Descriptive Epidemiology & Routine Analyses: Summarizing Data by Groups/Type/Location

Monica HuangCouncil of State and Territorial Epidemiologists

Next Step: Descriptive Analysis Reports are full of descriptive analysis – stratification by age

groups, sex, underlying conditions, location

Now you’ve figured out how to manage your data, it’s standardized, you have your forms set and the information you’re collecting. What do you do with it?

This is data for action! The data should be put to use, and the most straightforward use of the data is descriptive epidemiology

Questions: What types of analyses will be most important in your country? What are the best ways to use your surveillance data? What was the purpose in collecting all of this data?

Objectives of Descriptive Epidemiology

Evaluate trends in health and disease Frequency and distribution of disease

Comparisons between subgroups, regions, etc.

Provide information for planning, policy development

Identify problems to be studied in greater detail (example: correlations between a risk factor and an increased outcome)

Elements of Descriptive Epidemiology

Time Do disease patterns differ based on the time of year?

Seasonality Person

Do disease patterns differ based on person’s age or sex? Are certain groups of people more susceptible to

complications of disease? Place

Do disease patterns differ based on geographic location? Combinations of Time, Place and Person

E.g. age groups over time, stratified by location, etc.

Time Temperate climates usually have flu

season during the fall/winter months Tropical climates have a less

predictable flu season (e.g. may have several peaks throughout the season, and they may vary dramatically among regions of a country)

Differences in patterns are important Temporal patterns are often different

during an epidemic or pandemic

Percentage of Visits for Influenza-like Illness (ILI) Reported by the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet),

National Summary October 1, 2006 – September 24, 2011

0

1

2

3

4

5

6

7

8

9

10/7/

06

12/2/

06

1/27/0

7

3/24/0

7

5/19/0

7

7/14/0

79/8

/07

11/3/

07

12/29

/07

2/23/0

8

4/19/0

8

6/14/0

88/9

/08

10/4/

08

11/29

/08

1/24/0

9

3/21/0

9

5/16/0

9

7/11/0

99/5

/09

10/31

/09

12/26

/09

2/20/1

0

4/17/1

0

6/12/1

08/7

/10

10/2/

10

11/27

/10

1/22/1

1

3/19/1

1

5/14/1

17/9

/119/3

/11

Week

% o

f Vis

its fo

r ILI

% ILI National Baseline

Influenza Positive Tests Reported to CDC by U.S. WHO/NREVSS Collaborating Laboratories, National

Summary, 2007-08 through 2010-11

0

5

10

15

20

25

30

35

40

45

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

12,000

13,000

40 50 10 20 30 40 50 10 20 30 40 50 10 20 30 40 50 10 20

A (2009 H1N1)A (Unable to Subtype)A (H3)A (H1)A (Subtyping not Performed)BPercent Positive

2007-08 2010-112009-102008-09

Perc

ent

Posit

ive

Num

ber o

f Pos

itive

Sp

ecim

ens

Person Do certain characteristics make a

person more susceptible to infection or complications due to infection? Demographic data

Age and gender Underlying conditions

Neurologic disorders, pulmonary disease, genetic disorders, cardiac disease, immunosuppressive condition, endocrine disorders, mitochondrial disorders, renal disease, obesity, and pregnancy

Optional stratifications: Additional age

categories that inform vaccine policies:

0 – 12 or 12 – 24 months

May also combine age groups, if data is too sparse to break into larger groups

Other relevant groups for pre-determined analyses

Standard Age Stratifications

0 to < 2 years 2 to < 5 years 5 to < 15 years 15 to < 49 years 50 to < 65 years ≥ 65 years

Peak Percent of Patient Visits Due to ILI by Season and Age Group, 1998-2010

98-99

99-00

00-01

01-02

02-03

03-04

04-05

05-06

06-07

07-08

08-09

09-10

0

5

10

15

20

25

% IL

I

Percentage of Visits for ILI Reported by U.S. Outpatient Influenza-like Illness Surveillance

Network (ILINet) National Summary 2000-01 through 2009-10 by Age Group

Data as of week ending 20 February, 2010

0

5

10

15

20

25

10/7

/200

01/

27/2

001

5/19

/200

19/

8/20

0112

/29/

2001

4/20

/200

28/

10/2

002

11/3

0/20

023/

22/2

003

7/12

/200

311

/1/2

003

2/21

/200

46/

12/2

004

10/2

/200

41/

22/2

005

5/14

/200

59/

3/20

0512

/24/

2005

4/15

/200

68/

5/20

0611

/25/

2006

3/17

/200

77/

7/20

0710

/27/

2007

2/16

/200

86/

7/20

089/

27/2

008

1/17

/200

95/

9/20

098/

29/2

009

12/1

9/20

09

Week Ending

% o

f Vis

ists

for I

LI

% ILI 0-4 % ILI 5-24 % ILI 25-64 % ILI 65 and Older Age 25-49* Age 50-64*

Age-level comparison of percent positive over 10 influenza seasons

Influenza A(H3N2) predominant and non- predominant seasons, individuals aged >64

Standard Underlying Conditions

Chronic respiratory disease Asthma Diabetes Chronic cardiac disease Chronic liver disease Chronic renal disease Chronic neurological or neuromuscular

disease Immunodeficiency

Predominant virus type and percent positive

Place: Are things different or worse in one area vs. another?

Visualization of place information can be in Maps Graphs Tables

Place can be defined in different ways depending on what the question is you are asking and available information Geographic levels: region, state, county, city,

or site

Summary TableData for current week Data cumulative since October 3, 2010 (Week 40)

HHS Surveillance Regions*

Out-patient ILI†

% positive for flu‡ A (H3) 2009 A

(H1N1)A

(Subtyping not performed)

B Pediatric Deaths

Nation Normal 0.8% 17,599 10,946 11,737 13,944 105

Region 1 Normal 0.2% 1,728 923 97 462 3

Region 2 Normal 1.4% 1,498 377 1,448 537 11

Region 3 Normal 3.7% 2,983 2,570 860 1,042 10

Region 4 Normal 1.6% 1,483 1,436 3,180 3,963 18

Region 5 Normal 4.5% 2,145 1,527 464 1,361 21

Region 6 Normal 0.1% 2,191 570 2,318 2,582 18

Region 7 Normal 1.0% 717 538 289 680 1

Region 8 Normal 0.6% 1,735 691 2,124 1,890 9

Region 9 Normal 1.0% 1,998 1,477 763 1,287 12

Region 10 Normal 2.9% 1,121 837 194 140 2

*HHS regions (Region 1 CT, ME, MA, NH, RI, VT; Region 2: NJ, NY, Puerto Rico, U.S. Virgin Islands; Region 3: DE, DC, MD, PA, VA, WV; Region 4: AL, FL, GA, KY, MS, NC, SC, TN; Region 5: IL, IN, MI, MN, OH, WI; Region 6: AR, LA, NM, OK, TX; Region 7: IA, KS, MO, NE; Region 8: CO, MT, ND, SD, UT, WY; Region 9: AZ, CA, Guam, HI, NV; and Region 10: AK, ID, OR, WA).† Elevated means the % of visits for ILI is at or above the national or region-specific baseline.‡ National data are for current week; regional data are for the most recent three weeks.§ Includes all 50 states, the District of Columbia, Guam, Puerto Rico, and the U.S. Virgin Islands.

Important Considerations When summarizing data by place, you need a way

to correctly compare information, which includes correcting for confounders (variables or elements that influence the outcome but are not equal among groups) There may be differences in the way that sites report

(ex. a pediatrician will always report higher proportion of ILI than a practice that also sees adults)

There are ways that you can correct for these differences if they exist, but they require more advanced statistical methods, including: Baselines (discussed later) Data weighting

Data Display As part of CDC’s weekly report we

have deployed a web tool that allows users to look at differences in circulating viruses and the intensity of activity both by geographic region and time period

Link: http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html

Methods of Analysis Matter Different types of analyses lead to different

messages: Counts: easy to obtain; can be misleading Proportions: simple, clear, efficient analysis

Using an appropriate denominator (population, total visits, total specimens) makes data easier to interpret

Incidence rates: proportion of persons in a population who are sick during a specified period of time

Risk ratios or odds ratios provide a better understanding of the importance of the risk factor

Example: Incidence Rates

In a study of 2390 women between 16 and 49 years of age admitted to hospital for SARI, it was found that 482 were influenza positive in a one year period 482 / (2390 / 100,000) = 20,167

Therefore, there are influenza positive 20,167 per 100,000 women admitted to hospital in a one year

period

Types of Incidence Measures

Rate Numerator Denominator

Morbidity New cases of non-fatal disease

Total population at risk

Mortality Number of deaths from a disease causes)

Total population

Case-fatality Number of deaths from a disease

Number of cases of that disease

Attack Number of cases of a disease

Total population at risk during a specified period oobservation

Rates Commonly Used in Epidemiology

Crude For total population, not adjusted to reflect

contributions of different age groups to total (e.g. annual cancer mortality rate)

Category specific Based on the number of persons in the

category and the number of cases in that group (e.g. age-specific cancer mortality rate)

Age adjusted More appropriate comparisons when

differences in age distribution may mask real differences in the condition of interest

Importance of Denominators

------- total number of visits------- counts of ILI

Burkom et al. 2008

Conclusions You can learn a lot by looking at

differences in disease in terms of simple descriptives What are the normal patterns of

disease? Disease patterns will often differ based

on differences in person and place Changes in normal patterns occur

during major epidemics or pandemics

Your methods of analysis make a big difference in interpretation

Calculating Proportions, Rates, &

Preparing Graphs

Overview Descriptive epidemiology aims to evaluate

trends and allow comparisons by region and within subgroups Provides a basis for planning, policy making, etc. Helps to identify problems to be studied further

Data can be conveyed and compared easily using simple graphs Influenza activity Risk groups Age groups Weekly trends Circulating viruses

Types of Graphs

Line graphs

Bar charts

32%

21%21%

16%

11%HibPneumococcusRSVInfluenzaUnknown

Pie charts

Objectives - Use Excel to: Sort data Calculate:

Sum Sum total cases, total specimens tested, positive, etc.

Percentage/proportion Provides better picture of how widespread Important to collect denominators in order to

calculate! Rates

Gives idea of frequency in population, population level estimate of illness

Stratify cases by population groups, time, type of virus, etc.

Learning Objectives Use Excel to produce simple graphs

illustrating: Counts Proportions (must have denominator data!) Rates (where population denominator data is available) Activity over time Activity among different population groups

Keep track of site usage/performance – number of samples collected by site by month; number of SARI cases enrolled; consistency of reporting over time

Exporting data from Access

Sorting Data Sorting data is a simple way to group like

elements together, allowing for more simple construction of graphics Can sort data on a single variable, or create a sort based

on several levels of data

Sorting Data

Sorting Data Data sorted

by: Site name Date of Onset Sex

Creating Tables A well designed table is a good way to get

a quick look at your data, sorted or summarized by whichever variables you choose Sorting your tables prior to data cleaning can also be

useful • Example: Sorting by date might show that cases have

been recorded as occurring before surveillance was begun Summing data by site might help evaluate site

performance• Example: Summing by number of specimens submitted by

site might help to understand whether sites are meeting quotas

Table sorted by Site NameSite Name Patient ID Date of Consultation (ILI) Date of Admission (SARI) Date of Specimen Collection Age Sex Date of OnsetLab A NG0009 3/12/2011 3/12/2011 3/12/2011 28 Female 3/12/2011Lab A NG0015 4/18/2011 4/18/2011 4/21/2011 28 Female 4/18/2011Lab A NG0008 5/11/2011 5/11/2011 5/12/2011 34 Female 5/11/2011Lab A NG0010 5/13/2011 5/13/2010 5/13/2010 21 Female 5/13/2011Lab A NG0014 5/17/2011 5/17/2011 5/17/2011 34 Female 5/17/2011Lab A NG0016 5/19/2011 5/19/2011 5/19/2011 21 Female 5/19/2011Lab A NG0003 7/3/2011 7/4/2011 7/4/2011 40 Female 7/3/2011Lab A NG0002 8/30/2011 9/1/2011 9/1/2011 35 Female 8/30/2011Lab A NG0001 9/1/2011 9/3/2011 9/3/2011 30 Male 9/1/2010Lab A NG0012 2/15/2011 2/15/2011 2/15/2011 56 Male 2/15/2011Lab A NG0005 4/3/2011 4/13/2011 4/15/2011 25 Male 4/3/2011Lab A NG0006 5/9/2011 5/10/2011 5/10/2011 45 Male 5/9/2011Lab A NG0007 5/10/2011 5/10/2011 5/10/2011 56 Male 5/10/2011Lab A NG0011 5/14/2011 5/14/2011 5/14/2011 56 Male 5/14/2011Lab A NG0013 5/16/2011 5/21/2011 5/21/2011 56 Male 5/16/2011Lab A NG0004 10/30/2011 10/30/2011 10/30/2011 20 Male 10/30/2011Lab B NG0047 11/19/2010 11/19/2010 11/19/2010 32 Female 11/19/2010Lab B NG0061 1/3/2011 1/3/2011 1/3/2011 56 Female 1/3/2011Lab B NG0053 2/25/2011 2/25/2011 2/25/2011 87 Female 2/25/2011Lab B NG0076 4/18/2011 4/18/2011 4/18/2011 21 Female 4/18/2011Lab B NG0024 4/27/2011 4/27/2011 5/1/2011 21 Female 4/27/2011Lab B NG0074 5/16/2011 5/16/2011 5/16/2011 34 Female 5/16/2011Lab B NG0022 5/25/2011 5/25/2011 5/25/2011 34 Female 5/25/2011Lab B NG0023 5/26/2011 5/26/2011 5/28/2011 28 Female 5/26/2011Lab B NG0028 5/31/2011 5/31/2011 5/31/2011 39 Female 5/31/2011Lab B NG0029 6/1/2011 6/1/2011 6/1/2011 22 Female 6/1/2011Lab B NG0034 6/6/2011 6/6/2011 6/6/2011 34 Female 6/6/2011Lab B NG0036 6/8/2011 6/8/2011 6/8/2011 30 Female 6/8/2011Lab B NG0038 6/10/2011 6/10/2011 6/10/2011 21 Female 6/10/2011Lab B NG0040 6/12/2011 6/12/2011 6/12/2011 21 Female 6/12/2011Lab B NG0042 6/14/2011 6/14/2011 6/14/2011 34 Female 6/14/2011Lab B NG0046 6/18/2011 6/18/2011 6/18/2011 34 Female 6/18/2011Lab B NG0048 6/20/2011 6/20/2011 6/20/2011 21 Female 6/20/2011Lab B NG0051 6/23/2011 6/23/2011 6/23/2011 44 Female 6/23/2011Lab B NG0054 6/26/2011 6/23/2011 6/23/2011 68 Female 6/26/2011Lab B NG0057 6/29/2011 6/29/2011 6/29/2011 55 Female 6/29/2011Lab B NG0058 6/30/2011 6/30/2011 6/30/2011 34 Female 6/30/2011Lab B NG0060 7/2/2011 7/2/2011 7/2/2011 63 Female 7/2/2011Lab B NG0062 7/4/2011 7/4/2011 7/4/2011 34 Female 7/4/2011Lab B NG0064 7/6/2011 7/6/2011 7/6/2011 47 Female 7/6/2011Lab B NG0066 7/8/2011 8/7/2011 8/7/2011 34 Female 7/8/2011Lab B NG0068 7/10/2011 7/10/2011 7/10/2011 21 Female 7/10/2011Lab B NG0075 7/17/2011 7/17/2011 7/15/2011 43 Female 7/17/2011Lab B NG0080 7/22/2011 7/22/2011 7/22/2011 21 Female 7/22/2011Lab B NG0055 7/27/2011 7/27/2011 7/27/2011 28 Female 7/27/2011Lab B NG0030 8/2/2011 8/2/2011 8/2/2011 28 Female 8/2/2011Lab B NG0067 8/9/2011 8/9/2011 8/9/2011 44 Female 8/9/2011Lab B NG0041 8/13/2011 8/13/2011 8/13/2011 47 Female 8/13/2011Lab B NG0072 9/14/2011 9/14/2011 9/16/2011 33 Female 9/14/2011Lab B NG0079 10/21/2011 10/22/2011 10/22/2011 19 Female 10/21/2011Lab B NG0019 12/22/2010 12/25/2010 12/25/2010 28 Male 12/22/2010Lab B NG0070 1/12/2011 1/12/2011 1/12/2011 34 Male 1/12/2011Lab B NG0025 2/28/2011 2/28/2011 2/28/2011 65 Male 2/28/2011Lab B NG0017 3/20/2011 3/20/2011 3/21/2011 56 Male 3/20/2011Lab B NG0050 3/22/2011 3/22/2011 3/22/2011 34 Male 3/22/2011Lab B NG0035 5/7/2011 5/7/2011 5/7/2011 28 Male 5/7/2011Lab B NG0065 5/7/2011 5/7/2011 5/7/2011 56 Male 5/7/2011Lab B NG0018 5/21/2011 5/22/2011 5/22/2011 34 Male 5/21/2011

Pivot Tables/Charts Using pivot tables to create tables,

charts, and graphs helps to summarize and display data in a custom format

A very easy, effective way to summarize data and create charts

Pivot Tables/Charts Set up in the

exact same way in Access; can be used easily in either program

Pivot Tables/Charts Drag and

drop fields in any order

Pivot Tables/Charts Drag and

drop fields Use table to

make charts easily

Pivot Tables/Charts

Summary With well-cleaned and organized data, simple

tools are available for custom presentations and easy analysis

Making use of these simple tools makes the job of the analyst, reporter, data manager much easier

Messages can be conveyed easily using graphics produced in a straightforward manner

Exercise Produce a table showing number of specimens

submitted with test results, stratified by site Produce a line graph of SARI admissions by site,

over time

Thank you!

Questions?