Matching an Internet Panel Sample of Health Care Personnel to a Probability Sample

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1 Matching an Internet Panel Sample of Health Care Personnel to a Probability Sample Charles DiSogra Abt SRBI Carla L Black CDC* Stacie M Greby CDC* John Sokolowski Abt SRBI K.P. Srinath Abt SRBI Xin Yue CDC* Andrew Burkey Abt SRBI Sarah W Ball Abt Assoc. Sara MA Donahue Abt Assoc. AAPOR May 16, 2015 Hollywood, FL Immunization Services Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention

2 Background Centers for Disease Control and Prevention (CDC) uses influenza vaccination coverage data to: Monitor impact of vaccination programs Identify groups in need of vaccination services Health Care Personnel (HCP) are a special population prioritized for vaccination Routine vaccination of HCP can help reduce influenza-related illness among HCP and in health care settings Abt SRBI pg 2

3 Surveying a rare group: health care personnel (HCP) Probability-based survey could be costly and time-consuming Low prevalence of HCP: <6% of U.S. adults Compromise: Use opt-in Internet panel and List Non-probability opt-in panel and list are fit for purpose Can access large numbers of HCP Large sample: approximately 2,000 completes per 2-week period Provide information quickly (all Web mode) Short 2-week data collection period in November and in April Are affordable for the task Non-probability sources used for sample (since 2010): 1. Opt-in Internet panel (Survey Sampling, Inc.) 2. Online health professions membership list (WebMD) Abt SRBI pg 3

4 Limitations of non-probability sample HCP are all panel/list member volunteers who use the Internet Due to Internet and/or multi-survey exposure, HCP in survey may be better informed than HCP in general population Bias in estimate is unknown; poses threat to generalizability Standard errors and confidence intervals cannot be used with nonprobability samples (AAPOR Task Force Report, June 2013) Abt SRBI pg 4

5 Study Objective Objective Method Explore ways to mitigate the shortcomings of non-probability sample Assess the bias Assign a measure of precision to the estimate Match cases from Spring 2013 non-probability HCP survey with HCP cases from a 2013 national probability survey Assumption Purposely matched opt-in sample can resemble a probability sample Variance of estimate approximates that from a probability sample thus a confidence interval may be derived Matched sample estimate may not be similar to the probability sample s estimate due to existence of inherent bias Abt SRBI pg 5

6 Panel/List bias is assumed Quantify bias in the estimate = Matched panel/list sample Probability sample Difference is the inherent bias in the panel/list sample Abt SRBI pg 6

7 The comparison probability sample 2013 National Health Interview Survey (NHIS) In-person survey of population weighted to population totals Data collection throughout the year Has some of the same descriptive variables as the Panel/List sample To best align with Panel/List sample, NHIS sample limited to people: 18+ years Interviewed February July 2013 Work or volunteer in a health care profession (i.e., not retired) Work in a hospital, long-term, or ambulatory/outpatient care facility Responded to questions about influenza vaccination status Abt SRBI pg 7

8 Matching variables Variables in both surveys and with differences in the distribution between the panel and NHIS (age, race, education) don t explain differences in vaccination coverage between the samples Variables used by other studies not available in both surveys, e.g., political party affiliation, smoking status One difference between the panel and NHIS is Internet use Literature suggests that opt-in panel members are frequent internet users. Frequent internet users have attitudinal and behavioral differences compared to the general population Abt SRBI pg 8

9 Internet use: observed difference A difference between the two samples: Panel/List: All Internet users (online respondents English only) NHIS: Internet users and non-users (in-person, general population) There may be other attitude, behavioral, socio-political differences Internet use can be defined for both samples Internet use was thought to be the most logical available measure to define coverage differences between the two samples Opt-in panelists have been described as more active Internet users Abt SRBI pg 9

10 Flu Vaccination rate by Internet use Vaccination Rate for Panel/List and NHIS Samples (weighted*) Panel/List NHIS Total Panel/List Total NHIS Non-daily Internet Daily Internet Vaccination Rate 95% Conf. Interval 72.0% 62.6% 52.6% 65.5% * Both samples weighted to identical benchmarks The Panel/List sample is missing non-internet users Daily Internet has a higher observed vaccination rate as does Panel/List Difference by Internet use suggests a reason why Panel rate is higher Abt SRBI pg 10

11 Matching strategy Internet Use Propensity Leverage NHIS Internet use questions for matching Develop a propensity score based on frequency of Internet usage Daily Internet Use vs. Less than daily/no Internet Use Abt SRBI pg 11

12 Internet user propensity 1,944 Panel/List cases used in analysis Panel/List: We assume 100% to be daily users (for our purpose) NHIS weighted: 77% daily users, 23% less than daily/non-users Combined data used in a logistic regression model predicting likelihood of a Less-than-Daily user (i.e., propensity) Abt SRBI pg 12

13 Internet user propensity An exploratory investigation pursued for the propensity model Nine variables evaluated for prediction of Less-than-Daily user Stepwise analysis found six as significant for use (i.e., p<.05 level) education race/ethnicity age group income work setting occupation p >.05 = Census region, gender, marital status Abt SRBI pg 13

14 Vaccination and Internet propensity score Vaccination Rate and Quintiles of Internet Use Propensity Vaccination rate 100% 90% 80% 70% 60% 50% 40% 30% 90% 83% Panel/List (72%) NHIS (63%) 76% 77% 73% 72% 67% 61% 54% 47% 20% 10% 0% 1st High Use 2nd 3rd 4th 5th Low Use Quintile Percents rounded to whole numbers Abt SRBI pg 14

15 Matching rate Matched cases from both samples on their propensity score value 96% of Panel/List cases matched to NHIS cases Source Matched Did Not Match Total Cases Count Percent Count Percent Count Panel/List 1,867 96% 77 4% 1,944 Abt SRBI pg 15

16 Ratio adjustment weights Problem Single NHIS case matched to multiple Panel/List cases Solution Multiple NHIS cases matched to a single Panel/List case Multiple NHIS cases matched to multiple Panel/List cases A ratio adjustment for each of the matched Panel/List cases Ratio Adj. Weight = (number of NHIS cases in a match) (number of Panel/List cases in same match) Example: When 3 NHIS cases match 5 Panel/List cases Ratio adjustment weight = 3/5 = 0.60 Abt SRBI pg 16

17 Ratio adjustment weights Ratio adjust 1,867 matched Panel/List cases 4.9% had ratio adj. wgt. =1.00 e.g., 1/1, 2/2, 3/3, etc. 86.0% had ratio adj. wgt. <1.00 e.g., 1/2, 1/3, 2/3, etc. 9.1% had ratio adj. wgt. >1.00 e.g., 2/1, 3/2, 4/3, etc. Sum of ratio adjusted weighted Panel/List cases equals the number of matched NHIS cases Abt SRBI pg 17

18 Population weights for HCP Base weights: Panel/List = 1.00 x the ratio adjustment weight NHIS = NHIS sample adult file design weight (weight prior to NHIS post stratification adjustment) Determine number of HCP in U.S. population: Post-stratification raking procedure using national HCP benchmark estimates for each occupation category Bureau of Labor Statistics Occupational Employment and Wage Estimates o o HCP occupations (10 categories) Health care work settings (Hospital, Long-term care, Ambulatory/Outpatient care) Current Population Survey o o Race/ethnicity (black non-hispanic, Hispanic, white and other non-hispanic) Gender (Male, Female) o Age (<35, 35-44, 45-54, 55-64, 65) o Census region (Northeast, Midwest, South, West) Abt SRBI pg 18

19 Overview of method No Match Not used Panel/List cases Matched ratio adjusted cases Population weighted 14.5 million HCP Internet usage propensity score Matched Compare NHIS cases SAME NUMBER NHIS cases Population weighted 14.5 million HCP No Match Abt SRBI pg 19

20 Results Source Original Panel/List Matched Panel/List NHIS Sample Influenza vaccination rate (%) Standard error 95% CI lower bound 95% CI upper bound 72.0 Precision (% pts) ± ±3.8 Estimated inherent bias = 8.8% No overlap tested as different Abt SRBI pg 20

21 Example: vaccination rate by occupation groups 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% NHIS Panel/List Matched Panel/List 0% MD-DDS NP/PA Nurses Allied Pharm Techs Aides Adm/Mgr Support Matching made estimates further away from NHIS estimates in most cases (5 out of 9) Abt SRBI pg 21

22 Examples: vaccination rates for two subgroups Race/Ethnicity Age Group NHIS Panel/List Matched Panel/List NHIS Panel/List Matched Panel/List 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Hispanic White NH Black NH Other NH 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Matching had different effects on subgroup estimates Abt SRBI pg 22

23 HCP matching procedure limitations NHIS HCP sample chosen as the available alternative reference for comparison/validation NHIS HCP sample is not a true comparable reference: HCP are a domain of a national, year-round sample of adults Design weights intended for a national cluster sample weighting scheme Not all HCP are included Variance estimations are not strictly based on NHIS sample design Results hinge on Internet usage propensity Propensity score matching restricted to available variables All NHIS and Internet panel/list results are self-report Possible mode effects between in-person NHIS and Internet panel/list Abt SRBI pg 23

24 HCP matching procedure conclusions Approximates a probability sample from a non-probability sample Approximates a confidence interval around the Panel/List s estimate (overall precision of ±4.3%) Provides non-arbitrary variance estimation for trend comparisons Given the limitations, inherent vaccination rate bias is estimated to be +8.8% for the Spring 2013 Panel/List Matching may provide information to enhance the design of future samples using Internet panels/lists Suggest over-recruit HCP who have less education, older, Hispanic or Black, or work as medical assistants/aides Abt SRBI pg 24

25 Matching an Internet Panel Sample of Health Care Personnel to a Probability Sample Thank You! Charles DiSogra c.disogra@srbi.com The findings and conclusions in this presentation are those of the authors and do not necessarily represent the views of Centers for Disease Control and Prevention. Abt SRBI pg 25

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