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

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.

A Review of Evaluations of Electronic Event-based Biosurveillance Systems

Objective

To assess evaluations of electronic event-based biosurveillance systems (EEBS’s) and define priorities for EEBS evaluations.

Introduction

EEBS’s that use near real-time information from the Internet are an increasingly important source of intelligence for public health organizations (1, 2). However, there has not been a systematic assessment of EEBS evaluations, which could identify uncertainties about current systems and guide EEBS development to effectively exploit digital information for surveillance.

Methods

We searched PubMed and consulted EEBS experts to identify EEBS’s that met the following criteria: uses publicly-available Internet info sources, includes events that impact humans, and has global scope. We constructed a list of 17 key evaluation variables using guidelines for evaluating health surveillance systems, and identified the key variables included in evaluations per EEBS, as well as the number of EEBS’s evaluated for each key variable (3,4).

Results

We identified 10 EEBS’s and 17 evaluations (EEBSYear startedNo. evaluationsNo. key variables assessedArgus200557BioCaster200659EpiSpider200624Gcni-Db201214GODSn200613GPHIN1997710Health Map2006712MedlSys200624ProMed1994512PULS200625Open in a separate window

Conclusions

While EEBS’s have demonstrated their usefulness and accuracy for early outbreak detection, no evaluations have cited specific examples of public health decisions or outcomes resulting from the EEBS. Future evaluations should discuss these critical indicators of public health utility. They also should assess the novel aspects of EEBS and include variables such as policy readiness, system redundancy, input/output geography (5); and test the effects of combining EEBS’s into a “super system”.     

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.     

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

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

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

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 (2010 to 8/26 (year end total)2011 to 8/26 (year end total)2012 to 8/26Heat-related FD visits214(319)195(304)246Heat-related 911 calls102(169)73(128)163Number of days > 80F102(148)99(152)123Number of days > 90F72(100)67(96)87Number of days > 100F18(23)14(17)27Number of days > 105F3(3)4(4)7Open in a separate windowAge groups were similarly distributed in total ED visits, heat-related ED visits and EDC calls, with a 18 to 44 year old majority (37%, 37%, and 42% respectively), followed by 45 to 64 year olds (23%, 21%, 23%). Total ED visits did not increase during summer months, and were therefore not found to be correlated to temperature (ρ=−0.06, p=0.46) or heat-related EDC calls (ρ=0.07, p=0.4). Heat-related ED visits however, were positively correlated with both EDC calls (ρ=0.85, p< 0.001) and temperatures (ρ=0.59, p<0.001). Heat-related EDC calls were also correlated with temperature (ρ=0.56, p<0.001).

Conclusions

Due to small numbers of heat-related visits relative to total ED visits, any effects that increased temperatures may have on total ED visits are undetectable. Total ED volume should therefore not be used as an indicator for measuring the impact of heat on LAC’s population. Filtering chief complaints to obtain heat-specific ED visits, however, enables patterns of increase to emerge which correlate with higher temperatures and heat-related emergency dispatch calls. About 35% of the week to week variation in heat-related ED visits, and 32% of the week to week variation in heat-related EDC calls can be explained by week to week variations in temperature. That heat-related visits were similarly distributed in age as all visitors suggests that heat does not disproportionately affect children and the elderly any more than the other acute health conditions that bring visitors to the ED. Syndromic analysis of ED data and EDC can provide baselines for health conditions such as hyperthermia that are otherwise difficult to obtain.     

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.     

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.     

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     

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%     

Case Based Surveillance for Measles in Lagos,South Western Nigeria,September 2011

Objective

The objective of this study was to describe the performance of the measles surveillance in Lagos, characterize the epidemiologic pattern of measles infection and determine the measles vaccine efficacy.

Introduction

Measles is a vaccine preventable disease that has been successfully eliminated in some parts of the world. It causes high morbidity and mortality with the potential of large outbreaks. About a third of reported measles cases involve one or more complications including diarrhea, pneumonia, otitis media, blindness, post infections encephalitis and subacute sclerosing panencephalitis. It is however, one of the leading causes of childhood morbidity and mortality in Nigeria despite availability of safe and effective vaccines

Methods

We obtained the measles surveillance data for all the 20 Local Governments Areas (LGAs) in Lagos and reviewed all the measles case based investigation forms between the period 1st January to 31st December 2010.The WHO Recommended Surveillance Standards for Measles was used. Data was analyzed using EPI INFO version 3.5.3.

Results

Of the 615 suspected measles cases, 63(10.2%) were laboratory confirmed (measles IGM+) and 3(0.5%) clinically confirmed. Cases investigated within 48 hours was 222 (36%) (target ≥ 80%), 510 (83%) had adequate blood sample collected (target ≥ 80%) and 595 (97%) of sample results were received from the lab within 7 days (target ≥ 80%). The surveillance system sensitivity was 6.5/100,000 (target >2/100,000) with a predictive value positive of 10.73%. The overall attack rate was 0.73/100,000 population with 1 mortality (case fatality rate 1.5%). The Under 1 year attack rate (8.33/100,000) was higher than the 1– 4 years attack rate (3.48/100,000) (p= 0.01). Those vaccinated with at least 1 dose of measles vaccine had a 3 times lower risk of measles infection than the unvaccinated. The proportion of unvaccinated cases was 36%. The measles vaccine efficacy was 60%.

Conclusions

The quality of surveillance need to be strengthened by improving the time lapse between notification and investigation of suspected cases. Measles is still a significant cause of morbidity particularly among the under 1 year age group.The proportion of unvaccinated cases is also high, suggesting a low vaccine coverage among susceptibles.Prompt investigation of cases, good vaccine coverage and high vaccine efficacy are all vital in eliminating measles from Nigeria.Morbidity and Mortality rates

Nontraumatic Oral Health Classification for Alternative Use of Syndromic Data

Objective

To develop a nontraumatic oral health classification that could estimate the burden of oral health-related visits in North Carolina (NC) Emergency Departments (EDs) using syndromic surveillance system data.

Introduction

Lack of access to regular dental care often results in costly, oral health visits to EDs that could otherwise have been prevented or managed by a dentist (1). Most studies on oral health-related visits to EDs have used a wide range of classifications from different databases, but none have used syndromic surveillance data. The volume, frequency, and included details of syndromic data enabled timely burden estimates of nontraumatic oral health visits for NC EDs.

Methods

Literature review, input by subject matter experts (SMEs), and analysis of syndromic data was used to create the nontraumatic oral health classification. BioSense, a near real-time, national-level, electronic health surveillance system was the source of the NC ED syndromic data. Visits with at least one oral health-related ICD-9-CM code were extracted for NC fiscal years 2008–2010. Univariate analyses of chief complaint (CC) and final diagnosis data along with SME consultation were used to determine the CC substrings and ‘white list’ of ICD-9-CM codes used as inclusion criteria to classify visits as oral health-related. These analyses and consultations also determined the trauma-related codes and substrings used to exclude visits.

Results

Oral Health-Related ICD-9-CM CodesWhite List ICD-9-CM CodesOral Health-Related CC Substrings521.x780.60388.70Tooth and ache522.x305.1682.0Tooth and abscess523.x401.9786.2Tooth and pain525.x784.0478.19Tooth and abcess^**528.x^*784.2780.6Dental526.9Open in a separate windowx = includes all numbers under this ICD-9-CM subheading^*Except 528.3 and 528.5^**Most common misspelling of abscessIn summary, an ED visit had a nontraumatic oral health classification if it contained 1) an oral health-related CC substring with no trauma-related ICD-9-CM codes or CC substrings or 2) an oral health-related ICD-9 code accompanied by no oral health-related or trauma-related CC substrings and with no other diagnosis codes except for those on the whitelist.

Conclusions

There is increasing demand to determine ways to use syndromic surveillance data in an alternative way for population health surveillance. This use of BioSense data provided a practical classification of patient records for the tracking of nontraumatic oral health-related visits to NC EDs. Visit estimates created using this classification in combination with other pertinent information could prove useful to policymakers when deciding upon resource allocation aimed at reducing this unnecessary burden on the NC ED system. The large volume of records in syndromic surveillance systems offers substantial weight of evidence for alternative use in epidemiological studies; however, accurate classification of records is required to select cases of interest. While data volume precludes validation of every included record, a combination of human expertise and data analysis can provide credible classification criteria.     

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 Visits per 10,000 EligiblesSFY 2008SFY 2009SFY2010All Age9.59.99.20–14 yrs1.91.81.815–19 yrs8.49.07.920–29 yrs42.643.439.630–49 yrs22.924.222.550+ yrs9.52.52.4Open in a separate windowCounty-level rates showed the same age pattern to varying degrees. Detailed analysis showed problem areas, with rates in 21 of 100 counties exceeding 60 per 10,000 eligibles for the 20–29 year age group. Plots and tables complemented understanding of the ED oral health visit burden by age and county. The state total ED burden for oral health problems was ∼2% (0.2% – 9.7% by county).

Conclusions

Judicious use of syndromic data with external information, such as the detailed Medicaid denominators and the Method of Payment codes for each visit above, can give quantified estimates for policy-related public health issues. In the current study, the derived oral health visit rates gave numerical detail to concerns about the use of NC EDs for nontraumatic oral health problems by low-income persons affected by the economic recession. Results also show rate variation by county and can be combined with access-to-care data to inform planning of effective local measures to improve access to dental services and thus reduce the ED visit burden.