Prospective syndromic surveillance of emergency department visits has been used for near-real time tracking of communicable diseases to detect outbreaks or other unexpected disease clusters. The utility of syndromic surveillance for tracking cardiovascular events, which may be influenced by environmental factors and influenza, has not been evaluated. We developed and evaluated a method for tracking cardiovascular events using emergency department free-text chief complaints.
There were three phases to our analysis. First we applied text processing algorithms based on sensitivity, specificity, and positive predictive value to chief complaint data reported by 11 New York City emergency departments for which ICD-9 discharge diagnosis codes were available. Second, the same algorithms were applied to data reported by a larger sample of 50 New York City emergency departments for which discharge diagnosis was unavailable. From this more complete data, we evaluated the consistency of temporal variation of cardiovascular syndromic events and hospitalizations from 76 New York City hospitals. Finally, we examined associations between particulate matter ≤2.5 µm (PM2.5), syndromic events, and hospitalizations. Sensitivity and positive predictive value were low for syndromic events, while specificity was high. Utilizing the larger sample of emergency departments, a strong day of week pattern and weak seasonal trend were observed for syndromic events and hospitalizations. These time-series were highly correlated after removing the day-of-week, holiday, and seasonal trends. The estimated percent excess risks in the cold season (October to March) were 1.9% (95% confidence interval (CI): 0.6, 3.2), 2.1% (95% CI: 0.9, 3.3), and 1.8% (95%CI: 0.5, 3.0) per same-day 10 µg/m3 increase in PM2.5 for cardiac-only syndromic data, cardiovascular syndromic data, and hospitalizations, respectively.
Citation: Mathes RW, Ito K, Matte T (2011) Assessing Syndromic Surveillance of Cardiovascular Outcomes from Emergency Department Chief Complaint Data in New York City. PLoS ONE 6(2): e14677. https://doi.org/10.1371/journal.pone.0014677
Editor: J. Jaime Miranda, Universidad Peruana Cayetano Heredia, Peru
Received: May 28, 2010; Accepted: January 14, 2011; Published: February 14, 2011
Copyright: © 2011 Mathes et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Environmental Protection Agency, STAR Grant R833623010 and National Institute of Environmental Health Sciences (NIEHS) Center grant ES00260. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) implemented an active electronic prospective syndromic surveillance system in 2001 that monitors emergency department (ED) visits in near-real-time to identify temporal or spatial clusters in illness . A main advantage of syndromic systems over traditional surveillance is timeliness. As in other jurisdictions where near-real-time tracking of emergency department visits is carried out, one motivation for their development was concern about biological terrorism. Most uses of these data involve tracking syndromes that may indicate communicable disease outbreaks, including the onset of the influenza season , . Syndromic surveillance has identified increases in gastrointestinal illness following a power outage , but applications of such near-real-time syndromic surveillance systems for tracking non-communicable health conditions that may be related to environmental events, such as heat related illness , ,  have been more limited.
Cardiovascular disease (CVD) morbidity has been linked to ambient air pollution especially fine particles , , , , , influenza activity  public smoking bans , , and to natural and manmade disasters , , . Typically these phenomena are investigated months or years after they occur as administrative hospital admission data become available. While syndromic surveillance of emergency department visits might allow more timely identification of these impacts, in some cases guiding public health response, the feasibility and utility of near-real-time CVD syndrome tracking using emergency department data has not been explored.
In this paper, we describe the methods of developing a CVD syndromic definition using data from emergency department visits in NYC and comparing temporal patterns in CVD events defined by syndromic coding to hospital discharges in NYC using discharge data collected by the Statewide Planning and Research Cooperative System (SPARCS) of New York State. We also examined the relationship between ambient fine particles and the CVD syndromic data and hospitalizations to test the comparability of environmental effects on these CVD morbidity indicators.
The New York City DOHMH Institutional Review Board approved this study involving existing data that could not be linked to individuals and were thus analyzed anonymously.
ED Data Collection
Data from the EDs of 50 hospitals in New York City (comprising approximately 95% of all ED visits in New York City) is sent to the DOHMH daily via direct file transfer or as an email attachment. Electronic files contain date and time of each patient admission, age, sex, residential zip code, and the patient's reason of visit or chief complaint, recorded as a free-text field. The data do not contain personal identifiers such as name, birth date, or address. Table 1 summarizes the data sources used in this analysis.
Syndrome coding ED data (11 hospitals, 2000–2002).
At the time this study was conducted, discharge diagnoses for emergency department visits were not routinely reported as part of the syndromic surveillance system. To compare chief complaint defined syndromes to discharge diagnoses, we used data from a subset (n = 11) of the 50 hospital EDs for the period January 1, 2000 through June 30, 2002, which included both chief complaint text and discharge diagnosis code, based on the International Classification of Diseases, Ninth Revision (ICD-9). Discharge diagnosis data before 2000 and after 2002 were unavailable. There were a total of 2,831,891 patient visits. We excluded 943,963 ED visits (33.3%) that had a missing or illegible chief complaint description, 3812 ED visits (0.3%) that were missing an ICD-9 diagnosis code, 3948 (0.1%) that were missing both, and 1,299,463 (45.9%) that were 39 years of age or younger given CVD outcomes are predominantly observed in older individuals. Thus our syndrome coding of ED data consisted of 580,841 (20.5%) patient visits with a valid chief complaint, ICD-9 diagnosis code, and recorded age of 40 years or older (median age: 54, interquartile range (IQR): 46–67).
Developing a Syndromic Definition.
The New York City DOHMH has developed a syndromic surveillance system that uses ED visit chief complaint information and other non-identifying data elements reported electronically each day from hospitals in New York City. Chief complaint text is scanned to identify syndromes of interest classified as respiratory, sepsis, cold, diarrhea, rash, fever/influenza, asthma, and vomiting. Details of the methods used in this system have been published elsewhere . We developed chief complaint text scanning criteria to identify possible CVD emergency department visits and compared trends in such visits to trends in hospital admissions from CVD causes.
We developed an algorithm that scans the free-text chief complaint field for character strings indicative of CVD events. CVD keywords include, for example, “heart attack,” “heart racing,” “chest pain,” “blood pressure,” “stroke,” etc. We included both events (e.g. myocardial infarction) and symptoms (e.g. chest pain) in our algorithm because the chief complaint text is completed by triage staff and, in some cases, records the patient's complaint verbatim while other descriptions correspond to a possible a diagnosis rather than a complaint. The coding algorithm is programmed to capture common misspellings and abbreviations of CVD keywords. The conditions included, comprised of a CVD keyword or combination of CVD keywords, were based on previous studies of air pollution/weather events and CVD outcomes , , and include ischemic heart disease, myocardial infarction, dysrhythmia, heart failure, hypertension, chest pain, stroke, and shortness of breath. Using our algorithms, binary variables were coded representing the presence or absence of one or more of each of the CVD condition keywords.
We then classified principal discharge ICD-9 codes in the range representing cardiovascular diseases (405–441) and symptoms (785–786) into seven categories corresponding to different keywords (Table 2). For example, the keyword “chest pain” corresponds to ICD-9 code 786.5 while “heart attack” corresponds to ICD-9 code 410.
Our initial analysis proceeded in two phases. First, we evaluated the predictive value, sensitivity, and specificity of daily counts of individual CVD symptoms and conditions (i.e. ischemic heart disease/myocardial infarction, dysrhythmia, heart failure, hypertension, chest pain, stroke, and shortness of breath) using chief complaint and ICD-9 discharge code, the latter used as the gold standard. After evaluating individual symptoms and conditions, we estimated the predictive value, sensitivity, and specificity of seven individual or combinations of syndromic keywords (Table 2). After evaluating multiple combinations of the seven keyword categories, we defined two syndromes , one based on cardiac-only keywords (Syndrome 1) and the second based on a broader set of cardiovascular keywords (Syndrome 2), that had the best overall fit based on predictive value, sensitivity, and specificity (Table 3).
Temporal analysis ED data (N = 50 hospitals, 2004–6).
We limited our primary analysis to 2004–2006 because fewer hospitals reported ED data prior to 2004 and because hospitalizations data after 2006 were unavailable to us during the analysis phase of this study. For the study period, there were a total of 9,689,368 ED visit records (before exclusions). We excluded 200,324 ED patient visits (2.1%) that were missing a chief complaint description, 6 (<0.1%) that were missing age, 5,645,386 (58.3%) with a recorded age of 39 years and younger, and 35,234 visits that were recorded between December 22, 2006 and December 31, 2006 because hospitalization data between these dates were incomplete. Thus, a total of 3,808,418 (39.3%) subject visits were included from ED visits data for further analysis (median age: 55, interquartile range (IQR): 47–69).
Inpatient admission data
Hospital inpatient admission data were obtained from a statewide acute care hospital discharge database, known as SPARCS. This analysis used discharge records for New York City residents hospitalized at any New York State hospital during the calendar years 2004–6 (n = 3,774,537 discharge records before exclusions). An ICD-9 discharge code was available for all records. We excluded 1,512,485 (40.3%) patients 39 years of age and younger, 425,279 (11.3%) patients with a reported elective or unknown hospitalization type, 75,756 (2.0%) patients without an admission date, and 7400 (0.2%) hospitalizations between December 22 and December 31, 2006 because a large proportion of admissions near the end of that year may not have been discharged in 2006 and would not be included in our data. Thus, a total of 1,733,908 (45.9%) hospitalizations from SPARCS data sources between January 1, 2004 and December 21, 2006 are included in this analysis, with a median age of 66, (IQR: 53–78). The same method we used for classifying ED ICD-9 discharge codes was used for hospitalization discharge codes. To confirm our assumption that CVD outcomes occur primarily in older patients, we calculated the number of CVD-related hospitalizations by age group. Approximately 95% of individuals with a CVD-related hospitalization were> = 40 years of age.
(1) Temporal analysis
We compared the patterns of temporal variation in daily counts of CVD syndromic classifications to counts of CVD-related non-elective hospital admissions using graphical displays (time-series plots and scatterplots) and time series models. In order to explore the day of week and seasonal trends, we plotted mean visits and standard errors by season and day of week. Furthermore, we created a holiday indicator given CVD-related ED visits and hospitalizations may be related to the work week. To compare the correlation between our CVD syndromic classifications and CVD-related hospitalizations, residuals from Poisson generalized linear models (GLMs), adjusting for seasonal/temporal trends, holidays, and day-of-week patterns, were estimated and plotted. The seasonal and temporal trends were estimated by fitting natural cubic splines of study days with 15 degrees of freedom (5 degrees of freedom per year); this “optimum” degrees of freedom was based on an evaluation of the sum of absolute values of partial auto-correlation function (up to 30 day lags) of the residuals , . We calculated the percent deviance explained by season, day-of-week, and holiday patterns by estimating the deviance from a null GLM and a full GLM model with the season/temporal trend, day-of-week, or holiday term; the full deviance was subtracted from the null deviance and then divided by the null deviance. Finally, we fit time series models using a GLM separately for cold (October to March) and warm (April to September) weather months that included a seasonal smoothing function, day of week and holiday terms, and the count of CVD syndromic events with separate terms for lags 0–3 days as predictors of CVD hospitalizations. The percent increase in CVD hospitalizations and 95% confidence intervals (95% CI) were estimated per interquartile range increase in CVD syndromic counts. The goal was to assess the extent to which CVD syndromic events and hospital admissions co-vary temporally above and beyond any shared seasonal, day of week, and holiday patterns.
(2) Analysis of Associations between Ambient Fine Particles and CVD Syndrome and Hospitalizations
In order to assess the usefulness of the CVD ED syndrome data as a health outcome indicator to study the impact of environmental risk factors, we investigated whether CVD ED syndrome counts and hospital admissions were temporally associated with concentrations of ambient fine particles with aerodynamic diameter less than or equal to 2.5 micrometer (PM2.5), which have been shown to exacerbate or trigger CVD events. PM2.5 data from all the monitors within a 20-mile radius of the geographic center of NYC were obtained from Environmental Protection Agency's Air Quality System. The daily average values from these monitors were used for analysis. We used a model specification similar to those used in recent air pollution epidemiological studies , . A Poisson regression model was fitted to estimate percent excess risk of CVD ED syndrome counts per 10 µg/m3 increase in PM2.5, adjusting for: (1) temporal trends and seasonal cycles using natural cubic splines (NS) of study days using 7 degrees of freedom per year; (2) immediate temperature effects using NS of same-day temperature with 3 degrees of freedom; (3) delayed temperature effects using NS of the average of lag 1- through 3-day temperature with 3 degrees of freedom; (4) day-of-week, and accommodating over-dispersion. We examined PM2.5 effects at lag 0 through 3 days. Because PM2.5 effects may be modified by season, we also analyzed the data for warm (April–September) and cold (October–March) seasons. All analyses were conducted using SAS (version 9.1, SAS Institute Inc., Cary, NC) and R (version 2.9.0, R Development Core Team).
Developing a syndromic CVD definition
Of 580,841 ED visits in our syndrome coding data, a total of 44,427 (7.6%) were classified into 1 of the 7 individual CVD conditions or symptoms defined by ICD-9 diagnosis code (Table 2). The most common CVD-related event was chest pain (n = 24,523) and the least common was heart failure (n = 165). Sensitivity varied widely, from 0.007 for MI to 0.482 for chest pain. Similarly, positive predictive value (PPV) varied greatly, from 0.010 for MI to 0.398 for chest pain. Specificity was high for all individual syndromic criteria. For our two broader CVD syndromic definitions, we observed low sensitivity and PPV (<0.40) when compared to the corresponding ED ICD-9 discharge code, though specificity for both definitions was high (>0.95) (Table 3).
CVD Syndromic Data from 50 NYC Emergency Departments and 76 NYC Hospitals, 2004–2006
The mean number of citywide ED visits per day among adults age 40 and above between January 1, 2004 and December 21, 2006 was 3507 (range 2061 to 4585). During this same period, a total of 337,823 ED visits met syndromic definition #1, an indicator of cardiac condition, (mean: 311 visits per day, range: 163–525, approximately 9% of all ED visits); while 501,155 ED visits met syndromic definition #2, corresponding to a broader range of cardiovascular conditions, (mean: 461 visits per day, range: 260 to 727, approximately 13% of all ED visits). A daily mean of 1597 hospital visits (range: 858 to 2080) were recorded between 2004 and 2006. Of these hospitalizations, 393,063 were observed to be CVD-related (mean: 362 hospitalizations per day, range: 195 to 511, approximately 23% of all hospitalizations).
Daily counts and 7-day moving average of Syndrome 1, Syndrome 2, and CVD-related hospitalizations for 2004 to 2006 is shown in Figure 1. A weak seasonal pattern is observed as CVD syndromic ED visits rise modestly in the colder months and decline in the warmer months. The difference in mean CVD visits between summer and winter seasons is less than 10% for both syndromic definitions (298 in summer/320 in winter for Syndrome 1 and 438/477 for Syndrome 2).
A strong day of week pattern was observed for CVD syndromes and hospitalizations. Figure 2 shows mean and standard error of the CVD ED syndrome and hospitalizations by day of week and season. CVD visits to the ED were highest on Monday (range: 8–18% higher than the weekly average) and gradually decline before experiencing their nadir on the weekend (range: 17–21% below the weekly average). Hospitalizations follow a similar pattern, as Monday admissions were 15–21% higher than the weekly average and weekend admissions were 27–31% below the weekly average.
The amount of deviance explained by seasonal, day of week, and holiday trends differs greatly (Table 4). Seasonal and holiday patterns were found to explain little of the variance seen in the time series while day of week patterns explained approximately 57% and 62% of deviance in the time series of Syndromes 1 and 2, respectively, and 80% in the time series of hospitalizations. Holidays tended to coincide with the lowest syndromic and hospitalization counts shown in Figure 1, though the small amount of variance explained by holidays was expected given only ten holidays occur per year.
To explore the temporal correlation between the CVD syndromic definitions and hospitalizations by season, we plotted lagged cross-correlations of the residuals from our GLMs. We observed a strong same-day cross-correlation between the syndromic definitions and hospitalizations that appeared to be consistent throughout all seasons (results not shown). We also examined the correlation between our syndromic definitions and CVD-related hospitalization after adjusting for the strong day of week pattern and weaker seasonal and holiday variation. Scatter plots of the residuals shows moderate correlation between the two syndromes and hospitalizations (Figure 3). Daily counts of ED visits for syndromes 1 and 2 were observed to have a correlation of 0.59 and 0.61, respectively, with CVD-related hospitalization counts. Finally, the daily variation in CVD-related hospitalization was observed to be associated with ED visits for the CVD syndromic definitions during the warm and cold seasons. For Syndrome 1, an interquartile range increase in CVD syndrome counts was associated with a 10% increase in CVD-related hospital admissions during the warm season (95% CI: 9–12%) and a 14% increase in CVD-related hospitalization admissions in the cold season (95% CI: 13–16%, Table 5). Risk of hospital admission was similar for Syndrome 2. Higher CVD syndrome counts for lag 1–3 were also predictive of higher CVD hospitalization rates, though temporal autocorrelation in each time series was likely responsible for part of this observed association.
In the analysis of associations between PM2.5 and the CVD health outcomes, we found that both lag structure and seasonal patterns of associations between PM2.5 and these outcomes were very similar: the strongest associations occurred on lag 0 day and in colder season (Figure 4). The estimated percent excess risks at lag 0 day in cold season were 1.9% (95% CI: 0.6, 3.2), 2.1% (95% CI: 0.9, 3.3), and 1.8% (95%CI: 0.5, 3.0) per 10 µg/m3 increase in PM2.5 for Syndrome 1, Syndrome 2, and hospitalizations, respectively.
We found that emergency department chief complaint data has only fair predictive value for CVD diagnoses. However, despite substantial misclassification in individual cases, temporal patterns in CVD syndrome ED visits are quite consistent with those observed for CVD hospital admissions. As discussed below, we feel these findings show that CVD syndrome tracking using emergency department data, while having important limitations, may be useful for near-real time tracking CVD morbidity in relation to environmental events – providing more timely indication of departures from expectation than are available from traditional diagnosis based surveillance using hospital administrative data.
Chief complaint descriptions are brief (generally less than 50 characters long) and are typically recorded before a patient has been evaluated and treated, thus not capturing all information relevant a diagnosis. The poor overall agreement between our syndromic definitions and corresponding discharge diagnosis, as defined by the ED, is not surprising, given conditions like heart failure and dysrhythmia are not commonly found in chief complaints while other complaints, namely chest pain and shortness of breath, may be CVD-related but are non-specific enough to be related to many non-CVD events. For example, a common chief complaint among ED patients with a CVD-related ICD-9 discharge code was “I am not feeling well.” Our finding is consistent with other studies comparing chief complaint and ICD-9 discharge code in other syndromic studies of non-CVD events , .
Temporal trends of our CVD syndrome ED visits, and CVD-related hospitalizations to a lesser extent, show a somewhat weak seasonal variation. This finding is generally consistent with previous studies documenting seasonal variation in CVD morbidity , , , , though the seasonal trend we observed was not the dominant feature of our time-series. In general, CVD events, particularly myocardial infarction, peak in the colder months and reach their nadir in the warmer months, and increased risk for CVD events may be higher in warm climates that experience cold temperatures . While prior studies primarily focused on hospital admissions and not ED visits, it is encouraging that the seasonal pattern of our two CVD syndromic definitions reflects the general pattern observed among CVD-related hospitalizations. An anomalous increase of ED and hospital CVD events in early September 2004 may be explained by capture of CVD symptoms and concerns following President Bill Clinton's coronary artery bypass surgery in New York on September 6, 2004 and does not appear to reflect a weather event or other type of event. The observed increase of CVD syndromic events and CVD-related hospital admissions further suggests our syndromic definition is able to effectively capture and describe sudden temporal CVD anomalies.
We found variance among CVD events is driven primarily by day of week patterns, particularly by the start of the work week. Similarly, several , , , but not all , studies show that CVD events peak on Monday and gradually decline to a minimum of events on the weekend, suggesting a consistent day of week pattern for CVD events. It is generally thought that the start of the work week leads to increased stress levels which in turn increases risk of a cardiac event , , . This interpretation is supported by the large decline in syndromic ED visits and CVD-related hospitalizations we observed when Monday is a holiday. It is also possible that the increase in visits on Monday could be related to a delay in seeking care during the weekend and holidays. Some studies have observed a larger seasonal variation in CVD events compared to weekly variation , , while others have found no day of week pattern , although only acute myocardial infarction hospital admissions were typically analyzed. The inclusion of other CVD events, along with myocardial infarction, into our syndromic and hospitalization analyses may be responsible for this difference in day of week patterns we observed with these latter studies. The daily (and seasonal) time-series agreement between our syndromic definitions and hospital discharges may suggest those ED-based CVD events that we capture are more serious, thus reflecting temporal patterns seen in CVD-related hospitalizations. Conversely, it is also possible that those with less severe CVD symptoms may not seek ED care.
The two syndromic definitions were modestly correlated with hospitalizations, even after accounting for seasonal, day of week, and holiday patterns. We stratified our analyses by warm/cold season given the association of CVD events with temperature. Both syndromic definitions were found to predict risk of CVD-related hospitalization during the warm and cold season. This finding was strongest on the same day which was expected given our observation of a strong same-day cross-correlation of our CVD syndromes with CVD-related hospitalizations. Overall this suggests that the CVD syndromic definitions we developed are good indicators of short-term temporal variation in CVD-related hospitalization, beyond that expected due to seasonal, day of week, and holiday variation.
We found that the CVD syndromic definitions and CVD hospitalizations showed a very similar pattern of associations with PM2.5. These associations were stronger in the colder season, which is consistent with a recent multi-city study reporting that associations between PM2.5 and CVD hospitalizations were stronger in Northeast and in the cold season . The magnitude of PM2.5 effects estimated was also comparable between the syndromic definitions and hospitalizations. Further, our results agree with a prior study from Australia, which showed an effect of PM2.5 on risk of CVD syndromic visits to the ED . The result supports the use of CVD ED syndromic series as a near-real time health outcome indicator of CVD morbidity in response to exposure to environmental risk factors.
There are several strengths to this study. We achieved near-complete capture (approximately 95%) of emergency department chief complaint data in New York City between 2004 and 2006. In addition, specificity of our syndromic definitions was high. We also utilized ICD-9 discharge codes rather than admission codes for both ED visits (when available) and hospital visits. There is greater potential for a miscoded diagnosis during admission versus discharge, due to either nonspecific symptoms and/or unconfirmed diagnoses, and we were able to minimize this potential misclassification by utilizing discharge codes. This study also has several limitations. We excluded approximately two-thirds of ED visits due to age restrictions and missing or illegible chief complaint data. However, this did not obscure the temporal patterns that paralleled those in CVD hospital admissions. Second, we assumed ICD-9 discharge codes from ED and hospital records represented correct diagnoses. While ICD-9 discharge codes have been found to correspond fairly well to true disease , , correlation is not perfect. Indeed, assignment of ICD-9 codes may be more related to billing practices than true disease . However, it is unlikely that such misclassification of discharge diagnoses could explain the temporal tracking of CVD syndromic events with CVD hospital discharge ICD-9 discharge codes.
In summary, near real-time monitoring of emergency department chief complaint data may be useful for timely surveillance of CVD morbidity. A primary purpose of syndromic surveillance is to detect citywide increases in illness , and one of the primary advantages of chief complaint data over ICD-9 discharge data is timeliness. Given that chief complaint information is generally available to the NYC DOHMH within 24 hours after a visit to the ED, we have the ability to recognize sudden changes in CVD-related events. While an ICD-9 diagnosis code may have a higher agreement with one's true condition compared to a chief complaint that is recorded before physician examination, the lag time involved in obtaining hospital administrative data currently precludes its use in near real-time surveillance. While further evaluation is needed, near-real-time CVD surveillance could provide valuable situational awareness in the acute phase or immediate aftermath of natural or manmade disasters involving exposures that could exacerbate cardiovascular disease.
Conceived and designed the experiments: RWM KI TDM. Analyzed the data: RWM KI TDM. Wrote the paper: RWM KI TDM.
- 1. Heffernan R, Mostashari F, Das D, Karpati A, Kulldorff M, et al. (2004) Syndromic surveillance in public health practice, New York City. Emerg Infect Dis 10: 858–864.
- 2. Marsden-Haug N, Foster VB, Gould PL, Elbert E, Wang H, et al. (2007) Code-based syndromic surveillance for influenzalike illness by International Classification of Diseases, Ninth Revision. Emerg Infect Dis 13: 207–216.
- 3. Mocny M, et al. (2003) A Comparison of Two Methods for Biosurveillance of Respiratory Disease in the Emergency Department: Chief Complaint vs ICD9 Diagnosis Code. Acad Emerg Med 10: 513.
- 4. Espino JU, Wagner MM (2001) Accuracy of ICD-9-coded chief complaints and diagnoses for the detection of acute respiratory illness. Proc AMIA Symp 164–168.
- 5. Marx MA, Rodriguez CV, Greenko J, Das D, Heffernan R, et al. (2006) Diarrheal illness detected through syndromic surveillance after a massive power outage: New York City, August 2003. Am J Public Health 96: 547–553.
- 6. Bassil KL, Cole DC, Moineddin R, Craig AM, Lou WY, et al. (2009) Temporal and spatial variation of heat-related illness using 911 medical dispatch data. Environ Res 109: 600–606.
- 7. Bassil KL, Cole DC, Moineddin R, Gournis E, Schwartz B, et al. (2008) Development of a surveillance case definition for heat-related illness using 911 medical dispatch data. Can J Public Health 99: 339–343.
- 8. Turner RM, Muscatello DJ, Zheng W, Willmore A, Arendts G (2007) An outbreak of cardiovascular syndromes requiring urgent medical treatment and its association with environmental factors: an ecological study. Environ Health 6: 37.
- 9. Brook RD, Franklin B, Cascio W, Hong Y, Howard G, et al. (2004) Air pollution and cardiovascular disease: a statement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation 109: 2655–2671.
- 10. Curriero FC, Heiner KS, Samet JM, Zeger SL, Strug L, et al. (2002) Temperature and mortality in 11 cities of the eastern United States. Am J Epidemiol 155: 80–87.
- 11. Koken PJ, Piver WT, Ye F, Elixhauser A, Olsen LM, et al. (2003) Temperature, air pollution, and hospitalization for cardiovascular diseases among elderly people in Denver. Environ Health Perspect 111: 1312–1317.
- 12. Samet JM, Dominici F, Curriero FC, Coursac I, Zeger SL (2000) Fine particulate air pollution and mortality in 20 U.S. cities, 1987–1994. N Engl J Med 343: 1742–1749.
- 13. Schwartz J (1997) Air pollution and hospital admissions for cardiovascular disease in Tucson. Epidemiology 8: 371–377.
- 14. Jansen AG, Sanders EA, Hoes AW, van Loon AM, Hak E (2007) Influenza- and respiratory syncytial virus-associated mortality and hospitalisations. Eur Respir J 30: 1158–1166.
- 15. Khuder SA, Milz S, Jordan T, Price J, Silvestri K, et al. (2007) The impact of a smoking ban on hospital admissions for coronary heart disease. Prev Med 45: 3–8.
- 16. Sargent RP, Shepard RM, Glantz SA (2004) Reduced incidence of admissions for myocardial infarction associated with public smoking ban: before and after study. Bmj 328: 977–980.
- 17. Brown DL (1999) Disparate effects of the 1989 Loma Prieta and 1994 Northridge earthquakes on hospital admissions for acute myocardial infarction: importance of superimposition of triggers. Am Heart J 137: 830–836.
- 18. Feng J, Lenihan DJ, Johnson MM, Karri V, Reddy CV (2006) Cardiac sequelae in Brooklyn after the September 11 terrorist attacks. Clin Cardiol 29: 13–17.
- 19. Shedd OL, Sears SF Jr., Harvill JL, Arshad A, Conti JB, et al. (2004) The World Trade Center attack: increased frequency of defibrillator shocks for ventricular arrhythmias in patients living remotely from New York City. J Am Coll Cardiol 44: 1265–1267.
- 20. Peng RD, Dominici F, Louis TA (2006) Model choice in time series studies of air pollution and mortality. Journal of the Royal Statistical Society Series A 169: 179–203.
- 21. Touloumi G, Samoli E, Pipikou M, Le Tertre A, Atkinson R, et al. (2006) Seasonal confounding in air pollution and health time-series studies: effect on air pollution effect estimates. Stat Med 25: 4164–4178.
- 22. Chapman WW, Dowling JN, Wagner MM (2005) Classification of emergency department chief complaints into 7 syndromes: a retrospective analysis of 527,228 patients. Ann Emerg Med 46: 445–455.
- 23. Fischer T, Lundbye-Christensen S, Johnsen SP, Schonheyder HC, Sorensen HT (2004) Secular trends and seasonality in first-time hospitalization for acute myocardial infarction–a Danish population-based study. Int J Cardiol 97: 425–431.
- 24. Gerber Y, Jacobsen SJ, Killian JM, Weston SA, Roger VL (2006) Seasonality and daily weather conditions in relation to myocardial infarction and sudden cardiac death in Olmsted County, Minnesota, 1979 to 2002. J Am Coll Cardiol 48: 287–292.
- 25. Sayer JW, Wilkinson P, Ranjadayalan K, Ray S, Marchant B, et al. (1997) Attenuation or absence of circadian and seasonal rhythms of acute myocardial infarction. Heart 77: 325–329.
- 26. Spencer FA, Goldberg RJ, Becker RC, Gore JM (1998) Seasonal distribution of acute myocardial infarction in the second National Registry of Myocardial Infarction. J Am Coll Cardiol 31: 1226–1233.
- 27. Barnett AG, Dobson AJ, McElduff P, Salomaa V, Kuulasmaa K, et al. (2005) Cold periods and coronary events: an analysis of populations worldwide. J Epidemiol Community Health 59: 551–557.
- 28. Kinjo K, Sato H, Shiotani I, Kurotobi T, Ohnishi Y, et al. (2003) Variation during the week in the incidence of acute myocardial infarction: increased risk for Japanese women on Saturdays. Heart 89: 398–403.
- 29. Kriszbacher I, Boncz I, Koppan M, Bodis J (2008) Seasonal variations in the occurrence of acute myocardial infarction in Hungary between 2000 and 2004. Int J Cardiol 129: 251–254.
- 30. Willich SN, Lowel H, Lewis M, Hormann A, Arntz HR, et al. (1994) Weekly variation of acute myocardial infarction. Increased Monday risk in the working population. Circulation 90: 87–93.
- 31. Douglas AS, Dunnigan MG, Allan TM, Rawles JM (1995) Seasonal variation in coronary heart disease in Scotland. J Epidemiol Community Health 49: 575–582.
- 32. Gruska M, Gaul GB, Winkler M, Levnaic S, Reiter C, et al. (2005) Increased occurrence of out-of-hospital cardiac arrest on Mondays in a community-based study. Chronobiol Int 22: 107–120.
- 33. van der Palen J, Doggen CJ, Beaglehole R (1995) Variation in the time and day of onset of myocardial infarction and sudden death. N Z Med J 108: 332–334.
- 34. Bell ML, Ebisu K, Peng RD, Walker J, Samet JM, et al. (2008) Seasonal and regional short-term effects of fine particles on hospital admissions in 202 US counties, 1999–2005. Am J Epidemiol 168: 1301–1310.
- 35. Fisher ES, Whaley FS, Krushat WM, Malenka DJ, Fleming C, et al. (1992) The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. Am J Public Health 82: 243–248.
- 36. Heffernan R, Mostashari F, Das D, Besculides M, Rodriguez C, et al. (2004) New York City syndromic surveillance systems. MMWR Morb Mortal Wkly Rep 53(Suppl): 23–27.