Public Health Information Network (PHIN) Series I

Public Health Information Network (PHIN) Series I
is for Epi Epidemiology basics for non-epidemiologists

Series Overview Introduction to: The history of Epidemiology
Specialties in the field Key terminology, measures, and resources Application of Epidemiological methods

Series I Sessions Title Date
“Epidemiology in the Context of Public Health” January 12 “An Epidemiologist’s Tool Kit” February 3 “Descriptive and Analytic Epidemiology” March 3 “Surveillance” April 7 “Epidemiology Specialties Applied” May 5

NCCPHP Training Web site:
Session I – V Slides VDH will post PHIN series slides on the following Web site: NCCPHP Training Web site:

Site Sign-in Sheet FAX: (804) 225 - 3888
Please submit your site sign-in sheet and session evaluation forms to: Suzi Silverstein Director, Education and Training Emergency Preparedness & Response Programs FAX: (804)

Series I Session IV “Surveillance”

What to Expect. . . Today Introduction to the applications, limitations, and interpretation of public health surveillance data

Session Overview Introduction to Public Health Surveillance
Passive, active, and syndromic surveillance VA communicable disease law Paper-based surveillance of reportable diseases Applications and limitations Federal Public Health Surveillance CDC’s role Data sources Surveillance reporting examples

Session Overview (cont’d.)
Techniques for Review of Surveillance Data Considerations when working with surveillance data Access data sources for rate numerators and denominators Descriptive epidemiology Graph and map surveillance rates

Today’s Learning Objectives
Upon completion of this session, you will: Recognize the applications and limitations of current public health surveillance practices Understand the function of three different types of surveillance: active, passive, and syndromic Be familiar with federal public health surveillance systems relevant to epidemiology programs

Today’s Learning Objectives
Understand the reciprocal pathway of data exchange through county, state, and federal surveillance efforts Be familiar with the Virginia paper-based surveillance system for reportable diseases Recognize the potential benefits of National Electronic Disease Surveillance System (NEDSS) implementation in Virginia Recognize the utility of Epi Info software for surveillance data analysis

Today’s Presenters Amy Nelson, PhD, MPH Consultant NCCPHP
Lesliann Helmus, MS Surveillance Chief Division of Surveillance and Investigation Office of Epidemiology, Virginia Department of Health Sarah Pfau, MPH

What is Surveillance?

What is Surveillance? CDC: The ongoing systematic collection, analysis, and interpretation of health data, essential to the planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination to those who need to know. Let’s begin with what surveillance is. The World Health Organization defines surveillance as the “Systematic ongoing collection, collation, and analysis of data and the timely dissemination of information to those who need to know so that action can be taken.” The Centers for Disease Control and Prevention has a similar definition. “The ongoing systematic collection, analysis, and interpretation of health data, essential to the planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination to those who need to know.” Both of these definitions summarize the main aspects of performing surveillance. It is the systematic collection of data, analysis of that data, and the dissemination of that information to those people who can use it. The CDC definition goes one step further and mentions the use of surveillance to develop, implement and then evaluate public health policy and action. In a sense, this definition can be viewed as an information feedback loop, which I’ll illustrate on the next few slides. For those of you who attended the February session, the next couple of slides will be a review about the broad concept of surveillance. But I will then move on to explain the different types of surveillance and their applications and limitations.

Standardized data collection -Physicians -Laboratories -STD clinics
-Community health clinics County and state health departments and CDC who analyze data using statistical methods Cases of disease are identified by a physician, laboratory or other health provider according to a standard case definition. That case is then reported to the local health department, the state health department and the CDC where epidemiologists collect the data and analyze it.

-Community health clinics County and state health departments and CDC who analyze data using statistical methods Dissemination to those who need to know Dissemination to those who need to know The information obtained from analysis is then distributed in the form of a weekly update, report, conference, seminar or other means to a variety of people that can benefit from the information. Such beneficiaries would be physicians and laboratorians because knowing the frequency and distribution of diseases occurring in their area helps in differential diagnosis and allocation of resources for testing. Various health officials use the information in making relevant and effective decisions about the public’s health. -Public health officials -Health directors -Health policy officials

-Community health clinics County and state health departments and CDC who analyze data using statistical methods Dissemination to those who need to know Dissemination to those who need to know The information obtained by health officials can be used to initiate interventions, such as immunizations, alerting the public to known or newly identified risk factors, targeting education and resources to a particular population of people, or to change or implement any number of public health practices. Change in public health practice (vaccination, reduction of risk factors, medical intervention, etc.) -Public health officials -Health directors -Health policy officials Public health planning and intervention

-Community health clinics County and state health departments and CDC who analyze data using statistical methods Dissemination to those who need to know Public health evaluation Dissemination to those who need to know Finally, as these changes occur, those who initially identify and report cases of disease will see changes in the frequency and distribution of those diseases as a result of the changes in public health practice. They will continue to report the diseases they find, which in turn, aids the county, state and federal health agencies in evaluating the effectiveness of their planning and interventions. Change in public health practice (vaccination, reduction of risk factors, medical intervention, etc.) -Public health officials -Health directors -Health policy officials Public health planning and intervention

NNDSS & NETSS The National Notifiable Disease Surveillance System (NNDSS) Disease-specific epidemiologic information 60 nationally notifiable infectious diseases 10 non-notifiable infectious diseases The National Electronic Telecommunications System for Surveillance (NETSS) Nationally, this flow of information is known as the National Notifiable Diseases Surveillance System. The CDC maintains a list of 60 nationally notifiable infectious diseases as well as 10 non-notifiable diseases. These are infectious diseases that the CDC deems very important to public health, and they recommend that all states report them. In reality, which diseases get reported varies by state as each state mandates which diseases are important to track. This makes sense because many diseases are geographically specific. So, according to this state law, physicians, laboratories and other health providers are required to report specific conditions to the local and state health departments (usually using a paper-based reporting system). I will return to the topic of state communicable disease law and the paper-based reporting system momentarily. After the state health department receives information on reportable infectious diseases, it electronically transmits the cases and specific epidemiologic information to the CDC. This system of electronic reporting from the state to CDC is called the National Electronic Telecommunications System for Surveillance, or NETSS. It has been the mainstay of electronic national disease reporting since 1989.

Elements of Surveillance
Mortality reporting – legally required Morbidity reporting – legally required Epidemic reporting Timely reporting Laboratory investigations Individual case investigations Epidemic field investigations Analysis of data There are numerous elements of public health surveillance, some of which I have already mentioned. While reporting of mortality and morbidity from nationally notifiable diseases is required by law, state and local health departments also encourage reporting of unusual clusters of disease, outbreaks, or epidemics, even for non-reportable conditions since they may nevertheless be important public health conditions that need to be addressed. However, the simple reporting of these diseases and conditions is not enough to maintain a surveillance system; the reporting must be done in a timely manner. This is probably the greatest limitation to current surveillance. If public health officials are making public health decisions based on surveillance data, the more timely and accurate that data is, the better the decision making can be. This is especially true during epidemics. Officials need to get that information quickly in order to implement preventive measures. While the reporting of disease is ongoing, the investigation of those cases is also ongoing. As you learned in the February session, public health laboratories play an important role in investigating the occurrence of disease because they have the ability to not only identify causative agents, but also describe them in more genetic detail, which aids the epidemiologist. Individual cases of disease should be investigated by state or local health officials to determine if there are other cases involved that went unnoticed, and finally, the most obvious is to investigate cases during an epidemic to determine the cause and appropriate preventions. Reporting, laboratory work, investigation, and prevention are all occurring simultaneously. For example, health officials do not always need to have a specific diagnosis in order to implement prevention. Finally, for all of this information to be useful, it needs to be analyzed so epidemiologists can look for trends, clusters, or other epidemiologic occurrences. Mortality: Because this type of surveillance is legally required, many countries have complete data available. Furthermore, data are collected via an organized system of regional or national tabulation. Morbidity: Although morbidity surveillance is also legally required, not all diseases – particularly viral ones – are included in what is reportable. Furthermore, under-reporting is often an issue. Epidemic reporting: Single cases of e.g., foodborne illness may often go unreported, but reporting improves when there are clusters of cases or an epidemic with a case definition to standardize reporting. Absenteeisms from work or school can provide an indication as to whether or not there are clusters of cases and a potential epidemic that warrants further investigation. Laboratory Investigations: Critical for a confirmed diagnosis of respiratory viruses, viral encephalitis, and gastroenteritis. In the United States, laboratory reporting is required for Tuberculosis, HIV, and Syphilis. Case investigations: In areas where a disease has been nearly eradicated or never before reported (for example, polio might qualify in either of those categories), public health professionals should investigate each case. Epidemic Field Investigation: Local health departments, in conjunction with the CDC, will conduct field investigations when there is an increase in the number of cases or deaths from a disease of public health importance.

Types of Surveillance Passive Active Syndromic
There are three main ways to perform surveillance: Passive, Active and syndromic. I will discuss each of these individually.

Passive Surveillance Laboratories, physicians, or other health care providers regularly report cases of disease to the local or state health department based on a standard case definition of that particular disease. The form of surveillance I have been talking about to this point is passive surveillance. It is the most common form of surveillance and occurs when laboratories, physicians, or other health care providers regularly report cases of disease to the local and state health department based on a standard case definition for particular diseases. The key characteristic of passive surveillance is that the health care providers or laboratories initiate the forwarding of data to the health department.

Communicable Disease Reporting: Passive Surveillance
Lab Physician Hospital Public CDC LHD This diagram illustrates that physicians and other health providers report mostly to the local health department, while laboratories report mostly to the state health department. The local health department, state health department, and CDC all report data to each other. This two-way cycle of reporting provides a basic “checks and balances” component to the surveillance system and assures that the diseases and conditions required to be reported by law are in fact being reported. State

VA Reportable Diseases
This image shows the list of Virginia’s reportable diseases, toxic effects, conditions, and outbreaks. Reporting of the diseases listed here is required by state law; all conditions are to be reported to a city or county health department. The Commonwealth of Virginia’s Board of Health administers Regulations for Disease Reporting and Control that describe which diseases must be reported, and how reporting should be done. Items listed in UPPER CASE and bold are extremely contagious and should be reported within 24 hours of suspected or confirmed diagnosis by the most rapid means available. All others in the list should be reported on an Epi-1 form [paper-based surveillance] within three days of suspected or confirmed diagnosis. I have circled three unique items in the list that are more general than specific, but nevertheless deemed high priority. Cancers are also reportable. If you are interested in learning more about that topic, you can contact the Virginia Department of Health’s Virginia Cancer Registry at (804) The conditions in the list with an asterisk (*) are reportable by directors of laboratories in addition to physicians and directors of medical care facilities. Physicians and directors of medical care facilities should report Influenza by number of cases only (e.g., the total number per week and by type of influenza, if available). Later in today’s session, I will talk about Virginia’s Influenza surveillance in detail.

Paper-based Surveillance
VA Epi-1 Reporting Form for Paper-based Surveillance Here is a picture of Virginia’s standardized Epi-1 form for communicable disease reporting. As physicians and other health providers identify cases of reportable disease, they fill out this form and send it to their local health department. The form contains basic demographic and contact information that allow the public health officials to investigate the case in a more thorough manner.

VA Communicable Disease Law
Communicable disease statutes are in Chapter 2 of Title 32.1 of the Code of Virginia. These articles are incorporated into and referenced throughout the State Board of Health’s Regulations for Disease Reporting and Control This slide is primarily for your reference. Communicable disease statutes are in Chapter 2 of Title 32.1 of the Code of Virginia. You can research that legislation in detail by visiting the URL provided at the top of this slide. However, the critical elements of these articles are actually incorporated into and referenced throughout the State Board of Health’s Regulations for Disease Reporting and Control. What to report and how to report it is spelled out in the Regulations, which you can access via the second URL listed on this slide. So now that I have described passive surveillance and the specifics of reporting regulations in Virginia, let’s move on to look at active surveillance. . .

Active Surveillance Local or state health departments initiate the collection of specific cases of disease from laboratories, physicians, or other health care providers. A second form of surveillance is active surveillance. This occurs when the health department initiates the collection of data from public health laboratories, physicians or other health care providers.

Communicable Disease Reporting: Active Surveillance
Lab Physician Hospital CDC LHD This diagram is similar to the previous one for passive surveillance, but there are some subtle differences. The solid arrows indicate that the state or local health department initially contacts the laboratory, hospital or physicians to request information on any cases of a particular disease they have seen, treated and / or diagnosed during a given period of time. The laboratory, hospital or physician will then provide that information, which is forwarded on to the state and / or CDC. State

Active Surveillance Applications
Outbreak investigations Other times when complete case ascertainment is desired (e.g., research study) As you can imagine, and have maybe even experienced, the process of contacting health providers and laboratories can be very time consuming. It involves phone call after phone call, and maybe even a site visit to review medical records. So why would you want to perform active surveillance? The most common situation is during an outbreak investigation. You want to find every possible case that you can, and so you are regularly in contact with hospitals and physicians to see if anyone who has been seen meets the established case definition. There may be other times where you want complete case ascertainment, such as part of a research study, or if a complete epidemiologic description is needed, maybe in a newly recognized population. Because active surveillance is so timely and is initiated by the health official who wants specific information, this process usually provides fairly complete data. Unfortunately, because of resource and time constraints, active surveillance cannot be performed at all times for all diseases.

Question & Answer Opportunity
What questions do you have about passive or active surveillance?

Syndromic Surveillance
The ongoing, systematic collection, analysis, interpretation, and application of real-time indicators for disease that allow for detection before public health authorities would otherwise identify them. The third type of surveillance that I want to discuss today is syndromic surveillance. It can be defined as the ongoing, systematic collection, analysis, interpretation, and application of real-time indicators for disease that allow for detection before public health authorities would otherwise identify them. Syndromic Surveillance data are collected in real-time, meaning that the data are automated so they can be received daily. This definition of syndromic surveillance is parallel in many ways to the definition of surveillance in general. The ongoing, systematic collection, analysis and interpretation of data is still the primary focus. However, in syndromic surveillance, the time component and the type of data collected are different from what we see in passive or active surveillance. The type of data collected is not actual cases of disease. Instead, we are interested in indicators of specific diseases.

What are “indicators of disease?”
“Indicators” are clinical signs that we can categorize into syndromes, but NOT a specific diagnosis! Example: Cough + Sore throat + Fatigue + Fever = Influenza-Like Illness Indicators of disease are clinical signs that we categorize into syndromes. Several indicators can make up a syndrome, but not all are required to indicate a syndrome. Indicators associated with influenza-like illness are cough, runny nose, sore throat, fatigue and/or fever. Here is an example: If a person is complaining of a cough, fever and running nose, they would be seen as having a influenza-like illness. But this is not an exhaustive list of indicators. Other combinations could also be considered influenza-like illness, such as a person with a sore throat, fatigue and fever. It’s important to note that indicators of disease are not comparable to a fixed case definition during an outbreak investigation, in which a case-patient might be defined as having a minimum of two or three of the possible symptoms in a list of symptoms. Using indicators to designate a syndrome means that we do NOT have a specific diagnosis for any one patient. Rather, if we see clusters of syndromes, the health department is alerted to investigate. These investigations will determine if in fact there is an outbreak of a specific disease causing the observed syndromes, or if they are unrelated. The example shown here is fictitious: CDC’s actual ILI definition used for sentinel influenza surveillance is fever >100 degrees F with cough or sore throat.

Common Syndromes under Surveillance
Gastroenteritis Influenza like illness (ILI) Meningitis / Encephalitis Rash / Fever Botulinic Hemorrhagic Here is a list of the types of syndromes that are typically monitored in syndromic surveillance systems: Gastroenteritis; Influenza-like illness; Meningitis/encephalitis; Rash/fever; Botulinic; and Hemorrhagic. I will talk about Influenza-like illness surveillance in detail later in today’s session.

Why Do Syndromic Surveillance?
Early detection of clusters in naturally occurring outbreaks or a BT event Minimizes mortality & morbidity Characterize outbreak Magnitude, rate of spread, effectiveness of control measures Quick investigation Detection of unexplained deaths There are many reasons why a health department would undertake syndromic surveillance: The early detection of clusters in naturally occurring outbreaks or a BT event can help to minimize mortality & morbidity. Furthermore, syndromic surveillance is useful for characterizing an outbreak in terms of magnitude, rate of spread, and the effectiveness of control measures. One can also undertake a quick investigation to follow up on alerts from a syndromic surveillance system. And finally, the system can detect unexplained deaths.

Syndromic vs. Traditional Surveillance
Phase II Acute Illness Phase I Initial Symptoms Traditional Disease Detection Early Detection Gain of 2 days Theoretically, syndromic surveillance systems are supposed to be able to detect outbreaks days in advance of a traditional surveillance system. This graph depicts that potential advantage of syndromic versus traditional surveillance. Syndromic surveillance alerts us to early detection of large clusters of syndromes. Here, the blue line shows that less than 2 days after exposure, the syndromic surveillance system allows public health professionals to detect an outbreak. The red line depicts our traditional surveillance system; it picks up the disease 4 days after exposure, when specific cases of disease have been diagnosed. In this example, syndromic surveillance allowed health officials to detect the outbreak 2 days before traditional surveillance would have. And the benefits of early detection are well known: Early detection leads to early intervention, which decreases further transmission of disease. Effective Treatment Period Source: Johns Hopkins University / DoD Global Emerging Infections System

Limitations of Syndromic Surveillance
Inadequate specificity: false alarms Uses resources in investigation Inadequate sensitivity: failure to detect outbreaks/BT events Outbreak is too small Population disperses after exposure, cluster not evident I have presented you with information about syndromic surveillance’s potential as a means of detecting outbreaks more rapidly than with traditional surveillance systems. However, it is important to keep in mind the limitations of a syndromic surveillance system. Poor specificity results in a high number of false alarms; that is, the system detects an event that is not there. This then occupies resources to investigate a non-existent problem, and also desensitizes first responders. Alternatively, a syndromic surveillance system may have inadequate sensitivity. It may fail to detect smaller outbreaks or BT events. Furthermore, if a population disperses after exposure, the system may not be able to identify a cluster.

Limitations of Syndromic Surveillance
Costly Staff expertise required Formal evaluation of syndromic surveillance systems are incomplete Syndromic Surveillance Systems are also costly because they require the resources to bring together many sources of syndromic data, including hospitals, ambulance transport, poison control, and even school absenteeism. Furthermore, these systems require significant staff expertise and dedicated time. And to date, formal evaluations of existing syndromic surveillance systems are nearly nonexistent. Therefore, it is important to keep in mind that syndromic surveillance systems are not meant to replace regular surveillance systems, but rather to enhance them.

Surveillance Applications
Now that you are familiar with the function and the main types of surveillance, I want to discuss some broad applications and limitations.

Applications Establish Public Health Priorities
Aid in determining resource allocation Assess public health programs Facilitate research Determine baseline for detection of epidemics Early detection of epidemics Estimate magnitude of the problem Determine geographical distribution Surveillance data can be used to establish public health priorities, aid in determining resource allocation and assess public health programs. It is also used to determine a baseline for the detection of epidemics and for early detection of epidemics.

Establish Public Health Priorities:
Frequency (incidence / prevalence, mortality, years of life lost) Severity (case fatality rate, hospitalization, disability) Cost (direct, indirect) Surveillance data can help public health officials establish public health priorities by indicating the frequency of disease, mortality or other health outcomes, such as years of life lost. By indicating the severity of disease through calculating fatality rates or tracking hospitalization or morbidity, health officials can assess both the magnitude and urgency of intervention. And finally, surveillance data can help address cost and resource issues, which help public health officials know what can be done with the resources available.

Resource Allocation TUBERCULOSIS: Reported cases per 100,000 population, United States and U.S. territories, 2002 Surveillance data can also be used to determine resource allocation. Here is an example where, when the data are mapped, one can clearly identify states in which the greatest amount of resources might be needed to prevent or treat cases of Tuberculosis. The southern and southeastern United States have the highest rates of Tuberculosis. Source:

Assess Public Health Programs
Gonorrhea: reported cases per 100,000 population, United States, A third application of surveillance data is for assessing public health programs. On the left, we see that when Gonorrhea data are graphed by race and ethnicity, the rate among Black, non-Hispanic population was more than six times higher than for any other race or ethnicity in 1987, and although it has generally continued to decline (with a plateau over the course of a few years) for 13 years, the rate was still just under six times higher in Therefore, while one might be able to conclude that a prevention intervention targeting the Black, non-Hispanic population has had an impact on decreasing Gonorrhea rates, public health professionals might now need to determine the cause of the persistent disparity in rates among the Black, non-Hispanic population as compared to all other races / ethnicities. When we look at the same Gonorrhea data graphed by gender, we see that the gap in differences in rates among men versus women has been eliminated over the course of the same 13 years. Data Graphed by Race and Ethnicity Data Graphed by Gender Source:

Determine Baseline Rates
TUBERCULOSIS among U.S.-born and foreign-born persons, by year, United States, Surveillance data are also used to determine background levels of disease in a population. This graph shows rates of tuberculosis among U.S. and foreign-born persons over time. Often times when trying to better understand your baseline rates of disease, it is helpful to stratify the data so you can look at them by specific variables of interest. In this example, one’s birthplace is of particular importance. You might also stratify by age, sex, race or even month, if the disease is seasonal. Like the previous slide, this graph also shows the effect of public health programming. You can see that prevention programs have reduced the number of TB in the U.S.-born population, but have had no effect on the foreign-born population. In terms of baseline disease, we expect to see the majority of TB in the U.S. occur among the foreign-born population. If we saw an increase in the number of cases among the U.S.-born, this would indicate a possibly unusual event and require further investigation. *For 120 cases, origin of patients was unknown.

Early Detection of Epidemics
Boston, MA Finally, surveillance data can help in the early detection of epidemics. This graph shows flu trends by month in Boston, with several years worth of data aggregated to show trends within the year. As we expect, flu cases increase in the winter months and peak around December and January. Yet the variability in the cold months is much larger than it is in the warmer months and flu cases could peak as early as November or as late as February. A final comment on the applications of surveillance data. For the most part, we have shown you national level data, with the exception of this slide. However, everything applies just as much to the state and county levels of surveillance as well. Graphing and mapping trends and geographic occurrence of disease can reveal valuable information

Surveillance Limitations
So now that we have considered the many applications of surveillance, let’s address the limitations. . .

Limitations Uneven application of information technology Timeliness
Paper-based versus Electronic Timeliness Reporting time requirement Reporting burden Completeness Unreported cases Incomplete reports Probably one of the greatest limitations to surveillance comes from the uneven application of information technology. Surveillance involves all levels of public health, from physicians to the CDC, yet the routes for sharing disease information vary. The traditional surveillance system (NETSS) relies on disease report cards that physicians and other health officials fill out for every case and mail to the local health department. At the local health department, more comprehensive surveillance forms are filled out for each case. The forms and the report cards are mailed to the state health department, where they are entered into a computer and electronically transmitted using the old NETSS system to CDC. Many surveillance systems rely on a similar flow of information, but some specific surveillance systems have more up-to-date information technology that streamlines data reporting. There is a push to make all reporting electronic, but that has not happened yet. In the meantime, the uneven application of technology hinders surveillance systems and also leads to other limitations. As I have said, timeliness is essential in public health surveillance and response. Because of this fact, Virginia law states that reportable infectious disease conditions must be reported within 3 days, and that severe diseases – those in bold on the list of reportable diseases that I showed you - need to be reported within 24 hours. In spite of these state mandates, there is still a lag in the reporting of disease. This could occur for a number of reasons that we will not address here, but one of them is the burden the paper-based system puts on physicians and health providers. It is somewhat cumbersome, and often physicians do not take the time to fill out the report cards and initiate the reporting process. Furthermore, many cases of disease go unreported. This could be because a physician does not fill out a disease report card after diagnosing a disease, or it occurs simply because many diseases are self-limiting and people do not seek medical care. We know, for example, that cases of salmonella or other food-borne disease are grossly underreported, because only a small percentage even seek out medical care. On the other hand, more severe diseases, like encephalitis or meningitis, are fairly well reported because of their severity. However, just because a physician reports a case of disease does not mean the state receives all the information. The report card could be incomplete, or the surveillance form that the county health department initiates could be incomplete. The information on the card and the form are important for epidemiologists to understand the disease distribution and dynamics within the state. If these sources are incomplete, they are returned to the county with a request to identify the missing information. This, then, leads to the burden of reporting. The cases and/or physicians have to be contacted again, and asked questions about illness, that, by this point, could have occurred nearly four weeks prior.

Limitations: Multiple Categorical Systems
Current Situation Program Specific Reports and Summaries MMWR Annual Summaries MMWR Weekly Tables Statistical Surveys for Chronic Diseases, Injuries and Other Public Health Problems HARS STD*MIS TIMS NNDSS EIP Systems PHLIS CDC Varied communications methods and security - specific to each system - including diskettes, , direct modem lines, etc. Reporting by Paper Form, Telephone & Fax Data Sources State Health Dept EIP Systems This slide is meant to overwhelm you. It shows a number of different systems that begin at the county level and continue up a surveillance specific stream to the federal level, each independent of the other. The final surveillance limitation I want to talk about is that, once all the surveillance information is collected, there is no single data storage location, and therefore no single point of access for all surveillance data. Both at the federal and state levels, and sometimes at the county level, there are separate divisions for data on Sexually Transmitted Diseases, food-borne illnesses, vaccine-preventable illness, etc. While it is necessary to have dedicated divisions, data are then stored only locally within each division. It can often be difficult to integrate data from different datasets if an epidemiologist needs data on numerous diseases. This can hinder the flow of information, especially for health officials who work across all of these divisions. HARS STD*MIS TIMS NETSS PHLIS Physicians Varied communications methods and security - specific to each system- including paper forms, diskettes, , direct modem lines, etc. Chart Review Lab Reports HARS STD*MIS TIMS NETSS EIP Systems* PHLIS * EIP Systems (ABC, UD, Foodnet) City/County Health Department STD*MIS (Optional at the Clinic) TIMS (Optional at the Clinic)

National Electronic Disease Surveillance System (NEDSS)
NEDSS is not a surveillance system Electronically integrate existing surveillance systems for easy data collection, storage and access Security to meet confidentiality needs Currently, the CDC is collaborating with state health departments to develop a system that would address many of the limitations I just described. This system, called the national electronic disease surveillance system, or NEDSS, is not an actual surveillance system, in the traditional sense of the word. Rather, it is an Internet system that, according to the CDC, will “electronically integrate and link together a wide variety of surveillance activities and will facilitate more accurate and timely reporting of disease information to CDC and state and local health departments.” NEDSS aims to improve timeliness by establishing electronic reporting systems, which will hopefully reduce the reporting burden for many health providers. It also aims to make surveillance data more accessible for health officials by having a central system for storing and accessing data. This is a monumental task that has made progress, but is still not ready to be nationally implemented. The enormity of the task is due in part to the variety of data types and sources that make up the existing surveillance systems, but also, due to the use of the Internet, it is necessary to develop specific security measures to address the confidentiality issues inherent to transmitting sensitive health information electronically. ADD A SEGUE HERE FOR LESLIANN SINCE SHE IS WORKING ON NEDSS IN VA. . .

Guest Lecturer: Virginia’s Surveillance Practices and Challenges
Lesliann Helmus, MS Surveillance Chief Division of Surveillance and Investigation Office of Epidemiology, VDH

Overview Challenges in conducting surveillance
NEDSS – tool to improve surveillance Application – Hepatitis A example Focus - our Passive surveillance system – core of information we rely on - focus on ID surveillance

Surveillance Challenges in Virginia
Quality of the data Balancing priorities Discrepancies and perspectives Translating data into information

Quality of the data “The Government is very keen on amassing statistics. They collect them, add them, raise them to the nth power; take the cube root and prepare wonderful diagrams. But you must never forget that everyone of these figures comes in the first instance from the village watchman who puts down what he damn well pleases.” Sir Josiah Stamp ( ) - Head of the Inland Revenue Department of the UK First of the challenges - Quality of the data Meaningfulness of surveillance data is limited by its quality, completeness and timeliness - needed to understand disease patterns – characterize by person, place and time - however, reporting is time consuming and outside physician focus on patient care

Quality of the Data ‘Tip of the iceberg’
Completeness of case ascertainment Completeness and accuracy of case information Timeliness of reports Sentinel indicators Completeness of case ascertainment - For reporting to occur: - the patient must be seen in a health care setting, - a diagnosis or suspected diagnosis must be made - and the health care provider must be motivated to report Completeness and accuracy of information received on cases – - Labs are good, systematic reporters - They may not have access to basic demographic information, no access to clinical information Timeliness of reports - Some conditions have lengthy diagnostic processes - Some facilities report when it is convenient - Routing and reliance on mail can cause delays – see next slide ‘Tip of the iceberg’

Flow of Reports in Virginia
Reporter Central Office District Regional Office District Reporter -> (CO ->) (District->) District -> Region -> Central Office -> CDC - spreads out case reports so that it is more difficult to recognize problem - delays recognition and the opportunity for intervention Involvement of Region – - provide guidance in challenging situations - identify and coordinate cross jurisdictional events - provide surge capacity Central Office CDC

Quality of the Data ‘Tip of the iceberg’
Completeness of case ascertainment Completeness and accuracy of case information Timeliness of reports Sentinel indicators Sentinel indicators - Astute physician who calls when they note something of concern - laws provide obligation to report and protection for confidentiality - Evidence of underlying situation - Helps identify unusual and emerging conditions value – ‘Tip of the iceberg’ - Assume consistent biases in reporting (consistent degree of incompleteness) - Allows understanding of background, monitoring for changes and investigation and control Small investments in improving reporting can yield large improvements in data quality ‘Tip of the iceberg’

*Numbers may be inflated due to duplicates
Balancing Priorities Year Reported HCV+ Test Results* Acute Hep C Cases Acute Hep A Cases 2001 1,265 3 167 2002 1,365 15 163 2003 4,313 141 2004 10,725 21 145 Acute Hep C has been a reportable condition since 1998. Requirements for reporting of Chronic Hep C became effective July 28, 2004 Result was broader reporting of lab findings - some may be chronic Hep C – but HD’s don’t have time to investigate - Repeated investigations of positives on chronic cases waste time - developing guidelines to help prioritize Time spent on high and low priority diseases - Low severity and/or no public health intervention – no reporting - Opportunity to disrupt transmission (TX, prophylaxis, environmental measures) Time spent on surveillance (case finding, missing data, analysis) vs control *Numbers may be inflated due to duplicates

Discrepancies and Perspectives
Clinical vs surveillance case definitions Cases ‘worked’ vs cases ‘counted’ Place of exposure, residence, diagnosis Re-infection or duplicate report Stats by date of onset, diagnosis, report Clinical vs surveillance case definitions Surveillance definitions used to ensure that we are all counting the same thing. Generally more rigorous than clinical case definition, but intended for different purpose. Districts and disease specific programs often want to measure the work burden. From Hep C data – can see where this would be important. Similarly - districts interested in burden of disease originating in their district (control issue) and occurring in residents of their district (disease rates), and often involved in follow-up on cases diagnosed in their district (workload). Another problem is repeat reports on someone - may be new infection - may be another test on same infection Need decisions made consistently and on a sound basis There may be reasons for looking at data presented by different dates – just need to be clear about which is being used. Challenges in - making local and state stats match when different criteria are used. - ensuring that correct data is used for the purposes

Translating Data into Information
Provides the basis for public health action Requires sound analysis and interpretation Extracts meaningful, actionable findings Requires clear presentation of complex issues The data must be translated into meaningful information - Forms the basis for action - Requires that the data be correctly used and interpreted - Must pull out the meaningful information from all the background - Requires clear presentation of complex issues

A Tool to Improve Surveillance
NEDSS A Tool to Improve Surveillance

National Electronic Disease Surveillance System
Centralized data system for disease surveillance in Virginia Person based system – links health events Accessed through the VDH network Ensures data confidentiality and integrity Supports electronic data submissions Will modify processes for managing reports Shared database with surveillance data from all over Virginia, accessible to the district and the central office Events linked to a person – not held in isolation Utilized ‘web’ technology – not dependent on users PC. Nothing installed on users computer. Uses encrypted ‘secure’socket’ transmission of information. Access based on jurisdiction, disease areas, and privileges (read, write, edit) Two factor authentication Regular data back-ups. Building a ‘hot’ offsite system to allow quick switch-over in disaster situation Allows large labs to send batches of reports that load directly into the system (after some QC) There will definitely be some changes in how we do things – hopefully thy will be overdue improvements

Benefits from NEDSS Faster recognition of health problems
Electronic transmission from large facilities (provides better data, faster) Simultaneous district/region/central office access to the data Reduced data entry (maybe more data quality assurance) Won’t have to wait for paper record to make its way through the system. Will know whether district considers it as final or still under investigation.

Benefits from NEDSS Greater consistency in data interpretation
Shared case status (cases definitions) Shared dates ‘As needed’ guidance and coaching Shared updates Shared reports

Benefits from NEDSS Shift in effort Cases entered once
Trail for chronic cases Processing of electronic transmissions Ability to monitor reporters activity

Benefits from NEDSS Bigger picture
Earlier look at data across jurisdictions Identification of people with co-infections More effective analysis across diseases

Benefits from NEDSS Shared tools
High level tools with low level maintenance for users Shared expertise

Surveillance Application Example
Hepatitis A

Source: Virginia Disease Control Manual
Example – Hepatitis A Imaginary case Show Surveillance process, using NEDSS, where appropriate (closely tied to investigation but focusing on data side) Fecal-oral transmission Isolate case if high risk for spread Vaccine preventable Immune globulin for contacts Report within 24 hours Source: Virginia Disease Control Manual

Reporting Initial report Follow-up documentation
Phone call – 24/7 availability of Health Dept Would trigger follow-up with case Follow-up documentation Clinical (‘morbidity’) report Laboratory report

Enter Information into NEDSS
Generally don’t receive complete information - need to contact physician for basic information like DOB, sex, residence

Key Information from Provider
From this information – - Prioritize case (Hep a - high priority) - Begin case work-up (Investigation side – but there are important surveillance components that support and guide the investigation and that are derived from the investigation)

Laboratory Report Information
Electronic transmission from Lab? Important - Documentation confirming infection

Patient Interview: Risk Information
Forms in NEDSS replaces CDC disease-specific paper forms – systematically collect disease specific data to assist in monitoring trends Pull downs for recording answers Determine that patient is receiving care Identify source of patient’s exposure Identify others exposed by patient Food service worker Day care attendee Prophylax contacts - 10 day window Information from patient interview used for disease control - exclude health care workers and daycare attendees from work/daycare - identify contacts for preventive treatment (immune globulin and vaccine) Data entered for analysis to better understand transmission patterns in the community

Look for Bigger Picture
Sporadic case ? Part of ongoing outbreak ? Beginning of new outbreak ? Day care age? Subgroup? General community? Important to see if this case fits into a bigger picture. Analysis of surveillance data for patterns Shown – reports module in NEDSS (reports that follow are not from NEDSS but are similar to what it provides)

Surveillance Data Analysis: Line Lists
Visual review of key information to look for events of concern Often includes patient names to help pick out family groupings

Surveillance Data Analysis: Cross-tabs
Hepatitis A Cases Reported in the Past Week Age Group Risk 0-4 5-19 20-64 65+ Foodservice 3 Daycare 1 No Risk 2 Cross tabs to look at age, sex, race and other relevant variables. (There was a Hep A outbreak in MSM – ID’d by fact that someone noted preponderance of young adult males. Confirmed with cross check of names against STD registry, where information on sex of sexual partners was available.)

Surveillance Data Analysis: Time Trends
Provides comparison to what is ‘normal’ for the area. Compare to previous years to identify seasonal trends May also do analysis of lab findings. Pulsed field gel electrophoresis shows genetic distance of organisms. Can help define whether a specific case if part of an outbreak or if a case has a novel strain. Analysis of data may point to other cases in the community that could be part of an outbreak It may provide perspective on how unusual this case is. Monitors success in controlling spread.

Confirm Case – Submit Notification
Cases meeting case definition – submitted for notification to CDC Virginia database allows analysis by district and region and central office staff Helps assure agreement on - case status (whether case meets surveillance case definition) - dates (onset, diagnosis, report) - jurisdiction in which case is counted

Statewide Review and Analysis
Monthly data Annual Data Virginia Epidemiology Bulletin See Your District Epidemiologist Larger numbers of cases provide statistical stability to identify patterns and trends that are not apparent at the district level Exchange information with CDC on - unusual cases (SARS, Hanta virus, unusual strains) - coordinate on multi-state events Clearing house for official state statistics Pages on VDH web site where surveillance data is available

What questions do you have? If you have no further questions, we are now going to take a five minute break. When the session resumes, Amy will return to discuss Federal Surveillance.

5 minute break

Federal Public Health Surveillance
In this next portion of today’s session, I want to talk with you about the federal perspective of surveillance. But keep in mind that as the diagrams in the beginning of this session illustrated, surveillance data reporting really occurs on a two-way, or reciprocal path. County-level data travel up to the state and then the federal level, and then data reports trickle back down to the state, where you can often access or request either aggregate or county-specific data tables.

CDC’s Role in Surveillance
Support the states Provide training and consultation in public health surveillance Distribute and oversee funding Receive, collate, analyze, and report data Suggest changes to be considered in public health surveillance activities Report to the World Health Organization as required and appropriate Without the involvement of local health providers and local health departments, routine surveillance would not even exist. However, it is also safe to say that, without the involvement of the Centers for Disease Control and Prevention at the federal level, there would be no standardization of public health information. Furthermore, data might not extend beyond state borders. The work of CDC surveillance provides a national perspective that informs state and local officials of disease trends around the country for the purpose of improving the public’s health. The Centers for Disease Control and Prevention play an important role in public health surveillance for numerous reasons: They support the state’s efforts to perform routine surveillance by providing training and consultation. They also distribute and oversee the funding that allows most states to perform surveillance. Additionally, the CDC is responsible for receiving, collating, analyzing and reporting all the data that flow into the various agencies. From the analysis of this surveillance data, in conjunction with current research, they suggest changes to improve public health surveillance activities. And finally, the CDC reports disease information of global importance to the World Health Organization. Examples would be influenza and measles activity, among others.

CDC Surveillance Data Reporting
TABLE II. Provisional cases of selected notifiable diseases, United States, weeks ending June 5, 2004, and May 31, 2003 (22nd week) One of the primary route through which the CDC reports its data is the Morbidity and Mortality Weekly Report, or MMWR. At the end of each week’s publication, there is a selected list of reported disease current through that week. Here is an example of a table from the June 11, 2004 MMWR. Only a few diseases are shown here. Data were collected from January 1st through June 5th of 2004, and are broken down into reported cases by region and state. For example, you can see that the New England region reported 370 new cases of Acquired Immunodeficiency Syndrome (AIDS) during that time, and that nearly 50% of those cases – were reported from the state of Connecticut.

More detailed surveillance information is published in the MMWR annual summary of notifiable diseases. In this publication, data for all reportable diseases are published, along with a brief summary of the year’s activity and information on the recent case definition. At the end of the report are summary tables and maps. Here is a map showing the rate of newly reported AIDS cases per 100,000 people in the U.S. and its territories in 2002. The information in the CDC’s Morbidity and Mortality Weekly Report can prove to be an invaluable source for learning about and understanding the broad scope of disease surveillance around the country. It also allows health providers and public health officials to see what is going on in the states around them. Furthermore, the publication of these reports shows the collaborative nature of surveillance. As I have said, without the work of health providers and local health departments, these data would not be available.

Federal Data Sources Over 100 federal surveillance systems
Collect data on over 200 infectious and non-infectious conditions such as: Active Bacterial Core Surveillance (ABCs) Foodborne Diseases Active Surveillance Network (FoodNet) National West Nile Virus Surveillance System (ArboNet) Viral Hepatitis Surveillance Program (VHSP) Waterborne-Disease Outbreak Surveillance System Influenza Sentinel Physicians Surveillance Network In addition to the national notifiable disease surveillance system, the CDC maintains over 100 other surveillance systems; some include international collaborators. These surveillance systems collect data on over 200 infectious and non-infectious conditions from a variety of sources and participants. Some examples of ones that may be more familiar to some of you are: 1. The Active Bacterial Core Surveillance program, or ABCs; 2. The Foodborne Diseases Active Surveillance Network, or FoodNet, which is a collaborative project among CDC, 9 Emerging Infections Program sites, the U.S. Department of Agriculture (USDA), and the U.S. Food and Drug Administration (FDA). It consists of active surveillance for foodborne diseases and related epidemiologic studies designed to help public health officials better understand the epidemiology of foodborne diseases in the United States; 3. The National West Nile Virus Surveillance System; The Viral Hepatitis Surveillance Program which collects clinical, serologic, and epidemiologic data pertaining to risk factors of disease acquisition; The Waterborne-Disease Outbreak Surveillance System that is maintained by the U.S. Environmental Protection Agency (EPA), and the Council of State and Territorial Epidemiologists (CSTE). It is a collaborative surveillance system of the occurrences and causes of waterborne-disease outbreaks associated with drinking water and recreational water. Outbreak reports are collected annually and are published every 2 years as an MMWR Surveillance Summary. Approximately 260 physicians around the country report each week the total number of patients seen and the number of those patients with influenza-like illness by age group.

Federal Surveillance Resources
CDC Morbidity and Mortality Weekly Report (MMWR) CDC Office of Surveillance I would encourage you to make use of the wealth of national surveillance information that is available on the CDC website. You can search the MMWR for specific topics or simply review the latest report. The Office of Surveillance web site has numerous links to surveillance systems and published surveillance reports. This is also a good web site for learning more about the surveillance activities that the CDC is involved in.

Council of State and Territorial Epidemiologists (CSTE) http://www
Collaborates with CDC to recommend changes in surveillance, including what should be reported / published in MMWR Develops case definitions Develops reporting procedures Before we move into some examples of national and local surveillance activities, let me mention the Council of State and Territorial Epidemiologists, or CSTE. This is an organization that began in the 1950s with the purpose of giving state and local epidemiologists the opportunity to be involved in decision making about disease reporting and health policies. In many ways, the organization facilitates discussion between the CDC and local public health officials. Specifically, the CSTE collaborates with CDC to recommend changes in surveillance, including what should be reported and published in the MMWR. CSTE also develops and modifies case definitions and reporting procedures, and then presents these to CDC as formal recommendations. I encourage you to visit their website at

Example: ArboNet ArboNet is a cooperative surveillance system maintained by CDC and 57 state and local health departments for detecting and reporting the occurrence of domestic arboviruses. Let’s move on to look at some of surveillance systems at the national level. I first want to describe the ArboNet surveillance system. ArboNet is a cooperative surveillance system maintained by the Centers for Disease Control and Prevention and 57 state and local health departments for detecting and reporting the occurrence of domestic arboviruses, which are viruses transmitted primarily by ticks and mosquitoes. ArboNet was initially designed to track the recent emergence of West Nile virus in the United States in However, as the West Nile expanded across the country, it became evident that increased surveillance was being done for all domestic arboviruses. So, in 2003, ArboNet began collection of data on additional arboviruses.

ArboNet - Data Human Dead bird Equine Mosquito
Encephalitis, meningitis, fever, viremic blood donors, other Dead bird Equine Mosquito Sentinel animals (chicken, pigeon, horse) Other non-human mammals ArboNet collects a wide variety of epidemiologic and ecologic data. In addition to human demographics, the system collects information on the clinical presentation of disease: Encephalitis, meningitis, fever (which is very common, but not well reported due to it’s self-limiting nature), a new category for viremic blood donors identified through donation sites, and other clinical presentations. Ecologic data include species and location for dead birds, equines, mosquitoes, sentinel animals (which are primarily chickens, but pigeons and horses have been used) and other non-human mammals, such as dogs, cats and squirrels.

ArboNet – Surveillance Issues
“Real-time” reporting Novel occurrence of West Nile virus Web-based reporting (states) Still relies on paper-based reporting (local) Incorporates ecologic data NEDSS compatible Duplicity of human case reporting So, how does ArboNet stack up against some of the limitations we have discussed about existing surveillance systems? First of all, ArboNet is a “real-time” reporting system. Real-time is in quotes, because we can never completely capture when disease occurs, but we do know that some reports of dead birds and even human cases are received by ArboNet within a few weeks of when laboratory tests are positive. This is by far one of the fastest reporting times among national surveillance systems. This could be due to a couple of factors. First, the novel nature of West Nile virus has had many health officials working quickly to identify and track its occurrence. The same novelty does not exist for most other reportable diseases. Second, ArboNet has been streamlined to allow very simple web-based reporting through a secure website that any state can access. However, the ArboNet system still relies on paper-based reporting of human cases at the local levels, so there are still issues of timeliness and completeness. ArboNet is also unique in that it collects information on ecologic data. Why would we be interested in dead birds and sick horses? It turns out that identifying disease in these animals is a strong predictor of higher levels of virus transmission. By watching ecologic trends, it helps public health officials know when to implement preventive efforts, such as educational campaigns and mosquito spraying. As ArboNet continues to evolve, it becomes more and more compatible with the future NEDSS system. Once that system is up and running, ArboNet will simply be a module within the NEDSS system. However, in part because we are moving towards NEDSS but still have not abandoned NETSS, state health officials are reporting human cases of arboviral disease twice: once to the ArboNet system, and once to through the traditional NETSS system for use in Morbidity and Mortality Weekly Report publications. Eventually this duplicity will be eliminated, as there will be the capacity to store all surveillance data in a central, electronic location through NEDSS.

ArboNet - Diseases West Nile virus St. Louis Encephalitis virus
Eastern Equine Encephalitis virus Western Equine Encephalitis virus California serogroup viruses (i.e., La Crosse) Powassan Encephalitis virus Japanese Encephalitis virus Dengue virus The diseases that the national ArboNet system currently collects data on are: West Nile virus; St. Louis Encephalitis virus; Eastern Equine Encephalitis virus; Western Equine Encephalitis virus; California serogroup viruses (which is primarily La Crosse encephalitis); Powassan Encephalitis virus; Japanese Encephalitis virus; and Dengue virus.

What is West Nile Virus? Transmitted to humans via bites from infected mosquitoes Infection usually asymptomatic; some people have fever, headache, rash, swollen lymph glands. No infections documented in the Western Hemisphere until 1999; then 46 U.S. states reported WNV activity in 2003! So what is West Nile Virus, and why do we devote surveillance efforts to it? [READ SLIDE FIRST] The virus is transmitted to humans through mosquito bites; person to person transmission has not been documented. Mosquitoes become infected when they feed on infected birds that have high levels of WNV in their blood. Most human infections are asymptomatic; less than 1 percent develop encephalitis or meningitis. But there is neither a vaccine nor a specific treatment for West Nile Virus.

Virginia Arboviruses VA Department of Health
VA Arbovirus Surveillance and Response Plan State Laboratory of Public Health (VA Department of General Services) VA Department of Agriculture and Consumer Services Local Department of Agriculture Veterinary Laboratories VA Department of Game and Inland Fisheries U.S. Army Center for Health Promotion and Preventive Medicine Virginia administers its ArboNet surveillance system in the Division of Zoonotic and Environmental Epidemiology in the Office of Epidemiology. The Division developed the ArboVirus surveillance plan, which it updates annually with an Interagency Arboviral Task Force that includes the Department of Game and Inland Fisheries and Department of Parks and Recreation. Participation in Virginia’s surveillance system is voluntary. But data are collected from county health departments, county surveillance programs, and the other agencies listed on this slide. The Department of Game and Inland Fisheries collects and submits dead birds-primarily raptors such as hawks or owls- for testing. While Virginia laboratories usually limit West Nile Virus testing to dead crows, blue jays, and raptors, the Department of Game and Inland Fisheries might also submit unusual bird species to the state Wildlife Center for processing and then further analysis at the state public health laboratory.

West Nile Virus Infections: 2004
Cumulative Dead Bird West Nile Virus Infections: 2004 Every week, Virginia submits ArboNet West Nile virus data to the U.S. Geological Survey where they are translated into county level maps. This map is current through January 11, 2005 and shows the cumulative number of dead birds infected with West Nile Virus (in pink), counties that have identified positive and counties that are testing dead birds (in green), and counties with no bird data (in yellow) across the state of Virginia. You can access these interactive maps yourself at the URL on the bottom of the slide. In Virginia, area West Nile virus programs are tailored by Health Districts to suit local needs and resources, so not all localities are collecting information on dead birds or testing them. Individual programs may include testing mosquitoes or dead birds for the virus, collecting phone reports of dead birds, collecting information about other animals that can be infected, such as chickens or horses, and providing community education and outreach. You may want to contact your local health department for information on the program in your area and for guidelines on reporting or collecting dead birds where you live.

Cumulative Human West Nile Virus Cases: 2004
And here is a map showing the cumulative number of human cases of West Nile Virus (in pink) in 2004 in Virginia [the map was posted to the Web on January 11, 2005]. Specifically, the cases were located in: Accomack County (1 case) Augusta County (1) Chesterfield County (1) Fairfax County (1) Roanoke City (1)

Cumulative Human West Nile Virus Cases: 2004
And finally, zooming out a bit geographically, here is a U.S. map with the cumulative number of human West Nile Virus cases (in pink) reported in You can see that the mid-western and western portions of the country had the highest number of cases.

Video Clip: Public Health Grand Rounds
“Preparing for West Nile Virus: Will Your Community be Next?” (May 2001). Notice: Surveillance lessons learned by public health officials How the manifestation of the virus makes it easy to involve the general public in surveillance efforts How quickly West Nile Virus has spread across the U.S. in only a few years Segment Let’s now look at a short video segment of interviews with public health officials in Boston, Massachusetts. This will be the only clip that you view in today’s session. Before you watch the clip, I want to give you some background information. In 1999, an outbreak of West Nile Virus affected the residents of New York City. There were a total of 60 cases and 7 deaths. This was the first time that West Nile Virus had been identified in the United States! The next place that West Nile Virus struck was Boston. And the public health officials in Boston had to implement surveillance and control of a potential outbreak on a ‘learn as you go’ basis since West Nile Virus was new to our country at the time. In this video clip, you are going to hear from both Boston public health officials and a CDC official regarding the practice and utility of West Nile Virus surveillance. As you listen, pay particular attention to: The surveillance lessons learned by public health officials (for example, they determined that they could test random samples of dead birds versus every dead bird to ascertain the presence of West Nile Virus) 2. How the manifestation of the virus makes it easy to involve the general public in surveillance efforts since bird surveillance is the “primary tool” 3. And finally, you will see a U.S. map towards the end of the segment. If you keep in mind the national map with “cumulative human West Nile Cases” for 2004 that I just showed you (the map shows positive / confirmed cases in the vast majority of states), you will gain an appreciation for how quickly West Nile Virus has spread across the country in only a few years!

Example: Influenza Let’s now look at a second example of surveillance at both the national and state levels: Influenza surveillance.

U.S. Influenza Surveillance
World Health Organization (WHO) and National Respiratory and Enteric Virus Surveillance System (NREVSS) collaborating laboratories State and Territorial Epidemiologists’ Reports 122 Cities Mortality Reporting System U.S. Influenza Sentinel Providers Surveillance Network (voluntary) The Influenza Branch at CDC collects and reports information on influenza activity in the United States each week from October through May. The U.S. influenza surveillance system has four separate components designed to provide a national picture of influenza activity: World Health Organization (WHO) and National Respiratory and Enteric Virus Surveillance System (NREVSS) collaborating laboratories; State and Territorial Epidemiologists’ Reports; 122 Cities Mortality Reporting System; and U.S. Influenza Sentinel Providers Surveillance Network. All influenza activity reporting by states and health-care providers is voluntary. Therefore, not all states participate in these surveillance efforts. We will focus on the Sentinel Providers Surveillance Network in this discussion, since the state of Virginia is a participant.

U.S. Influenza Surveillance
Does. . . Find out when and where influenza is circulating Determine what type of influenza viruses are circulating Detect changes in the influenza viruses Track influenza-related illness Measure the impact influenza is having on deaths in the United States Does Not. . . Ascertain how many people have become ill with influenza during the influenza season The influenza data reported to CDC through specific sentinel sites identify when and where influenza is circulating, what strain of virus is circulating, whether or not there are changes over time in virus activity, and the morbidity and mortality due to influenza. However, because the data are not collected from every health care provider in the country, the data do not ascertain the total number of people who have become ill with influenza during the influenza season.

Influenza-like Illness Case Definition
The Influenza-Like Illness case definition for CDC’s surveillance system is: Fever of 100 degrees Fahrenheit or higher 2. AND cough OR sore throat.

CDC Sentinel Influenza Surveillance
Let’s look at an image of a graph from the CDC web site. The red line represents the current Sentinel Influenza Surveillance season. The green line represents last year’s Sentinel Influenza Surveillance season (2003 – 2004), and the purple line represents the year before that (2002 – 2003). The horizontal, dotted line represents the national baseline of 2.5%. The data plotted on the y-axis of this graph are not raw numbers of cases of Influenza; rather, they represent the “percentage of visits for Influenza-Like Illness” per the case definition that I just showed you. And Influenza surveillance is tabulated weekly, so each tic mark on the x-axis represents a lapse of one week’s time. As of early March at the national level, CDC reported that the proportion of patient visits to sentinel providers for ILI was above the national baseline for seven consecutive weeks, but had finally declined in the last week of February. In this image, you can see the seven red line plot points that are above the baseline. . .

CDC Sentinel Influenza Surveillance
In the south Atlantic region, which includes the states of Florida, Georgia, South Carolina, North Carolina, Virginia, West Virginia, Maryland, and Delaware [and the District of Columbia], this graph shows that like the data at the national level, data are finally showing a decline in the percent of provider visits for Influenza-Like Illness after many successive weeks of increasing percentages. Per CDC documentation, the regional variability of data prohibit a comparison of this graph line to the national baseline of 2.5%.

VA Influenza Surveillance
Goal: “. . .to detect outbreaks of influenza as early and quickly as possible in order to facilitate early public health intervention and to specify the organisms involved.” Passive surveillance Laboratory surveillance Active sentinel component The goal of Virginia Influenza surveillance efforts is: “. . .to detect outbreaks of influenza as early and quickly as possible in order to facilitate early public health intervention and to specify the organisms involved.” Earlier I described the four components of CDC’s Influenza surveillance efforts, of which one is Sentinel surveillance. Virginia has three components, one of which is sentinel surveillance. One component is Passive surveillance Influenza information is received from physicians, persons in charge of medical care facilities, and directors of laboratories who are required by the Regulations for Disease Reporting and Control to report Influenza cases to the health department. The population under surveillance is all residents of Virginia, and reports are recorded weekly throughout the calendar year [this differs from the sentinel surveillance cycle, which includes the fall, winter, and spring months only]. This information is reported to local health departments and then relayed to the state health department where it is tabulated weekly. 2. Another component of surveillance takes place at the Laboratory level The state laboratory attempts to identify the various strains of influenza virus present in the state so that a comparison between the strains present in the community and vaccine may be made and recommendations on antiviral therapy formulated 3. Active Sentinel As I mentioned, Virginia is one of the voluntary participants in CDC’s Sentinel Surveillance program for Influenza. We will focus on the active sentinel component of Virginia’s Influenza surveillance since it is part of the CDC efforts at the national level.

Virginia Active Sentinel Surveillance
Virginia Department of Health conducts active surveillance with physicians around the state Season is October - April 60 – 70 physicians represent medical practices in each of the state’s five health planning regions Primarily family practice or internal medicine The Virginia Department of Health conducts active surveillance with approximately 60 to 60 physicians around the state, from October to April each year. The physicians represent medical practices in each of the state’s five health planning regions. While the physicians primarily family practice or internal medicine, they may also represent the specialties of pediatrics, acute care centers, emergency medicine, infectious diseases, and OB / GYN.

VA Influenza-like Illness Surveillance Activity
Here is a bar chart from the Virginia Department of Health’s Influenza Surveillance Web page. This graph was current as of March 7th, Unlike the CDC line graphs that show the percent of physician visits that were for Influenza-Like Illness, this chart illustrates the extent of Influenza-like Illness activity, by week, for the surveillance seasons in 2003 to 2004 and 2004 to 2005. When reading this graph, you can use the Centers for Disease Control and Prevention (CDC) Influenza Activity Codes to interpret the y-axis: 0 = No Activity 1 = Sporadic 2 = Local 3 = Regional 4 = Widespread No Activity: Overall clinical activity remains low and there are no lab confirmed cases. Sporadic: Isolated cases of lab confirmed influenza in the state; ILI activity is not increased. OR A lab confirmed outbreak in a single institution in the state; ILI activity is not increased. Local: Increased ILI within a single region AND recent (within the past 3 weeks) laboratory evidence of influenza in that region. ILI activity in other regions is not increased. Two or more institutional outbreaks (ILI or lab confirmed) within a single region AND recent (within the past 3 weeks) lab confirmed influenza in that region. Other regions do not have increased ILI and virus activity is no greater than sporadic in those regions. Regional: Increased ILI in >2 but less than half of the regions AND recent (within the past 3 weeks) lab confirmed influenza in the affected regions. Institutional outbreaks (ILI or lab confirmed) in >2 and less than half of the regions AND recent lab confirmed influenza in the affected regions. Widespread: Increased ILI and/or institutional outbreaks (ILI or lab confirmed) in at least half of the regions AND recent (within the past 3 weeks) lab confirmed influenza in the state. I am providing the definitions for these categories of ILI activity surveillance for you so you can appreciate how specific they are, and how much detail goes into reporting. The x-axis of this graph tells you that the surveillance cycle runs from the last week in October until the end of April (VA collects its baseline incidence data for six weeks, beginning in the second week of October). You can infer from this graph that widespread Influenza-Like Illness occurred and then dissipated two months earlier in the previous surveillance cycle, and that widespread activity occurred for six weeks. So far in this season, there have been four weeks of widespread activity.

Access VA and CDC Reports
Reports of Influenza Activity in the Virginia Surveillance Program: CDC reports and charts containing national and regional data: If you want to read the most current information about “Influenza Activity in the Virginia Surveillance Program”, please visit the URL at the top of this slide. To review CDC reports and charts generated with national and regional data, please visit the second URL listed on this slide.

Before we end our session today, what questions do you have for any of the three speakers?

5 minute break

Guest Lecturer: Techniques for Analysis of Surveillance Data
Sarah Pfau, MPH Consultant, NCCPHP We have covered a lot of ground today about what surveillance is, some different forms of surveillance, and diverse examples of those forms. Sarah Pfau is now going to talk to you about Analyzing and interpreting surveillance data using Epi Info software.

Overview Considerations when working with surveillance data
Descriptive Epidemiology Access surveillance data in Microsoft Excel or Access formats Access online census data Analyze surveillance data Throughout today’s presentation, you have seen many graphs and maps. You have probably noticed how helpful those images are in allowing you to quickly evaluate the distribution of or time trends in surveillance data that have been collected. I therefore want to encourage you to generate these images with your own county or state-level data in Epi Info software. I also want to talk to you about more considerations for working with surveillance data, such as why you should use rates versus raw data when generating time trend graphs and maps. And I will provide you with the step-by-step information that you need to access both Virginia and census data tables online so you can calculate your own rates for graphing or mapping. And finally, I will talk briefly about some considerations when interpreting analysis results or graphs generated with surveillance data.

Considerations Surveillance data primarily yield descriptive statistics Know the inherent strengths and weaknesses of a data set Examine data from the broadest to narrowest Before I talk about data analysis, I want to mention some larger issues that you should consider as you work with surveillance data. The first is that surveillance data primarily yield only descriptive statistics. And while these have many uses, you need to keep in mind that you cannot use surveillance data for the purpose of hypothesis testing analyses. But surveillance data – particularly when graphed or mapped – can describe patterns of disease or injury among your population of interest. You should also know the inherent strengths and weaknesses of a data set: Both Amy and Lesliann talked today about how the variation of data collection and reporting methods across different surveillance systems or even among different providers or health care settings can impact the overall reliability or validity of a data set. And finally, try to examine surveillance data from the broadest to the narrowest perspectives. For example, before you attempt to look for any potential correlations between multiple causal factors and an outcome, look at only one condition at a time. Furthermore, you might just look at raw numbers first, then crude rates, then adjusted rates. I will define and explain the difference between these terms in a moment. Or, you might look at rates for the total population, then stratify rates by race or gender.

Rely on Computers to: Generate Simple, Descriptive Statistics
Tables: frequencies, proportions, rates Graphs: bar, line, pie Maps: census tracts; counties; districts Aggregate or Stratify Rates State versus county Multiple weeks or months or years Entire population versus age, gender, or race specific When you are working with surveillance data, you can use computer software programs to facilitate the generation of descriptive statistics. You might then present or communicate those statistics in the form of tables, graphs, or maps. You have seen all of those forms in examples today. They include: [READ SLIDE PART A] And, just as I mentioned analyzing surveillance from the broadest to the narrowest categories, you can use computer software programs – particularly relational database programs like Epi Info – to quickly look at rates in subsets of a population. [READ SLIDE PART B]

Rely on Public Health Professionals to:
Contact health care providers and laboratories to obtain missing data; Interpret laboratory tests; Make judgments about epidemiological linkages; Identify or correct mistakes in data entry; and Determine if epidemics are in progress. While computer software makes life a lot easier in terms of analyzing large data sets, it is important to remember that there are many jobs that only an Epidemiologist, not a computer, can do. After a computer generates number tables or graphs, an Epidemiologist still needs to review, evaluate, and interpret surveillance data. For example, only Epidemiologists can: Contact health care providers and laboratories to obtain missing data; Interpret laboratory tests; Make judgments about epidemiological linkages; Identify or correct mistakes in data entry; and Determine if epidemics are in progress. These concepts may sound familiar to you – they are a review of some of the “tools” in an Epidemiologist’s “toolkit” that I discussed in February’s session! Most importantly, only local public health professionals, including Epidemiologists, are familiar with the population, historical disease patterns, existing or new reporting systems, and resources in your community that are critical to understanding and using surveillance data appropriately.

Descriptive Epidemiology
Surveillance Data Descriptive Epidemiology As I mentioned, surveillance data primarily yield descriptive statistics.

Person, Place, and Time Person: What are the patterns of a disease among different populations? Place: What are the patterns of a disease in different geographic locations? Time: What are the patterns of a disease when compared at different times (e.g., by month, year, decade) ? Throughout this series, you have been hearing that the key elements of Descriptive Epidemiology are: Person, Place, and Time. Well, we are going to revisit that triad yet again today! Descriptive statistics generated with surveillance data can contribute to this aspect of epidemiologic investigations. Analysis of surveillance data often begins by summarizing it according to person, place, and time. That is, looking for patterns of disease or health outcomes among different populations, in different places, at different times. Surveillance data provide valuable information about the types of disease control and prevention efforts we need to undertake, and who we should target in order to most efficiently prevent, if not limit, the spread of disease. And remember: it is good to start with the simplest of analyses and move to the more complex. For example, you might begin by graphing data for the entire population, and then graph data by race or gender. It is very important to do this, because a population-based trend might mask an even more extreme trends within a sub-population.

Tuberculosis Cases: United States 1992 - 2002
To illustrate the importance of analyzing surveillance data by sub-population as well as the entire population, let’s re-visit a line graph that you saw earlier in today’s presentation. If you were to generate a line graph of the average rates of Tuberculosis in these years, you might see an overall decrease (represented by the green line here) and think that prevention and treatment have been highly successful. And yet, when you stratify the data into U.S. versus Foreign-born rates, you quickly see that the significant decrease in rates has only been occurring among U.S. born people (represented by the red line), while the rates among foreign-born people (represented by the yellow line) have either plateaued or even slightly increased from time to time. Source:

Raw Numbers versus Rates
Surveillance data are reported in aggregate form, but in what are referred to as “raw numbers”: these numbers certainly do their job of quantifying the incidence of disease [there’s that incidence concept that Kim taught you last month!], but they are not particularly helpful for providing an accurate picture within the context of the total population. I am therefore going to discuss how to use rates versus raw data to make inferences about surveillance data.

Ratio A ratio is any [fraction] obtained by dividing one quantity by another; the numerator and denominator are distinct quantities, and neither is a subset of the other. - Teutsch and Churchill (1994). Rates, Proportions, and Percentages are all some form of a Ratio. Before I can talk about rates, I first need to define ratios. You may have heard the terms rates and ratios used synonymously. However, a rate is a more narrow definition of a Ratio. A Ratio is essentially a fraction obtained by dividing one quantity by another. The numerator and denominator are distinct quantities, and neither is a subset of the other. Rates, Proportions, and Percentages are all some form of a Ratio. However, proportions are unique in that the numerator represents a subset of the denominator.

What Do Rates Do? Measures the frequency of an event over a period of time Includes a numerator (e.g., disease frequency for a period of time) and a denominator (e.g., population) When you use Rates, they measure the frequency of an event over a specific period of time. The rate numerator will most likely be disease frequency, and the denominator will most likely be a population statistic – you practiced setting up numerators and denominators in the activities for last month’s session on “Descriptive and Analytic Epidemiology.”

Rates provide frequency measures within the context of the population.
Why Use Rates? Raw Surveillance Data Total Population Crude Rate X 104 City A 10 1,000 .01 100 per 10,000 City B 1,000,000 .00001 .1 per 10,000 So why use rates versus raw data? While raw data do measure the frequency of an event, they measure that frequency out of context. You have no way of knowing whether or not a value warrants further investigation. For example, if you have 1000 people in your community and 10 cases of a disease, that could be a lot more significant than if you have 1 million people in your city and 10 cases of a disease. Furthermore, getting back to the importance of starting with the broadest and then investigating more narrow sub-sets of data, if all 10 cases in either population occur within only one age group, race, or gender, this might influence your decision to investigate further. Here, you can see that the rate in City A is much greater than that for City B – 1,000 times greater!. Sometimes people take this rate and multiply it by a multiple of 10 in order to convert it to a whole number versus a decimal. I have done this by multiplying both crude rates by 10 to the 4th, or 10,000, and the result is in the far right column. As long as you multiply the rates for Cities A and B by the same multiple of 10, you will still have a valid comparison. Of course, in either scenario, knowing the baseline or expected “average” number of cases per month or per year for a particular disease can also be very helpful; long term surveillance data can help you determine that baseline. You may remember that earlier in the session you saw a baseline plot for the national level Influenza-like Illness data graphed by CDC, and that CDC referenced that baseline as a comparison with data for the current surveillance season. Rates provide frequency measures within the context of the population.

Crude versus Specific Rates
Crude Rate: Rate calculated for the total population Specific Rate: Rate calculated for a sub-set of the population (e.g., race, gender, age) There are several types of rates that you can calculate. Two of them are called “Crude” and “Specific.” A Crude Rate is the rate calculated for the total population, while a Specific Rate is the rate calculated for a subset of the population. It is best to begin your descriptive analyses with the crude rate, but then move on to looking at specific rates. Another more advanced type of calculation is “adjusted” or “standardized” rates. That is beyond the scope of today’s session. However, if you want to learn more about calculating and interpreting adjusted rates, please see the references and resources slides at the end of this session. [Remember that you can access and download this complete presentation after the session airs]. There is a particularly helpful article published by the National Center for Health Statistics of the Centers for Disease Control and Prevention if you ever need to calculate age-adjusted rates. Furthermore, it is not uncommon to come across age-adjusted rates in professional journal articles, so you may want to refer to the resource simply to gain a better understanding of the methodology.

Rate Numerator: VA Reportable Disease Surveillance Data
Office of Epidemiology, Virginia Department of Health Call: (804) 864 – 8141 When you calculate rates at the state or county level, you will need a “count” of reportable diseases at the state or county level for the numerator of the fraction. While there are numerous surveillance systems and sources of national, state, and / or county level data for a large number of public health indicators, I am highlighting this Virginia resource as one that you may find most useful. You can access data summary tables, some of which include both raw numbers and rates, for Virginia’s reportable diseases in .PDF format on the web site provided here. And remember that the Office of Epidemiology s a weekly surveillance report to Epidemiologists and Health Directors in the local health departments across the state. If you need to actually download and analyze the data that you find in the online summary tables, you will need to the Office of Epidemiology to request raw data tables. The address is provide on this slide. Be sure you include your name and contact information in your . You will then receive the data tables in Microsoft Excel format; I will discuss the implications for working with a Microsoft Excel data table in Epi Info software in a few moments. The data tables will not include personal identifiers, so you should not need to worry about confidentiality issues.

Rate Denominator: U.S. Census Data
Click on the “State & County Quick Facts” hyperlink Choose VA in the dropdown menu and click on GO Click on the “Browse Data Sets for Virginia” hyperlink at the top of the Quick Facts data table Click on the “Virginia Counties” hyperlink for ‘Population by Race and Hispanic or Latino Origin’ Open a new, blank file in Microsoft Excel Highlight table cells on the Census web page, click CTRL + C to copy data, then paste into the same number of cells in the Excel spreadsheet Name / save the Excel file in the Epi2000 folder on your c:\ drive For the denominator portion of your fraction, you will need to use population totals. The best place to find these is the U.S. Census Bureau web site [ While I will not have time to walk you through accessing and downloading census data, here are some step-by-step instructions for accessing the data. After the final step of saving the data table in an Excel spreadsheet, you will need to reformat the file per the importing criteria / restrictions in Epi Info that I will discuss in a moment.

Import Data from Microsoft Excel or Access into Epi Info
Because both the state communicable disease and census data tables will be downloadable in Excel format, I want to make sure that you know how to import them into Epi Info software for analysis, graphing, and mapping.

“Read / Import” Command
The “Read” command in Epi Info is the first command that you use to open or import a data table for analysis, whether you want to work with a file generated in Epi Info, or one generated in a different software program (for example, Microsoft Excel, Microsoft Access, or dBASE).

“Read / Import” Dialogue Window
Import files from alternative Software programs In addition to reading in existing Epi Info data tables, the “Read / Import” command imports data tables from other software programs such as Microsoft Access and Excel.

Import Restrictions for Microsoft Excel Files
There can be no spaces in either the Excel file name or the column and row header cells, or sheet names within an Excel file. You can, however, have spaces in other file names in the directory path. These three components of an Excel file cannot contain characters (e.g., !) The Excel file cannot contain any duplicate field names. The Excel file must be saved in the path: c:\Epi2000 folder – NOT the c:\Epi_Info folder that tends to operate as the default folder for Epi Info files. I mentioned earlier that there are importing criteria that require reformatting of an Excel file before you can work with a data table in Epi Info. These are: 1. There can be no spaces in either the Excel file name or the column and row header cells, or sheet names within an Excel file. You can, however, have spaces in other file names in the directory path. 2. These three components of an Excel file cannot contain characters such as a number sign, an ampersand, or an exclamation point. 3. The Excel file cannot contain any duplicate field names. I have also noted from personal experience that your data table MUST begin in cell A1 in a spreadsheet. Otherwise, the column headers do not import properly to become field names in Epi Info. 4. Finally, the Excel file must be saved in the path: c:\Epi2000 folder – NOT the c:\Epi_Info folder that tends to operate as the default folder for Epi Info files.

Import Restrictions for Microsoft Access Files
There can be no spaces in either the file name or the table or form names within an Access file. You can, however, have spaces in other file names in the directory path. These file components cannot contain characters (e.g., !) The Access file must be saved in the path: c:\Epi2000 OR c:\Epi_Info folder. Although I will not need to work with a Microsoft Access file today, I want to also provide you with the similar importing criteria for Access files. They are: There can be no spaces in either the file name or the table or form names within an Access file. You can, however, have spaces in other file names in the directory path. These file components cannot contain characters (e.g., !) The Access file must be saved in the path: c:\Epi2000 OR c:\Epi_Info folder.

Online Epi Info Training
“Importing and Exporting Data Tables” After today’s session, if you want to spend more time learning how to import non-Epi Info data tables into Epi Info, a new online, self-instructional module is now available. To access the training, please go to the URL listed here, which is part of the North Carolina Center for Public Health Preparedness Training web site. This online instruction uses two different types of media: it is part PowerPoint slides with a narrated lecture (and you just might recognize the voice!), and part “motion capture” segments that have captured my on-screen demonstrations in real-time so you can observe how to complete a task in the software. The motion capture segments are also accompanied by a narrated lecture. I encourage you to take this free online session if you need to learn how to Read in, import, or export data tables in Epi Info software.

Analyze Surveillance Data
So once you have successfully imported an Excel data table into Epi Info, you can graph and map the statistics.

Sample Analyses Time trend graph of NC data over ten years, by year for Salmonella cases Raw data Rates Maps of Salmonella rates by county: 2000 Raw Data versus Rates Choropleth I am going to illustrate two different ways to assess the same surveillance data set. The first way is to generate time trend graphs that literally illustrate the pattern of a disease in the population over a distinct period of time I am going to show you two different time trend line graphs for ten year’s worth of Salmonellosis surveillance data in North Carolina: a. One with raw data; and b. One with rate data. 2. The second way is to generate maps that illustrate the geographic distribution of cases I am going to show you several different maps that demonstrate how the distribution of raw data versus rates will look

Graph Surveillance Data
I’ll begin with time trend line graphs

Line Graph: Raw Data Here is a line graph representing raw data for the sum of reported cases of Salmonella in all North Carolina counties for each of the years 1992 through You can see in the data table at the bottom of the graph that the number of cases was lowest in 1998 at 3927, and highest in 1993 at 6678 (but nearly as high again in 2002 at 6608). I generated this graph in Epi Info.

Line Graph: Rate Data As I mentioned, a rate is defined as measuring the frequency of an event over a period of time, with a population value in the denominator. The rule of thumb is that you should use the population at the beginning of your time span for the denominator. I was able to calculate rates with unique denominators for each year with U.S. Census population estimates and projections for the state of North Carolina. The total population from the 1990 census was more than 6.5 million, while the 2000 census population was greater than 8 million. As I illustrated in the mock data table a few minutes ago, it is often helpful to multiply a crude rate by a multiple of ten. Note that these rates are “per 10,000 people” because I multiplied the crude rates by 104.

Archived U.S. Census Population Estimates
National State County Estimates: present and past Projections: future I used archived U.S. Census population estimates, by year, to calculate the rates for the line graph that I just showed you. The URL for a direct link to those tables is provided here for your convenience. The estimates are available at the national, state, and county levels. When you search for data on the Census web site, keep in mind that “estimates” provide numbers for the present and past, and “projections” provide numbers for the future.

Line Graph Raw Data Rates
Here we can compare the raw data and rate data line graphs side-by-side to see how they differ. While the differences may appear to be subtle, you can see a distinct adjustment in two segments of the graph line from 1999 to My main point here is to emphasize the utility of using rates versus raw data. It is helpful to graph and evaluate both distributions so as to not draw incorrect inferences about the descriptive statistics. Keep in mind that I have only illustrated what you might do with state level data. You could then compare trends between several counties in a region, for example, by graphing multiple lines at once with each line representing unique county data. Or, you could graph both national and state data and compare the two lines at once. Raw Data Rates

Generating a Line Graph: Considerations
Use an x-axis scale to show a trend over time Select an interval size that contains enough detail for the purpose of the graph Label x- and y-axes Now that you have seen how informative surveillance data line graphs can be, I want to point out a few details that you should keep in mind when you generate a line graph with any software program: Remember that you will use an x-axis scale to plot a trend over time, and a y-axis scale to plot a frequency or ratio Select an x-axis interval size that contains enough detail for the purpose of the graph – for example, does a line graph with weekly intervals tell a much better story than a line graph with monthly or bi-monthly intervals? You may have to experiment with a few graphs until you find the right level of detail in the x-axis intervals. Also, always remember to clearly label your x- and y- axes, and include a legend and title for your graph.

Map Surveillance Data I now have some maps to show you. I generated each map using Epi Info’s “Epi Map” component. The first one is a map that shows raw numbers, by county, for the frequency of a disease in the population – this was generated with raw numbers for the year 2000. The remaining images are choropleth- or color coded maps – that illustrate the extent of disease rates, by county, for they year 2000 with color gradients to represent lower versus higher rates. In the interest of time, I will not be able to demonstrate how to generate the maps – But I want to show you what you can do in Epi Info.

Epi Map Instruction “Generating Maps”
After today’s session, if you want to spend more time learning how to use the Epi Map component of Epi Info, two new online, self-instructional modules will be available after April 18th, 2005. To access the training, please go to the URL listed here, which is part of the North Carolina Center for Public Health Preparedness Training web site. Just like the online instruction for importing and exporting data tables, this online session uses two different types of media: it is part PowerPoint slides with a narrated lecture, and part “motion capture” segments that have captured my on-screen demonstrations in real-time so you can observe how to complete a task in the software. The motion capture segments are also accompanied by a narrated lecture.

Raw Data Map North Carolina Salmonella Cases by County: 2002
Here is an Epi Info map by county for the number of Salmonella cases in North Carolina in the year 2000. The number of cases per county ranged from only 1 in many counties to as many as 92 in one county, with a Mean number of cases of 12.6 per county. In the year 2000, 14 counties did not report any cases of Salmonella. Data source: NC Communicable Disease Data by county for 2000, General Communicable Disease Control Branch, Epidemiology Section, Division of Public Health

Choropleth Map North Carolina Salmonella Cases by County: 2002
Here I have mapped the same raw numbers for the year 2000, but have added a choropleth – or color coded element – to further illustrate the concentration of cases. You might choose to not show county-specific numbers on this map since the choropleth legend identifies the range of numbers of cases by color gradient. Data source: NC Communicable Disease Data by county for 2000, General Communicable Disease Control Branch, Epidemiology Section, Division of Public Health

Choropleth Map North Carolina Salmonella Rates by County: 2002
Here is another choropleth map, but two components are different: First, I mapped RATES versus raw numbers for this map. I calculated the rates using North Carolina communicable disease data by county for 2000 in the numerator, and U.S. Census population totals by county for the denominators for each rate calculated. As the legend shows you, rates ranged from .10 to 6.16 per 10,000 people by county. The mean rate was 1.45. The second difference is that I labeled the counties with their respective names versus rates this time. You can really do a lot of things to customize maps in Epi Info. For example, you could change the color coding on this map from greens to blues, or reds, oranges, and yellows. And as I just noted, you could also choose to label your counties with the numeric value or county name, or no name at all. But the goal, of course, is facilitating the review and evaluation of surveillance data. Rate numerators: NC Communicable Disease Data for 2000 Rate denominators: U.S. Census population data, by county, for 2000

Raw Data So here are the two maps lined up together so you can see how different the choropleth settings look when I graph raw data versus rates. These maps tell me that had I just graphed and assessed the distribution of raw numbers, I would have missed the high concentration of cases in the western, southern, and eastern parts of the state! I hope that this comparison further illustrates the benefit of calculating surveillance data rates as a means of providing a context for the raw numbers in the surveillance population. Rates

Data Interpretation: Considerations
Underreporting Inconsistent Case Definitions Has reporting protocol changed? Has the case definition changed? Have new providers or geographic regions entered the surveillance system? Has a new intervention (e.g., screening or vaccine) been introduced? So once you generate a time trend line graph or choropleth map with your surveillance data, what types of inferences might you draw? I have a few guidelines listed here for your consideration. Keep in mind that the surveillance data interpretation considerations on this slide apply to all descriptive analyses no matter how you illustrate the data. Earlier today, Amy discussed the first two items on this slide – underreporting and inconsistent case definitions - and how they impact the quality of surveillance data and overall surveillance systems. The other considerations listed on this slide really come down to a “common sense” approach to evaluating surveillance data. In addition to a change in rates that might be due to a legitimate outbreak, you need to also consider other possible explanations for trends that you observe. For example, has a reporting protocol or case definition changed? For example, if a case definition used to include 4 symptoms and now includes only two, surveillance efforts might capture many more individuals, and rates could appear to increase. Have new providers or geographic regions entered the surveillance system? If yes, the raw numbers might appear to be increasing, when in fact, if you calculate rates that include the new geographic region populations in the denominator, you may or may not see an increase in disease rates. One more important question to ask is, “Has a new intervention been introduced?” If a new vaccine has been introduced, you might be able to pinpoint its introduction into the population and a subsequent decrease in rates over time with the line graph. Conversely, if a screening protocol has changed, (for example, last year only high school freshmen were screened for dental caries, and this year freshmen and sophomores were screened), raw numbers may be deceiving. And sometimes, depending on the disease being screened for, health care providers may indirectly end up identifying and reporting cases of a related disease. As a result, raw numbers for two diseases might increase! An example of this might be screenings for one specific sexually transmitted disease and the simultaneous identification and treatment of others.

Online Surveillance Trainings
NC Center for Public Health Preparedness Direct link to 13 surveillance trainings Well, this concludes my portion of today’s session. I hope that today’s comprehensive session has taught you more about working with public health surveillance data. We have covered a lot of information, so there may be sub-topics that you want to explore further. The North Carolina Center for Public Health Preparedness has thirteen online trainings on public health surveillance on its Training web site. The URL for that web site is provided here for your reference. And please also remember to see the “references and resources” slides for this session if you are seeking additional resources.

Before we end our session today, what questions do you have for any of the three speakers?

Session Summary Surveillance is the ongoing systematic collection, analysis, and interpretation of health data, essential to the planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination to those who need to know. There are three broad forms of surveillance: passive, active, and syndromic. Passive and active differ primarily in the way in which data are reported to local health departments from health care providers, but both document confirmed cases. Syndromic surveillance involves collecting and analyzing real-time indicators for disease in an effort to identify an outbreak earlier than a traditional surveillance system will; however, cases are not confirmed via one standardized, case definition. I want to thank all of you for attending today’s session. Before we adjourn, I will review the key concepts that were addressed:

Session Summary Surveillance data have many applications, including: establishing public health priorities; aiding in determining resource allocation; assessing public health programs; determining baseline rates for detection of epidemics; and early detection of epidemics. The uneven application or availability of technologies, combined with the reporting burden and decentralized system of paper-based reporting, are inherent limitations of surveillance. Furthermore, electronic and paper-based reporting are only reliable when reporting practices are standardized and public health professionals and practitioners are trained in surveillance protocol and public health laws.

Session Summary Federal and state or local surveillance go hand-in-hand; they are the result of a collaborative, reciprocal pathway for data collection and reporting. When analyzing and interpreting surveillance data, it is advisable to graph rates versus raw data. It is also advisable to investigate broad, total population rates prior to evaluating specific rates for population strata such as race or gender.

Session IV Slides Following this program, please visit the Web site below to access and download a copy of today’s slides if you have not already done so:

Don’t Forget! Please submit your site sign-in sheet and session evaluation forms to: Suzi Silverstein Director, Education and Training Emergency Preparedness & Response Programs FAX: (804)

Next Session: May 5th Final Session in this 5-part Series
“Epidemiology Specialties Applied” Disaster Environmental Forensic Finally, I want to remind you that next month’s session titled, “Epidemiology Specialties Applied” will be the last in our five-part series on ‘Epidemiology for Non-Epidemiologists.’ The first two sessions in this series provided you with an overview of the practice of Epidemiology, including an introduction to some specialties and the many technological, institutional, and community-based tools that Epidemiologists have to work with. The third and fourth sessions were more technical, because we wanted you to gain an understanding of the key elements of descriptive and analytic epidemiology as a means of facilitating your work with district Epidemiologists in Virginia. In the final session, we will be shifting back to content that is more informative than technical. I will moderate the session, but you will hear from three different specialists talk in detail about the practice of three Epidemiology specialties: Disaster, Environmental, and Forensic. As the speakers discuss case studies and their experience in the field, I think that you will be able to identify with the underlying methods of Epidemiological practice that you have learned in these first four sessions. So I hope that you will be able to join us in May. I will look forward to connecting with you across the Poly Com network at that time! Thank you.

References and Resources
Bonetti, M. et al (August 2003). Syndromic Surveillance PowerPoint Presentation. Harvard Center for Public Health Preparedness. CDC case definitions CDC infectious disease surveillance systems CDC Integrated project: National electronic diseases surveillance system

CDC nationally notifiable infectious diseases CDC Notifiable diseases/deaths in selected cities weekly information. MMWR. June 4, 2004/53(21); CDC Division of Public Health Surveillance and Informatics, Epidemiology Program Office General Communicable Disease Control Branch, Epidemiology Section, Division of Public Health, NC Department of Health and Human Services. Reportable Communicable Diseases – North Carolina.

Klein, R. and Schoenborn, C. (January 2001). Age Adjustment Using the 2000 Projected U.S. Population. Healthy People 2010 Statistical Notes: No National Center for Health Statistics, Centers for Disease Control and Prevention. Last, J.M. (1988). A Dictionary of Epidemiology, Second Edition. New York: Oxford University Press. Teutsch, S. and Churchill, R. (1994). Principles and Practice of Public Health Surveillance. New York: Oxford University Press. U.S. Department of the Interior, U.S. Geological Survey (January 19, 2005). Virginia Department of Health Web site:

NC Center for Public Health Preparedness Surveillance Trainings: “Surveillance” “Utilizing Infectious Disease Surveillance Data” “Acute Disease Surveillance and Outbreak Investigation” “Syndromic Surveillance in North Carolina, 2003” “North Carolina Communicable Disease Law” “Introduction to Surveillance” “Communicable Disease Surveillance in North Carolina”

Public Health Information Network (PHIN) Series I

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