Surveillance of Heat-related Morbidity: Relation to Heat-related Excess Mortality

Objective

To describe the extent to which heat-illness indicators increase with extreme heat and to evaluate the association among daily weather, heat-related illness and natural cause mortality.

Introduction

The impact of heat on mortality is well documented [1–3] but deaths tend to lag extreme heat and mortality data is generally not available for timely surveillance during heat waves. Recently, systems for near-real time surveillance of heat illness have been reported [4] but have not been validated as predictors of heat related mortality. In this study, we examined the associations among weather, indicators of heat-related ambulance calls and emergency department visits and excess natural cause mortality in New York City (NYC).

Methods

We analyzed daily weather conditions, emergency medical system (EMS) calls flagged as heat-related by EMS dispatchers, emergency department (ED) visits classified as heat-related based on chief complaint text, and natural cause deaths. EMS and ED data were obtained from data reported daily to the city health department for syndromic surveillance. We fit generalized linear models to assess the relationships of daily counts of heat related EMS and ED visits to natural cause deaths after adjustment for weather conditions during the months of May–September between 1999 and 2008.

Results

We observed an increase in mean total calls to EMS and a decrease in mean total visits to EDs during 10 observed heat waves (maximum heat index ≥90° F (Fahrenheit) for four or more consecutive days with the first three days ≥95° F and at least one day ≥100°F) in NYC between 1999 and 2008. Both EMS and EDs experienced an increase in heat-related incidents during heat waves though the increase in heat-related EMS calls was much greater. A modest increase in mean natural cause deaths was also observed. Controlling for temporal trends, an 11% (95% confidence interval (CI): 5–18) and 7% (95% CI: 4–9) increase in natural cause mortality was associated with an increase from the 50th percentile to 99th percentile of same-day and one-day lagged heat-related EMS calls and ED visits, respectively. After controlling for both temporal trends and weather, we observed a 10% (95% CI: 4–16) increase in natural cause mortality associated with one-day lagged heat-related EMS calls and a 5% mortality increase with one-day lagged ED visits (95% CI: 2–8).

Conclusions

Heat-related EMS calls and ED visits lagged one day predicted natural cause mortality in our temporal and weather-adjusted model. In particular, risk of mortality rapidly increased as the number of heat-related EMS calls approached high levels (>100 heat-related calls/day). Heat-related illness can be tracked during heat waves using EMS and ED data which are indicators of heat associated excess natural cause mortality during the warm weather season.     

Monitoring the Impact of Heat Waves with Emergency Service Utilization Data in Los Angeles County

Objective

To assess current indicators for situational awareness during heat waves derived from electronic emergency department (ED) and 911 emergency dispatch call (EDC) center data.

Introduction

Los Angeles County’s (LAC) early event detection system captures over 60% of total ED visits, as well as 800 to 1,000 emergency dispatch calls from Los Angeles City Fire (LACF) daily. Both ED visits and EDC calls are classified into syndrome categories, and then analyzed for aberrations in count and spatial distribution. During periods of high temperatures, a heat report is generated and sent to stakeholders upon request. We describe how syndromic surveillance serves as an important near real-time, population-based instrument for measuring the impact of heat waves on emergency service utilization in LAC.

Methods

Daily electronic ED registration data, EDC calls, and high temperatures from Palmdale, California were queried from January 1, 2010 to August 26, 2012 and aggregated into Centers for Disease Control (CDC) weeks. A custom “heat exposure” category was created by searching ED chief complaints for key terms such as “Heat stroke,” “hyperthermia,” “overheat,” and relevant ICD9 diagnosis codes. Similarly, EDC calls were classified if related to “heat exposure.” Pearson correlation tests were used to determine correlation between total ED visits, heat-related ED visits, heat-related EDC calls, and daily maximum temperatures.

Results

Thus far 2012 has exceeded counts cumulative to August 26th for the past two years in the number of heat-related ED visits, heat-related EDC calls, and hot days (

COPD-Related ED Visits in North Carolina: Hospitalizations and Return Visits

Objective

To investigate hospital admissions and short-term return visits resulting from chronic obstructive pulmonary disease (COPD)-related emergency department (ED) visits.

Introduction

COPD is a prevalent chronic disease among older adults; exacerbations often result in ED visits and subsequent hospital admissions. [1,2] A portion of such patients return to the ED within a few days or weeks. [3] In this study, we investigated patterns of hospital admissions and short-term return visits resulting from COPD-related ED visits.

Methods

We performed a population-based study of ED visits for COPD using state-wide surveillance data from NC DETECT[4], including all ED visits made by NC residents aged ≥45 years in 2008–2009. Visits were considered COPD-related if the first- or second-listed discharge diagnoses contained one of the following ICD-9-CM codes: 491.*, 492.*, 493.2*, 494.*, or 496.*. Hospital admissions were captured by ED disposition codes. If a patient had made another COPD-related ED visit within the prior 3 or 30 days, we defined the current visit as a 3-day or 30-day return visit. We compared the prevalence of hospitalization and 3- and 30-day return visits by age, sex, and payment method. We also described the disposition patterns for return visit pairs.

Results

There were 97,511 COPD-related ED visits made by adults age 45 and older in NC in 2008–2009, made by 64,568 individuals. HOSPITAL ADMISSIONS: Nearly half (46.3%) of all COPD-related ED visits resulted in hospital admission. Hospitalization prevalence increased with age, but there were no differences by gender. ED visits that were non-insured (self-pay) or paid by Medicare or Medicaid were less likely to lead to hospitalization than those with private insurance. RETURN VISITS: 1.6% (1607) of the COPD-related ED visits were categorized as 3-day return visits and 11.2% (10922) were considered 30-day return visits. There were no statistical differences by gender for 3-day returns, while 30-day returns were more likely to be made by men. Prevalence of return visits for both intervals initially increased with age compared to the 45–49 years age group, then decreased steadily after age 65. Visits that were non-insured or paid by Medicare or Medicaid were statistically more likely to be 3-day or 30-day returns than those paid by private insurance. DISPOSITION PATTERNS: We also examined the permutations of 1st and 2nd ED visit dispositions that make up these return visit pairs. While many return visits were discharged at both visits in the return visit pair, a substantial proportion were admitted at one or both visits. Surprisingly, in 8% of the 3-day return visit pairs, the patient was hospitalized at the 1st ED visit but yet still returned to the ED within 3 days; for the 30-day visit pairs, 37% returned despite the patient being admitted at the 1st visit.

Conclusions

This population-based study describes the short-term outcomes of a large number of COPD-related ED visits using a unique state-wide surveillance system. We found a high prevalence of hospital admissions and return ED visits, including many repeat hospitalizations. This study also demonstrates how surveillance data can be used for research on “acute on chronic” disease epidemiology.     

Tracking Drug Overdose Trends in Ohio using ED Chief Complaints

Objective

Preliminary analysis was completed to define, identify, and track the trends of drug overdoses (OD), both intentional and unintentional, from emergency department (ED) and urgent care (UC) chief complaint data.

Introduction

The State of Ohio, as well as the country, has experienced an increasing incidence of drug ODs over the last three decades [3]. Of the increased number of unintended drug OD deaths in 2008, 9 out of 10 were caused by medications or illicit drugs [1]. In Ohio, drug ODs surpassed MVCs as the leading cause of injury death in 2007. This trend has continued through the most current available data [3]. Using chief complaint data to quickly track changes in the geographical distribution, demographics, and volume of drug ODs may aid public health efforts to decrease the number of associated deaths.

Methods

Chief complaint data from ED/UC visits were collected and analyzed from Ohio’s syndromic surveillance application for 2010–2012. Ninety-six percent of all Ohio ED visits were captured during this timeframe. Due to the nonspecific nature of chief complaints as well as the lack of detail given upon registration at the ED/UC, attempting to separate visits into intentional vs. unintentional was not feasible. Therefore, a fairly specific classifier was created to define all potential ED/UC visits related to drug ODs. The data were analyzed, using SAS v 9.3, via time series analyses, and stratified by age, gender, and geographic region. Although these data types are pre-diagnostic in nature, they are more readily accessible than discharge data.

Results

On average, Ohio observed approx 66 ED/UC visits per day related to drug ODs from 2010–2012. The data show an increasing trend from 2010 through 2012 as well as a slight seasonal trend with higher visits observed in the spring/summer months as opposed to the autumn/winter months (Figure 1). The data showed that females attributed to a higher frequency of the drug ODs than males by approximately 4 ED/UC visits per day. Other data sources show a higher incidence in males than females related to unintentional drug ODs [3]. The highest age category attributing to the increase was the 18–39 years of age for both males and females, as shown in Figure 2. Population rates were calculated to identify those counties most affected by drug ODs. The data showed the highest rates of ED/UC visits related to drug ODs to be found in mostly rural areas of Ohio.Open in a separate windowFigure 1ED Visits Related to Drug Overdoses by Day, Ohio, 2010–12Open in a separate windowFigure 2ED Visits Related to Drug Overdoses by Age Group, Ohio, 2010–12

Conclusions

The annual death rate from unintentional drug poisonings by Ohio residents has increased from 3.6 in 2000 to 13.4 per 100,000 population in 2010[3]. As a result, the Ohio Governor created a Drug Abuse Task Force in 2009[4]. Ohio legislation (HB 93) implemented a prohibition on the operation of pain management clinics without a license on June 19, 2011[3]. According to this preliminary analysis, ED/UC visits related to drug ODs 1 year post-implementation of HB 93 continue to increase. It is unclear if HB 93 has slowed the rate of increase. Additionally, pre-diagnostic data has significant limitations including the significant possibility of misclassifying non-OD patient encounters as ODs. Further study of post-diagnostic data to confirm these trends is warranted.     

The 2006 California Heat Wave: Impacts on Hospitalizations and Emergency Department Visits

Background

Climate models project that heat waves will increase in frequency and severity. Despite many studies of mortality from heat waves, few studies have examined morbidity.

Objectives

In this study we investigated whether any age or race/ethnicity groups experienced increased hospitalizations and emergency department (ED) visits overall or for selected illnesses during the 2006 California heat wave.

Methods

We aggregated county-level hospitalizations and ED visits for all causes and for 10 cause groups into six geographic regions of California. We calculated excess morbidity and rate ratios (RRs) during the heat wave (15 July to 1 August 2006) and compared these data with those of a reference period (8–14 July and 12–22 August 2006).

Results

During the heat wave, 16,166 excess ED visits and 1,182 excess hospitalizations occurred statewide. ED visits for heat-related causes increased across the state [RR = 6.30; 95% confidence interval (CI), 5.67–7.01], especially in the Central Coast region, which includes San Francisco. Children (0–4 years of age) and the elderly (≥ 65 years of age) were at greatest risk. ED visits also showed significant increases for acute renal failure, cardiovascular diseases, diabetes, electrolyte imbalance, and nephritis. We observed significantly elevated RRs for hospitalizations for heat-related illnesses (RR = 10.15; 95% CI, 7.79–13.43), acute renal failure, electrolyte imbalance, and nephritis.

Conclusions

The 2006 California heat wave had a substantial effect on morbidity, including regions with relatively modest temperatures. This suggests that population acclimatization and adaptive capacity influenced risk. By better understanding these impacts and population vulnerabilities, local communities can improve heat wave preparedness to cope with a globally warming future.     

Using Syndromic Emergency Department Data to Augment Oral Health Surveillance

Objective

To utilize an established syndromic reporting system for surveillance of potentially preventable emergency department (ED) oral health visits (OHV) in New York City (NYC).

Introduction

NYC Department of Health and Mental Hygiene recently reoriented its oral health care strategy to focus on health promotion and expanded surveillance. One surveillance challenge is the lack of timely OHV data; few dental providers are in our electronic health record project, and statewide utilization data are subject to delays. Prior research has examined OHV using ICD-9-CM from ED records, and has suggested that diagnostic specificity may be limited by ED providers’ lack of training in dental diagnoses (1–3). We considered our existing ED syndromic system as a complement to periodic population-based surveys. This system captures approximately 95% of all ED visits citywide; 98% of records have a completed chief complaint text field whereas only 52% contain an ICD-9-CM diagnosis.

Methods

We used chief complaint text to define OHV in two ways: (1) a basic definition comprised of ‘TOOTH’ or ‘GUM’ in combination with a pain term (e.g., ‘ACHE’); (2) a more inclusive definition of either specific oral health diagnoses (e.g., ‘PULPITIS’) or definition (1). For both definitions, we excluded visits likely to have stemmed from trauma (e.g., ‘ACCIDENT’). Data from 2009–2011 were analyzed by facility, patient age and residential zip code, and day/time using SAS v9.2 (SAS Institute; Cary, NC).

Results

OHV in 2009–2011 totaled 72,410 (def. 1) and 103,594 (def. 2), or 0.6% and 0.9% of all ED visits, respectively. OHV (def. 2) spiked at age 18 and were highest among 18 to 29 year olds (Fig. 1). Neighborhood OHV rates (def. 2) ranged from 74 to 965 per 100,000 persons. 59% of OHV occurred between 8am and 6pm (Fig. 2). Highly specific dental conditions were rare; terms such as “tooth ache” were most common.Open in a separate windowFig 1OHV (def.2) by age, 2009–2011Open in a separate windowFig 2OHV (def.2) by day/time, 2009–2011

Conclusions

Findings suggest that OHV are a particular problem among ages 18 to 29. This pattern may reflect lower insurance coverage among young adults. The proportion of daytime visits suggests that EDs are substituting for regular dental treatment and there may be opportunities to promote daytime linkages to office-based dental providers.A well-established syndromic reporting system holds promise as a method of OHV surveillance. Strengths include near complete chief complaint reporting, rapid availability, and the potential to identify populations and facilities that could benefit from expanded access and preventive education. Limitations include the need to gather site-specific facility information (e.g., presence of dental residents, coding practices) to better understand patterns. Also, the absence of some important fields in the syndromic system (e.g., insurance coverage, income) limit assessment of the degree to which cost barriers may be driving OHV.     

Evaluation of Heat-related Illness Surveillance Based on Chief Complaint Data from New Jersey Hospital Emergency Rooms

Objective

The purpose of this evaluation is to characterize the relationship between a patient’s initial hospital emergency room chief complaint potentially related to a heat-related illness (HRI) with final primary and secondary ICD-9 diagnoses.

Introduction

The NJ syndromic surveillance system, EpiCenter, developed an algorithm to quantify HRI visits using chief complaint data. While heat advisories are released by the National Weather Service, an effective HRI algorithm could provide real-time health impact information that could be used to provide supplemental warnings to the public during a prolonged heat wave.

Methods

Data on NJ hospital emergency room visits were evaluated using two data sources: 1) the EpiCenter syndromic surveillance system of emergency room visits; and 2) the Uniform Bill-Patient Summaries (UB) system containing diagnosis data on all hospital visits. Three years of data (2009–2011) were selected, for the time window of May 1 to September 30. The UB data used for matching with the EpiCenter data were limited to facilities participating in EpiCenter during the evaluation period. (EpiCenter facilities captured about 1/3 of all heat-related diagnoses in 2009, increasing to about 2/3 in 2011.) The ICD-9 codes of interest included 992.0–992.9 and external cause of injury codes E900.0 and E900.9. We evaluated the sensitivity and positive predictive value (PPV) of the EpiCenter algorithm in relation to the patients’ eventual diagnoses coded in the UB data.

Results

During the 15 months of data examined, there were a total of 871 people identified with HRI visits based on the EpiCenter algorithm. Over the same time period in the same emergency room facilities, there were a total of 2,146 people with a primary or secondary HRI diagnosis in UB. The algorithm for the EpiCenter’s HRI definition had a sensitivity of 16% (348/2,146) when any primary or secondary ICD or E-code matched; the PPV was 40% (348/871). When data during a major heat event (July 21–23, 2011) was examined separately, both sensitivity (23%) and PPV (59%) improved.Graph 1 presents the 2011 daily number of HRI visits from EpiCenter data and the subset of UB data from facilities also reporting to EpiCenter. The pattern in the EpiCenter data tracked with the UB data for HRI visits and correctly identified several major episodes in 2011.The major heat-related illness episode of July 2011 was selected to evaluate the non-matched EpiCenter and UB data. A total of 210 (95%) of the non-matched UB cases were able to be matched to EpiCenter chief complaint data. The EpiCenter information displayed a diverse range of general complaints, including syncope, dizziness, weakness, and headache. Similarly, non-matched EpiCenter data were compared to UB data to examine diagnoses, and 22 (48%) of the EpiCenter HRI cases were matched to UB diagnostic data. Diagnosis codes for these cases were for a variety of conditions classified under “general symptoms”; fluid balance disorders; asthma; diabetes; and unspecified hypertension.

Conclusions

The evaluation found that using chief complaint data to monitor HRI was relatively insensitive in comparison to the UB diagnosis codes, with a sensitivity of just over 16% for any UB HRI diagnosis. Sensitivity and PPV improved during a peak heat event.The evaluation of the non-matched data (both EpiCenter and UB) provided little guidance for modifying the algorithm. While expanding the algorithm to include complaints such as syncope, dizziness, or weakness may capture a few more HRI cases, it would also likely result in a greater number of false positive cases (i.e., higher background noise).Though not especially sensitive, EpiCenter data did identify all major episodes of HRI in 2011. The degree of correspondence indicates that the EpiCenter HRI algorithm provides a useful real-time gauge of the daily HRI trends.

Graph 1.

Open in a separate window2011 HRI visits identified by EpiCenter data and UB data subset for the same EpiCenter reporting facilities.     

Time of Arrival Analysis in NC DETECT to Find Clusters of Interest from Unclassified Patient Visit Records

Objective

To describe a collaboration with the Johns Hopkins Applied Physics Laboratory (JHU APL), the North Carolina Division of Public Health (NC DPH), and the UNC Department of Emergency Medicine Carolina Center for Health Informatics (CCHI) to implement time-of-arrival analysis (TOA) for hospital emergency department (ED) data in NC DETECT to identify clusters of ED visits for which there is no pre-defined syndrome or sub-syndrome.

Introduction

TOA identifies clusters of patients arriving to a hospital ED within a short temporal interval. Past implementations have been restricted to records of patients with a specific type of complaint. The Florida Department of Health uses TOA at the county level for multiple sub-syndromes (1). In 2011, NC DPH, CCHI and CDC collaborated to enhance and evaluate this capability for NC DETECT, using NC DETECT data in BioSense 1.0 (2). After this successful evaluation based on exposure complaints, discussions were held to determine the best approach to implement this new algorithm into the production environment for NC DETECT. NC DPH was particularly interested in determining if TOA could be used for identifying clusters of ED visits not filtered by any syndrome or sub-syndrome. In other words, can TOA detect a cluster of ED visits relating to a public health event, even if symptoms from that event are not characterized by a predefined syndrome grouping? Syndromes are continuously added to NC DETECT but a syndrome cannot be created for every potential event of public health concern. This TOA approach is the first attempt to address this issue in NC DETECT. The initial goal is to identify clusters of related ED visits whose keywords, signs and/or symptoms are NOT all expressed by a traditional syndrome, e.g. rash, gastrointestinal, and flu-like illnesses. The goal instead is to identify clusters resulting from specific events or exposures regardless of how patients present – event concepts that are too numerous to pre-classify.

Methods

In late 2011, NC DPH and JHU APL signed a Software License Agreement and soon thereafter CCHI received the TOA software package. In May 2012, the TOA controller was adapted and set up to run against ED visit data for all NC DETECT hospitals. The TOA looks for clusters in all ED visits by hospital based solely on arrival time in both 30-minute and 60-minute intervals. There is no pre-classification of the chief complaints or triage notes into syndromes. TOA alerts are viewable on the NC DETECT Web application and, as of August 2012, users are able to document any actions taken on these alerts.

Results

From April 15, 2012 to July 31, 2012, TOA generated 173 alerts across all 115 hospitals reporting to NC DETECT. The TOA identified a group of scabies-related ED visits that was not captured in another syndrome. The TOA also identified clusters identified by hospitals as disaster-related which included misspellings that had not been previously identified, e.g. “diaster” and “disater,” as well as events involving out-of-town groups that will not be identified spatially (Cluster Event TypeNumber of ED Visits in TOA alert / Number of visits related to the specific event clusterSample chief complaints in clusterDisaster-related13/9Disaster abd pain, diaster flu, disaster phyc, diaster anxity, diaster blackScabies16/8Scabies 7FER, Scaobes 7FER, 5M FOLLOW UPSame out of town location17/7Cough, laceration(s)Open in a separate window

Conclusions

Our preliminary review of TOA shows that this algorithm approach can be helpful for identifying clusters of ED visits that are not captured by existing syndromes and can be used to identify hospital coding schemes for disaster events. The TOA will continue to be monitored in our production environment and evaluated for additional effectiveness. We will also explore tools that will display counts of terms within a TOA alert to assist in signal investigation.     

Enhanced Influenza Surveillance using Telephone Triage Data in the VA ESSENCE Biosurveillance System

Objective

To evaluate the utility and timeliness of telephone triage (TT) for influenza surveillance in the Department of Veterans Affairs (VA).

Introduction

Telephone triage is a relatively new data source available to biosurveillance systems.1–2 Because early detection and warning is a high priority, many biosurveillance systems have begun to collect and analyze data from non-traditional sources [absenteeism records, over-the-counter drug sales, electronic laboratory reporting, internet searches (e.g. Google Flu Trends) and TT]. These sources may provide disease activity alerts earlier than conventional sources. Little is known about whether VA telephone program influenza data correlates with established influenza biosurveillance.

Methods

Veterans phoning VA’s TT system, and those admitted or seen at a VA facility with influenza or influenza-like-illness (ILI) diagnosis were included in this analysis. Influenza-specific ICD-9-CM coded emergency department (ED) and urgent care (UC) visits, hospitalizations, TT calls, and ILI outpatient visits were analyzed covering 2010–2011 and 2011–2012 influenza seasons (July 11, 2010–April 14, 2012). Data came from 80 VA Medical Centers and over 500 outpatient clinics with complete reporting data for the time period of interest. We calculated Spearman rank-order coefficients, 95% confidence intervals and p-values using Fisher’s z transformation to describe correlation between TT data and other influenza healthcare measures. For comparison of time trends, we plotted data for hospitalizations, ED/UC visits and outpatient ILI syndrome visits against TT encounters. We applied ESSENCE detection algorithms to identify high-level alerts for influenza activity. ESSENCE aberration detection was restricted to the 2011–2012 season because limited historical TT and outpatient data from 2009–2010 was available to accurately predict aberrancy in the 2010–2011 season. We then calculated the peak measure of healthcare utilization during both influenza seasons (2010–2011 and 2011–2012) for each data source and compared timing of peaks and alerts between TT and other healthcare encounters to assess maximum healthcare system usage and timeliness of surveillance.

Results

There were 7,044 influenza-coded calls, 564 hospitalizations, 1,849 emergency/urgent visits, and 416,613 ILI-coded outpatient visits. Spearman rank correlation coefficients were calculated for influenza-coded calls with hospitalizations (0.77); ED/UC visits (0.85); and ILI-outpatient visits (0.88), respectively (P< 0.0001 for all correlations). Peak influenza activity occurred on the same week or within 1 week across all settings for both seasons. For the 2011–2012 season, TT alerted with increased influenza activity before all other settings.

Conclusions

Data from VA telephone care correlates well with other VA data sources for influenza activity. TT may serve to augment these existing clinical data sources and provide earlier alerts of influenza activity. As a national health care system with a large patient population, VA could provide a robust early-warning system for influenza if ongoing biosurveillance activities are combined with TT data. Additional analyses are needed to understand and correlate TT with healthcare utilization and severity of illness.     

Early Detection of Influenza Activity Using Syndromic Surveillance in Missouri

Objective

To assess how weekly percent of influenza-like illness (ILI) reported via Early Notification of Community-based Epidemics (ESSENCE) tracked weekly counts of laboratory confirmed influenza cases in five influenza seasons in order to evaluate the early warning potential of ILI in ESSENCE and improve ongoing influenza surveillance efforts in Missouri.

Introduction

Syndromic surveillance is used routinely to detect outbreaks of disease earlier than traditional methods due to its ability to automatically acquire data in near real-time. Missouri has used emergency department (ED) visits to monitor and track seasonal influenza activity since 2006.

Methods

The Missouri ESSENCE system utilizes data from 84 hospitals, which represents up to 90 percent of all ED visits occurring in Missouri statewide each day. The influenza season is defined as starting during Centers for Disease Control and Prevention (CDC) week number 40 (around the first of October) and ending on CDC week 20 of the following year, which is usually at the end of May.A confirmed influenza case is laboratory confirmed by viral culture, rapid diagnostic tests, or a four-fold rise in antibody titer between acute and convalescent serum samples. Laboratory results are reported on a weekly basis. To assess the severity of influenza activity, all flu seasons were compared with the 2008–09 season, which experienced the lowest influenza activity based on laboratory data. Analysis of variance (ANOVA) was applied for this analysis using Statistical Analysis Software (SAS) (version 9.2).The standard ESSENCE ILI subsyndrome includes ED chief complaints that contain keywords such as “flu”, “flulike”, “influenza” or “fever plus cough” or “fever plus sore throat”. The ESSENCE ILI weekly percent is the number of ILI visits divided by total ED visits.Time series of weekly percent of ILI in ESSENCE were compared to weekly counts of laboratory confirmed influenza cases. Spearman correlation coefficients were calculated using SAS. The baseline refers to the mean of three flu seasons with low influenza activity (2006–07, 2008–09 and 2010–11 seasons). The threshold was calculated as this baseline plus three standard deviations.The early warning potential of the ESSENCE weekly ILI percent was evaluated for five consecutive influenza seasons, beginning in 2006. This was accomplished by calculating the time lag between the first ESSENCE ILI warning versus the first lab confirmed influenza warning. A warning was identified if either lab confirmed case counts or weekly percent of ILI crossed over their respective baselines.

Results

For each influenza season evaluated, weekly ILI rates reported via ESSENCE were significantly correlated with weekly counts of laboratory-confirmed influenza cases (Figure 1 shows that two influenza seasons (2007–08 and 2009–10) were more severe than others examined based on the ESSENCE percent ILI threshold analysis, this result is consistent with the examination of severity of influenza activity based on lab confirmed influenza data (p<0.05).Open in a separate windowFigure 1Number and baseline of lab confirmed influenza cases, ESSENCE weekly ILI percent and baseline, and ESSENCE ILI threshold for five consecutive influenza seasons, 2006–2011.

Table 1

Correlation between laboratory confrmed influenza cases and ESSENCE ILI weekly percent in five influenza seasons, 2006–2011

Can We Use Syndromic Surveillance Data to Identify Primary Care Visits to NYC EDs?

Objective

To develop a syndrome classification based on patient chief complaint to (1) estimate the proportion of primary care-related emergency department (ED) visits in New York City (NYC) hospitals and (2) explore predictors of such visits.

Introduction

NYC EDs saw nearly 4 million visits in 2011. Studies have demonstrated that non-urgent visits can account for more than 50% of visits to EDs [1,2]. Designed to provide rapid diagnosis and first-line treatment of serious illness, EDs often function as a primary care site due to their accessibility. Unfortunately, use of EDs for primary care may affect their ability to meet the needs of severely ill patients.

Methods

We examined syndromic surveillance data from 45 hospitals in NYC for 2011 and classified visits into a primary care syndrome based on the chief complaint field. Data from 4 hospitals were omitted due to data quality issues, as were records from non-NYC residents. Primary care (PC) syndrome visits included visits recorded as referrals, screenings, suture removal/dressing changes, or medication refills; records with a blank or non-informative (e.g. “X”) chief complaint field were omitted from analysis. Using unique patient IDs, we identified patients who visited the same ED multiple times in the previous 12 months. A hierarchical generalized linear mixed effects model with hospital-level random effects was used to explore patient characteristics associated with PC syndrome visits. The model included a random intercept for hospital and the following covariates: duplicate visit, patient gender and age group (ages 0–4, 5–17, 18–64, and 65+), and time of visit (midnight to 8 AM, 8 AM to midnight). Covariates for month and day of week were included to control for temporal trends in ED visits. Model parameters were estimated by maximum likelihood. Estimation was performed in SAS version 9.2 [3] using the GLIMMIX procedure.

Results

Citywide, 7.5% (N=190,431) of visits to EDs during 2011 were classified as PC syndrome visits, but varied by hospital with a median of 4.6% (IQR: 3% to 9%) across hospitals. The average proportion of PC syndrome visits varied by hospital. Of the 45 hospitals included in the analysis, 18 had a lower baseline, 13 were the same, and 14 had a higher baseline than the city mean. Hospitals with a larger census had a larger proportion of PC syndrome visits.Age had a significant effect on the odds of a PC syndrome visit; ages 0–4 had the greatest odds of a PC syndrome visit relative to the 65+ age group. Visits from patients ages 5–17 and 18–64 were also more likely to be primary care visits. Patients with repeat visits were more likely to have PC syndrome visits. Female gender and early morning visits (12A–8A) were associated with lower odds of a PC syndrome visit.

Conclusions

With limited detail on patient visits, our syndrome likely under-counts primary care visits to EDs. However, the relationships between our explanatory variables—age, time of day, and duplicate visits—and PC syndrome visits are consistent with the literature on ED usage for primary care. Gender is an exception [1], but earlier findings may be confounded by the fact that females seek health care more frequently in general. The variation in PC syndrome visits among NYC EDs is significant and may be explained by hospital or community measures not captured in our model, such as clinic wait times, ED capacity, or insurance coverage. In fact, disparities in such predictors of PC syndrome visits could be targets for interventions. Our ability to replicate previous findings on the use of EDs for primary care visits suggests that syndromic data may be a near real-time data source for following trends in such visits.Predictors of PC Syndrome Visits

Differentiating ZIP Codes in Syndromic Data; What Can They Tell Us?

Objective

To classify visits to NYC emergency departments (ED) into NYC residential, NYC PO Box or commercial building, commuters to NYC, and out-of-town visitors. To describe patterns in each group, to evaluate how they differ, and to consider how the differences can affect syndromic surveillance analyses and results.

Introduction

The NYC Department of Health and Mental Hygiene (DOHMH) ED syndromic surveillance system receives data from 95% of all ED visits in NYC totaling 4 million visits each year. The data include residential ZIP code as reported by the patient. ZIP code information has been used by the DOHMH to separate visits into NYC and non-NYC for analysis; and, a closer examination of non-NYC visits may further inform disease surveillance.

Methods

Visits were initially differentiated into six home ZIP code types. NYC residential ZIP codes, PO Boxes and commercial buildings were identified with 2010 US Census and data from the SAS institute (SAS Institute Inc., Cary, NC, USA). Commuter visits to the EDs were classified as any ZIP codes from the NYC Core Based Statistical Area (CBSA; United States Office of Management and Budget). Out of town visits were identified using with the 2010 US Census. Unknown ZIP codes included all of those ZIP codes that were not identified by any of the previous methods, and missing ZIP codes were those that were blank. ZIP codes were verified with the United States Postal Service website (www.usps.com).Once ZIP codes were categorized, spatial and temporal trends in total ED visits by home ZIP code type were analyzed.

Results

Of the approximately 4 million ED visits in NYC during 2011, the number of visits by commuters and out-of-town visitors were 125,236 (3.1%) and 45,158 (1.1%) respectively (Figure 1). There were 4,676 (0.1%) visits with a NYC PO Box or building ZIP codes and 48,077 (1.2%) visits with a missing or non-interpretable ZIP codes. The majority of commuter and out-of-town ED visits occur at a smaller set of hospitals. Out-of town visitors mostly visited hospitals in Manhattan rather than hospitals in the outer boroughs. While the seasonal trends and day-of-week patterns for the NYC residents and the commuters appear to be fairly similar, this is not the case for out-of-town visitors. For example, total ED visit trends correlated well for NYC residents and commuters (r=0.77), but there was no correlation between NYC residents and out-of-town visitors (r=−0.18) over time. The number of ED visits among out-of-town visitors was higher during summer months and the winter holiday season, and this trend may have reflected the larger number of visitors during these periods. Day-of-week patterns were similar for NYC residents and commuters with weekdays associated with larger numbers of visits compared to weekends. However, the opposite was found true for out-of-town visitors with larger number of visits occurring over the weekends compared to weekdays.Open in a separate windowFigure 1:Total ED visits from the NYC Core Based Statistical Area (CBSA) and the surrounding region.

Conclusions

Considerable differences in temporal trends were found among out-of-town visitors, NYC residents, and commuters to NYC. Out-of-town visitors also tend to visit EDs located in Manhattan rather than in the outer boroughs. These results suggest that out-of-town visitors represent a unique population ED visitors. Analyzing NYC residents, commuters, and out-of-town visitors separately may provide additional information that could prove useful to daily syndromic surveillance activities.     

Adapting Syndromic Surveillance Systems to Increase Value to Local Health Departments

Objective

Our objective was to describe changes in use following syndromic surveillance system modifications and assess the effectiveness of these modifications.

Introduction

Syndromic surveillance systems offer richer understanding of population health. However, because of their complexity, they are less used at small public health agencies, such as many local health departments (LHDs). The evolution of these systems has included modifying user interfaces for more efficient and effective use at the local level. The North Carolina Preparedness and Emergency Response Research Center previously evaluated use of syndromic surveillance information at LHDs in North Carolina. Since this time, both the NC DETECT system and distribution of syndromic surveillance information by the state public health agency have changed. This work describes use following these changes.

Methods

Data from NC DETECT were used to assess the number of users and usage time. Staff from 14 NC LHDs in 2009 and from 39 LHDs in 2012 were surveyed (May–August of 2009 and June of 2012) to gather information on the mode of access to syndromic surveillance information and how this information was used. Data were analyzed to assess the link between the mode of access and use of syndromic surveillance data.

Results

System changes made between 2009 and 2012 included the creation of “dashboards” (Figure 1) which present users with LHD-specific charts and graphs upon login and increases in the distribution of syndromic surveillance information by the state public health agency. The number of LHD-based NC DETECT system users increased from 99 in 2009 to 175 in 2012. Sixty-two of 72 respondents completed the 2012 survey (86%). Syndromic surveillance information was used in 28/40 LHDs (70%) for key public health tasks. Among 20 NC EDSS leads reporting an outbreak in the past year, 25% reported using data from NC DETECT for outbreak response, compared to 23% in 2009 (Figure 2). Among 30 responding NC EDSS leads, 57% reported using data from NC DETECT to respond to seasonal events such as heat-related illness or influenza, compared to 46% in 2009. NC DETECT data were reported to have been used for program management by 30% (compared to 25% in 2009), and to have been used in reports by 33% (compared to 23% in 2009).Open in a separate windowFigure 1:NC DETECT dashboardsOpen in a separate windowFigure 2:Uses of syndromic surveillance information, communicable disease staff 2009 (13 LHDs) and 2012 (31 LHDs)

Conclusions

Changes in how syndromic surveillance information was distributed supported modest increases in use in LHDs. Because use of syndromic surveillance data at smaller LHDs is rare, these modest increases are important indicators of effective modification of the NC syndromic surveillance system.     

Utility of Syndromic Surveillance Using Novel Clinical Data Sources

Objective

To document the current evidence base for the use of electronic health record (EHR) data for syndromic surveillance using emergency department, urgent care clinic, hospital inpatient, and ambulatory clinical care data.

Introduction

Historically, syndromic surveillance has primarily involved the use of near real-time data sent from hospital emergency department (EDs) and urgent care (UC) clinics to public health agencies. The use of data from inpatient and ambulatory settings is now gaining interest and support throughout the United States, largely as a result of the Stage 2 and 3 Meaningful Use regulations [1]. Questions regarding the feasibility and utility of applying a syndromic approach to these data sources are hampering the development of systems to collect, analyze, and share this potentially valuable information. Solidifying the evidence base and communicating the results to the public health surveillance community may help to initiate and build support for using these data to advance surveillance functions.

Methods

We conducted a literature search in the published and grey literature that scanned for relevant articles in the Google Scholar, Pub Med, and EBSCO Information Services databases. Search terms included: “inpatient/ambulatory electronic health record”; “ambulatory/inpatient/hospital/outpatient/chronic disease syndromic surveillance”; and “EHR syndromic surveillance”. Information gleaned from each article included data use, data elements extracted, and data quality indicators. In addition, several stakeholders who provided input on the September 2012 ISDS Recommendations [2] also provided articles that were incorporated into the literature review.ISDS also invited speakers from existing inpatient and ambulatory syndromic surveillance systems to give webinar presentations on how they are using data from these novel sources.

Results

The number of public health agencies (PHAs) routinely receiving ambulatory and inpatient syndromic surveillance data is substantially smaller than the number receiving ED and UC data. Some health departments, private medical organizations (including HMOs), and researchers are conducting syndromic surveillance and related research with health data captured in these clinical settings [2].In inpatient settings, many of the necessary infrastructure and analytic tools are already in place. Syndromic surveillance with inpatient data has been used for a range of innovative uses, from monitoring trends in myocardial infarction in association with risk factors for cardiovascular disease [3] to tracking changes in incident-related hospitalizations following the 2011 Joplin, Missouri tornado [3].In contrast, ambulatory systems face a need for new infrastructure, as well as pose a data volume challenge. The existing systems vary in how they address data volume and what types of encounters they capture. Ambulatory data has been used for a variety of uses, from monitoring gastrointestinal infectious disease [3], to monitoring behavioral health trends in a population, while protecting personal identities [4].

Conclusions

The existing syndromic surveillance systems and substantial research in the area indicate an interest in the public health community in using hospital inpatient and ambulatory clinical care data in new and innovative ways. However, before inpatient and ambulatory syndromic surveillance systems can be effectively utilized on a large scale, the gaps in knowledge and the barriers to system development must be addressed. Though the potential use cases are well documented, the generalizability to other settings requires additional research, workforce development, and investment.     

Long-Term Asthma Trend Monitoring in New York City: A Mixed Model Approach

Objective

Show the benefits of using a generalized linear mixed model (GLMM) to examine long-term trends in asthma syndrome data.

Introduction

Over the last decade, the application of syndromic surveillance systems has expanded beyond early event detection to include long-term disease trend monitoring. However, statistical methods employed for analyzing syndromic data tend to focus on early event detection. Generalized linear mixed models (GLMMs) may be a useful statistical framework for examining long-term disease trends because, unlike other models, GLMMs account for clustering common in syndromic data, and GLMMs can assess disease rates at multiple spatial and temporal levels (1). We show the benefits of the GLMM by using a GLMM to estimate asthma syndrome rates in New York City from 2007 to 2012, and to compare high and low asthma rates in Harlem and the Upper East Side (UES) of Manhattan.

Methods

Asthma related emergency department (ED) visits, and patient age and ZIP code were obtained from data reported daily to the NYC Department of Health and Mental Hygiene. Demographic data were obtained from 2010 US Census. ZIP codes that represented high and low asthma rates in Harlem and the UES of Manhattan were chosen for closer inspection. The ratio of weekly asthma syndrome visits to total ED visits was modeled with a Poisson GLMM with week and ZIP code random intercepts (2). Age and ethnicity were adjusted for because of their association with asthma rates (3).

Results

The GLMM showed citywide asthma rates remained stable from 2007 to 2012, but seasonal differences and significant inter-ZIP code variation were present. The Harlem ZIP code asthma rate that was estimated with the GLMM was significantly higher (5.83%, 95% CI: 3.65%, 9.49%) than the asthma rate in UES ZIP code (0.78%, 95% CI: 0.50%, 1.21%). A linear time component to the GLMM showed no appreciable change over time despite the seasonal fluctuations in asthma rate. GLMM based asthma rates are shown over time (Figure 1).Open in a separate windowFigure 1:Harlem ZIP code (red), the Upper East Side ZIP code (blue), and citywide (black) estimates are shown as dotted lines surrounded by 30% credibility bands in solid lines.

Conclusions

GLMMs have several strengths as statistical frameworks for monitoring trends including:

1. Disease rates can be estimated at multiple spatial and temporal levels,
2. Standard error adjustment for clustering in syndromic data allows for accurate, statistical assessment of changes over time and differences between subgroups,
3. “Strength borrowed” (4) from the aggregated data informs small subgroups and smooths trends,
4. Integration of covariate data reduces bias in estimated rates.

GLMMs have previously been suggested for early event detection with syndromic surveillance data (5), but the versatility of GLMM makes them useful for monitoring long-term disease trends as well. In comparison to GLMMs, standard errors from single level GLMs do not account for clustering and can lead to inaccurate statistical hypothesis testing. Bayesian hierarchical models (6), share many of the strengths of GLMMS, but are more complicated to fit. In the future, GLMMs could provide a framework for grouping similar ZIP codes based on their model estimates (e.g. seasonal trends and influence on overall trend), and analyzing long-term disease trends with syndromic data.     

Using GI Syndrome Data as an Early Warning Tool for Norovirus Outbreak Activity

Objective

To assess the relationship between emergency department (ED) and urgent care center (UCC) chief complaint data for gastrointestinal (GI) illness and reported norovirus (NV) outbreaks to develop an early warning tool for NV outbreak activity. The tool will provide an indicator of increasing NV outbreak activity in the community allowing for earlier public health action to mitigate NV outbreaks.

Introduction

Norovirus infection results in considerable morbidity in the United States where an estimated 21 million illnesses, 70,000 hospitalizations, and 800 deaths are caused by NV annually (1). Additionally, NV is responsible for approximately 50% of foodborne outbreaks (1). Between January 2008 and June 2012, 875 NV outbreaks were reported to the Virginia Department of Health (VDH). To assist in detecting possible disease outbreaks such as NV, VDH utilizes the web-based Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) to monitor and detect public health events across Virginia. ESSENCE performs automated parsing of chief complaint text into 10 syndrome categories, including a non-specific GI syndrome that serves as a proxy for GI illnesses like NV.

Methods

ED and UCC chief complaints parsed into the ESSENCE GI syndrome category were compared to confirmed and suspected NV outbreaks across four years. In this study, the analysis periods were defined as week 21 through week 20 of the subsequent year. GI syndrome visits as a proportion of all ED and UCC visits and NV outbreak counts were aggregated by week. Time-series, correlation, and logistic regression analyses were performed. Low NV outbreak activity weeks were defined as those with 4 or fewer outbreaks, and high NV outbreak activity weeks were those with 5 or more outbreaks. Based on low NV outbreak activity weeks, baseline and threshold values for the weekly percent of GI syndrome visits were calculated for each analysis period. Baseline calculation was the average weekly percent of GI syndrome visits from week 21 to week 31 and threshold value was baseline plus two standard deviations. Weekly percent of GI syndrome visits was compared to the threshold value to serve as an indicator of increasing NV outbreak activity.

Results

The study period was from May 18, 2008 to May 19, 2012 (Fig 1). A total of 1,425,728 GI syndrome visits and 804 confirmed and suspected NV outbreaks were analyzed. Weekly visits to ED and UCC facilities with GI syndrome were highly correlated with outbreaks of NV in the community (r =0.809, p <.0001). Median and mean number of NV outbreaks per week were 2 and 4, respectively (range 0–23). NV outbreaks were more prominent during the winter months with peaks occurring between weeks 3–9. Median and mean percent of GI syndrome visits per week were 10.2% and 10.5%, respectively (range 8.9%–12.8%). Weeks with high NV outbreak activity were more likely to occur when the weekly percent of GI syndrome visits had surpassed the threshold value (OR =110.7, 95% CI: 31.9–384.8). On average, weekly percent of GI syndrome visits surpassed the threshold value 1.25 weeks prior to the start of high NV outbreak activity weeks (range 0–3).

Conclusions

These results support the use of syndromic surveillance GI illness data as an early warning indicator of increasing NV outbreak activity in Virginia. This study identified that GI syndrome visits crossed a calculated threshold value on average 1.25 weeks before the initiation of high NV outbreak activity. Although NV outbreaks occur year round, this study attempted to identify an indicator to trigger meaningful risk communication to the community immediately prior to high NV outbreak activity with the goal of reducing the magnitude of NV outbreaks. This early warning tool for NV outbreak activity will be implemented in the following year to validate its effectiveness and timeliness in mitigating NV outbreaks in Virginia.Open in a separate windowPercent of Emergency Department and Urgent Care Center Visits with GI Syndrome and Reported Norovirus Outbreaks, Virginia, May 2008-May 2012.     

An Integrated Syndromic Surveillance System for Monitoring Scarlet Fever in Taiwan

Objective

To develop an integrated syndromic surveillance system for timely monitoring and early detection of unusual situations of scarlet fever in Taiwan, since Hong Kong, being so close geographically to Taiwan, had an outbreak of scarlet fever in June 2011.

Introduction

Scarlet fever is a bacterial infection caused by group A streptococcus (GAS). The clinical symptoms are usually mild. Before October, 2007, case-based surveillance of scarlet fever was conducted through notifiable infectious diseases in Taiwan, but was removed later from the list of notifiable disease because of improved medical care capacities. In 2011, Hong Kong had encountered an outbreak of scarlet fever (1,2). In response, Taiwan developed an integrated syndromic surveillance system using multiple data sources since July 2011.

Methods

More than 99% of the Taiwan population is covered by National Health Insurance. We first retrospectively evaluated claims data from the Bureau of National Health Insurance (BNHI) by comparing with notifiable diseases reporting data from Taiwan Centers for Disease Control (TCDC). The claims data included information on scarlet fever diagnosis (ICD-9-CM code 034.1), date of visits, location of hospitals and age of patients from outpatient (OPD), emergency room (ER) and hospital admissions. Daily aggregate data of scarlet fever visits or hospitalizations were prospectively collected from BNHI since July 2011. Over 70% of the deaths in Taiwan are reported to the Office of Statistics of Department of Health electronically. We obtained daily data on electronic death certification data and used SAS Enterprise Guide 4.3 (SAS Institute Inc., Cary, NC, USA) for data management and analysis. Deaths associated with scarlet fever or other GAS infections were identified by text mining from causes of death with keywords of traditional Chinese ‘scarlet fever’, ‘group A streptococcus’ or ‘toxic shock syndrome’ (3).

Results

From January 2006 to September 2007, the monthly OPD data with ICD-9-CM code 034.1 from BNHI showed strong correlation with TCDC’s notifiable disease data (r=0.89, p<0.0001). From July 6, 2008 (week 28) through July 28, 2012 (week 30), the average weekly numbers of scarlet fever visits to the OPD, ER and hospital admissions were 37 (range 11–70), 7 (range 0–20) and 3 (range 0–9). Eighty-five percent of the scarlet fever patients were less than 10 years old. In Taiwan, scarlet fever occurred year-round with seasonal peaks between May and July (Fig. 1). From January 2008 to July 2012, we identified 12 potential patients (9 males, age range 0–82 years) who died of GAS infections. No report had listed ‘scarlet fever’ as cause of death during the study period.Open in a separate windowFig. 1.Weekly numbers of nationwide scarlet fever OPD and ER visits, and hospital admissions, with baseline OPD visits and 95% confidence interval calculated by a Serfling’s model, week 28 of 2008 to week 30 of 2012.

Conclusions

Taiwan has established an integrated syndromic surveillance system to timely monitor scarlet fever and GAS infection associated mortalities since July 2011. Syndromic surveillance of scarlet fever through BNHI correlated with number of scarlet fever cases through notifiable disease reporting system. Text mining from cause of death with the used keywords may have low sensitivities to identify patients who died of GAS infection. In Taiwan, syndromic surveillance has also been applied to other diseases such as enterovirus, influenza-like illness, and acute diarrhea. Interagency collaborations add values to existing health data in the government and have strengthened TCDC’s capacity of disease surveillance.     

Content Analysis of Tobacco-related Twitter Posts

Objective

We present results of a content analysis of tobacco-related Twitter posts (tweets), focusing on tweets referencing e-cigarettes and hookah.

Introduction

Vast amounts of free, real-time, localizable Twitter data offer new possibilities for public health workers to identify trends and attitudes that more traditional surveillance methods may not capture, particularly in emerging areas of public health concern where reliable statistical evidence is not readily accessible. Existing applications include tracking public informedness during disease outbreaks [1].Twitter-based surveillance is particularly suited to new challenges in tobacco control. Hookah and e-cigarettes have surged in popularity, yet regulation and public information remain sparse, despite controversial health effects [2,3]. Ubiquitous online marketing of these products and their popularity among new and younger users make Twitter a key resource for tobacco surveillance.

Methods

We collected 7,300 tobacco-related Twitter posts at 15-day intervals from December 2011 to July 2012, using ten general keywords such as cig^* and hookah. Each tweet was manually classified using a tri-axial scheme, capturing genre (firsthand experience, joke, news, …), theme (underage usage, health, social image, …), and sentiment (positive, negative, neutral). Machine-learning classifiers were trained to detect tobacco-related vs. irrelevant tweets as well as each of the above categories, using Naïve Bayes, k-Nearest Neighbors, and Support Vector Machine algorithms. Finally, phi correlation coefficients were computed between each of the categories to discover emergent patterns.

Results

The most prevalent genre of tweets was personal experience, followed by categories such as opinion, marketing, and news. The most common themes were hookah, cessation, and social image, and sentiment toward tobacco was more positive (26%) than negative (20%). The most highly correlated categories were social image–underage, marketing–e-cigs, and personal experience–positive sentiment. E-cigarettes were also correlated with positive sentiment and new users (even excluding marketing posts), while hookah was highly correlated with positive sentiment, pleasure, and social relationships. Further, tweets matching the term “hookah” reflected the most positive sentiment, and “tobacco” the most negative (Figure 1). Finally, negative sentiment correlated most highly with social image, disgust, and non-experiential categories such as opinion and information.The best machine classification performance for tobacco vs. nontobacco tweets was achieved by an SVM classifier with 82% accuracy (baseline 57%). Individual categories showed similar improvements over baseline.

Conclusions

Several novel findings speak to the unique insights of Twitter surveillance. Sentiment toward tobacco among Twitter users is more positive than negative, affirming Twitter’s value in understanding positive sentiment. Negative sentiment is equally useful: for example, observed high correlations between negative sentiment and social image, but not health, may usefully inform outreach strategies. Twitter surveillance further reveals opportunities for education: positive sentiment toward the term “hookah” but negative sentiment toward “tobacco” suggests a disconnect in users’ perceptions of hookah’s health effects. Finally, machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, allowing for automated tobacco surveillance applications.Open in a separate windowSentiment in “hookah” tweets is disproportionately more positive than in “cig” and especially “tobacco” tweets.     

Comparing Syndromic Surveillance and Poison Center Data for Snake Bites in Missouri

Objective

This study intends to use two different surveillance systems available in Missouri to explore snake bite frequency and geographic distribution.

Introduction

In 2010, there were 4,796 snake bite exposures reported to Poison Centers nationwide (1). Health care providers frequently request help from poison centers regarding snake envenomations due to the unpredictability and complexity of prognosis and treatment. The Missouri Poison Center (MoPC) maintains a surveillance database keeping track of every phone call received. ESSENCE, a syndromic surveillance system used in Missouri, enables surveillance by chief complaint of 84 different emergency departments (ED) in Missouri (accounting for approximately 90% of all ED visits statewide). Since calling a poison center is voluntary for health care providers, poison center data is most likely an underestimation of the true frequency of snake envenomations. Comparing MoPC and ESSENCE data for snake envenomations would enable the MoPC to have a more accurate depiction of snake bite frequency in Missouri and to see where future outreach of poison center awareness should be focused.

Methods

Archived data from Toxicall®, the MoPC surveillance system, was used to query the total number of snake bite cases from 01/01/2007 until 12/31/2011 called into the MoPC center by hospitals that also participate ESSENCE. Next, ESSENCE data was used to estimate the total number of snake envenomations presenting to EDs in Missouri. This was accomplished using the same date range as well as searching for key terms in the chief complaints that would signify a snake bite. The results of each datasearch were compared and contrasted by Missouri region.

Results

The Toxicall® search showed a total of 324 snake bite cases. The initial ESSENCE data query showed a total of 1983 snake bite cases. After certain data exclusions, there was a total of 1763 ESSENCE snake bite visits. This suggests that approximately 18% of all snake bite visits reported in Missouri ESSENCE were called into the MoPC. The results are demonstrated by Missouri region in Figure 1. This figure also shows that the greatest number of ESSENCE visits for snake bites were reported by Southwest region hospitals whereas the Eastern region hospitals placed the greatest number of calls to MoPC regarding snake bites.Open in a separate windowFigure 1:ESSENCE Snake Bites Cases Compared to Toxicall® Snake Bite Cases in Missouri by Region

Conclusions

The total number of snake bite cases from Missouri ESSENCE ED visits is much greater than the number of snake bites cases called into the MoPC by ESSENCE participating hospitals. This underutilization of the poison center demonstrates the increased need for awareness of the MoPC’s free services. In Missouri, the MoPC should target hospitals in the Southwest region for outreach in particular based on these findings. Poison centers are staffed by individuals trained in all types of poisonings and maintain a list of consulting physicians throughout the United States experienced in management and treatment of venomous snake bites (2). Any healthcare facility would benefit from MoPC assistance. Finally, syndromic surveillance allows for quick and easy data compilation, however there are some difficulties when attempting to search for a particular condition. Communication and partnership between two different public health organizations will be beneficial toward future public health studies.     

Automated Surveillance of Outpatients with Pneumonia: A Performance Evaluation

Objective

To determine if influenza surveillance should target all patients with acute respiratory infections (ARI) or only track pneumonia cases.

Introduction

Effective responses to epidemics of infectious diseases hinge not only on early outbreak detection, but also on an assessment of disease severity. In recent work, we combined previously developed ARI case-detection algorithms (CDA) [1] with text analyses of chest imaging reports to identify ARI patients whose providers thought had pneumonia. In this work, we asked if a surveillance system aimed at patients with pneumonia would outperform one that monitors the full severity spectrum of ARI.

Methods

Time series of daily casecounts (backgrounds) were created by applying either an ARI CDA (ARI ICD-9 codeset [1]) or a Pneumonia CDA (ARI ICD-9 codes AND chest imaging obtained AND positive results from automated text analysis that identify those chest imaging reports that support the diagnosis of pneumonia) to electronic medical record (EMR) entries related to outpatient encounters at the VA Maryland Health Care System. We used an age-structured metapopulation influenza epidemic model for Baltimore to inject factitious influenza cases into backgrounds. Injections were discounted by the known sensitivity of the ARI CDA [1]. For injections into the pneumonia backgrounds time series, factitious ARI cases were further discounted by the expected pneumonia rate in the modeled influenza epidemic (10%). From the time of injection, EARS or CUSUM statistics [2,3] were applied on each successive day on paired back-ground+injection vs. background-only time series. Each injection-prospective-surveillance cycle was repeated 52 times, each time with the injection shifted to a different week of the one-year study period (2010–11). We computed: 1) the “Detection Delay”, the average time from injection to the first alarm present in the back-ground+injection dataset but absent from the background-only dataset; 2) the “False-Alarm Rate” (FAR), defined as the number of unique false-alarms originating in the background-only dataset during the study year, divided by 365 days. To create activity monitoring operating characteristic (AMOC) curves, we empirically determined the corresponding Delay-FAR pairs over a wide range of alarm thresholds.

Results

The Figure compares AMOC curves for otherwise identical surveillance systems that included either any ARI outpatient visits (red circles, using the EARS W2c statistic [2] and blue triangles, using the CUSUM statistic) or pneumonia (blue triangles, using the CUSUM statistic modified for sparse data [3]). Note that Detection Delay (y-axis) is lower at any given FAR when surveillance aims at patients with pneumonia. Sensitivity analysis suggests that this advantage remains true when pneumonia complicates influenza ≥ 5% of the time.

Conclusions

Our results suggest that EMR-based influenza surveillance that targets patients with pneumonia can outperform systems that monitor all ARI patients.Open in a separate window