descriptive epidemiology & routine analyses: summarizing data by groups/type/location monica...
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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/
07
3/24/
07
5/19/
07
7/14/
07
9/8/0
7
11/3
/07
12/2
9/07
2/23/
08
4/19/
08
6/14/
08
8/9/0
8
10/4
/08
11/2
9/08
1/24/
09
3/21/
09
5/16/
09
7/11/
09
9/5/0
9
10/3
1/09
12/2
6/09
2/20/
10
4/17/
10
6/12/
10
8/7/1
0
10/2
/10
11/2
7/10
1/22/
11
3/19/
11
5/14/
11
7/9/1
1
9/3/1
1
Week
% o
f Vis
its fo
r IL
I
% 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)
B
Percent Positive
2007-08 2010-112009-102008-09
Perc
en
t Po
siti
ve
Num
ber
of
Posi
tive
Speci
mens
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
0-4
9.05205905205908
9.36645147171463
7.09512578616352
9.57102125453602
7.3527350866
539
21.6432778932779
9.44791666666667
7.79028982831825
10.3028503562945
11.1963312720707
8.67162313310085
13.3354114713217
5-24
7.59903801189124
6.61225762852834
6.26157212590474
3.80885542937347
5.2296290656
21
10.8123201689956
6.47016000831984
4.30561635722194
4.82724993034271
8.41689886602753
9.39782114210463
13.7
25-64
3.50967124598456
5.88278968470029
2.30678412496594
2.08531695476673
1.50862524017652
3.88497065113528
3.68015579803535
2.02571348042581
1.55199883192119
3.60293168485822
1.8 3.8
65+
1.5430900621
118
3.32825906448934
1.25883843544366
1.24751716207269
0.799301634067163
3.10222524536002
1.7652304948
181
1.36865945566012
1.02266359504389
1.73426685981008
0.728213240925584
0.808937113348209
25-49
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.7
50-64
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.2
3
8
13
18
23
% I
LI
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 V
isis
ts f
or
ILI
% 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 Name
Site Name Patient ID Date of Consultation (ILI) Date of Admission (SARI) Date of Specimen Collection Age Sex Date of Onset
Lab 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?