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1 Supplementary Online Content 1 Sullivan K, Capp G, Gilreath TD, Benbenishty R, Roziner I, Astor RA. Substance abuse and other adverse outcomes for military-connected youth in California: results from a large-scale normative population survey. JAMA Pediatr. Published online August 17, doi: /jamapediatrics eappendix 1. California Healthy Kids Survey Measures of Lifetime and Recent Substance Use, Physical Victimization, Nonphysical Victimization, and Weapon Carrying eappendix 2. Sensitivity Analysis for Unmeasured Confounding Effects of Socioeconomic Status ereferences etable 1. Odds Ratios for Lifetime Substance Use etable 2. Odds Ratios for Recent Substance Use etable 3. Odds Ratios for Physical Victimization etable 4. Odds Ratios for Nonphysical Victimization and Weapon Carrying This supplementary material has been provided by the authors to give readers additional information about their work.

2 2 eappendix 1. California Healthy Kids Survey Measures of Lifetime and Recent Substance Use, Physical Victimization, Nonphysical Victimization, and Weapon Carrying Number of Times During your life, how many times have you used the following substances? or more A whole cigarette A B C D E F One full drink of alcohol (such as a can of beer, glass of wine, wine cooler or shot of A B C D E F liquor) Marijuana (pot, weed, grass, hash, bud) A B C D E F Inhalants (things you sniff, huff or breathe to get high, glue, paint, aerosol sprays, gasoline, poppers, gases) A B C D E F Cocaine, Methamphetamine, or any amphetamines (meth, speed, crystal, crank, ice) A B C D E F Ecstasy, LSD or other psychedelics (acid, mescaline, peyote, mushrooms) A B C D E F Prescription pain killers, tranquilizers or sedatives A B C D E F Ritalin or Adderall (JIF, R-ball, Skippy) or other prescription stimulant A B C D E F Cough or cold medicines or other over-the-counter medicines A B C D E F During the past 30 days, on how many days did you use Number of Days Cigarettes A B C D E F At least one drink of alcohol A B C D E F Marijuana (pot, weed, grass, hash, bud) A B C D E F Inhalants (things you sniff, huff or breathe to get high) A B C D E F Cocaine, methamphetamine, or any amphetamines (meth, speed, crystal, crank, ice) A B C D E F Ecstasy, LSD or other psychedelics (acid, mescaline, peyote, mushrooms) A B C D E F During the past 12 months, how many times on school property have you Happened on School Property or 3 4 or more Been pushed, shoved, slapped, hit, or kicked by someone who wasn t just kidding A B C D around Been afraid of being beaten up A B C D Been in a physical fight A B C D Had mean rumors or lies spread about you A B C D Had sexual jokes, comments or gestures made to you A B C D Been made fun of because of your looks or the way you talk A B C D Had your property stolen or deliberately damaged, such as your car, clothing or books A B C D Had mean rumors or lies spread about you on the internet A B C D Seen someone carrying a gun, knife, or other weapon A B C D Been threatened or injured with a weapon (gun, knife, club, etc.) A B C D Carried a gun A B C D Carried any other weapon (such as a knife or club) A B C D

3 eappendix 2. Sensitivity Analysis for Unmeasured Confounding Effects of Socioeconomic Status 3 Generally, for SES to be a confounder, two conditions must occur: (1) the independent variable (military connectedness) must be related to the potentially confounding variable (SES) and (2) the possible confounder (SES) must be related to the outcome (substance use, victimization, or weapon-carrying). Available data suggest that in our current all-volunteer force, military families are characterized by similar or higher SES than the general civilian population. 1,2 These data suggest that, if anything, our results may be an underestimation of the actual relationship between military-connectedness and our outcomes. Further, empirical findings on the relationship between SES and our outcomes have been mixed. 3 Taken together, this literature suggests there is little reason to be concerned about the potential for unmeasured confounding of SES, because at least one and possibly both of the specified conditions have not been met in this case. Nevertheless, to measure the possible boundaries of confounding by unmeasured SES, we conducted two separate analyses. First, although we do not have student-level SES data in our dataset, we have an SES proxy (the proportion of students in a school eligible for free or reduced-price meals) at the school level. Though we are uncomfortable including this variable in our analyses, because its unit of measurement is different than the other variables in our model, we examined whether there is a relationship between the proportion of military-connected students and the proportion of students receiving free or reduced-price meals at a given school. We found a small and negative correlation indicating that a greater proportion of higher SES students is related to a larger proportion of military-connected children. In addition to this analysis, we also conducted the requested sensitivity analysis. We followed two methods described by Schneeweiss 4 to guide our calculations. To conduct these analyses we needed to estimate or extract values from our data. Here I detail the steps we took to accurately estimate each of these values: 1. The prevalence of the confounder (Pc): Following Reed, Bell, and Edwards, 5 we used maternal educational attainment as a proxy for SES. However, we chose to use national prevalence estimates rather than the prevalence in the Reed et al. article. 5 Using census data, 6 we estimated the prevalence of the confounder at The prevalence of the exposure (Pe): We used the prevalence of military-connectedness in our data, which is approximately The apparent relative risk (ARR): For the purposes of these analyses, we used the two extreme odds ratio values in our data: 1.28 (the odds of being made fun of for one s looks) and 2.20 (the odds of carrying a gun). 4. The relationship between the exposure (military connectedness) and the confounder (SES) (ORec): Although the exact odds ratio was not available in the Reed et al. article, 5 we were able to calculate an approximate odds ratio based on the data they present and the prevalence of military and deployed parents nationally. By our calculations, this odds ratio is We used several values around this number, ranging from 0.80 to The relationship between the confounder (SES) and the outcomes (RRcd): There were several possible sources of odds ratios to estimate this value. To be conservative, we used larger estimates from the literature. Using these sources, we have estimated the odds ratio for the relationship between SES and weapon carrying to be 1.48, 7 for the relationship between SES and various substance use outcomes to be between 1.25 and 1.46, 8 and for the relationship between SES and victimization to be We used several values around these estimates, ranging from 0.90 to Using the rule-out approach specified by Schneeweiss, 4 we explored which values of ORec and RRcd would be needed to fully explain the odds ratio measuring the relationship between exposure and outcome. We repeated this procedure for two extreme odds ratio values in our data (i.e., 1.28 and 2.20). The results showed that plausible values of the RRcd would need to be paired with implausible (negative and very small, e.g., 0.18, or very large, e.g., 3.26) values of ORec, or vice versa, to nullify our results. Using the external adjust approach, we estimated the extent to which our odds ratio estimates could be biased under different assumptions of the RRcd and ORec values. Under the relatively plausible assumptions previously specified, our outcomes may be biased by -2.16% to +3.83%. In other words, for our lowest odds ratio, 1.28, which measures the relationship between military connectedness and being made fun of for one s looks, the actual estimate could fall between approximately 1.24 and 1.30.

4 4 ereferences 1. Hosek J, Wadsworth SM. Economic conditions of military families. Future Child. 2013;23(3): U.S. Department of Defense. Who is volunteering for today s military? myths versus facts. Accessed April 26, Hanson MD, Chen, E. Socioeconomic status and health behaviors in adolescence: a review of the literature. J Behav Med. 2007;30(3): Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 2006;15(5): Reed SC, Bell JF, Edwards TC. Adolescent well-being in Washington state military families. Am J Public Health. 2011;101(9): U.S. Census Bureau. Educational attainment in the United States: Accessed April 26, Kodjo CM, Auinger P, Ryan SA. Demographic, intrinsic, and extrinsic factors associated with weapon carrying at school. Arch Pediatr Adolesc Med. 2003;157(1): Humensky JL. Are adolescents with high socioeconomic status more likely to engage in alcohol and illicit drug use in early adulthood? Subst Abuse Treat Prev Policy. 2010;5: Jansen PW, Verlinden M, Dommisse-van Berkel A, Mieloo C, van der Ende J, Veenstra R, et al. Prevalence of bullying and victimization among children in early elementary school: do family and school neighbourhood

5 5 etable 1. Odds Ratios for Lifetime Substance Use Cigarettes Alcohol Marijuana Other Drugs Prescription a OR 95% OR 95% OR 95% OR 95% OR 95% Boys (vs. girls) 1.36* * * * Grade (vs. seventh grade) Ninth grade 3.55* * * * * th grade 6.46* * * * * Nontraditional 20.1* * * * * Race (vs. white) Black * * * * Latino 1.31* * * * * Other 0.63* * * * * Mixed 1.16* * * * * Military (vs. nonmilitary) 1.53* * * * * a Only lifetime-not recent-use of prescription drugs assessed by the California Healthy Kids Survey. *95% indicates significant odds ratio.

6 etable 2. Odds Ratios for Recent Substance Use a Cigarettes Alcohol Marijuana Other Drugs OR 95% OR 95% OR 95% OR 95% Boys (vs. girls) 1.45* * * * Grade (vs. seventh grade) Ninth grade 2.01* * * * th grade 3.10* * * * Nontraditional 11.5* * * * Race (vs. white) Black 1.12* * * * Latino 1.12* * * * Other 0.64* * * * Mixed 1.07* * * Military (vs. nonmilitary) 1.59* * * * a Only lifetime-not recent-use of prescription drugs assessed by the California Healthy Kids Survey. *95% indicates significant odds ratio. 6

7 etable 3. Odds Ratios for Physical Victimization Pushedslapped-hitkicked Fear of being beaten up Been in fight OR 95% OR 95% OR 95% Boys (vs. girls) 1.49* Grade (vs. seventh grade) Ninth grade 0.54* th grade 0.33* Nontraditional 0.44* Race (vs. white) Black 1.08* Latino 1.10* Other 0.93* Mixed 1.16* Military (vs. 1.42* nonmilitary) 1.45 * 95% indicates significant odds ratio * * * * * * * * * * * * * * * * Property damaged Threatened with weapon Seen weapon OR 95% OR 95% OR 95% 1.23* * * * * * * * * * * * * * * * * * * * * * * * * *

8 etable 4. Odds Ratios for Nonphysical Victimization and Weapon Carrying Rumors spread Sexual jokesgestures Made fun of for looks Cyber victimization Carry gun on campus OR 95% Boys (vs. girls) 0.57* Grade (vs. seventh grade) Ninth grade 0.74* th grade 0.64* Nontraditional 0.74* Race (vs. white) Black 0.86* Latino 0.87* Other 0.80* Mixed 1.05* Military (vs. 1.34* nonmilitary) 1.37 * 95% indicates significant odds ratio. OR 95% 0.63* * * * * * OR 95% 0.74* * * * * * * * Carry knife or other weapon on campus OR 95% OR 95% OR 95% 0.58* * * * * * * * * * * * * * * * * * * * * * *