

COVID-19 has impacted United States workers and workplaces in multiple ways including workplace violence events (WVEs). This analysis scanned online media sources to identify and describe the characteristics of WVEs related to COVID-19 occurring in the United States during the early phases of the pandemic.

METHOD

Publicly available online media reports were searched for COVID-19-related WVEs during March 1-October 31, 2020. A list of 41 keywords was used to scan four search engines using Natural Language Processing (NLP). Authors manually reviewed media reports for inclusion using the study definition and to code variables of interest. Descriptive statistics were calculated across three types of violence: non-physical, physical, and events with both physical and non-physical violence.

RESULTS

The search of media reports found 400 WVEs related to COVID-19 during March 1-October 31, 2020. Of the WVEs, 27% (n = 108) involved non-physical violence, 27% (n = 109) physical violence, and 41% (n = 164) both physical and non-physical violence. Nineteen WVEs could not be assigned to a specific type of violence (5%). Most occurred in retail and dining establishments (n = 192, 48%; n = 74, 19%, respectively). Most WVEs related to COVID-19 were perpetrated by a customer or client (n = 298, 75%), but some were perpetrated by a worker (n = 61, 15%). Most perpetrators were males (n = 234, 59%) and acted alone (n = 313, 79%). The majority of WVEs were related to mask disputes (n = 286, 72%). In 22% of the WVEs, the perpetrator coughed or spit on a worker while threatening infection from SARS-CoV-2, the virus that causes COVID-19.

DISCUSSION

This analysis demonstrated that media scraping may be useful for workplace violence surveillance. The pandemic resulted in unique violent events, including those perpetrated by workers. Typical workplace violence prevention strategies may not be effective in reducing COVID-19-related violence. More research on workplace training for workers during public health crises is needed.

Implementing a Machine Learning Screening Tool for Malnutrition: Insights from Qualitative Research Applicable to Other ML-Based CDSS (Preprint)

BACKGROUND

Machine learning (ML)-based clinical decision support systems (CDSS) are popular in clinical practice settings but are often criticized for being limited in usability, interpretability, and effectiveness. Evaluating the implementation of ML-based CDSS is critical to ensure CDSS is acceptable and useful to clinicians and helps them deliver high-quality health care. Malnutrition is a common and underdiagnosed condition among hospital patients, which can have serious adverse impacts. Early identification and treatment of malnutrition are important.

OBJECTIVE

This study aims to evaluate the implementation of an ML tool, Malnutrition Universal Screening Tool (MUST)-Plus, that predicts hospital patients at high risk for malnutrition and identify best implementation practices applicable to this and other ML-based CDSS.

METHODS

We conducted a qualitative postimplementation evaluation using in-depth interviews with registered dietitians (RDs) who use MUST-Plus output in their everyday work. After coding the data, we mapped emergent themes onto select domains of the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework.

RESULTS

We interviewed 17 of the 24 RDs approached (71%), representing 37% of those who use MUST-Plus output. Several themes emerged: (1) enhancements to the tool were made to improve accuracy and usability; (2) MUST-Plus helped identify patients that would not otherwise be seen; perceived usefulness was highest in the original site; (3) perceived accuracy varied by respondent and site; (4) RDs valued autonomy in prioritizing patients; (5) depth of tool understanding varied by hospital and level; (6) MUST-Plus was integrated into workflows and electronic health records; and (7) RDs expressed a desire to eventually have 1 automated screener.

CONCLUSIONS

Our findings suggest that continuous involvement of stakeholders at new sites given staff turnover is vital to ensure buy-in. Qualitative research can help identify the potential bias of ML tools and should be widely used to ensure health equity. Ongoing collaboration among CDSS developers, data scientists, and clinical providers may help refine CDSS for optimal use and improve the acceptability of CDSS in the clinical context.

NextGen Public Health Surveillance and the Internet of Things (IoT)

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

Hawke's Bay pharmacists' activities during a campylobacter contamination of public water supply in Havelock North during 2016

INTRODUCTION In August 2016 contamination of the local water supply resulted in a significant gastroenteritis outbreak in Hawke's Bay. The significance of the initial test result was recognised early, partly as a result of information provided by a Havelock North pharmacist to health authorities about an unusual number of requests for anti-diarrhoeal medication. AIM To describe the breadth of activities undertaken by pharmacists working in Hawke's Bay in August 2016, following Campylobacter jejuni contamination of the public water supply in Havelock North, New Zealand. METHODS All pharmacists and hospital pharmacy management staff working in Hawke's Bay in 2017 were eligible to complete the qualitative online questionnaire. Additionally, information was requested from stakeholders with known relevant experiences. Free-text responses were thematically analysed using a general inductive approach. RESULTS Thirteen pharmacists and two ancillary staff from community pharmacy, hospital pharmacy, general practice, management, emergency response and dispensary management responded to the survey. Analysis of responses revealed three overarching themes and six sub-themes. The first was public wellbeing, with sub-themes of community information, local emergency response and pharmacy operational management. The second was pharmaceutical distribution, with a sub-theme of stock management. The third theme was clinical medicine management, with sub-themes of acute symptom management and medicine management. DISCUSSION The pharmacy profession appears to have played an important role in public wellbeing, pharmaceutical distribution and medicine therapy management during the outbreak. It is likely that through their actions, responding pharmacists reduced demand on other primary care services and prevented hospitalisations. Further research directions include exploring the effectiveness of community pharmacists in public health surveillance and the use of endorsed public health information to ensure consistent delivery of health messages.

Cutaneous leishmaniasis among Syrian refugees in Jordan

INTRODUCTION

Old World cutaneous leishmaniasis (CL) is one of the most prevalent vector-borne diseases within the World Health Organization's Eastern Mediterranean Region. The conflict in the Syrian Arab Republic generated large population movements and raised concerns about spreading of CL to countries where Syrians have relocated, including Jordan.

METHODS

A review of electronic and paper-based registries of CL cases in Jordan was conducted to assess burden of disease and associated socio-demographic factors.

RESULTS

Increasing numbers of CL cases have been reported in Jordan between 2010 and 2016 (from 140 to 281), paralleled by significant increases in incidence rate (from 2.09 per 100 000 person-years, 95% CI (1.77-2.47), to 2.87 per 100 000 person-year, 95% CI (2.55-3.22), p = 0.002) and in the proportion of cases who are Syrian (from 8.6%-55.2%, p < 0.001). Syrian refugees have higher odds of presenting with leishmaniasis than Jordanian nationals (adjusted OR 7.1, 95% CI (6.3-8.0), p = 0.000).

CONCLUSIONS

Presence of large numbers of Syrian refugees within Jordan has so far not contributed to increased risk of developing CL for Jordanians, however surveillance, diagnosis and case management for CL should be reinforced to meet the increased burden. Electronic surveillance can help identify priority populations and areas for interventions.

Barriers to Breastfeeding in Disasters in the Context of Iran

IntroductionNatural disasters have many effects on vulnerable groups, especially infants and children. Protecting breastfeeding in disasters is important, because artificial feeding puts a lot of risk to the child. In disasters, artificial nutrition is dangerous to children and its supplementation requires special equipment. There is little information on the nutritional status of infants after disasters in Iran.ProblemThe purpose of this study was to explore the barriers to appropriate lactation after disasters in Iran.

METHOD

This was a qualitative study using a content analysis method. A total of 19 midwives with disaster-relief experiences were approached for interview. Data were collected using semi-structured interviews. Data analysis was performed using the Graneheim's approach.

RESULTS

The categories of maternal factors, neonatal factors, management factors, and context-base factors were extracted from the data.

CONCLUSION

The challenges of social support, mothers' self-efficacy, educated staff for disasters, and privacy for breastfeeding can be considered as important barriers to breastfeeding in disasters. Training programs, as well as health system support, can help overcome the breastfeeding barriers in disasters. MirMohamadaliIeM, Khani JazaniR, SohrabizadehS, Nikbakht NasrabadiA. Barriers to breastfeeding in disasters in the context of Iran. Prehosp Disaster Med. 2019;34(1):20-24.

Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study

Background

New nursing homes (NH) data warehouses fed from residents’ medical records allow monitoring the health of elderly population on a daily basis. Elsewhere, syndromic surveillance has already shown that professional data can be used for public health (PH) surveillance but not during a long-term follow-up of the same cohort.

Objective

This study aimed to build and assess a national ecological NH PH surveillance system (SS).

Methods

Using a national network of 126 NH, we built a residents’ cohort, extracted medical and personal data from their electronic health records, and transmitted them through the internet to a national server almost in real time. After recording sociodemographic, autonomic and syndromic information, a set of 26 syndromes was defined using pattern matching with the standard query language-LIKE operator and a Delphi-like technique, between November 2010 and June 2016. We used early aberration reporting system (EARS) and Bayes surveillance algorithms of the R surveillance package (Höhle) to assess our influenza and acute gastroenteritis (AGE) syndromic data against the Sentinelles network data, French epidemics gold standard, following Centers for Disease Control and Prevention surveillance system assessment guidelines.

Results

By extracting all sociodemographic residents’ data, a cohort of 41,061 senior citizens was built. EARS_C3 algorithm on NH influenza and AGE syndromic data gave sensitivities of 0.482 and 0.539 and specificities of 0.844 and 0.952, respectively, over a 6-year period, forecasting the last influenza outbreak by catching early flu signals. In addition, assessment of influenza and AGE syndromic data quality showed precisions of 0.98 and 0.96 during last season epidemic weeks’ peaks (weeks 03-2017 and 01-2017) and precisions of 0.95 and 0.92 during last summer epidemic weeks’ low (week 33-2016).

Conclusions

This study confirmed that using syndromic information gives a good opportunity to develop a genuine French national PH SS dedicated to senior citizens. Access to senior citizens’ free-text validated health data on influenza and AGE responds to a PH issue for the surveillance of this fragile population. This database will also make possible new ecological research on other subjects that will improve prevention, care, and rapid response when facing health threats.

How Prepared Are We for Possible Bioterrorist Attacks: An Approach from Emergency Medicine Perspective

Preparedness for bioterrorist attacks and early recognition of specific agents are essential for public health. Emergency departments may play an important role in this field. The large spectrum of bioterrorism involves not only disastrous terrorism with mass casualties, but also microevents using low technology but producing civil unrest, disruption, disease, disabilities, and death. It aims not only to cause mortality and morbidity, but also to lead to social and political disruption. Preparedness appears to be the most potent defense against possible bioterrorist events. In this article, we aim to create awareness against biological agents and underline the importance of emergency departments in this public health problem.

Right Here Right Now (RHRN) pilot study: testing a method of near-real-time data collection on the social determinants of health

BACKGROUND

Informing policy and practice with up-to-date evidence on the social determinants of health is an ongoing challenge. One limitation of traditional approaches is the time-lag between identification of a policy or practice need and availability of results. The Right Here Right Now (RHRN) study piloted a near-real-time data-collection process to investigate whether this gap could be bridged.

METHODS

A website was developed to facilitate the issue of questions, data capture and presentation of findings. Respondents were recruited using two distinct methods - a clustered random probability sample, and a quota sample from street stalls. Weekly four-part questions were issued by email, Short Messaging Service (SMS or text) or post. Quantitative data were descriptively summarised, qualitative data thematically analysed, and a summary report circulated two weeks after each question was issued. The pilot spanned 26 weeks.

RESULTS

It proved possible to recruit and retain a panel of respondents providing quantitative and qualitative data on a range of issues. The samples were subject to similar recruitment and response biases as more traditional data-collection approaches. Participants valued the potential to influence change, and stakeholders were enthusiastic about the findings generated, despite reservations about the lack of sample representativeness. Stakeholders acknowledged that decision-making processes are not flexible enough to respond to weekly evidence.

CONCLUSION

RHRN produced a process for collecting near-real-time data for policy-relevant topics, although obtaining and maintaining representative samples was problematic. Adaptations were identified to inform a more sustainable model of near-real-time data collection and dissemination in the future.

Mental Health-related Emergency Department Visits Associated With Cannabis in Colorado

BACKGROUND

Cannabis legalization in Colorado resulted in increased cannabis-associated health care utilization. Our objective was to examine cooccurrence of cannabis and mental health diagnostic coding in Colorado emergency department (ED) discharges and replicate the study in a subpopulation of ED visits where cannabis involvement and psychiatric diagnosis were confirmed through medical review.

METHODS

We collected statewide ED International Classification of Diseases, 9th Revision, Clinical Modification diagnoses from the Colorado Hospital Association and a subpopulation of ED visits from a large, academic hospital from 2012 to 2014. Diagnosis codes identified visits associated with mental health and cannabis. Codes for mental health conditions and cannabis were confirmed by manual records review in the academic hospital subpopulation. Prevalence ratios (PRs) of mental health ED discharges were calculated to compare cannabis-associated visits to those without cannabis. Rates of mental health and cannabis-associated ED discharges were examined over time.

RESULTS

Statewide data demonstrated a fivefold higher prevalence of mental health diagnoses in cannabis-associated ED visits (PR = 5.35, 95% confidence interval [CI], 5.27-5.43) compared to visits without cannabis. The hospital subpopulation supported this finding with a fourfold higher prevalence of psychiatric complaints in cannabis attributable ED visits (PR = 4.87, 95% CI = 4.36-5.44) compared to visits not attributable to cannabis. Statewide rates of ED visits associated with both cannabis and mental health significantly increased from 2012 to 2014 from 224.5 to 268.4 per 100,000 (p < 0.0001).

CONCLUSIONS

In Colorado, the prevalence of mental health conditions in ED visits with cannabis-associated diagnostic codes is higher than in those without cannabis. There is a need for further research determining if these findings are truly attributed to cannabis or merely coincident with concurrent increased use and availability.

National and Regional Representativeness of Hospital Emergency Department Visit Data in the National Syndromic Surveillance Program, United States, 2014

OBJECTIVE

We examined the representativeness of the nonfederal hospital emergency department (ED) visit data in the National Syndromic Surveillance Program (NSSP).

METHODS

We used the 2012 American Hospital Association Annual Survey Database, other databases, and information from state and local health departments participating in the NSSP about which hospitals submitted data to the NSSP in October 2014. We compared ED visits for hospitals submitting data with all ED visits in all 50 states and Washington, DC.

RESULTS

Approximately 60.4 million of 134.6 million ED visits nationwide (~45%) were reported to have been submitted to the NSSP. ED visits in 5 of 10 regions and the majority of the states were substantially underrepresented in the NSSP. The NSSP ED visits were similar to national ED visits in terms of many of the characteristics of hospitals and their service areas. However, visits in hospitals with the fewest annual ED visits, in rural trauma centers, and in hospitals serving populations with high percentages of Hispanics and Asians were underrepresented.

CONCLUSIONS

NSSP nonfederal hospital ED visit data were representative for many hospital characteristics and in some geographic areas but were not very representative nationally and in many locations. Representativeness could be improved by increasing participation in more states and among specific types of hospitals. (Disaster Med Public Health Preparedness. 2016;10:562-569).

Missouri K-12 school collection and reporting of school-based syndromic surveillance data: a cross sectional study

BACKGROUND

School participation in collecting and reporting syndromic surveillance (SS) data to public health officials and school nurses' attitudes regarding SS have not been assessed.

METHODS

An online survey was sent to Missouri Association of School Nurses members during the 2013/2014 school year to assess whether K-12 schools were collecting and reporting SS data. Z-scores were used to assess collection versus reporting of SS indicators. Logistic regressions were used to describe factors predicting nurses' collection and reporting of SS indicators: all-cause absenteeism, influenza-like illness and gastrointestinal illness. Univariate predictors were assessed with Chi-Squares.

RESULTS

In total, 133 school nurses participated (33.6 % response rate). Almost all (90.2 %, n = 120) collect at least one SS indicator; half (49.6 %, n = 66) report at least one. Schools are collecting more SS data than they are reporting to the health department (p < .05 for all comparisons). Determinants of school nurses' collection of SS data included perceived administrative support, and knowledge of collecting and analyzing SS data. The strongest predictive factors for reporting SS data were the perception that the health department was interested in SS data and being approached by the health department to collect SS data.

CONCLUSION

Schools are collecting SS indicators at a relatively high rate, yet less than half of the data is reported to public health officials. Findings from this study indicate that public health officials can increase access to school-based SS data by approaching schools about collecting and reporting this important data.

Big bad data: law, public health, and biomedical databases

The accelerating adoption of electronic health record (EHR) systems will have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs. Because of clinicians' workloads, poor user-interface design, and other factors, EHR data can be erroneous, miscoded, fragmented, and incomplete. In addition, public health findings can be tainted by the problems of selection bias, confounding bias, and measurement bias. These flaws may become all the more troubling and important in an era of electronic "big data," in which a massive amount of information is processed automatically, without human checks. Thus, we conclude the paper by outlining several regulatory and other interventions to address data analysis difficulties that could result in invalid conclusions and unsound public health policies.

Developing a health system approach to disaster management: A qualitative analysis of the core literature to complement the WHO Toolkit for assessing health-system capacity for crisis management

BACKGROUND The World Health Organisation's (WHO) sixty-fourth World Health Assembly in May 2011 adopted a resolution on 'strengthening national health emergency and disaster management capacities and resilience of health systems'. Disaster management is a topical issue globally and countries are being encouraged to improve their disaster preparedness, along with growing international commitment to strengthening health systems. Lessons identified from disasters have not been effectively collated; essential experience is forgotten. METHODS This paper describes the analysis of the worldwide experience of disasters through a health systems approach. A systematic search of the core literature from January 2000 to November 2011 was conducted. Components drawn from the WHO's Global assessment of national health sector emergency preparedness and response baseline survey were combined with WHO's six health system building blocks (or levers) to act as the initial analysis anchors, with a further grounded theory qualitative analysis of the literature allowing the identification of emerging themes and insights. The priority areas identified by this literature review were then compared with the topics covered by the new expert-consensus-derived Toolkit for assessing health-system capacity for crisis management developed by the WHO Regional Office for Europe. FINDINGS 143 publications identified from a literature search were analysed and appraised. Themes and examples from the literature demonstrate how health system strengthening should contribute to disaster management. Priority areas under-represented in the WHO Toolkit and identified by the qualitative analysis are discussed. INTERPRETATION Collation and analysis of the disaster management literature identifies how health system strengthening can promote resilience and efficient recovery in the face of disasters. These findings support and complement the WHO Toolkit. Countries can use the literature evidence with the WHO Toolkit to assess their disaster management capacities and identify priorities for strengthening their health system.

CITATION

Bayntun C, Rockenschaub G, Murray V. Developing a health system approach to disaster management: A qualitative analysis of the core literature to complement the WHO Toolkit for assessing health-system capacity for crisis management. PLOS Currents Disasters. 2012 Aug 22. doi: 10.1371/5028b6037259a.

Application of change point analysis to daily influenza-like illness emergency department visits

BACKGROUND

The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends.

OBJECTIVE

To evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI.

METHODOLOGY AND PRINCIPAL FINDINGS

Data collected through the Distribute project (isdsdistribute.org), which aggregates data on ED visits for ILI from over 50 syndromic surveillance systems operated by state or local public health departments were used. The performance was compared of the cumulative sum (CUSUM) CPA method in combination with EARS and the performance of three CPA methods (CUSUM, structural change model and Bayesian) in detecting change points in daily time-series data from four contiguous US states participating in the Distribute network. Simulation data were generated to assess the impact of autocorrelation inherent in these time-series data on CPA performance. The CUSUM CPA method was robust in detecting change points with respect to autocorrelation in time-series data (coverage rates at 90% when -0.2≤ρ≤0.2 and 80% when -0.5≤ρ≤0.5). During the 2008-9 season, 21 change points were detected and ILI trends increased significantly after 12 of these change points and decreased nine times. In the 2009-10 flu season, we detected 11 change points and ILI trends increased significantly after two of these change points and decreased nine times. Using CPA combined with EARS to analyze automatically daily ED-based ILI data, a significant increase was detected of 3% in ILI on April 27, 2009, followed by multiple anomalies in the ensuing days, suggesting the onset of the H1N1 pandemic in the four contiguous states.

CONCLUSIONS AND SIGNIFICANCE

As a complementary approach to EARS and other aberration detection methods, the CPA method can be used as a tool to detect subtle changes in time-series data more effectively and determine the moving direction (ie, up, down, or stable) in ILI trends between change points. The combined use of EARS and CPA might greatly improve the accuracy of outbreak detection in syndromic surveillance systems.

Self-reported fever and measured temperature in emergency department records used for syndromic surveillance

Many public health agencies monitor population health using syndromic surveillance, generally employing information from emergency department (ED) visit records. When combined with other information, objective evidence of fever may enhance the accuracy with which surveillance systems detect syndromes of interest, such as influenza-like illness. This study found that patient chief complaint of self-reported fever was more readily available in ED records than measured temperature and that the majority of patients with an elevated temperature recorded also self-reported fever. Due to its currently limited availability, we conclude that measured temperature is likely to add little value to self-reported fever in syndromic surveillance for febrile illness using ED records.

Veterinary syndromic surveillance: Current initiatives and potential for development

This paper reviews recent progress in the development of syndromic surveillance systems for veterinary medicine. Peer-reviewed and grey literature were searched in order to identify surveillance systems that explicitly address outbreak detection based on systematic monitoring of animal population data, in any phase of implementation. The review found that developments in veterinary syndromic surveillance are focused not only on animal health, but also on the use of animals as sentinels for public health, representing a further step towards One Medicine. The main sources of information are clinical data from practitioners and laboratory data, but a number of other sources are being explored. Due to limitations inherent in the way data on animal health is collected, the development of veterinary syndromic surveillance initially focused on animal health data collection strategies, analyzing historical data for their potential to support systematic monitoring, or solving problems of data classification and integration. Systems based on passive notification or data transfers are now dealing with sustainability issues. Given the ongoing barriers in availability of data, diagnostic laboratories appear to provide the most readily available data sources for syndromic surveillance in animal health. As the bottlenecks around data source availability are overcome, the next challenge is consolidating data standards for data classification, promoting the integration of different animal health surveillance systems, and also the integration to public health surveillance. Moreover, the outputs of systems for systematic monitoring of animal health data must be directly connected to real-time decision support systems which are increasingly being used for disease management and control.

Accuracy of syndrome definitions based on diagnoses in physician claims

Background

Community clinics offer potential for timelier outbreak detection and monitoring than emergency departments. However, the accuracy of syndrome definitions used in surveillance has never been evaluated in community settings. This study's objective was to assess the accuracy of syndrome definitions based on diagnostic codes in physician claims for identifying 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory including influenza-like illness) in community clinics.

Methods

We selected a random sample of 3,600 community-based primary care physicians who practiced in the fee-for-service system in the province of Quebec, Canada in 2005-2007. We randomly selected 10 visits per physician from their claims, stratifying on syndrome type and presence, diagnosis, and month. Double-blinded chart reviews were conducted by telephone with consenting physicians to obtain information on patient diagnoses for each sampled visit. The sensitivity, specificity, and positive predictive value (PPV) of physician claims were estimated by comparison to chart review.

Results

1,098 (30.5%) physicians completed the chart review. A chart entry on the date of the corresponding claim was found for 10,529 (95.9%) visits. The sensitivity of syndrome definitions based on diagnostic codes in physician claims was low, ranging from 0.11 (fever) to 0.44 (respiratory), the specificity was high, and the PPV was moderate to high, ranging from 0.59 (fever) to 0.85 (respiratory). We found that rarely used diagnostic codes had a higher probability of being false-positives, and that more commonly used diagnostic codes had a higher PPV.

Conclusions

Future research should identify physician, patient, and encounter characteristics associated with the accuracy of diagnostic codes in physician claims. This would enable public health to improve syndromic surveillance, either by focusing on physician claims whose diagnostic code is more likely to be accurate, or by using all physician claims and weighing each according to the likelihood that its diagnostic code is accurate.

Prediction of gastrointestinal disease with over-the-counter diarrheal remedy sales records in the San Francisco Bay Area

Background

Water utilities continue to be interested in implementing syndromic surveillance for the enhanced detection of waterborne disease outbreaks. The authors evaluated the ability of sales of over-the-counter diarrheal remedies available from the National Retail Data Monitor to predict endemic and epidemic gastrointestinal disease in the San Francisco Bay Area.

Methods

Time series models were fit to weekly diarrheal remedy sales and diarrheal illness case counts. Cross-correlations between the pre-whitened residual series were calculated. Diarrheal remedy sales model residuals were regressed on the number of weekly outbreaks and outbreak-associated cases. Diarrheal remedy sales models were used to auto-forecast one week-ahead sales. The sensitivity and specificity of signals, generated by observed diarrheal remedy sales exceeding the upper 95% forecast confidence interval, in predicting weekly outbreaks were calculated.

Results

No significant correlations were identified between weekly diarrheal remedy sales and diarrhea illness case counts, outbreak counts, or the number of outbreak-associated cases. Signals generated by forecasting with the diarrheal remedy sales model did not coincide with outbreak weeks more reliably than signals chosen randomly.

Conclusions

This work does not support the implementation of syndromic surveillance for gastrointestinal disease with data available though the National Retail Data Monitor.