Use of Syndromic Data to Determine Oral Health Visit Burden on Emergency Departments

Objective

The objective was to use syndromic surveillance data from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool NCDETECT and from BioSense to quantify the burden on North Carolina (NC) emergency departments of oral health-related visits more appropriate for care in a dental office (ED). Calculations were sought in terms of the Medicaid-covered visit rate relative to the Medicaid-eligible population by age group and by county.

Introduction

Concern over oral health-related ED visits stems from the increasing number of unemployed and uninsured, the cost burden of these visits, and the unavailability of indicated dental care in EDs [1]. Of particular interest to NC state public health planners are Medicaid-covered visits. Syndromic data in biosurveillance systems offer a means to quantify these visits overall and by county and age group.

Methods

Using BioSense data received from NCDETECT, 60.8 million records from 12.9 million ED visits were collected, covering all NC visits for state fiscal years (SFY) 2008–2010. Roughly 4% of visits were dropped because of patient residence zip codes missing or outside NC. A careful multi-step procedure involving both dentist consultants and data analysis was used to derive classification criteria for visits whose main reason was a nontraumatic oral health problem [2]. This procedure yielded 243,970 visits by ∼174,600 patients based on hospital-specific patient identifiers. Nontraumatic oral health-related visits were collected in a study set with added fields for method of payment, patient residence county, and age group. Based on previous studies, consultant preferences, and NC Medicaid eligibility guidelines, selected age groups were 0–14, 15–19, 20–29, 30–49, 50+ years. Stratified counts of Medicaid-eligibles were obtained from the NC Dental Director by study year. Using these tables and the ED visit study set, rates of nontraumatic oral health-related Medicaid visits per 10,000 eligibles were tabulated by county and age group for each study year. Demographics of multiple-visit patients were also studied.

Results

Rates of ED oral health-related visits were substantially higher for young adults than for other age groups. From statewide rates in

Detection of Patients with Influenza Syndrome Using Machine-Learning Models Learned from Emergency Department Reports

Objective

Compare 7 machine learning algorithms with an expert constructed Bayesian network on detection of patients with influenza syndrome.

Introduction

Early detection of influenza outbreaks is critical to public health officials. Case detection is the foundation for outbreak detection. Previous study by Elkin el al. demonstrated that using individual emergency department (ED) reports can better detect influenza cases than using chief complaints [1]. Our recent study using ED reports processed by Bayesian networks (using expert constructed network structure) showed high detection accuracy on detection of influenza cases [2].

Methods

The dataset used in this study includes 182 ED reports with confirmed PCR influenza tests (Jan 1, 2007–Dec 31, 2009) and 40853 ED reports as control cases from 8 EDs in UPMC (Jul 1, 2010–Aug 31, 2010). All ED reports were deidentified by De-ID software with IRB approval.An NLP system, Topaz, was used to extract relevant findings and symptoms from the reports and encoded them with the UMLS concept unique identifier codes [2]. Two subsets were created: DS1-train (67% of cases) and DS1-test (remaining 33%).The algorithms used for training the models are: Naïve Bayes Classifier, Efficient Bayesian Multivariate Classification (EBMC) [3], Bayesian Network with K2 algorithm, Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Networks (ANN) and Random Forest (RF).The predictive performance of each method was evaluated using the area under the receiver operator characteristic (AUROC) and the Hosmer-Lemeshow (HL) statistical significance testing, that describes the lack-of-fit of the model to the dataset.

Results

The evaluation results of all the models using DS1-test, including the AUROC, its confidence interval, p-value (between each algorithm and the expert) and the calibration with HL are shown in ConclusionsAll models achieved high AUROC values. The pairwise comparison of p-values in Figure 1.One limitation of the study is that the test dataset has low influenza prevalence, which may bias the detection algorithm performance. We are in the process of testing the algorithms using higher prevalence rate.The same process could also be applied to other diseases to further research the generalizability of our method.Predictive performance and Calibration

Applying Zero-inflated Mixed Model to School Absenteeism Surveillance in Rural China

Objective

To describe and explore the spatial and temporal variability via ZIMM for absenteeism surveillance in primary school for early detection of infectious disease outbreak in rural China.

Introduction

Absenteeism has great advantages in promoting the early detection of epidemics1. Since August 2011, an integrated syndromic surveillance project (ISSC) has been implemented in China2. Distribution of the absenteeism generally are asymmetry, zero inflation, truncation and non-independence3. For handling these encumbrances, we should apply the Zero-inflated Mixed Model (ZIMM).

Methods

Data for this study was obtained from the web-based data of ISSC in 62 primary schools in two counties of Jiangxi province, China from April 1th, 2012 to June 30st, 2012. The ZIMM was used to explore: 1)the temporal and spatial variability regarding occurrence and intensity of absenteeism simultaneously, and 2) the heterogeneity among the reporting primary schools by introducing random effects into the intercepts. The analyse was processed in the SAS procedure NLMIXED4.

Results

The total 4914 absenteeism events were reported in the 62 primary schools in the study period. The rate of zero report was 49.88% (Fig. 1). According to ZIMM, there are fixed and random effect parameters in this model (Open in a separate windowFig. 1Absenteeism from Apr. 1st to Jun. 30th 2012

Table 1

Fixed parameters and variance components estimates for the absenteeism using ZIMM

Evaluation of Cholera and Other Diarrheal Disease Surveillance System,Niger State,Nigeria-2012

Objective

To determine how the cholera and other diarrheal disease surveillance system in Niger state is meeting its surveillance objectives, to evaluate its performance and attributes and to describe its operation to make recommendations for improvement.

Introduction

Cholera causes frequent outbreaks in Nigeria, resulting in mortality. In 2010 and 2011, 41,936 cases (case fatality rate [CFR]-4.1%) and 23,366 cases (CFR-3.2%) were reported (1). Reported cases in Nigeria by week 26, 2012 was 309 (CFR-1.29%) involving 20 Local Government Areas in 6 States. In Nigeria, there are currently eleven (11) States including Niger state at high risk for cholera/bloodless diarrhea outbreaks.In 2011, Niger state had 2472 cholera cases (CFR-2%) and 45,111 other diarrhea diseases cases, recorded in more than half of state Purpose of surveillance system is to ensure early detection of cholera and other diarrheal cases and to monitor trends towards evidence-based decision for management, prevention and control.

Methods

We conducted evaluation in July, 2012. We used CDC guideline on surveillance system evaluation (2001) as guide to assess operation, performance and attributes (2). We conducted key informant/in-depth interviews with stakeholders. We examined cholera action plans for preparedness and response, conducted laboratory assessment, extracted and analyzed cholera surveillance (2005–2012) for frequencies/proportions using Microsoft Excel. Thematic analysis was done for qualitative data. We shared findings with stakeholders at all levels.

Results

Surveillance system was setup for early detection and monitoring towards evidence-based decision. State government funds system. Case definition used is highly sensitive and is any patient aged 5 years or more who develops acute watery diarrhea, with/without vomiting. Though simple case definition, laboratory confirmation makes surveillance complex. A passive system, active during outbreaks; has formal and informal sources of information and part of Integrated Disease Surveillance and Response (IDSR) system and flow(fig.1). It takes 24–48 hours between outbreaks onset, confirmation and response.Line list showed undefined/poorly labeled outcomes. Of 2472 cases in 2011 1320 (49%) were found in line list. 2011 monthly data completeness was 75%. So far in 2012, 5(0.02%) of all diarrhea cases were cholera. System captures only age as sociodemographics.Of 11 suspected cholera cases tested during 2011 epidemic, 7 confirmed as cholera (PPV-63%). Of 3 rumours of cholera outbreaks (January 2011-July 2012), one (PPV-33%) was true. Acceptability of system is high among all stakeholders interviewed. Timeliness of monthly reporting was 68.7% (Performance attributesExcellent (>90%)Very good (80–89%)Good (70–79%)Average (60–69%)Fair (50–59%)Poor (<50%)Simplicity•Flexibility•Data Quality•Acceptability•Sensitivity•Positive Predictive Value•Representativeness•Timeliness•Stability•Open in a separate windowLaboratory can isolate Vibro cholerae isolation but has no Cary Blair transport medium and cholera rapid test kits.

Conclusions

Evaluation revealed that surveillance system is meeting its objectives by early detection and response to cholera outbreaks. System is simple, stable, flexible, sensitive with poor data quality, low PPV, fair laboratory capacity and moderate timeliness. We recommended electronic and internet-based reporting for timeliness and data quality improvement; and provision of laboratory consumables.Open in a separate window     

Using Medications Sales from Retail Pharmacies for Syndromic Surveillance in Rural China

Objective

To use an unconventional data - pharmaceutical sales surveillance for the early detection of respiratory and gastrointestinal epidemics in rural China.

Introduction

Drug sales data as an early indicator in syndromic surveillance has attracted particular interest in recent years (1, 2), however previous studies were mostly conducted in developed countries or areas. In China, many people (around 60%) choose self-medication as their first option when they encounter a health problem (3), and electronic sales information system is gradually used by retail pharmacies, which makes drug sales data become a promising data source for syndromic surveillance in China.

Methods

This experimental study was conducted in four rural counties in central China. From Apr. 1st 2012, there are 56 retail pharmacies joined the study, including 21 county pharmacies and 35 township pharmacies. 123 drugs were selected under surveillance based on the analysis of local historical sales volume and consultation with local pharmacists, including 19 antibiotics, 15 antidiarrheal medications, 9 antipyretics, 41 compound cold medicine, and 39 cough suppressants. Daily sales volume of the selected drugs was recorded into the database by pharmacy staff at each participating unit via electronic file importing or manual entering. Figure 1 showed the user interface for data viewing, query and export. Field training and supervision were regularly conducted to ensure the data quality.Open in a separate windowFigure 1User interface in the system for data viewing, query and export

Results

From Apr. 1st to Jun. 30th 2012, there were 103814 sales records reported in the system, including 44464 (42.83%) records from county pharmacies and 59350 (57.17%) from township pharmacies. Among all surveillance drugs, the sales of compound cold medicine accounted for the largest proportion (43.42%), followed by antibiotics (22.52 %), cough suppressants (18.50%), antidiarrheal drugs (9.49%) and antipyretics (6.06 %). More than 80% data were reported into the system within 24 hours after the sales date, and the reporting timeliness of county pharmacies improved with time (Report time after sales dateAprilMayJuneCounty N(%)Township N(%)County N(%)Township N(%)County N(%)Township N(%)within 24 h11359(75.84)16653(85.6)12483(81.80)17749(87.99)12225(85.93)15977(81.01)24 h-48h1523(10.25)1001(5.15)1487(9.74)1760(8.72)1321(9.29)2224(11.28)later than 48h2084(13.91)1801(9.26)1290(8.45)663(3.29)680(4.78)1522(7.72)Sum149781945515260201721422619723Open in a separate window

Conclusions

Although the current reporting timeliness and completeness are satisfying, it is noteworthy the quality of data is not stable during the beginning phase of the implementation. Further validation of the data will be required. To ensure the accuracy of data and the effective and sustainable deployment of the system, it is imperative to establish a data sharing policy between pharmacies and public health agencies, and achieve automated data collection to avoid additional human labor involvement.     

Computerized Text Analysis to Enhance Automated Pneumonia Detection

Objective

To improve the surveillance for pneumonia using the free-text of electronic medical records (EMR).

Introduction

Information about disease severity could help with both detection and situational awareness during outbreaks of acute respiratory infections (ARI). In this work, we use data from the EMR to identify patients with pneumonia, a key landmark of ARI severity. We asked if computerized analysis of the free-text of clinical notes or imaging reports could complement structured EMR data to uncover pneumonia cases.

Methods

A previously validated ARI case-detection algorithm (CDA) (sensitivity, 99%; PPV, 14%) [1] flagged VAMHCS outpatient visits with associated chest imaging (n = 2737). Manually categorized imaging reports (Non-Negative if they could support the diagnosis of pneumonia, Negative otherwise; kappa = 0.88), served as a reference for the development of an automated report classifier through machine-learning [2]. EMR entries related to visits with Non-Negative chest imaging were manually reviewed to identify cases with Possible Pneumonia (new symptom(s) of cough, sputum, fever/chills/night sweats, dyspnea, pleuritic chest pain) or with Pneumonia-in-Plan (pneumonia listed as one of two most likely diagnoses in a physician’s note). These cases were used as reference for the development of the EMR-based CDAs. CDA components included ICD-9 codes for the full spectrum of ARI [1] or for the pneumonia subset, text analysis aimed at non-negated ARI symptoms in the clinical note [1] and the above-mentioned imaging report text classifier.

Results

The manual review identified 370 reference cases with Possible Pneumonia and 250 with Pneumonia-in-Plan. Statistical performance for illustrative CDAs that combined structured EMR parameters with or without text analyses are shown in the ConclusionsAutomated text analysis of chest imaging reports can improve our ability to separate outpatients with pneumonia from those with a milder form of ARI.

CDA Number	1	2	3	4	5	6	7	8	9	10	11	12
	Possible Pneumonia						Pneumonia-in-Plan
CDA Components
(Pneumonia ICD-9 Codes	•	•					•	•
(ARI ICD-9 Codes			•	•	•	•			•	•	•	•
OR Text of Clinical Notes)					•	•					•	•
AND Chest Imaging Obtained	•	•	•	•	•	•	•	•	•	•	•	•
AND Text of Imaging Reports		•		•		•		•		•		•
Sensitivity (%)	36.8	28.4	85.9	58.4	99.7	66.2	52	40.8	93.6	68.8	100	74.8
Specificity (%)	95.4	99.7	29.8	98.5	2.2	98	95.4	99.6	29.8	96.8	2.3	95.7
PPV (%)	55.3	93.8	16	86.1	13.7	83.3	52.8	91.1	12	68.5	9.3	63.6
NPV (%)	91	90	93.2	93.8	98.1	95	95.2	94.4	98	97	100	97.4
F-Measure	44.2	43.6	27	69.6	24.1	73.8	52.4	56.4	21	68.6	17	68.7

Open in a separate window     

Mental Illness and Co-morbid Conditions: BioSense 2008 – 2011

Objective

The purpose of this paper was to analyze the associated burden of mental illness and medical comorbidity using BioSense data 2008–2011.

Introduction

Understanding the relationship between mental illness and medical comorbidity is an important aspect of public health surveillance. In 2004, an estimated one fourth of the US adults reported having a mental illness in the previous year (1). Studies showed that mental illness exacerbates multiple chronic diseases like cardiovascular diseases, diabetes and asthma (2). BioSense is a national electronic public health surveillance system developed by the Centers for Disease Control and Prevention (CDC) that receives, analyzes and visualizes electronic health data from civilian hospital emergency departments (EDs), outpatient and inpatient facilities, Veteran Administration (VA) and Department of Defense (DoD) healthcare facilities. Although the system is designed for early detection and rapid assessment of all-hazards health events, BioSense can also be used to examine patterns of healthcare utilization.

Methods

We used 4 years (2008 – 2011) of BioSense civilian hospitals’ EDs visit data to perform the analysis. We searched final diagnoses for ICD-9 CM codes related to mental illness (290 – 312), schizophrenia (295), major depressive disorder (296.2 – 296.3), mood disorder (296, 300.4 and 311) and anxiety, stress & adjustment disorders (300.0, 300.2, 300.3, 308, and 309). We used BioSense syndromes/sub-syndromes based on chief complaints and final diagnoses for comorbidity. For the purpose of this study, comorbidity was defined broadly as the co-occurrence of mental and physical illness in the same person regardless of the chronological order. The proportion was calculated as the number of mental health visits associated with comorbidity divided by the total number of mental illness relevant visits. We ranked the top 10 proportions of comorbidity for adult mental illness by year.

Results

From 2008–2011, there were 4.6 million visits where mental illness was reported in the EDs visits. Average age of those reported mental illness was 44 years, 55% were women and 45% were men. More women were reported with anxiety (67%), mood (66%), and major depressive disorders (59%) than men; while men were reported more with schizophrenia (56%) than women (44%). The most common comorbid condition was hypertension, followed by chest pain, abdominal pain, diabetes, nausea & vomiting and dyspnea (Rank20082009201020111HypertensionHypertensionHypertensionHypertension2Chest PainChest PainChest PainChest Pain3Abdominal painAbdominal painAbdominal painAbdominal pain4Diabetes mellitusDiabetes mellitusDiabetes mellitusDiabetes mellitus5Nausea & vomitingNausea &. vomitingNausea & vomitingNausea & vomiting6DyspneaDyspneaDyspneaDyspnea7Injury, NOSTailshallshalls8FallsAsthmaHeadacheHeadache9HeadacheHeadacheAsthmaInjury, NOS10AsthmaInjury, NOSInjury, NOSAsthmaOpen in a separate window

Conclusions

This study supports prior findings that adult mental illness is associated with substantial medical burden. We identified 10 most common comorbid condition associated with mental illness. The major limitation of this work was that electronic data does not allow determination of the causal pathway between mental illness and some medical comorbidity. In addition, data represents only those who have access to healthcare or those with health seeking behaviors. Familiarity with comorbid conditions affecting persons with adult mental illness may assist programs aimed at providing medical care for the mentally ill.     

Selecting Targeted Symptoms/Syndromes for Syndromic Surveillance in Rural China

Objective

To select the potential targeted symptoms/syndromes as early warning indicators for epidemics or outbreaks detection in rural China.

Introduction

Patients’ chief complaints (CCs) as a common data source, has been widely used in syndromic surveillance due to its timeliness, accuracy and availability (1). For automated syndromic surveillance, CCs always classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. However, in rural China, most outpatient doctors recorded the information of patients (e.g. CCs) into clinic logs manually rather than computers. Thus, more convenient surveillance method is needed in the syndromic surveillance project (ISSC). And the first and important thing is to select the targeted symptoms/syndromes.

Methods

Epidemiological analysis was conducted on data from case report system in Jingmen City (one study site in ISSC) from 2004 to 2009. Initial symptoms/syndromes were selected by literature reviews. And finally expert consultation meetings, workshops and field investigation were held to confirm the targeted symptoms/syndromes.

Results

10 kinds of infectious diseases, 6 categories of emergencies, and 4 bioterrorism events (i.e. plague, anthrax, botulism and hemorrhagic fever) were chose as specific diseases/events for monitoring (Respiratory casesGastrointestinal casesEmergenciesName%Name%NameEvents (No.)^*Pulmonary tuberculosis82.38^†Hand-foot-mouth diseases41.73^†A(H1N1)10^†Mumps9.14^†Bacillary dysentery28.56^†Mumps5^†Measles3.35^†Hepatitis A15.36^†Hand-foot-mouth diseases1^†Varicella2.00^† Infectious diarrhea6.58^†Bacillary dysentery1^†Influenza/A(H1N1)1.79^†Hepatitis E4.30^†Food poisoning2^†Rubella0.72Typhoid3.03^†Unknown reason dermatitis1Scarlet fever0.44Paratyphoid0.22Pertussis0.15Amebic dysentery0.22Meningococcal meningitis0.03Total100.00Total100.00Total20Open in a separate window^*Chronic infectious diseases (excluded).^†Selected specific diseases (top 5) or events (non-infectious excluded).

Table 2

List of symptoms/syndromes

Potential use of multiple surveillance data in the forecast of hospital admissions

Objective

This paper describes the potential use of multiple influenza surveillance data to forecast hospital admissions for respiratory diseases.

Introduction

A sudden surge in hospital admissions in public hospital during influenza peak season has been a challenge to healthcare and manpower planning. In Hong Kong, the timing of influenza peak seasons are variable and early short-term indication of possible surge may facilitate preparedness which could be translated into strategies such as early discharge or reallocation of extra hospital beds. In this study we explore the potential use of multiple routinely collected syndromic data in the forecast of hospital admissions.

Methods

A multivariate dynamic linear time series model was fitted to multiple syndromic data including influenza-like illness (ILI) rates among networks of public and private general practitioners (GP), and school absenteeism rates, plus drop-in fever count data from designated flu clinics (DFC) that were created during the pandemic. The latent process derived from the model has been used as a measure of the influenza activity [1]. We compare the cross-correlations between estimated influenza level based on multiple surveillance data and GP ILI data, versus accident and emergency hospital admissions with principal diagnoses of respiratory diseases and pneumonia & influenza (P&I).

Results

The estimated influenza activity has higher cross-correlation with respiratory and P&I admissions (ρ=0.66 and 0.73 respectively) compared to that of GP ILI rates (Surveillance dataCross correlations / lags^*−2−1012Respiratory disease admissionsGP ILI0.480.530.550.500.45Estimated influenza activity0.590.660.660.580.46P&I admissionsGP ILI0.570.620.650.590.49Estimated influenza activity0.660.730.730.650.53Open in a separate window^*negative lags refer to correlations between lagged surveillance data and hospital admissions

Conclusions

The use of a multivariate method to integrate information from multiple sources of influenza surveillance data may have the potential to improve forecasting of admission surge of respiratory diseases.     

The Use of the International Classification of Diseases,Ninth Revision (ICD-9) Coding in Identifying Chronic Hepatitis B Virus Infection in Health System Data: Implications for Surveillance

Objective

To evaluate the sensitivity, specificity, positive and negative predictive values of the ICD-9 coding system for surveillance of chronic hepatitis B virus infection (HBV) using data from an observational cohort study in which ICD-9-coded HBV cases were validated by chart review.

Introduction

In the United States, 800,000- 1.4 million people are chronically infected with hepatitis B virus (HBV); these persons are at increased risk for chronic liver disease and its sequelae (CDC, 2010; Wasley, 2010). Current national viral hepatitis surveillance is a passive laboratory-initiated reporting system to state or local health departments with only 39 health departments reporting chronic HBV infection in the National Notifiable Disease Surveillance System (NNDSS). Since active HBV surveillance can be expensive and labor-intensive, the ICD-9 coding system has been proposed for surveillance of chronic hepatitis B.

Methods

We examined the electronic health records (EHRs) available as part of an existing cohort study of persons with chronic viral hepatitis. Records from 1.6 million adult patients who had one or more services from 2006–2008 in four integrated health care systems were reviewed. Complex algorithms using laboratory data and/or use of qualifying hepatitis B ICD-9 codes were applied to EHR patient data to create the chronic HBV cohort. Disease status was manually validated by abstractor review of the medical record. Sensitivity, specificity, positive and negative predictive values were calculated based upon presence of either one hepatitis B-specific ICD-9 code or two such ICD-9 codes separated by at least six months.

Results

Of 1,652,055 adult patients, 2,202 (0.1%) met criteria for inclusion into the chronic HBV cohort. Of the 2,202 confirmed cases, the sensitivity of use of one ICD-9 code was 83.9%, positive predictive value was 61.0%, specificity was 99.9% and the negative predictive value was over 99.9% (ConclusionsOur findings suggest that use of one or two hepatitis B specific ICD 9 codes can identify cases with chronic HBV infection. For health departments with access to electronic medical records, collection of ICD-9 data may be useful for surveillance and potentially improve reporting of chronic HBV infection.Measurement of sensitivity, specificity, and predictive values of using one hepatitis B-specific ICD-9 code among persons receiving services from four health care systems from 2006–2008

	Confirmed HBV case	Not a HBV case	Total
One ICD-9 code	1,847	1,182	3,029
No ICD-9 code	355	1,648,671	1,649,026
Total	2,202	1,649,853	1,652,055

Open in a separate windowSensitivity= 1,847/2,202= 83.9%Specificity= 1,648,671/1,649,853= 99.9%Positive predictive value= 1,847/3,029= 61.0%Negative predictive value= 1,648,671/1,649,026= >99.9%     

Serum Zinc Concentration and Acute Diarrhea in Children from Different Regions of Uzbekistan

Objective

To study the blood serum zinc concentration in children with acute diarrhea (AD) in in-patient facilities before and after therapy.

Introduction

There are several reports of zinc deficiency in pathogenesis of acute and chronic diarrhea. The literature review showed children with diarrhea and chronic gastroduodenitis performed zinc deficiency in majority of cases (1). The normal values of zinc in blood serum are 12.8–27.8 μmol/l (2). There is a threshold of 13μmol/l zinc concentration for zinc deficiency diagnosis. The zinc level 8.2 μmol/l and below is poor prognostic criteria (3).

Methods

Totally 102 children (1–14 years old) with AD in in-patient facility from different regions were studied for serum zinc concentration before and after treatment. Termez and Saraosie cities are located in south of Uzbekistan, in the region with high negative impact from the nearly Tajikistan located aluminum producing plant. The serum zinc level measured by neutron-activation method in the Institute of Nuclear Research (INR).

Results

The zinc concentration in serum significantly varied by the region (CitynZinc concentration, μmol/l (mean ± SD)Before treatmentAfter treatmentTashkent city (captial)3613.8±1.512.5±1.3Termez city409.1±0.087.47±0.01Saraosie city267.9±0.37.5±0.8Open in a separate windowThe level of zinc in children from Tashkent estimated at lower normal limit with reduction below normal values after treatment. Children from Termez during admission to the in-patient facilities were zinc deficient with further reduction to the poor prognostic level. Children in Saraosie admitted to the in-patient with significant zinc deficiency that remained on poor prognostic level after treatment.

Conclusions

The study results may indicate the treatment of AD in children do not replenish the zinc to the appropriate level. Though some confounding factors may contribute the observed zinc disorders the results may indicate environmental factors, such as pollution by aluminum producing plant emission to contribute the difference in zinc concentration and should be considered for the correction and treatment of AD in children.     

Paralysis Analysis: Investigating Paralysis Visit Anomalies in New Jersey

Objective

To describe the investigation of a statewide anomaly detected by a newly established state syndromic surveillance system and usage of that system.

Introduction

On July 11, 2012, New Jersey Department of Health (DOH) Communicable Disease Service (CDS) surveillance staff received email notification of a statewide anomaly in EpiCenter for Paralysis. Two additional anomalies followed within three hours. Since Paralysis Anomalies are uncommon, staff initiated an investigation to determine if there was an outbreak or other event of concern taking place. Also at question was whether receipt of multiple anomalies in such a short time span was statistically or epidemiologically significant.

Methods

In New Jersey, 68 of 81 total acute care and satellite Emergency Departments (EDs) are connected to EpiCenter, an online syndromic surveillance system developed by Health Monitoring Systems, Inc (HMS) that incorporates statistical management and analytical techniques to process health-related data in real time. Chief complaint text is classified, using text recognition methods, into various public health-related and other categories. Anomalies occur when any of several statistical methods detect increases in incoming data that are outside of established thresholds.After receiving three anomaly notifications related to Paralysis in a 4-hour time period, NJDOH surveillance data staff enlisted CDS and local epidemiologist colleagues to review the data and determine if there was an infectious cause.

Results

The first EpiCenter anomaly notification was received on July 11, 2012 at 1:22 pm as a result of increased ED visits classified as Paralysis based on facility location for the period beginning at noon on July 10, 2012. Using Cusum EMA analysis, 76 reported interactions exceeded the predicted value of 50.49 and the threshold of 70.72. The second anomaly, also based on facility location, was received at 3:20 pm and the third anomaly notification, based on home location, was received at 4:32 pm. Cusum EMA and Exponential Moving Average analysis methods detected these anomalies. Anomaly Date/Time24 hour end timeLocationAnalysis Method# InteractionsExpected Value/Threshold of Alert7/11/2012, 1:22 PM12:00 PMBy FacilityCusum EMA7650.49 / 70.727/11/2012,3:20 PM2:00 PMBy FacilityExponential Moving Average8050.47 / 79.647/11/2012,4:32 PM3:00 PMBy HomeCusum EMA7044.94/66.32Open in a separate windowCompiled data from all anomalies were reviewed by CDS epidemiology and surveillance staff to determine whether there was a public health event taking place. A total of 89 patients were seen in 39 (57%) of the 68 NJ facilities reporting to EpiCenter with no geographic centralization. Age and gender of patients were reviewed with no clear pattern discerned. Figure 1 shows the time distribution of these visits. Upon further investigation, it was determined that a moderate increase in Paralysis visits over a relatively short time span was sufficient to create an anomaly under the default threshold for those visits. Multiple analysis methods created multiple anomalies which gave an impression the event was of greater significance compared to a single anomaly. To follow up, NJDOH requested that local epidemiologists investigate within their jurisdictions by contacting hospitals directly where EpiCenter data proved inconclusive. Their reports confirmed NJDOH’s findings that the anomalies did not signal an event of public health concern.

Conclusions

This investigation of three Paralysis anomalies is an important introduction to the newly implemented system’s capabilities in anomaly detection, and also to anomaly investigation procedures developed by NJDOH for local surveillance staff. As a result of this experience, these anomaly investigation procedures are being fine-tuned. The fact that these sequential anomalies resulted in an investigation being undertaken highlights the importance in setting investigation- generating alert thresholds within EpiCenter at a level that will minimize “false” positives without risking the missing of “true” positives.Open in a separate window     

Improving ILI Surveillance using Hospital Staff Influenza-like Absence (ILA)

Objective

To address the feasibility and efficiency of a novel syndromic surveillance method, monitoring influenza-like absence (ILA) among hospital staff, to improve national ILI surveillance and inform local hospital preparedness.

Introduction

Surveillance of influenza in the US, UK and other countries is based primarily on measures of influenza-like illness (ILI), through a combination of syndromic surveillance systems, however, this method may not capture the full spectrum of illness or the total burden of disease. Care seeking behaviour may change due to public beliefs, for example more people in the UK sought care for pH1N1 in the summer of 2009 than the winters of 2009/2010 and 2010/2011, resulting in potential inaccurate estimates from ILI (1). There may also be underreporting of or delays in reporting ILI in the community, for example in the UK those with mild illness are less likely to see a GP (2), and visits generally occur two or more days after onset of symptoms (3). Work absences, if the reason is known, could fill these gaps in detection.

Methods

Weekly counts and rates of hospital staff ILA (attributed to colds or influenza) were compared to GP ILI consultation rates (Royal College of General Practitioners Weekly Returns Service)(4) for 15–64 year olds, and positive influenza A test results (PITR) for all inpatients hospitalised in the three London hospitals for which staff data were collected using both retrospective time series and prospective outbreak detection methods implemented in the surveillance package in R (5)

Results

Rates of ILA were about six times higher than rates of ILI. Data on hospital staff ILA demonstrated seasonal trends as defined by ILI. Compared to the ILI rates, ILA demonstrated a more realistic estimate of the relative burden of pandemic H1N1 during July 2009 (1) (Figure). ILA provides potentially earlier warnings than GP ILI as indicated by its ability to predict ILI data for the local region (p < 0.001), as well as its potential for daily ‘real time’ updates. Using outbreak detection methods and examining peak weeks, alarms and thresholds, ILA alarmed, reached threshold rates and peaked consistently earlier or in the same week as ILI and PITR, with the exception of the July 2009, suggesting that it may be predictive of both community and patient cases of influenza (Open in a separate windowFigure:Weekly counts of ILA among hospital staff (blue), PITR among hospital patients (orange), and ILI in the community (red) from April 2008 to March 2011 and prospective alarms for elevated counts (circles) using a Bayesian subsystem algorithm, using the previous six weeks as the reference for prediction. Data plotted by counts rather than rates for clarity.

Table:

Week of the year that alarms commenced and peaks were reached for each of the four official influenza events from March 2008 to April 2011.

Content Analysis of Syndromic Twitter Data

Objective

We present an annotation scheme developed to analyze syndromic Twitter data, and the results of its application to a set of respiratory syndrome-related tweets [1]. The scheme was designed to differentiate true positive tweets (where an individual is experiencing respiratory symptoms) from false positive tweets (where an individual is not experiencing respiratory symptoms), and to quantify more fine-grained information within the data.

Introduction

The popularity of Twitter, a social-networking service, creates the opportunity for researchers to collect large amounts of free, localizable data in real-time. Data takes the form of short, user-written messages, and has been employed for general syndromic surveillance [2] and surveillance of public attitudes toward the H1N1 flu outbreak [3]. Accessibility of tweets in real-time makes them particularly appropriate for use in early warning systems. Data collected through keyword search contains a significant amount of noise, however, annotation can help boost the signal for true positive tweets.

Methods

The annotation scheme was developed based on information relevant for early warning systems (e.g. who is experiencing symptoms, and when) as well as other information present in the tweets (e.g. aspirations regarding symptoms, or abuse of substances such as cough syrup). Categories included Experiencer: Self/Other, Temporality: Current/Non-Current, Sentiment: Positive/Negative, Information: Providing/Seeking, Language: Non-English, Aspiration, Hyperbole, and Substance Abuse. All categories with the exception of Language and Substance Abuse were defined in reference to diseases or symptoms. The scheme was applied to 1,100 respiratory syndrome-related tweets (544 false positive, 556 true positive) from a previously collected corpus of syndromic twitter data [2]. Inter-annotator agreement was calculated for 9% of the data (100 tweets).

Results

Inter-annotator agreement was generally good, however certain categories had lower scores. Categories for Experiencer, Temporality, Sentiment: Negative, Information: Providing, and Language all had Kappa values above .9, Sentiment: Positive, Aspiration, and Substance abuse had Kappa values above .7, and Information: Seeking and Hyperbole had Kappas above .6. There was good separation between true positive tweets and false positive tweets, especially for the Experiencer: Self, Temporality: Current, Sentiment: Negative, Aspiration, Hyperbole, and Substance Abuse categories (see % True Positive Tweets% False Positive TweetsExperiencer: Self98.20.4Temporality: Current98.70.2Sentiment: Negative79.71.7Information: Providing0.72.8Language: Non-English2.71.3Aspiration11.00.2Hyperbole18.30.2Substance Abuse1.38.1Open in a separate window

Conclusions

Future work will apply the scheme to other syndromes, including constitutional, gastrointestinal, neurological, rash, and hemorrhagic.     

Evaluating the Variation on Public Health’s Perceived Field Need of Communicable Disease Reports

Objective

To assess communicable disease report fields required by public health practitioners and evaluate the variation in the perceived utility of these fields.

Introduction

Communicable disease surveillance is a core Public Health function. Many diseases must be reported to state and federal agencies (1). To manage and adjudicate such cases, public health stakeholders gather various data elements. Since cases are identified in various healthcare settings, not all information sought by public health is available (2) resulting in varied field completeness, which affects the measured and perceived data quality. To better understand this variation, we evaluated public health practitioners’ perceived value of these fields to initiate or complete communicable disease reports.

Methods

We chose four diseases: Histoplasmosis, acute hepatitis B, hepatitis C and salmonella. We asked public health practitioners from Mar-ion County Health Department (MCHD) of Indianapolis to list the fields they felt were necessary when submitting a communicable disease report. We then asked them to evaluate those fields using the following criteria:Required – A critical case attribute, when missing or unknown, would make the task of initiating and/or closing a case impossible or exceedingly difficult.Desired – A case attribute allowing more complete epidemiologic profiles to be developed but, if missing, would not prohibit initiating and/or closing a case.Not applicable – A case attribute that is not usually collected to initiate and/or close a case for the particular condition.To quantify the need for the fields, we assigned a number to each response as follows:0 - Not applicable 1 - Desired  2- RequiredWe summed the numbers for each field for each disease and created a table for the perceived need of that field (The perceived need for the fieldFUNGALHEPATITISENTERICHistoplasmosisHepatitis B-acuteHepatitis CSalmonellaField NameInitiateCloseInitiateCloseInitiateCloseInitiateCloseSumDisease8888888864First Name8888888864Middle Initial4545454536Last Name8888888864Parent – First Name3434343428Parent – Middle Initial111111118Parent – Last Name3434343428Phone Number7778787859Street Number7878787860Street Name7878787860City7878787860Zip Code5757575748Country7878787860Date of Birth8888888864Age3434343428Sex6868686856Pregnant3478453438Race4848484848Ethnicity4747474744Health Care Worker45333357Food Service WorkerSchool (student/staff)Day care (attendee/staff)33Name of School/Day Care4511114724Part of and Outbreak4544344634Etiologic Agent7878787860Site of Infection6800006828Date of Diagnosis (m, d, y)5767676751Symptoms associated with infection5868575852If yes to Symptom: Onset Date (m, d, y)5768575750If yes to Symptoms: Pertinent Symp/Signs5858575750If yes to Symptoms: Died? (y/n)6868677856Lab tests(s) and results(s)8888888864Lab tests(s)/results(s) Date7878787860Treatment (name of antibiotic)4811115728Dosage4511114421Date initiated4511114623Antibiotic Resistance (y, n, nd)1100004511If yes, what antibiotic?000000347Reporting Facility Code2323232320If Hospital, Name Hospital7878787860Name of Physician and Address7878787860Record Number5656565644Person Reporting (other than physician)5656565644Telephone Number7777777756Telephone Number (2)2222222216Date of Report6666666648TOTAL (max possible 360)223273210248204241233285Open in a separate window

Results

The perceived needs table showed a difference between the fields needed to initiate or close a case. Moreover the perceived need for fields varied by disease as well. To assess the difference in perceived needs, we calculated the standard deviation of the fields (The Disagreement for the fieldFUNGALHEPATITISENTERICHistoplasmosisHepatitis B-acuteHepatitis CSalmonellaField NameInitiateCloseInitiateCloseInitiateCloseInitiateCloseSumDisease0.000.000.000.000.000.000.000.000.00First Name0.000.000.000.000.000.000.000.000.00Middle Initial0.000.430.000.430.000.430.000.431.73Last Name0.000.000.000.000.000.000.000.000.00Parent – First Name0.500.000.500.000.500.000.500.002.00Parent – Middle Initial0.500.500.500.500.500.500.500.504.00Parent – Last Name0.500.000.500.000.500.000.500.002.00Phone Number0.430.430.430.000.430.000.430.002.17Street Number0.430.430.430.000.430.000.430.001.73Street Name0.430.000.430.000.430.000.430.001.73City0.430.000.430.000.430.000.430.001.73Zip Code0.430.430.430.430.430.430.430.433.46County0.430.000.430.000.430.000.430.001.73Date of Birth0.000.000.000.000.000.000.000.000.00Age0.430.710.430.710.430.710.430.714.56Sex0.500.000.500.000.500.000.500.002.00Pregnant0.430.710.430.000.000.430.430.713.15Race0.000.000.000.000.000.000.000.000.00Ethnicity0.000.430.000.430.000.430.000.431.73Health Care Worker0.000.430.430.430.430.430.430.433.03Food Service WorkerSchool (student/staff)Day Care (attendee/staff)Name of School/Day Care0.000.430.430.430.430.430.000.432.60Part of an Outbreak0.000.430.000.000.430.710.000.502.07Etiologic Agent0.430.000.430.000.430.000.430.001.73Site of Infection0.500.000.000.000.000.000.500.001.00Date of Diagnosis (m, d, y)0.430.430.500.430.500.430.500.433.67Symptoms associated with infection0.430.000.500.000.430.430.430.002.23If yes to Symptoms: Onset Date (m, d, y)0.430.430.500.000.430.430.430.433.10If yes to Symptoms: Pertinent Symp/Signs0.430.000.430.000.430.430.430.432.60If yes to Symptoms: Died? (y/n)0.500.000.500.000.500.430.430.002.37Lab test(s) and result(s)0.000.000.000.000.000.000.000.000.00Lab test(s)/result(s) Date0.430.000.430.000.430.000.430.001.73Treatment (name of antibiotic)0.000.000.430.430.430.430.430.432.60Dosage0.000.430.430.430.430.430.000.002.17Date initiated0.000.430.430.430.430.430.000.502.67Antibiotic Resistance (y, n, nd)0.430.430.000.000.000.000.000.431.30If yes, what antibiotic?0.000.000.000.000.000.000.000.470.47Reporting Facility Code0.470.820.470.820.470.820.470.825.15If Hospital, Name Hospital0.430.000.430.000.430.000.430.001.73Name of Physician and Address0.430.000.430.000.430.000.430.001.73Record Number0.430.500.430.500.430.500.430.503.73Person Reporting (other than physician)0.430.500.430.500.430.500.430.503.73Telephone Number0.430.430.430.430.430.430.430.433.46Telephone Number (2)0.000.000.000.000.000.000.000.000.00Date of Report0.500.500.500.500.500.500.500.504.00Open in a separate window

Conclusions

Data quality is essential, not only for research but to support routine public health practice as well. Many factors affect data quality; one of them is perceived need of the information by Public Health Practitioners. Despite working with public health stakeholders from the same organization we observed variation in their perceived needs for these fields to initiate or close a communicable case. These results highlight another source of the problem regarding health information quality and its goodness of fit issues.     

Co-morbidity Factors Associated with Influenza in Nigeria

Objective

To analyze Influenza surveillance data from 2009 to 2010 the Northern, Southern, and Western zones in Nigeria and determined co-morbidity factors associated with influenza in Nigeria.

Introduction

Influenza is viral illness that affects mainly the nose, throat, bronchi and occasionally, the lungs. Influenza viruses have been an under-appreciated contributor to morbidity and mortality in Nigeria. They are a substantial contributor to respiratory disease burden in Nigeria and other developing countries. Nigeria started influenza sentinel surveillance in 2008 to inform disease control and prevention efforts.

Methods

We conducted a cross sectional study on secondary data analysis of Influenza surveillance data from January 2009 to December 2010 obtained from Nigeria’s Federal Ministry of Health. Epidemiological data were obtained for suspected ILI and SARI cases defined in accordance with WHO Regional Office for Africa’s guidelines. Laboratory confirmation for presence of influenza viruses was done using real time PCR assays.Standardized case investigation forms used for sample collection were analyzed using Epi-Info software to generate frequency and proportions.

Results

Of the 5,860 suspected influenza cases reported between 2007 and 2011 from all the influenza sites in Nigeria, 1104 (18.8%) and 2,510 (42.8%) of the total cases were recorded in 2009 and 2010 respectively. A total of 296 (7.3%) were positive for Flu A, while 147 (2.9%) for Flu B. The Northern zone recorded a total of 1908(AR: 2.6/100,000) suspected cases while the Southern zone recorded 554(AR: 1.48/100,000) and the Western zone reported 549(2/100,000) suspected cases. Of the 443 that were positive 43 (1.5%) were health workers, 446 (8.0%) had co infection of chronic respiratory tract disease, 50(3.7%) had co infection with heart disease. Exposure to poultry was 2797(98.2%).

Conclusions

Co-morbidity factors associated with influenza viruses are an important contribution to the burden of respiratory illnesses in Nigeria predominantly affecting children less than 5years and adults 25years and above. Additional years of data are needed to better understand the co-morbidity factors associated epidemiology of influenza viruses in Nigeria.INFLUENZA AND CHRONIC OBSTRUCTIVE PULMONARY DISEASE (COPD)

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