Educational Epidemiology: Applying a Public Health Perspective to Issues and Challenges in Education

Transcription

1 Educational Epidemiology: Applying a Public Health Perspective to Issues and Challenges in Education Craig A. Mason, Ph.D. Acknowledgements: Shihfen Tu, Quansheng Song, Sriram Bhuvanagiri, Cecilia Cobo-Lewis Derek Chapman, Richard Urbano, and Keith Scott 1

2 Background Two key series of informatics initiatives leading to changes within Education Improved, longitudinal population-based data Data linkage with areas outside of education Create significant opportunities for researchers and policy makers interested in academic and long-term developmental outcomes in children Epidemiological and public health models 2

3 Statistician s Anonymous I m David, and I m a bean-counter 3

4 Overview Contrast epi / public health measures of effect with traditional behavioral measures of effect Review a pair of epi measures of effect Discuss application to developmental and/or educational research Briefly describe some methodological issues and developments Questions and discussion 4

5 Traditional Behavioral Research Focus on individual-level Not using the term as in multi-level modeling What is the effect of a risk factor on the level of some outcome for a person experiencing that risk factor Predicted GPA for a student who works 15 hours/week Valuable in identifying effective interventions targeting individuals Treatment for reducing behavior problems in adolescents Programs for improving math scores of high-risk students Clinical perspective 5

6 Public Health Perspective An alternative approach to the study of risk and health Epidemiology is the study of the distribution and determinants of disease in human populations Goal is to inform health professionals and the public at large in order for improvements in general health status to be made (Woodward, 1999) Policy decisions typically focus on impacting populations Highly effective in informing and shaping public policy on a broad range of topics 6

7 Applying Public Health to Ed Research How does a public health perspective differ from traditional educational/ developmental research? Focus on informing policy lead to inherently different measures of effect Examine rates and probabilities Categorical states or decisions Differentiate risk to individuals and communities Risk for individuals not have much impact on populations Informative with rare outcomes 7

8 Epidemiological Measures of Effect Not Ed Ed Total Smoke Not Smoke Total To illustrate, review two measures of effect Hypothetical data to for transparent calculations Random sample of 200 children Half exposed prenatally to cigarette smoke 30 of these children later receive special ed. services 8

9 Individual Level: Risk Ratio Not Ed Ed Total Smoke Not Smoke Total Risk Ratio is the increased likelihood or probability that an individual exposed to a risk factor will have an outcome, relative to the likelihood or probability of the outcome for an individual who does not experience the risk factor. 9

10 Calculation of Risk Ratio Not Ed Ed Total Smoke Not Smoke Total What is the likelihood have special education placement for a child whose mother smoked while pregnant? 10

11 Calculation of Risk Ratio Not Ed Ed Total p Smoke Not Smoke Total

12 Calculation of Risk Ratio Not Ed Ed Total p Smoke Not Smoke Total What is the probability of special education placement for a child whose mother did not smoke during pregnancy? 12

13 Calculation of Risk Ratio Not Ed Ed Total p Smoke Not Smoke Total The risk ratio is simply the probability observed among children whose mother s smoked, divided by the probability observed among children whose mother s did not smoke 13

14 Calculation of Risk Ratio Not Ed Ed Total p RR Smoke Not Smoke Total or 2.00 Indicates that children whose mother s smoked are twice as likely to receive special education services than are children whose mother s did not smoke. r 2 for this effect is

15 Developmental Epidemiology Inherently different than variance accounted for and means Consider autism as an example Predict males are more at risk for autism If gender had the strongest effect possible, what would we expect the data to look like? 15

16 Developmental Epidemiology Autistic Not Autistic Female Male Maximum correlation if all males are identified as having autistism, and all females are not However, only 1 in 100 are identified as having autism 16

17 Developmental Epidemiology Autistic Not Autistic Female Male If sample 1,000 2 individuals, half male, half female, and have maximum r. 01 effect, RR what would we see? 17

18 Community Level: Population Attributable Fraction (PAF) Not Ed Ed Total p RR Smoke Not Smoke Total RR indicate risk for an individual Does not indicate impact on community rates Rare risk factor with large RR may have minimal impact on population Common risk factor with small RR may have large impact on population 18

19 Calculation of PAF Not Ed Ed Total p RR Smoke Not Smoke Total The population attributable fraction assesses the percentage of cases of the outcome in the population that is related to the risk factor Mathematically, the percentage decrease in the outcome that would be observed if the probability or rate of the outcome among those exposed to the risk factor was equal to the probability or rate among those not exposed to the risk factor 19

20 Calculation of PAF Not Ed Ed Total p RR Smoke???? Not Smoke Total???? 200 The probability of special education placement among children whose mother s did not smoke was.10 What would happen if the probability of special education placement among children whose mother s smoked was also.10? 20

21 Calculation of PAF Not Ed Ed Total p RR Smoke???? Not Smoke Total???? 200 One hundred children were born to mothers who smoked, if the probability of special education placement among these children was.10, there would be 21

22 Calculation of PAF Not Ed Ed Total p RR Smoke 10?? Not Smoke Total 20?? youth receiving special education services 22

23 Calculation of PAF Not Ed Ed Total p RR Smoke 10?? Not Smoke Total 20?? 200 The total number of children does not change, consequently the number of children born to mothers who smoked who would not be receiving special education services would be the difference between 100 and 10, or.. 23

24 Calculation of PAF Not Ed Ed Total p RR Smoke Not Smoke Total Ninety children 24

25 The total number of children receiving special education services would now be 20 instead of 30. This reduction of 10 cases corresponds to 33.3% of the original 30 cases PAF=.333 or 33.3% Calculation of PAF Not Ed Ed Total p RR Smoke Not Smoke Total PAF p p e e RR RR

26 Application to Educational Data Value for informing policy What is an important risk factor for an individual may not be what is important for reducing rates across a community Estimating PAF requires an estimate of the prevalence of a risk factor in the population Possible with longitudinal population-based data PAF pe RR 1 p RR 1 e 1 26

27 Applying Public Health Models to Education / Developmental Research Public health measures of effect can prove valuable to inform education policy and research Particularly given longitudinal, population-based data But traditional developmental research also has much to offer public health epidemiology An area in which our research group is interested 27

28 Applying Developmental Models to Public Health Research How public health model of developmental outcomes differ from traditional epi research? Multiple state changes Traditional: Often predict one-time events (e.g., death) Developmental: Reoccurring events (e.g. arrests) Multi-causal processes in sequential models Traditional: Exposure predicting future disease/death Development: Complex causal sequences where early events (exposures) impact future events (exposures) 28

29 PAF With Temporally Sequenced Effects Obvious question of partialling Techniques exist for estimating a partial PAF Focus on adjusting the RR for other risk factors Address contemporaneous risk factors Can result in paradoxical or even impossible conclusions A A B C Not adjust for effect of a developmental sequence over time 29

30 PAF With Temporally Sequenced Effects Strategy for partitioning PAF that is analogous to partition variance Partition total PAF for a set of risk factors sequentially Allow one to examine direct and indirect effects of each risk factor on population rates Based on marginal probabilities weighted by the inverse of the RR between risk factors Number of very nice characteristics 30

31 That s all very nice, but.. Monty Python Life of Brian (and Martin White, UC Berkeley) All right. But apart from the sanitation, the medicine, education, wine, public order, irrigation, roads, the fresh water system, and public health What have the Romans ever done for us? --- Reg, spokesman for the People s Front of Judea 31

32 Smoking and Birth Weight Predicting EMH Smoking EMH Low Birth Weight 32

33 Smoking and Birth Weight Predicting EMH Smoking EMH Low Birth Weight Smoking total effect if 5.09% Smoking is related to 5.09% of cases of EMH If effect associated with smoking were eliminated, rates of EMH would decrease 5.09%* 33

34 Smoking and Birth Weight Predicting EMH Smoking EMH Low Birth Weight Indirect effect associated with smoking that occurs through increases in low birth weight is 1.43% 34

35 Smoking and Birth Weight Predicting EMH Smoking EMH Low Birth Weight Direct effect associated with smoking that is independent of low birth weight is 3.64% 35

36 Smoking and Birth Weight Predicting EMH Smoking 3.64% EMH 1.43% Low Birth Weight Balance reflects a small interaction between smoking and low birth weight Significant portion of the total effect passes through low birth weight 36

37 Smoking and Birth Weight Predicting EMH Smoking 3.64% EMH 1.43% Low Birth Weight Much of focus of the effect of smoking on developmental disabilities has been been based on belief the effect is largely due to smoking leading to increased risk for children being born low birth weight 37

38 Smoking and Birth Weight Predicting SED/EH Smoking SED/EH Low Birth Weight Smoking is related to 17.57% of cases of SED/EH Very large effect 38

39 Smoking and Birth Weight Predicting SED/EH Smoking SED/EH Low Birth Weight Indirect effect associated with smoking that occurs through increases in low birth weight is 1.06% 39

40 Smoking and Birth Weight Predicting SED/EH Smoking SED/EH Low Birth Weight Direct effect associated with smoking that is independent of low birth weight is 16.54% 40

41 Smoking and Birth Weight Predicting SED/EH Smoking 16.54% SED/EH 1.06% Low Birth Weight Nearly all impact on community rates is through processes other than low birth weight 41

42 Further Extension Smoking SED/EH Education Low Birth Weight PAF for direct effect of smoking upon SED/EH remain high even if control for mother s education Process behind this requires further research Soft neurological damage? Marker for future parenting problems? Epi can inform traditional lines of research 42

43 Examples emphasize capitalizing on population-based longitudinal data Also highlight the second change we have been seeing push to link education data with other child health and development data Newborn screening Early childhood education or childcare Early intervention services Autism surveillance Data Linkage 43

44 Record Linkage Newborn Screening Education Records ID First Last ID First Last John Smith John Smith Zbignew Brezinsky Zbignew Brezinski Deterministic: Records must match exactly to be linked Probabilistic: Records do not have to match across all fields in order to be linked 44

45 Probabilistic Matching Newborn Screening Education Records ID First Last ID First Last John Smith John Smith Zbignew Brezinsky Zbignew Brezinski For a possible pairing, a value is calculated that reflects the likelihood that the two records are (or are not) the same person Considers Quality of the data field Frequencies of specific values More open to further statistical analysis 45

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47 The Politics of Linkage Service Data: Craig A. Mason School Data: Craig A. Mason Two systems contain info on same individuals Would like to link data for public health research Schools can t see health service data, and vice-versa What solution may allow data to be linked, yet prevent contributors from seeing each other s identifying data Solution: Encrypt identifiers in both systems 47

48 Encrypted Linkage Key: Key: Service Data: Craig A. Mason School Data: Craig A. Mason Service Data: *Bj&!33t. School Data: yy#k66. School Data: Jf*72Coo. Service Data: Jf*72Coo. Linked Data: Jf*72Coo, Services, Grades 48

49 Summary This is an exciting time of opportunity for researchers interested in epidemiological research based on educational data A little ahead of the curve Methodological work still to do Clinical vs. public health perspective are both informative and valuable Just different approaches for different questions Public health models can help provide the correct answer to certain policy questions 49

50 Questions? Do you see application of PAF in your own research questions? Are there existing, archival data sets education or otherwise that may supplement your research?.and Thanks! 50