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

Objective

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

Introduction

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

Methods

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

Results

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

Conclusions

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

2002年湖南省流行性感冒监测结果分析

目的　对湖南省 2 0 0 2年流行性感冒 (流感 )的流行病学和病原学监测结果进行分析。　方法　逐月统计流感样病例(IL I) ,采集 IL I咽拭子标本用狗肾传代细胞进行流感病毒分离 ,采用血凝抑制方法 (HI)进行流感病毒型别鉴定。　结果　 2所监测医院 2 0 0 2年共统计上报的流感样病例 (IL I) 72 6 9例 ,其中 <15岁的 IL I占 80 .95 % ;≥ 15的 IL I占 19.0 5 %。 2 0 0 2年 3～ 12月共检测流感样病人咽拭子标本数 5 6 1份 ,分离出流感病毒 80株 ,并对 4 1株毒株进行了分型鉴定 ,其中 H3N2亚型 36株 (占 87.80 % ) ,B型 5株 (占 12 .2 0 % )。　结论　湖南省监测地区同时有 H3N2亚型和 B型流感病毒的流行 ,流行优势毒株为 H3N2亚型 ,未发现H1N1亚型毒株     

Analysis of Heat Illness using Michigan Emergency Department Syndromic Surveillance

Objective

The purpose of this work was to conduct an enhanced analysis of heat illness during a heat wave using Michigan’s Emergency Department Syndromic Surveillance System (MSSS) that could be provided to Public Health and Preparedness Stakeholders for situational awareness.

Introduction

The MSSS, described elsewhere (1), has been in use since 2003 and records Emergency Department (ED) chief complaint data along with the patient’s age, gender and zip code in real time. There were 85/139 hospital EDs enrolled in MSSS as of June 2012, capturing 77% of the annual hospital ED visits in Michigan. The MSSS is used routinely during the influenza season for situational awareness and is monitored throughout the year for aberrations that may indicate an outbreak, emerging disease or act of bioterrorism. The system has also been used to identify heat-related illnesses during periods of extreme heat. Very young children, the elderly, and people with mental illness and chronic diseases are at the highest risk of preventable heat-related illnesses including sunburn, heat exhaustion, heat stroke and/or death (2). During a heat wave in the summer of 2012, data was reviewed on an ad hoc basis to monitor potential increases in heat-related ED visits.

Methods

MSSS ED visits were queried to identify those with the primary complaints of: “heat”, “sun”, or “dehydration” including word derivatives and misspellings. The query excluded terms and misspellings such as “Sunday”, “heater”, and “heatlh”. Daily maximum temperatures for four major cities in Michigan were tracked using measures from the National Oceanic and Atmospheric Administration’s National Weather Service (3). Multiple analyses were performed. For this abstract, ED data from a 10-day period of sustained above normal temperatures are presented with data from the prior 10-day period used as reference.Visits were categorized into 1 of 3 syndromes based on the chief complaint: sun-associated, heat-associated, and dehydration. Gender, age group, and syndrome for the period of interest were compared to the reference period. Heat-related visits during the period of extreme heat were also analyzed by Michigan Public Health Preparedness Region.

Results

During the period of June 28–July 7, 2012 the South and Central regions of Michigan sustained maximum daily temperatures surpassing 90°F with maximum temperatures at or above 100°F on at least 2 days. Among the cities reviewed, a total of 9 high temperature records were set or tied during that period. The number of heat-related ED visits reported into MSSS increased compared to the previous period of June 18–June 27, 2012. Heat-associated ED visits such as heat exhaustion and heat stroke were more frequent than the reference period, 30.0% vs. 13.7% (p<0.0001). Sun-associated ED visits such as sunburn were lower compared to the reference period, 17.3% vs. 23.8% (p=0.01). Dehydration complaints were elevated among those 20-29 years of age, 17.7% vs. 10.0% (p=0.01). While the proportion of ED visits due to heat-related complaints was highest in the Central and Northwestern areas of the state, increases were observed in all regions of Michigan.On July 6, 2012 an initial analysis summary was issued via the Michigan Health Alert Network (MIHAN) to provide situational awareness related to a concurrent heat advisory for much of the state. By July 23, 2012 MDCH issued a media release reporting this increase in heat-related ED visits.

Conclusions

Although cases used in the analysis may not represent all potential cases of heat-related illness and also may represent non-heat-related illnesses, ED data are useful in describing trends in illness presentations over time. As the MSSS covers a large proportion of Michigan’s population, the data from the MSSS can be stratified by type of heat-related injury, age group, and region, providing detailed situational awareness to public health stakeholders. This type of in-depth analysis further contributes to our knowledge of heat events and allows public health to relay important information regarding the severity of the situation and information about groups at risk for illness.     

郴州市2004年流行性感冒监测结果分析

目的通过监测,了解郴州市流行性感冒流行情况及流行株,为国家流感监测网提供科学数据。方法对市区两所医院的儿科、内科门诊设流感样病例(ILI)监测点,定期上报数据,同时监测全市ILI暴发疫情,采集ILI标本进行实验室病毒分离鉴定。结果两所医院全年报告ILI 2 640例,占门诊就诊者的1%。儿童ILI 2 014例,占ILI总数的76.3%,儿童ILI病例占儿童就诊者总数的1.66%,成人ILI占成人就诊者总数的0.73%。从两起暴发ILI疫情中采集标本23份,分离到甲3型流感病毒A(H3N2)亚型4株。结论郴州市ILI全年均有发生,但3~8月为流行高峰,流行株为A(H3N2)亚型。     

Enabling Syndromic Surveillance in Pakistan

Objective

This work presents our first steps in developing a Global Real-time Infectious Disease Surveillance System (GRIDDS) employing robust and novel infectious disease epidemiology models with real-time inference and pre/exercise planning capabilities for Lahore, Pakistan. The objective of this work is to address the infectious disease surveillance challenges (specific to developing countries such as Pakistan) and develop a collaborative capability for monitoring and managing outbreaks of natural or manmade infectious diseases in Pakistan.

Methods

Utilizing our partner hospitals in the Lahore, Punjab area, we have begun developing a theoretical model of patient hospital visits with respect to diseases and syndromes within Pakistan. Our first thrust has focused on the collection, categorization and cleansing of data based on expert knowledge from our partnering institutions in Pakistan. Data consists of a patient’s home address and chief complaint which is then categorized into syndromes. Home addresses are geocoded utilizing the Google API with a resultant 72% accuracy. Unknown geolocations are aggregated only at the hospital level. Using this cleaned data, we employ methods similar to our previous work [1] on syndromic surveillance for early disease detection. Currently, we have collected over 600,000 patient records over 1.5 years.We employ the use of choropleth maps, isopleth maps utilizing kernel density estimation of patient addresses, traditional control chart methods such as exponentially weighted moving averages (EWMA), and a non-parametric time series analysis approach (seasonal trend decomposition using loess smoothing (STL) [2]) which requires only 90 days of historical data to be put into operation. The time series models are deployed as part of a real-time surveillance system in which temporal anomalies over regions can be analyzed and disease outbreaks reported.

Results

Figure 1 illustrates our visual analytics toolkit in operation. Here we see the location of our partner hospital in the Lahore region. The hospital coverage is in the most populous location of the city, providing data as a sentinel site for the overall health of the city. Currently, our system employs the use of interactive filters and linked isopleth or choropleth maps with time series analysis on mouse over.

Conclusions

Currently our research has focused on one partner location within the city of Lahore. Our ongoing work is focusing on the adoption of such a system to other regions of the country and the development of disease spread simulations (particularly Dengue Fever) utilizing baseline data collected by our partners. We plan to integrate these models into our visual analytics system for real-time planning and simulation.Open in a separate window     

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

Objective

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

Introduction

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

Methods

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

Results

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

Conclusions

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

Syndromic Surveillance for Outbreak Detection and Investigation

Objective

For the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data – telephone triage of acute gastroenteritis (Swedish Health Care Direct 1177), web queries about symptoms of gastrointestinal illness (Stockholm County’s website for healthcare information), and OTC pharmacy sales of anti-diarrhea medication.

Introduction

A large part of the applied research on syndromic surveillance targets seasonal epidemics, e.g. influenza, winter vomiting disease, rotavirus and RSV, in particular when dealing with preclinical indicators, e.g. web traffic (Hulth et al, 2009). The research on local outbreak surveillance is more limited. Two studies of teletriage data (NHS Direct) have shown positive and negative results respectively (Cooper et al, 2006; Smith et al, 2008). Studies of OTC pharmacy sales have reported similar equivocal performance (Edge et al, 2004; Kirian and Weintraub, 2010). As far as we know, no systematic comparison of data sources with respect to multiple point-source outbreaks has so far been published (cf. Buckeridge, 2007). In the current study, we evaluated the potential of three data sources for syndromic surveillance by analyzing the correspondence between signal properties and point-source outbreak characteristics.

Methods

The extracted data streams were compared with respect to nine waterborne and foodborne outbreaks in Sweden in 2007–2011. The analysis consisted of three parts: (1) the validation of outbreak signals by comparing signal counts during outbreak and baseline periods, (2) the estimation of detection limits by modeling signal rates (signal-to-case ratios), and (3) the evaluation of early warning potential by means of signal detection analysis.

Results

The four largest outbreaks generated strong and clear outbreak signals in the 1177 triage data. The two largest outbreaks produced signals in OTC sales of anti-diarrhea. No signals could be identified in the web query data. The outbreak detection limit based on triage data was about 100–1000 cases. For two outbreaks, triage data on diarrhea provided outbreak signals early on, weeks and months respectively, potentially serving the purpose of early warning.

Conclusions

The sensitivity and specificity were highest for telephone triage data on patient symptoms. It provided the most promising source of syndromic data for surveillance of point-source outbreaks. Currently, a project has been initialized to develop and implement a national system in Sweden for daily syndromic surveillance based on 1177 Health Care Direct, supporting regional and local outbreak detection and investigation.     

Recommendations for Syndromic Surveillance Using Inpatient and Ambulatory EHR Data

Objective

To develop national Stage 2 Meaningful Use (MUse) recommendations for syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record (EHR) data.

Introduction

MUse will make EHR data increasingly available for public health surveillance. For Stage 2, the Centers for Medicare & Medicaid Services (CMS) regulations will require hospitals and offer an option for eligible professionals to provide electronic syndromic surveillance data to public health. Together, these data can strengthen public health surveillance capabilities and population health outcomes (Figure 1).Open in a separate windowFigure 1:Syndromic surveillance data can inform public health functions.To facilitate the adoption and effective use of these data to advance population health, public health priorities and system capabilities must shape standards for data exchange. Input from all stakeholders is critical to ensure the feasibility, practicality, and, hence, adoption of any recommendations and data use guidelines.

Methods

ISDS, in collaboration with the Division of Informatics Solutions and Operations at the Centers for Disease Control and Prevention (CDC), and HLN Consulting, convened a multi-stakeholder Work-group of clinicians, technologists, epidemiologists, and public health officials with expertise in syndromic surveillance. Recommended MUse guidelines were developed by performing an environmental scan of current practice and by using an iterative, expert and community input-driven process. The Workgroup developed initial guidelines and then solicited and received feedback from the stakeholder community via interview, e-mail, and structured surveys. Stakeholder feedback was analyzed using quantitative and qualitative methods and used to revise the recommendations.

Results

The MUse Workgroup defined electronic syndromic surveillance (ESS) characteristics. Specifically, data are characterized by their timeliness, sensitivity rather than specificity, population focus, limited personally identifiable information, and inclusion of all patient encounters within a specific healthcare setting (e.g., emergency department, inpatient, outpatient). Based on stakeholder input (n=125) and Workgroup expertise, the guidelines identify priority syndromic surveillance uses that can assist with:

1. Monitoring population health;Informing public health services; andInforming interventions, health education, and policy by characterizing the burden of chronic disease and health disparities.

Similarly, the Workgroup identified data elements to support these uses in the hospital inpatient setting and possibly in the ambulatory care setting. They were aligned to previously identified emergency department and urgent care center data elements and Stage 1–2 clinical MUse objectives. Core data elements (required for certification) cover treating facility; patient demographics; subjective and objective clinical findings, including chief complaint, body mass index, smoking history, diagnoses; and outcomes. Other data elements were designated as extended (not required for certification) or future (for future consideration). The data elements and their specifications are subject to change based on applicable state and local laws and practices.Based on their findings and recommended guidelines detailed in the report, the Workgroup also identified community activities and additional investments that would best support public health agencies in using EHR technology with syndromic surveillance methodologies.

Conclusions

The widespread adoption of EHRs, catalyzed by MUse, has the potential to improve population health. By identifying and describing potential ESS uses of new sources of EHR data and associated data elements with the greatest utility for public health, the recommendations set forth by the ISDS MUse Workgroup will serve to facilitate the adoption of MUse policy by both healthcare and public health agencies.     

珠海市2006-2008年流感症状监测分析及预测

目的 了解广东省珠海市2006-2008年流感流行趋势及其病毒株变化特点,预测2009年流行趋势,为本地区防控流感提供科学依据。方法 收集2006-2008年珠海市流感监测哨点流感样病例(ILI)监测和暴发疫情监测资料信息,医院门诊、暴发疫情的流感病毒病原学监测资料,进行综合分析。采用季节性自回归移动平均(ARIMA)构建模型预测2009年ILI的趋势。结果 2006-2008年珠海市流感流行大致呈3-4月和6-7月的双峰型,平均ILI%为4.1%;门诊报告ILI中<5岁儿童为主,占50.3%,构成比逐年上升。哨点医院流感病毒分离阳性率为10.0%,2006年流感季节类型为A(H1N1)型和B型混合型,2007年为A(H3N2)型占优势,2008年为A(H1N1)型和A(H3N2)型混合型。暴发疫情主要发生在3-6月,流行病毒株与医院哨点监测基本一致。结论 珠海市流感流行呈现春夏季双峰型,ILI的高峰较流感病毒早4周左右;H3型、H1型、B型流感病毒交替成为年分离优势株。预测2009年季节性流感流行趋势平稳。     

症状监测与预警系统建立及应用

目的在2010上海世博会期间,于上海市浦东新区建立一个基于多数据源的症状监测与自动预警信息系统。方法选择医院、学校、宾馆和药店4类监测点,对已有的数据采集传输渠道进行修改、完善,实现监测数据每日自动化、电子化采集和传输,形成症状监测数据库,部署CUSUM模型和固定阈值两种方法对数据进行计算分析,实现自动预警,即时发布预警信号。结果系统持续运行了184 d,共采集数据近60万条,发出预警信号约800个,发现疾病聚集性事件11起,均得到及时响应与处理,未发展成突发公共卫生事件。结论症状监测与预警系统建立运行顺利,为上海世博会的召开提供了公共卫生保障,可在今后国内开展的类似大型活动中推广应用。     

Evaluating Utility of Cold-Injury Syndromic Surveillance Data in New York City

Objective

1) Develop cold exposure-related injury syndromic case definitions 2) use historical data to compare trends among cases identified in syndromic surveillance and cases identified in NY Statewide Planning and Research Cooperative System (SPARCS) hospital discharge data to evaluate representativeness and 3) develop regression models to examine relationships with cold weather conditions, and compare relationships across case definitions and data sources.

Introduction

Cold weather exposure-related injuries range from hypothermia to less severe conditions such as frost bite, trench foot, and chilblains, which are all preventable causes of mortality and morbidity. In recent years, NYC has successfully used syndromic surveillance of heat-related ED visits to inform emergency response during heat waves. Similar timely surveillance of cold-exposure related injuries could also inform public health protection measures during severe winter weather or cold season power outages. We conducted a retrospective analysis to compare hypothermia and cold-injury patient case characteristics, as well as temporal and meteorological correlates, between syndromic surveillance data and hospital discharge data.

Methods

Using chief complaint key words, we developed syndromic case definitions for 1) hypothermia only and 2) all injury caused by environmental cold exposure. Case definitions were applied to an archive of 2008–2010 cold season (October to April) syndromic surveillance data reported from a subset of NYC emergency departments (ED SS), representing 95% of all ED visits in NYC. Relevant ICD-9 codes (991, E901.0, E901.8, E901.9, E988.3) were applied to ED discharge data (ED Dx) to detect hypothermia and cold injury cases. Age, gender, and alcohol involvement were compared using tests of proportion to determine whether characteristics of cases identified through ED SS were representative of cases identified in ED Dx data. Poisson regression models were fit to estimate the relation of daily ED SS and Dx counts with daily temperature, snow depth, and other weather conditions. Models were adjusted for month, holiday, day of week, and year to account for potential temporal confounding.

Results

Fewer hypothermia and cold injury cases were detected with ED SS than with ED Dx but the two populations did not differ significantly with respect to age and sex. From 2008–2010, there were 292 hypothermia cases with an average annual rate of 1.2 per 100,000 people, and 445 cold injury cases (1.8 per 100,000) identified in ED SS data. Over the same time period, there were 566 hypothermia cases (2.3 per 100,000) and 933 cold injury cases (3.7 per 100,000) identified in ED Dx data. Daily counts of hypothermia and cold injury were correlated across data sources. In preliminary analyses using both case definitions, minimum daily temperature was associated with increases in daily ED SS and ED Dx counts. Mean daily snow depth was associated with ED SS and ED Dx cold injury cases, although not with hypothermia counts. Risk increased at lower temperatures for both case definitions.

Conclusions

There were no meaningful differences between ED SS and ED Dx weather models. Minimum temperature is associated with both case definitions. Snow depth is associated with cold injury. Daily minimum temperature and mean snow depth are potentially useful in determining timing of surveillance. Syndromic surveillance data are a timely means for monitoring hypothermia and other cold-related injuries, and could provide health departments with useful information during severe winter weather to guide prevention.     

贵州省2012-2019年度流感样病例监测结果分析

目的 分析贵州省2012-2019年流感监测结果,为全省流感防控提供依据。方法 通过中国疾病预防控制信息系统收集哨点医院流感样病例（ILI）监测数据,运用描述性流行病学方法对贵州省2012-2019年度流感监测结果进行分析。结果 贵州省2012-2019 年度哨点医院共报告ILI 728970 例, ILI 占门急诊就诊病例比例为6.49%（4.34%~7.25%）。儿内科急诊ILI%最高,为65.52%,ILI主要集中在15岁以下的儿童,占90%。随着年龄的增长,ILI 比例逐渐递减,60岁以上年龄组人群所占比重最低,为2.69%。ILI%呈现冬春季和夏季2个流行高峰。完成核酸检测流感样病例标本90380份,核酸阳性12090份,阳性率为13.38%（10%~20%）。核酸阳性时间和地区分布呈现交替升降,黔东南和黔西南每个年度核酸阳性率都高。2014-2015年度和2016-2017年度以季节H3流感为主要优势毒株,2013-2014年度和2017-2018年度以甲型H1N1流感和B型流感共同流行,2015-2016年度以季节H3流感和B型流感为优势株,2018-2019年度以季节H3型流感和甲型H1N1流感为主。结论 儿童和学生是重点防控对象,流感病毒流行优势株交替出现,应加强流感防控工作,及时做好流感监测和预测预报。     

学校传染病症状监测及暴发疫情分析

目的对学校传染病症状监测系统进行评价,对疫情暴发情况进行分析。方法天津市滨海新区汉沽疾控中心于2012年9月起在辖区34年学校中选取7所监测点学校,开展学生因病缺课传染病症状监测工作。并与监测点医院数据进行比对,对症状监测系统进行效果评价。结果 2012年9月3日—2013年1日15日(1个学期),监测点学校症状监测系统共报告缺课学生人数540例,符合流感样病例人数255例,占47.22%,普通感冒244例,腹痛、腹泻13例,肺炎11例。7所学校及时发现5所学校流感暴发事件,均为甲3型季节流感。其中2所中学,3所小学。5所学校累计报告流感样病例170例,波及人数506例,平均罹患率为3.09%。哨点医院监测发现学校流感样病例暴发疫情较学校监测时间晚了17 d。结论通过对学校症状监测,可对传染病疫情及时预警,补充哨点医院监测系统中的不足,及时发现暴发疫情,及时处置。     

Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study

BackgroundParticipatory epidemiology is an emerging field harnessing consumer data entries of symptoms. The free app Ada allows users to enter the symptoms they are experiencing and applies a probabilistic reasoning model to provide a list of possible causes for these symptoms.ObjectiveThe objective of our study is to explore the potential contribution of Ada data to syndromic surveillance by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb.MethodsWe extracted data for all assessments performed by Ada users in Germany over 3 seasons (2017/18, 2018/19, and 2019/20) and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age group), standardized for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson correlation coefficient.ResultsIn total, 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson correlation—2017-18: r=0.86, 95% CI 0.76-0.92; P<.001; 2018-19: r=0.90, 95% CI 0.84-0.94; P<.001; 2019-20: r=0.64, 95% CI 0.44-0.78; P<.001). However, there were differences in the exact timing and nature of the epidemic curves between years.ConclusionsWith careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems or to the syndromic surveillance of symptoms not covered by existing systems.