secondary data talk 2010
TRANSCRIPT
Publicly Available Secondary Data Sources: An Overview and an Example from Two Data Sources
Marion R Sills, MD, MPH
Department of Pediatrics, University of Colorado School of Medicine
GoalsHow do I find secondary data sets?
Once I find one, how do I know it’s right for me and my research question?
Example of a secondary data analysis
GoalsHow do I find secondary data sets?
Once I find one, how do I know it’s right for me and my research question?
Example of a secondary data analysis
Health Data OnlineAgency for Healthcare Research and Quality (AHRQ)
CDC WONDER
National Center for Health Statistics (NCHS)
Partners in Information Access for the Public Health Workforce
GoalsHow do I find secondary data sets?
Once I find one, how do I know it’s right for me and my research question?
Example of a secondary data analysis
GoalsOnce I find one, how do I know it’s right for me and my research question?
What types of questions was it designed to answer?
What data elements are available?How can I figure out if those data
elements are useful to me?
Two ExamplesHCUP (KID) used for background statement in a manuscript
NHAMCS and NHANES used for a full analysis for a manuscript
HCUP--KIDAn all-payer inpatient care database for children in the United States
2006 KID contains data from 6.6 million pediatric hospital discharges
Online data available via HCUPnet
HCUP--KIDQuestion: What is the utilization of inpatient resources for asthma among children?
Use: A background/significance statement for a grant
NHAMCS/NHANES Analysis Example
Questions:• What are pediatric norms for the shock
index (SI)? • Do these predict shock?
Use: Manuscript(s)
Shock Index (SI)
Triage tool
Monitoring tool
No established pediatric normal values
Heart rate (HR)
Systolic Blood Pressure (SBP)SI =
BackgroundElevated SI (> 0.90 adults)
Blood loss, admissions, ICU interventions, poor outcome
Inverse relationship with LV function Only 1 pediatric study of SI
Positive association with mortality Reduction in SI during transport was associated with improved
outcome
Initial ObjectiveTo evaluate the utility of shock index in an emergency department population of children
Utility as an early predictor of patient deterioration when measured
• Pre-hospital• At triage• Sequentially
(Modified) ObjectiveTo evaluate shock index as a predictor for admission in an emergency department population of children
SI evaluated independent of HR and SBP
Methods: Data SourcesHealthy Population
National Health and Nutrition Examination Survey (NHANES) 1999-2006
Emergency Department Population National Hospital Ambulatory Medical Care Survey (NHAMCS
ED) 2004-2006
Methods: Data sources
NHANES population Generate norms
NHAMCS ED Population
Address study question
Methods: Data sources
NHANES population Generate norms
NHAMCS ED Population
Address study question
No BP in < 8 yr Age limited to 8-21 yr
Methods: Data sources
Healthy population Generate norms
ED Population
Address study question
SI Norms Study: Data SourcesPediatric Age specific normal values
Calculate age- and gender-specific percentiles
Test of fit of logarithmic trend linesAll-ages population age- and gender
median values Calculate percentiles by age,
gender, and pregnancy status
SI Norms Study: ResultsNHANES 10,195 patients age 8-17 (41,048,417 weighted)
NHANES 32,819 age 8-85 (251,845,769 weighted)
Results: SI Percentiles in the NHANES Population
[n =13,308 (57.2 million, weighted)]
0.5
0.6
0.7
0.8
0.9
1
1.1
8 9 10 11 12 13 14 15 16 17
Age (y)
Sh
ock
In
de
x
25 %ile
50 %ile
95 %ile
75 %ile
Figure 3: Shock Index Median Value by Gender and Pregnancy Status, NHANES 1999-2006 Weighted Data, With Moving Average
Trendlines (3-Period)
.45
.50
.55
.60
.65
.70
.75
.80
.85
.90
8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84
Age (y)
Sh
oc
k In
de
x
Male
Non-pregnant female
Pregnant
3 per. Mov. Avg. (Non-pregnant female)3 per. Mov. Avg. (Male)
3 per. Mov. Avg.(Pregnant)
SI Norms Study: ConclusionsFirst report of pediatric age-specific normal values for SIFirst report of age and gender SI medians in an all-ages
population Gender, pregnancy and age contribute to SISmooth percentile trends for SI are best expressed as a
logarithmic function
Methods: Data sources
Healthy population Generate norms
ED Population
Address study question
Search for outcome measures
Candidate measures of “shock” Unweighted n, NHAMCS 1999-2006
Traumatic shock (958.4) 0
Non-trauma shock (785.5) 1
Anaphylactic shock (995.0, 995.6) 5
ICU admit 13
Died 9
CPR 6
Admit 848
Methods: Data sources
NHANESpopulation Generate norms
NHAMCS ED Population
Address study question
Age limited to 8-21 yrOutcome: admission
Methods: AnalysisLogistic regression was used to model the association between predictor variables and admission
Primary predictor • SI > 95th %• SI > 0.9
Methods: AnalysisCut-point for percentiles
Based on frequency distribution in the emergency department population
• 95th % for SI and HR• 25th % for SBP
Absolute cut-point of SI > 0.9 was based on adult literature
Methods: Logistic Regression
Model #1 #2
Outcome Admission Admission
1º independent variable SI > 95th % SI > 0.9
Methods: Logistic Regression
Model #1 #2
Outcome Admission Admission
1º independent variable SI > 95th % SI > 0.9
Other independent variables HR > 95th %
SBP < 25th %
Age, Gender, Race, Ethnicity, Payer
Results: ED populationNHAMCS ED Population
18,147 ED visits = 58.9 million visits, weightedPatients age 8-21 years 4 % were admitted
Variable Cut-Point Proportion
SI > 95th % 14%
SI > 0.9 19%
HR > 95th % 29%
SBP < 25th % 6%
SI > 95th % with normal HR, SBP < 1%
Results: ED population
Results: BivariateIn bivariate chi-square analyses, SI was associated with admission (p < 0.0001)SI > 95th %SI > 0.9
Results: Bivariate Analyses
Percent Admitted by SI Cutoff
0%
2%
4%
6%
8%
10%
SI > 95th % SI < 95th % SI > 0.9 SI < 0.9
Pe
rce
nt A
dm
itte
d
Results: Bivariate Analyses
Percent Admitted by SI Cutoff
0%
2%
4%
6%
8%
10%
SI > 95th % SI < 95th % SI > 0.9 SI < 0.9
Pe
rce
nt A
dm
itte
d
OR = 2.97
p < .0001
OR = 2.63
p < .0001
Model 1: Shock Index > 95th % for Age and Gender: Outcome = Admission
OR 95% CI
SI > 95th % 1.54 1.14 2.08
HR > 95th % 2.51 1.96 3.21
SBP < 25th % 1.24 0.87 1.77
Age, gender, race, ethnicity, and payer were not significant
Results: Multivariate Analysis
Model 2: Shock Index > 0.9: Outcome = Admission
OR 95% CI
Shock Index > 0.9 1.50 1.15 1.94
HR > 95th % 2.50 2.00 3.12
SBP < 25th % 1.27 0.90 1.79
Age 1.04 1.01 1.07
Results: Multivariate Analysis
Gender, race, ethnicity, and payer were not significant
LimitationsNo children under 8 years evaluated
Insufficient numbers Abnormal SI with normal HR and SBP “Shock” as outcome
Admission based on provider and patient
No ability to assess unscheduled return visits
ConclusionsShock index predicted hospital admission, independent of the impact of HR and SBP
Expressed as percentile or absolute value