The rationale for developing public health surveillance systems based on emergency department data

Diagnosing an overcrowded emergency department from its Electronic Health Records

Emergency department overcrowding is a complex problem that persists globally. Data of visits constitute an opportunity to understand its dynamics. However, the gap between the collected information and the real-life clinical processes, and the lack of a whole-system perspective, still constitute a relevant limitation. An analytical pipeline was developed to analyse one-year of production data following the patients that came from the ED (n = 49,938) at Uppsala University Hospital (Uppsala, Sweden) by involving clinical experts in all the steps of the analysis. The key internal issues to the ED were the high volume of generic or non-specific diagnoses from non-urgent visits, and the delayed decision regarding hospital admission caused by several imaging assessments and lack of hospital beds. Furthermore, the external pressure of high frequent re-visits of geriatric, psychiatric, and patients with unspecified diagnoses dramatically contributed to the overcrowding. Our work demonstrates that through analysis of production data of the ED patient flow and participation of clinical experts in the pipeline, it was possible to identify systemic issues and directions for solutions. A critical factor was to take a whole systems perspective, as it opened the scope to the boundary effects of inflow and outflow in the whole healthcare system.

An Evaluation of Syndromic Surveillance-Related Practices Among Selected State and Local Health Agencies

CONTEXT

Syndromic surveillance consists of the systematic collection and use of near real-time data about health-related events for situational awareness and public health action. As syndromic surveillance programs continue to adopt new technologies and expand, it is valuable to evaluate these syndromic surveillance systems and practices to ensure that they meet public health needs.

OBJECTIVE

This assessment's aim is to provide recent information about syndromic surveillance systems and practice characteristics among a group of state and local health departments.

DESIGN/SETTING

Information was obtained between November 2017 and June 2018 through a telephone survey using an Office of Management and Budget-approved standardized data collection tool. Participants were syndromic surveillance staff from each of 31 state and local health departments participating in the National Syndromic Surveillance Program funded by the Centers for Disease Control and Prevention. Questions included jurisdictional experience, data sources and analysis systems used, syndromic system data processing characteristics, data quality verification procedures, and surveillance activities conducted with syndromic data.

MEASURES

Practice-specific information such as types of systems and data sources used for syndromic surveillance, data quality monitoring, and uses of data for public health situational awareness (eg, investigating occurrences of or trends in diseases).

RESULTS

The survey analysis revealed a wide range of experiences with syndromic surveillance. Participants reported the receipt of data daily or more frequently. Emergency department data were the primary data source; however, other data sources are being integrated into these systems. All health departments routinely monitored data quality. Syndromes of highest priority across the respondents for health events monitoring were influenza-like illness and drug-related syndromes. However, a wide variety of syndromes were reported as priorities across the health departments.

CONCLUSION

Overall, syndromic surveillance was relevantly integrated into the public health surveillance infrastructure. The near real-time nature of the data and its flexibility to monitor different types of health-related issues make it especially useful for public health practitioners. Despite these advances, syndromic surveillance capacity, locally and nationally, must continue to evolve and progress should be monitored to ensure that syndromic surveillance systems and data are optimally able to meet jurisdictional needs.

Future perspectives of wastewater-based epidemiology: Monitoring infectious disease spread and resistance to the community level

Infectious diseases are acknowledged as one of the most critical threats to global public health today. Climate change, unprecedented population growth with accelerated rates of antimicrobial resistance, have resulted in both the emergence of novel pathogenic organisms and the re-emergence of infections that were once controlled. The consequences have led to an increased vulnerability to infectious diseases globally. The ability to rapidly monitor the spread of diseases is key for prevention, intervention and control, however several limitations exist for current surveillance systems and the capacity to cope with the rapid population growth and environmental changes. Wastewater-Based Epidemiology (WBE) is a new epidemiology tool that has potential to act as a complementary approach for current infectious disease surveillance systems and an early warning system for disease outbreaks. WBE postulates that through the analysis of population pooled wastewater, infectious disease and resistance spread, the emergence of new disease outbreak to the community level can be monitored comprehensively and in real-time. This manuscript provides critical overview of current infectious disease surveillance status, as well as it introduces WBE and its recent advancements. It also provides recommendations for further development required for WBE application as an effective tool for infectious disease surveillance.

Using Indirect Measures to Identify Geographic Hot Spots of Poor Glycemic Control: Cross-sectional Comparisons With an A1C Registry

OBJECTIVE

Focusing health interventions in places with suboptimal glycemic control can help direct resources to neighborhoods with poor diabetes-related outcomes, but finding these areas can be difficult. Our objective was to use indirect measures versus a gold standard, population-based A1C registry to identify areas of poor glycemic control.

RESEARCH DESIGN AND METHODS

Census tracts in New York City (NYC) were characterized by race, ethnicity, income, poverty, education, diabetes-related emergency visits, inpatient hospitalizations, and proportion of adults with diabetes having poor glycemic control, based on A1C >9.0% (75 mmol/mol). Hot spot analyses were then performed, using the Getis-Ord Gi* statistic for all measures. We then calculated the sensitivity, specificity, positive and negative predictive values, and accuracy of using the indirect measures to identify hot spots of poor glycemic control found using the NYC A1C Registry data.

RESULTS

Using A1C Registry data, we identified hot spots in 42.8% of 2,085 NYC census tracts analyzed. Hot spots of diabetes-specific inpatient hospitalizations, diabetes-specific emergency visits, and age-adjusted diabetes prevalence estimated from emergency department data, respectively, had 88.9%, 89.6%, and 89.5% accuracy for identifying the same hot spots of poor glycemic control found using A1C Registry data. No other indirect measure tested had accuracy >80% except for the proportion of minority residents, which had 86.2% accuracy.

CONCLUSIONS

Compared with demographic and socioeconomic factors, health care utilization measures more accurately identified hot spots of poor glycemic control. In places without a population-based A1C registry, mapping diabetes-specific health care utilization may provide actionable evidence for targeting health interventions in areas with the highest burden of uncontrolled diabetes.

Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City

Background

Monitoring and detecting sudden outbreaks of respiratory infectious disease is important. Emergency Department (ED)-based syndromic surveillance systems have been introduced for early detection of infectious outbreaks. The aim of this study was to develop and validate a forecasting model of respiratory infectious disease outbreaks based on a nationwide ED syndromic surveillance using daily number of emergency department visits with fever.

Methods

We measured the number of daily ED visits with body temperature ≥ 38.0 °C and daily number of patients diagnosed as respiratory illness by the ICD-10 codes from the National Emergency Department Information System (NEDIS) database of Seoul, Korea. We developed a forecast model according to the Autoregressive Integrated Moving Average (ARIMA) method using the NEDIS data from 2013 to 2014 and validated it using the data from 2015. We defined alarming criteria for extreme numbers of ED febrile visits that exceed the forecasted number. Finally, the predictive performance of the alarm generated by the forecast model was estimated.

Results

From 2013 to 2015, data of 4,080,766 ED visits were collected. 303,469 (7.4%) were ED visits with fever, and 388,943 patients (9.5%) were diagnosed with respiratory infectious disease. The ARIMA (7.0.7) model was the most suitable model for predicting febrile ED visits the next day. The number of patients with respiratory infectious disease spiked concurrently with the alarms generated by the forecast model.

Conclusions

A forecast model using syndromic surveillance based on the number of ED visits was feasible for early detection of ED respiratory infectious disease outbreak.

The Pakistan National Emergency Department Surveillance Study (Pak-NEDS): Introducing a pilot surveillance

BACKGROUND

Evidence-based decision making is essential for appropriate prioritization and service provision by healthcare systems. Despite higher demands, data needs for this practice are not met in many cases in low- and middle-income countries because of underdeveloped sources, among other reasons. Emergency departments (EDs) provide an important channel for such information because of their strategic position within healthcare systems. This paper describes the design and pilot test of a national ED based surveillance system suitable for the Pakistani context.

METHODS

The Pakistan National Emergency Department Surveillance Study (Pak-NEDS) was pilot tested in the emergency departments of seven major tertiary healthcare centres across the country. The Aga Khan University, Karachi, served as the coordinating centre. Key stakeholders and experts from all study institutes were involved in outlining data needs, development of the study questionnaire, and identification of appropriate surveillance mechanisms such as methods for data collection, monitoring, and quality assurance procedures. The surveillance system was operational between November 2010 and March 2011. Active surveillance was done 24 hours a day by data collectors hired and trained specifically for the study. All patients presenting to the study EDs were eligible participants. Over 270,000 cases were registered in the surveillance system over a period of four months. Coverage levels in the final month ranged from 91-100% and were highest in centres with the least volume of patients. Overall the coverage for the four months was 79% and crude operational costs were less than $0.20 per patient.

CONCLUSIONS

Pak-NEDS is the first multi-centre ED based surveillance system successfully piloted in a sample of major EDs having some of the highest patient volumes in Pakistan. Despite the challenges identified, our pilot shows that the system is flexible and scalable, and could potentially be adapted for many other low- and middle-income settings.

Pattern of presenting complaints recorded as near-drowning events in emergency departments: a national surveillance study from Pakistan

Background

Drowning is a heavy burden on the health systems of many countries, including Pakistan. To date, no effective large-scale surveillance has been in place to estimate rates of drowning and near-drowning in Pakistan. The Pakistan National Emergency Department Surveillance (Pak-NEDS) study aimed to fill this gap.

Methods

Patients who presented with a complaint of "near-drowning" were analyzed to explore patterns of true near-drowning (unintentional) and intentional injuries that led to the "near-drowning" complaint. Bivariate analysis was done to establish patterns among patients treated in emergency departments, including socio-demographic information, injury-related information, accompanying injuries, and emergency department resource utilization.

Results

A total of 133 patients (0.2% of all injury patients) with "near-drowning" as presenting complaints were recorded by the Pak-NEDS system. True near-drowning (50.0%) and intentional injuries that led to "near-drowning" complaints (50.0%) differed in nature of injuries. The highest proportion of true near-drowning incidents occurred among patients aged between 25-44 years (47.5%), and among males (77.5%). True near-drowning patients usually had other accompanying complaints, such as lower limb injury (40.0%). Very few patients were transported by ambulance (5.0%), and triage was done for 15% of patients. Eleven (27.5%) true near-drowning patients received cardiopulmonary resuscitation.

Conclusion

There was major under-reporting of drowning and near-drowning cases in the surveillance study. The etiology of near-drowning cases should be further studied. Patients who experienced non-fatal drownings were more commonly sent for medical care due to other accompanying conditions, rather than near-drowning event itself. There is also need for recognizing true near-drowning incidents. The results of this study provide information on data source selection, site location, emergency care standardization, and multi-sector collaboration for future drowning prevention studies.

Determining Chronic Disease Prevalence in Local Populations Using Emergency Department Surveillance

OBJECTIVES

We sought to improve public health surveillance by using a geographic analysis of emergency department (ED) visits to determine local chronic disease prevalence.

METHODS

Using an all-payer administrative database, we determined the proportion of unique ED patients with diabetes, hypertension, or asthma. We compared these rates to those determined by the New York City Community Health Survey. For diabetes prevalence, we also analyzed the fidelity of longitudinal estimates using logistic regression and determined disease burden within census tracts using geocoded addresses.

RESULTS

We identified 4.4 million unique New York City adults visiting an ED between 2009 and 2012. When we compared our emergency sample to survey data, rates of neighborhood diabetes, hypertension, and asthma prevalence were similar (correlation coefficient = 0.86, 0.88, and 0.77, respectively). In addition, our method demonstrated less year-to-year scatter and identified significant variation of disease burden within neighborhoods among census tracts.

CONCLUSIONS

Our method for determining chronic disease prevalence correlates with a validated health survey and may have higher reliability over time and greater granularity at a local level. Our findings can improve public health surveillance by identifying local variation of disease prevalence.

Survey of an Australian general emergency department: estimated prevalence of mental health disorders

Compared to the general population, people with mental health disorders have an increased risk of morbidity and mortality, and are associated with higher health-care costs and lost societal productivity. Evidence indicates that more people are presenting to emergency departments with mental health disorders and that this group represents a disproportionately large number of emergency department attendees. The study results indicate that around a third of people who attended the emergency department may have had a mental health disorder, which is more than that found in the general adult Australian population. The results also suggest that the majority of emergency department attendees that have a mental health disorder are not identified at this opportunistic point of contact. The emergency department is an ideal point of contact to screen people for mental health problems. If problems are identified early, and treatment is started early, then it is likely that more people would be helped before their mental health problem became severe. However, increased identification of mental health problems may have implications for mental health services in terms of workload and delivery. The aim of this study was to estimate the prevalence of mental health disorders in an Australian general emergency department. A cross-sectional survey was used to screen a sample of 708 patients, using the Kessler Psychological Distress Scale (K10). The mean age of participants was 50.2 years, and their mean K10 score was 19.96 (SD 7.83), with 24% categorized as having high or very high psychological distress. Seventeen per cent self-reported having a mental health issue. Post-probability calculations based on observed K10 scores estimated that 37% of participants had an actual mental health disorder. The results suggest the prevalence of mental health disorder is significantly higher in emergency department attendees than Australian population norms, supporting the contention that a substantial proportion of ED attendees has a mental health disorder that, in the majority of cases, is not investigated at this point of contact. There is potential to screen all emergency department attendees for the presence of mental health disorder; early identification of mental illness would enable early referral for treatment. However, if all patients are screened, then it is likely that more mental health conditions will be picked up. The implications for mental health nursing are that this may increase workload.

Cumulative query method for influenza surveillance using search engine data

BACKGROUND

Internet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea.

OBJECTIVES

The objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data.

METHODS

Our study was based on the local search engine, Daum (approximately 25% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson's correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient.

RESULTS

In validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of ≥.7, but 4 of 13 combined queries had an r value of ≥.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of ≥.7, but 6 of 15 combined queries had an r value of ≥.7.

CONCLUSIONS

Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set.

Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance

Background

Syndromic surveillance in emergency departments (EDs) may be used to deliver early warnings of increases in disease activity, to provide situational awareness during events of public health significance, to supplement other information on trends in acute disease and injury, and to support the development and monitoring of prevention or response strategies. Changes in mental health related ED presentations may be relevant to these goals, provided they can be identified accurately and efficiently. This study aimed to measure the accuracy of using diagnostic codes in electronic ED presentation records to identify mental health-related visits.

Methods

We selected a random sample of 500 records from a total of 1,815,588 ED electronic presentation records from 59 NSW public hospitals during 2010. ED diagnoses were recorded using any of ICD-9, ICD-10 or SNOMED CT classifications. Three clinicians, blinded to the automatically generated syndromic grouping and each other’s classification, reviewed the triage notes and classified each of the 500 visits as mental health-related or not. A “mental health problem presentation” for the purposes of this study was defined as any ED presentation where either a mental disorder or a mental health problem was the reason for the ED visit. The combined clinicians’ assessment of the records was used as reference standard to measure the sensitivity, specificity, and positive and negative predictive values of the automatic classification of coded emergency department diagnoses. Agreement between the reference standard and the automated coded classification was estimated using the Kappa statistic.

Results

Agreement between clinician’s classification and automated coded classification was substantial (Kappa = 0.73. 95% CI: 0.58 - 0.87). The automatic syndromic grouping of coded ED diagnoses for mental health-related visits was found to be moderately sensitive (68% 95% CI: 46%-84%) and highly specific at 99% (95% CI: 98%-99.7%) when compared with the reference standard in identifying mental health related ED visits. Positive predictive value was 81% (95% CI: 0.57 – 0.94) and negative predictive value was 98% (95% CI: 0.97-0.99).

Conclusions

Mental health presentations identified using diagnoses coded with various classifications in electronic ED presentation records offers sufficient accuracy for application in near real-time syndromic surveillance.

Correlation between national influenza surveillance data and google trends in South Korea

BACKGROUND

In South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea.

METHODS

Our study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearson's correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons.

RESULTS

The correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05).

CONCLUSIONS

In our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends.

Nontraumatic dental condition-related visits to emergency departments on weekdays, weekends and night hours: findings from the National Hospital Ambulatory Medical Care survey

Objective

To determine whether the rates of nontraumatic dental condition (NTDC)-related emergency department (ED) visits are higher during the typical working hours of dental offices and lower during night hours, as well as the associated factors.

Methods

We analyzed data from the National Hospital Ambulatory Medical Care Survey for 1997 through 2007 using multivariate binary and polytomous logistic regression adjusted for survey design to determine the effect of predictors on specified outcome variables.

Results

Overall, 4,726 observations representing 16.4 million NTDC-related ED visits were identified. Significant differences in rates of NTDC-related ED visits were observed with 40%–50% higher rates during nonworking hours and 20% higher rates on weekends than the overall average rate of 170 visits per hour. Compared with 19–33 year olds, subjects < 18 years old had significantly higher relative rates of NTDC-related ED visits during nonworking hours [relative rate ratio (RRR) = 1.6 to 1.8], whereas those aged 73 and older had lower relative rates during nonworking hours (RRR = 0.4; overall P = 0.0005). Compared with those having private insurance, Medicaid and self-pay patients had significantly lower relative rates of NTDC visits during nonworking and night hours (RRR = 0.6 to 0.7, overall P < 0.0003). Patients with a dental reason for visit were overrepresented during the night hours (RRR = 1.3; overall P = 0.04).

Conclusion

NTDC-related visits to ED occurred at a higher rate during nonworking hours and on weekends and were significantly associated with age, patient-stated reason for visit and payer type.

Using Emergency Department Data For Biosurveillance: The North Carolina Experience

Biosurveillance is an emerging field that provides early detection of disease outbreaks by collecting and interpreting data on a variety of public health threats. The public health system and medical care community in the United States have wrestled with developing new and more accurate methods for earlier detection of threats to the health of the public. The benefits and challenges of using Emergency Department data for surveillance are described in this chapter through examples from one biosurveillance system, the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). ED data are a proven tool for biosurveillance, and the ED data in NC DETECT have proved to be effective for a variety of public health uses, including surveillance, monitoring and investigation. A distinctive feature of ED data for surveillance is their timeliness. With electronic health information systems, these data are available in near real-time, making them particularly useful for surveillance and situational awareness in rapidly developing public health outbreaks or disasters. Challenges to using ED data for biosurveillance include the reliance on free text data (often in chief complaints). Problems with textual data are addressed in a variety of ways, including preprocessing data to clean the text entries and address negation.

The use of ED data for public health surveillance can significantly increase the speed of detecting, monitoring and investigating public health events. Biosurveillance systems that are incorporated into hospital and public health practitioner daily work flows are more effective and easily used during a public health emergency. The flexibility of a system such as NC DETECT helps it meet this level of functionality.

Research using emergency department-related data sets: current status and future directions

The 2009 Academic Emergency Medicine consensus conference focused on "Public Health in the ED: Surveillance, Screening and Intervention." One conference breakout session discussed the significant research value of health-related data sets. This article represents the proceedings from that session, primarily focusing on emergency department (ED)-related data sets and includes examples of the use of a data set based on ED visits for research purposes. It discusses types of ED-related data sets available, highlights barriers to research use of ED-related data sets, and notes limitations of these data sets. The paper highlights future directions and challenges to using these important sources of data for research, including identification of five main needs related to enhancing the use of ED-related data sets. These are 1) electronic linkage of initial and follow-up ED visits and linkage of information about ED visits to other outcomes, including costs of care, while maintaining de-identification of the data; 2) timely data access with minimal barriers; 3) complete data collection for clinically relevant and/or historical data elements, such as the external cause-of-injury code; 4) easy access to data that can be parsed into smaller jurisdictions (such as states) for policy and/or research purposes, while maintaining confidentiality; and 5) linkages between health survey data and health claims data. ED-related data sets contain much data collected directly from health care facilities, individual patient records, and multiple other sources that have significant potential impact for studying and improving the health of individuals and the population.

The 2005 Wellington influenza outbreak: syndromic surveillance of Wellington Hospital Emergency Department activity may have provided early warning

OBJECTIVES

To assess whether the Wellington Emergency Department (ED) Respiratory Syndromic Surveillance System may have provided early warning of the influenza outbreak in Wellington schools during 2005, and as a result might have provided the opportunity for an earlier or more effective public health response.

METHODS

All events of respiratory syndrome, as defined by selected ICD 10 codes, were extracted from Wellington Hospital ED for the dates 1 January 2004 to 31 December 2006, and analysed using the Centers for Disease Control and Prevention (CDC) surveillance program, Early Aberration Reporting System (EARS). Daily events were analysed for total counts and by lifecycle age group. Seven day moving averages of the numbers of events were also calculated.

RESULTS

This study indicated that the surveillance system may have provided early warning of a potential respiratory outbreak. Regular exceedance flags were generated nine days prior to the initial notification received by Regional Public Health (RPH). The surveillance system also provided information on the type of illness (respiratory), the groups affected (5-14 year olds), and the progression of the outbreak (peak, end).

CONCLUSIONS

The surveillance system might have worked by providing early notification of the outbreak. This may have prompted RPH to earlier investigate the potential outbreak and may have led to an earlier response.

IMPLICATIONS

Surveillance of Emergency Department activity may be useful for early public health response.

Syndromic surveillance: sensitivity and positive predictive value of the case definitions

The aim of the study was to measure the positive predictive value (PPV) and sensitivity of operational case definitions of 13 syndromes in a surveillance system based on the Emergency online database of the Lazio region. The PPVs were calculated using electronic emergency department (ED) medical records and subsequent hospitalizations to ascertain the cases. Sensitivity was calculated using a modified capture–recapture method. The number of cases that fulfilled the case definition criteria in the 2004 database ranged from 27 320 for gastroenteritis to three for haemorrhagic diarrhoea. The PPVs ranged from 99·3 to 20; sepsis, meningitis-like and coma were below 50%. The estimated sensitivity ranged from 90% for coma to 22% for haemorrhagic diarrhoea. Syndromes such as gastroenteritis, where the signs, symptoms, and exposure history provide immediate diagnostic implications fit this surveillance system better than others such as haemorrhagic diarrhoea, where symptoms are not evident and a more precise diagnosis is needed.

Health promotion and disease prevention in the emergency department

This article provides an overview of health promotion and disease and injury prevention concepts. It provides an emergency medicine perspective and reviews approaches that can be used in the emergency department. It discusses examples of innovative emergency medicine-based preventive activities including prevention in the prehospital setting. This article ends with a discussion of the importance of a system approach to prevention and suggests a role for a preventionist as a new member of the emergency medicine team.

Update on public health surveillance in emergency departments

The development of public health surveillance systems based on ED visits, in conjunction with other health and non-health-related data, is an important step to better understanding the health needs of the US population. There are multiple steps to develop a functional organization, and these actions require the support and involvement of many different partners. The ability to (1) analyze data; (2) distribute results; and (3) influence policy, funding, and patients' behavior are important outgrowths of these activities. This chapter discusses the opportunities and obstacles related to public health surveillance systems based on ED data.

Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance

BACKGROUND

Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC).

METHODS

We collected ED CCs from 2/1/94 - 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 - 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real).

RESULTS

Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets.

CONCLUSION

We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results.

The validity of chief complaint and discharge diagnosis in emergency department-based syndromic surveillance

OBJECTIVE

Emergency department (ED)-based syndromic surveillance systems are being used by public health departments to monitor for outbreaks of infectious diseases, including bioterrorism; however, few systems have been validated. The authors evaluated a "drop-in" syndromic surveillance system by comparing syndrome categorization in the ED with chief complaints and ED discharge diagnoses from medical record review.

METHODS

A surveillance form was completed for each ED visit at 15 participating Arizona hospitals between October 27 and November 18, 2001. Each patient visit was assigned one of ten clinical syndromes or "none." For six of 15 EDs, kappa statistics were used to compare syndrome agreement between surveillance forms and syndrome categorization with chief complaint and ED discharge diagnosis from medical record review.

RESULTS

Overall, agreement between surveillance forms and ED discharge diagnoses (kappa = 0.55; 95% confidence interval [CI] = 0.52 to 0.59) was significantly higher than between surveillance forms and chief complaints (kappa = 0.48; 95% CI = 0.44 to 0.52). Agreement between chief complaints and ED discharge diagnoses was poor for respiratory tract infection with fever (kappa = 0.33; 95% CI = 0.27 to 0.39). Furthermore, pediatric chief complaints showed lower agreement for respiratory tract infection with fever when compared with adults (kappa = 0.34 [95% CI = 0.20 to 0.47] vs. kappa = 0.44 [95% CI = 0.28 to 0.59], respectively).

CONCLUSIONS

In general, this syndromic surveillance system classified patients into appropriate syndrome categories with fair to good agreement compared with chief complaints and discharge diagnoses. The present findings suggest that use of ED discharge diagnoses, in addition to or instead of chief complaints, may increase surveillance validity for both automated and drop-in syndromic surveillance systems.

Work-related amputations in Michigan, 1997

BACKGROUND

Work-related amputations are of concern in Michigan and nationally. This study reports on 1 year of data on work-related amputations, which were treated in Michigan hospital emergency departments (ED) or as in-patients in Michigan.

METHODS

Michigan hospitals provided face sheets and discharge summaries of in-patient and ED visits for work-related amputations that occurred in 1997. Information was also obtained about worksite inspections associated with reported amputations from the Michigan Occupational Safety and Health Act (MIOSHA) program. Data from this study and from Michigan workers compensation were used to generate an estimate of the true numbers of work-related amputations in Michigan in 1997.

RESULTS

Three hundred thirty-nine work-related amputations were identified by hospitals. Powered saws and power presses were the leading sources of injury. MIOSHA completed 30 enforcement inspections related to these amputations. Our best estimate of the total numbers of work-related amputations in 1997 for Michigan was 693, of which 562 resulted in hospitalization or ED treatment.

CONCLUSIONS

In-patient and ED records provided information for identifying high risk groups and problem worksites in Michigan. Estimates generated from these data underscore that data on work-related amputations released by the Bureau of Labor Statistics (BLS), which reported 440 amputations in 1997, are a significant undercount--only 64%--of the true number of cases. Better integration of public health data into OSHA enforcement activity is needed.

Unintentional residential child injury surveillance in Hong Kong

OBJECTIVES

To provide an overall pattern of morbidity in unintentional residential childhood injuries (URCI) in Hong Kong.

METHODOLOGY

A cross-sectional telephone survey of caregivers of children aged under 16-years and adolescents suffering from URCI and admitted to three selected local Accident and Emergency Departments.

RESULTS

Falls, cuts and scalds were the most common external causes of URCI observed, while boys predominated in the sample population. Most of the observed URCI were of moderate to mild severity. Children of new immigrant mothers were more likely to receive first aid immediately after the incidents. Parents were aware of potentially injurious behaviour and intervened on occasion, but most resorted to verbal warnings only.

CONCLUSIONS

Prevalence of falls among observed URCI offers evidence in support of the hypothesis that the high population density in Hong Kong plays an integral role in understanding mechanisms of morbidity. Parents show concern about URCI but often lack substantial action that modifies injury risk. Considering the local injury differentials, an active prevention effort such as behavioural intervention and education for parents may be useful.