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What Can I Do With My Data?
Utilizing Existing Data for Analysis and Hypothesis Development
Falgunee Parekh, MPH, PhD
Agenda
• My Research Background
• Background on Analysis of Surveillance (or Initial) Data
• Case Study of Lassa Fever Data Analysis Utilizing Surveillance Data• Development of Collaboration
• Type of Existing Data
• Developing a research question
• Analysis Plan
• Results
• Questions and Discussion
Research Background• Infectious Disease Epidemiologist
• >15 years of experience
• Field Epidemiology and Clinical Research
• Disease Experience
• Malaria, Zika, Lassa Fever, Influenza,
• Zoonotic Diseases and One Health
Approach
• Country Experience
• Peru, Colombia, India, Azerbaijan, Tanzania, Democratic Republic of Congo,
Gabon, South Africa, Zimbabwe
Aims of Surveillance
• Allows for rapid detection of disease outbreaks
• Supports early identification of disease problems – endemic and non-endemic
• Provides an early warning system able to identify new and emerging diseases
• Assess the health status of a defined population (estimating level of occurrence/trends among diseases)
• Confirm absence of a specific disease
Uses and Applications of Surveillance Data
• Estimate the magnitude of the problem
• Detect epidemics/define a problem
• Evaluate control measures
• Facilitate health planning
• Determine geographic distribution of illness
• Portray the natural history of a disease
• Generate hypotheses, stimulate research
• Monitor changes in infectious agents and/or health practices
Example: Raw Dataset
Case
#
Date of Onset Disease Case
Classification
Age Gender
1 22/10/16 Anthrax Confirmed 19 M
2 25/10/16 Anthrax Not a case 17 M
3 19/10/16 Anthrax Probable 23 F
4 15/10/16 Anthrax Investigation
Pending
18 ?
5 23/10/16 Anthrax Confirmed 21 F
6 27/10/16 Anthrax Suspect 18 M
7 21/10/16 Anthrax Confirmed 25 F
Methods of Analysis of Surveillance Data
• Descriptive Methods• Analysis of the data by person, place and time
• Calculation of rates
• Use of tables, graphs, and maps
• Analytical methods• Cohort studies
• Case-Control studies
Developing a Data Analysis Plan
• To analyze data you need a data analysis plan• A series of steps to organize your work
• The data analysis plan must build upon itself• Start with simple descriptive statistics
• Build to more complex analyses
• Examine the data for possible errors and correct if possible at every step of the data analysis plan
Components of a Surveillance Analysis Plan
• Become familiar with the data
• Check for errors – “Clean” the data
• Analyze counts and rates by year, months, or weeks (Time)• Check for trends and seasonality
• Analyze data by regions or districts (Place)
• Analyze data by age and sex (Person)
• Subgroup analysis
Data Quality
• Missing Values
• Completeness of critical variables
• Data entry errors,
• Adherence to strict case definitions
• Biases• Severe cases tend to be reported more than mild cases
• Better surveillance in urban areas than rural
• Non-standard reporting
Collaborations
• Develop collaborations with other investigators • Fulfill your knowledge gaps
• Assist in development of analysis plan
• Allows for multiple perspectives in interpretation of analysis
• Allows for hypothesis development and continued collaboration on future projects
Case Study – Lassa Fever Data, Sierra Leone
Case Study – Lassa Fever Data, Sierra Leone
Viral Hemorrhagic Fevers (VHFs) pose serious biological threats and potent agents of bioterrorism
Ease of aerosolized dissemination Low infectious dose High morbidity/mortality rates Lack of effective vaccines or treatments
The outbreak of Ebola demonstrates the rapid spread of VHFs across borders and regions due to mobile populations
VHFs have serious impact on public health and heavy burden on health care infrastructure and agencies
Lassa Fever has been imported to other countries
BackgroundLassa Fever (LF) Lassa virus (LASV) is an arenavirus
Reservoir is the multimammate rat genus Mastomys
LF is NOT a rare disease
Endemic to West Africa and transmitted throughout the year
Occurs in several countries including Guinea, Liberia, Nigeria, and Sierra Leone
Estimated that 300,000 cases and 5,000 deaths occur annually
One of the only VHFs that can be prospectively studied
Understanding how LF spreads can better help us understand other disese like Ebola
LF in Sierra Leone 2004-2011
Study Objective
Characterize the morbidity/mortality, epidemiology and risk factors associated with clinical outcome for
infection with Lassa virus (LASV)
Description of Dataset – LF from Sierra Leone Developed Collaboration:
Sierra Leone Ministry of Health and Sanitation (MOHS) provided access to country-wide data on suspected LF cases
Surveillance and clinical data of suspected cases reported by MOHS, 2008 – 2013
Includes data on: Suspected Cases identified through passive and active surveillance Results of diagnostic laboratory testing Epidemiologic data collected from patient questionnaires and
clinical assessments Potential contacts identified and approached by active surveillance
team
LF Dataset
Study Methods:• Retrospective
analysis of data collected from surveillance of LF in Sierra Leone
• Assess epidemiologic risk factors associated with disease and mortality
Where Do I Start??
Analysis of Data by Person, Place and Time
Analysis by Person• Compare counts or
frequencies by:
• Age
• Gender
• Ethnicity
• Occupation
• Vaccination status
• Others?
Analysis by Place• Present geographic
distribution of counts or rates
• Where cases were reported
• Where exposures might occur
• Determine the geographic area with the highest rates of infection
Analysis by Time• Examine occurrence of disease
during particular time interval (years, months, weeks)
• Seasonal trends
• Analysis of time using person and place subcategories:
• Gender frequency over time
• Frequency in a region over time.
Analysis of Subgroups
• Analysis of sub-groups can reveal additional information
• Sub-Groups• Gender
• Children
• Ethnicity
• Individuals with outdoor occupations
• Combinations – (gender and ethnicity)
Develop an Analysis Plan
Univariate analysis
Temporal trend analysis across years
Risk factor analysis to assess predictors of disease and mortality AgeGenderOther subgroups
LF Results 2008-2013 – Univariate Analysis by Time
3348 suspected LF cases identified between 2008-2013:
27.0% were LF Positive
31.5% of LF Positive (n=872), Died
56.3% of suspected cases were Female
13.7% of suspected received Ribavirin treatment
178317
673776 806
598
3348
42 64191 192 222 194
905
19 34 57 66 59 40
275
0
500
1000
1500
2000
2500
3000
3500
4000
2008 2009 2010 2011 2012 2013 Total
Lassa Fever Enrollees, Diagnosis and Mortality 2008-2013
N LF Pos LF Died
LF Results – Analysis by Time2008-2013
The proportion of female suspected cases significantly increased over the years
Days Since Onset of Illness(DSOI) significantly different across the years
Appears to be decreasing
Characteristic 2008 2009 2010 2011 2012 2013 TotalChi-Sq. P-value
CA* Trend P-Value
N 178 317 673 776 806 598 3348
Female 84 (47.2) 177 (55.8) 356 (52.9) 460 (59.3) 473 (805, 58.7) 335 (56.0) 1,885 (3347, 56.3) .016 .026
Age in Years (Median) 25.5 (26.0) 25.0 (316, 25.0) 23.7 (670, 23.0) 24.3 (766, 24.0) 24.7 (788, 23.0) 23.7 (593, 22.0) 24.3 (3311, 23.0) .23** NA
Mean DSOI/days (Median)
9.6 (134, 8.0) 9.2 (307, 7.0) 8.6 (647, 6.0) 9.6 (600, 7.0) 8.2 (418, 6.0) 8.5 (323, 6.0) 8.9 (2429, 7.0) .0003** NA
*Cochran Armitage Trend test, **Krukal Wallis test
Characteristics of Suspected LF Cases by Year
LF ResultsTotal Suspected Enrollees with Defined LF Diagnosis, 2008-2013
0
5
10
15
20
25
30
35
2008 2009 2010 2011 2012 2013
Pro
po
rtio
n o
f Su
spec
ted
Cas
es
Year
LF Positive Cases and Ribavirin Treated by Year
% LF + %Ribavirin
p<.0001*
p<.0009*
0
10
20
30
40
50
60
2008 2009 2010 2011 2012 2013Pro
po
rtio
n o
f LF
Po
siti
ve
Year
LF Mortality by Year
%Mortality
p<.0001*
Increased prevalence may be due to improved detection and/or increasing transmission
More mild LF cases may be detected that don’t require Ribavirin treatment
* Cochran-Armitage Trend test
LF Results – Analysis by PlaceMap of Cases in Sierra Leone
• LF cases identified from districts that had previously not reported LF
Improved detection of LF
Improved awareness of the population at risk of LF
LF may be spreading
2008 2009 2010
2011 2012 2013
Courtesy of Marc Souris
LF Results – Analysis by PersonRisk Factors of Lassa Fever Diagnosis, 2008-2013
LF positive were of significantly younger age and had more days since onset of illness
LF negative were significantly more likely to have reported a death in their household, and contact with a LF case
Gender was not significantly different between LF positive and LF negative
Characteristic All Patients LF Non-LF P-value
N 3233 882 (27.3) 2351 (72.2)
Female 1823 508 (57.6) 1315 (55.9) NSMean Age (Median) 24.5 (24.0) 21.9 (20.0) 25.5 (25.0) <.0001*
Mean DSOI(Median) 8.96 (2377,7.0) 9.6 (723,8.0) 8.7 (1654,6.0) <.0001*
House Deaths 167(927) 36 (295,12.2) 131 (632,20.7) .0017
Contact with LF Case 770(2009) 145 (565,25.6) 625 (1444,43.3) <.0001
Ribavirin 454 (3218) 406 (871,46.6) 48 (2347,2.1) <.0001* Wilcoxon Rank Sum Test,
LF ResultsRisk Factors of Lassa Fever Mortality, 2008-2013
Non-Survivors were of significantly younger age (p=.0005)
Survivors significantly more likely to report household death or contact with LF case (p=.045, p<.0001)
Ribavirin significantly associated with mortality (p<.0001); most likely confounding factor and an indication of disease severity
Characteristic Total LF Non-Survivors Survivors P-valueN 856 271 (31.7) 585 (68.3)Female 495 (57.8) 146 (53.9) 349 (59.7) NS
Mean Age (Median) 21.7 (20.0) 18.7 (18.0) 23.1 (21.0) .0005*Mean DSOI(Median) 9.6 (704,8.0) 9.3 (230,8.0) 9.7 (474,7.0) NS*
House Deaths 36 (285) 3 (60,5.0) 33 (225,14.7) .045
Contact with LF Case 139 (549) 16 (141,11.4) 123 (408,30.2) <.0001
Ribavirin 405 (852) 156 (270,57.8) 249 (582,42.8) <.0001* Wilcoxon Rank Sum Test,
LF Results – Subgroup AnalysisChildren < 5 years of age vs. All Other Suspected LF Cases, 2008-2013
Children < 5 years were significantly more likely to be LF positive, receive Ribavirin treatment, and die from LF compared to all others
Children < 5 years were significantly more likely to have malaria
All others significantly more likely to report household death or contact with case; low sample size
Total Age<5 All Others P-value
N 3233 583 2650
LF Positive 882 (27.3) 198(34.0) 684(25.8) <.0001
LF Mortality (N=856) 271 (31.2) 83(193,43.0) 188(663,28.4) .0001
Ribavirin 454(3218,14.1) 107(582,18.4) 347(2636,13.2) .0011
Female 1823(56.4) 268(46.0) 1555(58.7) <.0001
Household Deaths 167(927,18.0) 7(95,7.4) 160(832,19.2) .0044
Contact with Case 770(2009,38.3) 76(309,24.6) 694(1700,40.8) <.0001
Mean DSOI 9.6(704,8.0) 8.0(392,7.0) 9.1(1985,7.0) NS
Malaria 152 (356,42.7) 57(87,65.5) 95(269,35.3) <.0001
• Among LF+, median DSOI for< 5years was 7.0 compared to 8.0
for all others (p=.065)
LF Results – Subgroup AnalysisPregnant vs. Non-pregnant females (14-49), 2008-2013
Total Pregnant Non-Pregnant P-value
N 345 162(47.0) 183(53.0) -
LF Positive 120(34.8) 63(38.9) 57(31.2) NS
LF Mortality 44(117,37.6) 32(61,52.5) 12(56(21.4) .0005
Ribavirin 71(343,20.7) 43(160,26.9) 28(15.3) .0083
Household Deaths 21(220,9.6) 5(68,7.4) 16(152,10.5) NS
Contact with Case 39(266,14.7) 10(107,9.4) 29(159,18.2) .04
Mean DSOI 8.4(278,6.0) 8.5(122,7.0) 8.4(156,6.0) NS
Malaria 41(117,35.0) 17(46,37.0) 24(71,33.8) NS
Pregnant women significantly more likely to receive Ribavirin treatment and die from LF
Non-pregnant women significantly more likely to report contact with case
Small sample size, so difficult to detect significance for other factors
LF Results – Subgroup AnalysisMalaria and LF Co-Infection, 2008-2013
Malaria testing results reported for 356 suspected LF cases
152 (42.7%) of suspected LF cases were Malaria positive
55/141 (39.0) of LF + patients were co-infected with malaria
Those who were co-infected were of significantly younger age compare to those who were only LF positive 7.0 years vs. 22.7 years (p=.0005)
The majority (41.8%) of co-infection cases occurred in children<5 years of age
No significant difference in mortality detected between co-infected and LF+ alone; low sample size
www.cdc.gov
Lassa Fever
p. falciparum Malaria
LF Dataset
Study Methods:• Retrospective
analysis of data collected from surveillance of LF in Sierra Leone
• Assess epidemiologic risk factors associated with disease and mortality
Summary of LF Results - Interpretation
LF prevalence significantly increased over the years and reported from new districts Could be due to improving detection, increasing transmission, or both
LF mortality significantly decreased over the years Earlier detection and improving clinical management may result in better
outcome
Ribavirin treatment significantly associated with mortalityThe most severe cases usually receive Ribavirin treatment
Ribavirin treatment probably a confounding factor, and an indicator of severe disease
Summary of LF Results – Interpretation and Hypothesis Generation
Summary of Results Young individuals, especially children < 5
years of age, were significantly more likely to be LF positive, to receive Ribavirin treatment, and to die from LF Early Detection and clinical care targeted
for LF infected young children may be critical to improving LF outcome
Pregnant women were significantly more likely to die for LF compared to non-pregnant counterparts
High prevalence of malaria co-infection, especially in younger age Impact of co-infections on LF outcome
needs to be further investigated
Hypothesis Development
• Young children have increased risk of LASV infection and severe LF
• Pregnant women have increased risk of severe LF and death
• Malaria exacerbates LASV infection and results in more severe LF outcome
ConclusionIf you have data, develop a step by step plan for analysis:
• Define objectives• Assess data quality• Develop collaborations • Develop study methods • Develop analysis plan
• Person, Place, Time
• Conduct analysis utilizing appropriate resources• Interpret Results• Present Results – Abstract, Presentation, or Manuscript• Develop Hypothesis for futures studies
Questions
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