AJPH EDITORIALS
young people need treatment, but that figure is six times greater than the number of available treatment slots.7 Therefore, there is a concomitant need to recruit and train more substance use treatment and mental health professionals, arm them with evidenced-based assessments and interventions, and develop opportunities for them to work with youths, families, and interdisciplinary teams of other professionals and service providers. Ideally, we also need to develop primary upstream interventions aimed at improving social determinants of health and reducing the number and severity of adverse childhood experiences or ACEs that may contribute to SUDs and delinquency, while simultaneously addressing factors contributing to the hyperincarceration of minority youths. In the interim, youths presenting at the various doorsteps of the justice system are in need of, and deserve, a second chance to turn their lives around. Clearly, this will be expensive. However, the estimated annual costs to society attributed to
substance-using, justice-involved youths is between 27.5 and 42.2 billion dollars.8 These include costs for law enforcement, detention, probation, and providing services for victims of juvenile felonies. And, just as clearly, the current standard of care is failing young men like Ricky. Conversely, it is estimated that if all substance-using juvenile and adult offenders were provided evidence-based treatment services during confinement and upon release back into the community, the annual costs may be upward of 12.6 billion dollars.3 While this represents a significant investment in resources, we would recoup our costs if just 11% of these individuals remained substance-free, out of jail or prison, and gainfully employed for one year. Further benefits to society accrue if these patterns continue over time—at levels reaching more than $90 000 per inmate per year with respect to savings in decreased crime, lower incarceration rates, and improved health coupled with the benefits of employment.3 At the Incarceration and Public Health Action Network—a coalition composed of schools of
public health and other academic institutions concerned about the impact of mass incarceration on the health of our communities—we look forward to working with community advocates, governmental and private funders, and others in the judicial, correctional, legal, educational, vocational, and health sectors to develop creative solutions designed to enhance health outcomes for individuals involved with the justice system. Since his referral to our program, Ricky’s viral load is undetectable, his substance use is reduced, he is employed, and he is going to college. But it took getting HIV for him to get the type of coordinated, integrated services needed to stabilize his life. We can, and must, do better. Alwyn T. Cohall, MD ACKNOWLEDGMENTS The Incarceration and Public Health Action Network (IPHAN) is grateful for support from the Ford Foundation, in addition to the Tow Foundation.
REFERENCES 1. Welty LJ, Harrison AJ, Abram KM, et al. Health disparities in drug- and
A Public Health of Consequence: Review of the June 2016 Issue of AJPH This month, in our invited editorial, Westreich et al.1 provide an important illustration of the implications that a public health of consequence lens has for causal thinking in the population health sciences. Centrally, Westreich et al. make two points. First, they note that thinking about the consequences of our work for population health elevates the importance of external validity. Simply put, our work is unlikely to be consequential if we cannot use it
June 2016, Vol 106, No. 6
AJPH
to guide inference about the populations whose health we aim to improve. This point may seem simple at face value, but it both challenges conventional wisdom in the field2 and upends our currently established hierarchy of study designs. As Westreich et al. and others continue to note, randomized clinical trials (RCTs) depend on stringent inclusion criteria, aimed at improving these studies’ internal validity. This, however, frequently limits the extent to which RCTs
are representative of broader populations, limiting the inferences we can extend from the findings of these studies to these same populations. This observation is readily
alcohol-use disorders: a 12-year longitudinal study of youths after detention. Am J Public Health. 2016;106(5):872–880. 2. Center for Behavioral Health Statistics and Quality. Behavioral Health Trends in the United States: Results From the 2014 National Survey on Drug Use and Health. HHS Publication No. SMA 15–4927, NSDUH Series H-50. 2015. Available at: http://www. samhsa.gov/data. Accessed January 5, 2016. 3. Behind the Bars: Substance Abuse and America’s Prison Population. New York, NY: The National Center on Addiction and Substance Abuse (CASA) at Columbia University; 2010. 4. Teplin LA, Elkington KS, McClelland GM, Abram KM, Mericle AA, Washburn JJ. Major mental disorders, substance use disorder, comorbidity and HIV-AIDS risk behaviors in juvenile detainees. Psychiatr Serv. 2005;56(7):823–828. 5. Grisso T. Adolescent offenders with mental disorders. Future Child. 2008;18(2):143–164. 6. Young DW, Dembo R, Henderson CE. A national survey of substance abuse treatment for juvenile offenders. J Subst Abuse Treat. 2007;32(3):255–266. 7. Nissen LB, Butts JA, Merrigan DM, and Kraft MK. The Reclaiming Futures Initiative: Improving Substance Abuse Interventions for Justice-Involved Youth. A Reclaiming Futures National Program Report. Portland, OR: Reclaiming Futures National Program Office, Portland State University; 2007. 8. Shoveling Up. The Impact of Substance Abuse on State Budgets. New York, NY: The National Center on Addiction and Substance Abuse (CASA) at Columbia University; 2001.
borne out by the abundant examples of RCT results3 coming in conflict with findings from equally well-done observational studies.4 This challenges our notion that RCTs represent a gold standard. While they might represent an outstanding study design to improve internal validity, they are less well suited to external validity and
ABOUT THE AUTHORS Sandro Galea is Dean and Professor, School of Public Health, Boston University, Boston, MA. Roger Vaughan is an AJPH editor, and is also the Vice Dean and Professor of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY. Correspondence should be sent to Roger Vaughan, Vice Dean and Professor of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032 (e-mail: [email protected]). Reprints can be ordered at http://www. ajph.org by clicking the “Reprints” link. This editorial was accepted April 7, 2016. doi: 10.2105/AJPH.2016.303230
Galea and Vaughan
Editorial
973
AJPH EDITORIALS
generalizable inference and may well be less informative for the goals of a public health of consequence. This is not intended to obviate the role of RCTs in population health research. It does, however, serve as a sobering reminder of the balance we must strike between internal validity and external validity concerns, and of how a public health of consequence lens helps us evaluate the scholarship that is of highest priority, toward the goal of informing efforts that aim to improve the health of populations. Second, Westreich et al. make the case to treat population intervention effects as a means to making population health science findings more accessible.5 This provides an equally important reminder of the need for accessible analytic representations that help us realize the promise of translation of population health scholarship. With Westreich et al’s words as framework, we highlight two articles in this month’s AJPH that address the two central points made in this framework; first we discuss an article that uses a populationbased sample to conduct informative science that can guide population-level intervention, and second, an article that presents interesting modeling data, accessibly, in a way that can plausibly inform, inflect, and influence an important public conversation. First, Keyes et al.6 demonstrate a link between early adolescent use of tobacco and marijuana and cocaine use by 12th grade. They suggest that each percentagepoint decrease in the prevalence of smoking in the 8th and 10th grade is associated with an 8% decrease in the prevalence of later marijuana use and a 14% to 23% decrease in later cocaine use. The Monitoring the Future data are nationally representative data, and the sheer number of people to whom this applies puts the
974
Editorial
Galea and Vaughan
importance of these findings in context. Nearly 10 million adolescents will have tried marijuana by the 12th grade, and one million will have tried cocaine. Importantly therefore, this article suggests a logical way forward for any effort by public health to reduce marijuana and cocaine use by millions later in life. As the authors note, [P]ublic health campaigns to reduce the burden of drug use among adolescents should focus on the early stages of adolescence when drug use habits are forming, and that prevention of cigarette smoking, and use of tobacco products more generally, may be a crucial component of a public health strategy.6(p1148)
This article provides us with an elegant example of how a population-based sample can illuminate the tremendous potential of interventions that target ubiquitous factors (like early adolescent smoking) toward the creation of healthier populations. It seems to us that the logical next step in this thinking would be inquiry about the most effective population-based approaches that successfully reduce smoking in these target populations, and that do so without widening intergroup differences and introducing health inequities. As a start, Cobb et al.7 present early evidence of the potential utility of a Facebook-based smoking cessation intervention, showing that those initially enrolled in an online smoking cessation program can act as viral agents and enroll others to participate at no additional cost. Whether this is effective in reducing smoking among adolescents remains to be seen. Second, Tsao et al.8 make an excellent contribution to the literature through an article that models the potential impact of
a $15 per hour minimum wage on preventable mortality in the New York City population. Their analyses suggest that this minimum wage could have prevented 2800 to 5500 premature deaths in New York City, principally among populations of color living in low-income neighborhoods. Any such modeling exercise must always be approached with caution, recognizing that findings from this work are only as robust as the assumptions that were used to inform and parametrize the model; to this end Tsao et al. do an admirable job of presenting, comprehensively, the limitations to their work. However, notwithstanding these limitations, their presentation of these findings, as the authors note, “adds to a growing body of work by health departments to resurrect the centrality of minimum wages to population health.”8(p1039) Importantly, it both does so and presents important data that are accessible to the nonspecialist and can contribute meaningfully to the public discussion about the minimum wage. As this article was going to press, Governor Cuomo had proposed a $15 per hour minimum wage in New York State.9 Further analyses demonstrating how this may influence premature death in the whole state would be welcome. It is in part the premise behind this monthly commentary that the lenses we adopt to inform our work are important: they shape how we think, the questions we ask, the studies we design, and how we analyze our data. An approach to population health science that prioritizes the potential public health consequences of our work will definitionally ask questions that matter to populations, draw inference that can inform how we might intervene to improve these
populations’ health, and present results in such a way as to inform those who can make change happen, locally and globally. These articles in this month’s issue provide a compelling illustration of these principles. Sandro Galea, MD, DrPH Roger Vaughan, DrPH, MS CONTRIBUTORS Both authors contributed equally to this editorial.
REFERENCES 1. Westreich D, Edwards JK, Rogawski ET, Hudgens MG, Stuart EA, Cole SR. Causal impact: epidemiologic approaches for a public health of consequence. Am J Public Health. 2016;106(6):1011–1012. 2. Rothman KJ, Gallacher JEJ, Hatch EE. Why representativeness should be avoided. Int J Epidemiol. 2013;42: 1012–1014. 3. DeKosky ST, Williamson JD, Fitzpatrick AL, et al.; Ginkgo Evaluation of Memory (GEM) Study Investigators. Ginkgo biloba for prevention of dementia: a randomized controlled trial. JAMA. 2008;300(19):2253–2262. 4. Andrieu S, Gillette S, Amouyal K, et al. Association of Alzheimer’s disease onset with ginkgo biloba and other symptomatic cognitive treatments in a population of women aged 75 years and older from the EPIDOS study. J Gerontol A Biol Sci Med Sci. 2003;58(4):M372–M377. 5. Ahern J, Hubbard A, Galea S. Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. Am J Epidemiol. 2009;169(9):1440–1147. 6. Keyes K, Hamilton A, Kandel DB. Birth cohorts analysis of adolescent cigarette smoking and subsequent marijuana and cocaine use. Am J Public Health. 2016; 106(6):1143–1149. 7. Cobb NK, Jacobs MA, Wileyto P, Valente T, Graham AL. Diffusion of an evidence-based smoking cessation intervention through Facebook: a randomized controlled trial. Am J Public Health. 2016;106(6):1099–1100. 8. Tsao TY, Konty KJ, Van Wye G, et al. Estimating potential reductions in premature mortality in New York City from raising the minimum wage to $15. Am J Public Health. 2016;106(6):1036–1041. 9. New York Gov. Cuomo signs $15 minimum wage law. Associated Press. April 4, 2016. Available at: http://pix11. com/2016/04/04/new-york-govcuomo-signs-minimum-wage-law. Accessed April 11, 2016.
AJPH
June 2016, Vol 106, No. 6
young people need treatment, but that figure is six times greater than the number of available treatment slots.7 Therefore, there is a concomitant need to recruit and train more substance use treatment and mental health professionals, arm them with evidenced-based assessments and interventions, and develop opportunities for them to work with youths, families, and interdisciplinary teams of other professionals and service providers. Ideally, we also need to develop primary upstream interventions aimed at improving social determinants of health and reducing the number and severity of adverse childhood experiences or ACEs that may contribute to SUDs and delinquency, while simultaneously addressing factors contributing to the hyperincarceration of minority youths. In the interim, youths presenting at the various doorsteps of the justice system are in need of, and deserve, a second chance to turn their lives around. Clearly, this will be expensive. However, the estimated annual costs to society attributed to
substance-using, justice-involved youths is between 27.5 and 42.2 billion dollars.8 These include costs for law enforcement, detention, probation, and providing services for victims of juvenile felonies. And, just as clearly, the current standard of care is failing young men like Ricky. Conversely, it is estimated that if all substance-using juvenile and adult offenders were provided evidence-based treatment services during confinement and upon release back into the community, the annual costs may be upward of 12.6 billion dollars.3 While this represents a significant investment in resources, we would recoup our costs if just 11% of these individuals remained substance-free, out of jail or prison, and gainfully employed for one year. Further benefits to society accrue if these patterns continue over time—at levels reaching more than $90 000 per inmate per year with respect to savings in decreased crime, lower incarceration rates, and improved health coupled with the benefits of employment.3 At the Incarceration and Public Health Action Network—a coalition composed of schools of
public health and other academic institutions concerned about the impact of mass incarceration on the health of our communities—we look forward to working with community advocates, governmental and private funders, and others in the judicial, correctional, legal, educational, vocational, and health sectors to develop creative solutions designed to enhance health outcomes for individuals involved with the justice system. Since his referral to our program, Ricky’s viral load is undetectable, his substance use is reduced, he is employed, and he is going to college. But it took getting HIV for him to get the type of coordinated, integrated services needed to stabilize his life. We can, and must, do better. Alwyn T. Cohall, MD ACKNOWLEDGMENTS The Incarceration and Public Health Action Network (IPHAN) is grateful for support from the Ford Foundation, in addition to the Tow Foundation.
REFERENCES 1. Welty LJ, Harrison AJ, Abram KM, et al. Health disparities in drug- and
A Public Health of Consequence: Review of the June 2016 Issue of AJPH This month, in our invited editorial, Westreich et al.1 provide an important illustration of the implications that a public health of consequence lens has for causal thinking in the population health sciences. Centrally, Westreich et al. make two points. First, they note that thinking about the consequences of our work for population health elevates the importance of external validity. Simply put, our work is unlikely to be consequential if we cannot use it
June 2016, Vol 106, No. 6
AJPH
to guide inference about the populations whose health we aim to improve. This point may seem simple at face value, but it both challenges conventional wisdom in the field2 and upends our currently established hierarchy of study designs. As Westreich et al. and others continue to note, randomized clinical trials (RCTs) depend on stringent inclusion criteria, aimed at improving these studies’ internal validity. This, however, frequently limits the extent to which RCTs
are representative of broader populations, limiting the inferences we can extend from the findings of these studies to these same populations. This observation is readily
alcohol-use disorders: a 12-year longitudinal study of youths after detention. Am J Public Health. 2016;106(5):872–880. 2. Center for Behavioral Health Statistics and Quality. Behavioral Health Trends in the United States: Results From the 2014 National Survey on Drug Use and Health. HHS Publication No. SMA 15–4927, NSDUH Series H-50. 2015. Available at: http://www. samhsa.gov/data. Accessed January 5, 2016. 3. Behind the Bars: Substance Abuse and America’s Prison Population. New York, NY: The National Center on Addiction and Substance Abuse (CASA) at Columbia University; 2010. 4. Teplin LA, Elkington KS, McClelland GM, Abram KM, Mericle AA, Washburn JJ. Major mental disorders, substance use disorder, comorbidity and HIV-AIDS risk behaviors in juvenile detainees. Psychiatr Serv. 2005;56(7):823–828. 5. Grisso T. Adolescent offenders with mental disorders. Future Child. 2008;18(2):143–164. 6. Young DW, Dembo R, Henderson CE. A national survey of substance abuse treatment for juvenile offenders. J Subst Abuse Treat. 2007;32(3):255–266. 7. Nissen LB, Butts JA, Merrigan DM, and Kraft MK. The Reclaiming Futures Initiative: Improving Substance Abuse Interventions for Justice-Involved Youth. A Reclaiming Futures National Program Report. Portland, OR: Reclaiming Futures National Program Office, Portland State University; 2007. 8. Shoveling Up. The Impact of Substance Abuse on State Budgets. New York, NY: The National Center on Addiction and Substance Abuse (CASA) at Columbia University; 2001.
borne out by the abundant examples of RCT results3 coming in conflict with findings from equally well-done observational studies.4 This challenges our notion that RCTs represent a gold standard. While they might represent an outstanding study design to improve internal validity, they are less well suited to external validity and
ABOUT THE AUTHORS Sandro Galea is Dean and Professor, School of Public Health, Boston University, Boston, MA. Roger Vaughan is an AJPH editor, and is also the Vice Dean and Professor of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY. Correspondence should be sent to Roger Vaughan, Vice Dean and Professor of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032 (e-mail: [email protected]). Reprints can be ordered at http://www. ajph.org by clicking the “Reprints” link. This editorial was accepted April 7, 2016. doi: 10.2105/AJPH.2016.303230
Galea and Vaughan
Editorial
973
AJPH EDITORIALS
generalizable inference and may well be less informative for the goals of a public health of consequence. This is not intended to obviate the role of RCTs in population health research. It does, however, serve as a sobering reminder of the balance we must strike between internal validity and external validity concerns, and of how a public health of consequence lens helps us evaluate the scholarship that is of highest priority, toward the goal of informing efforts that aim to improve the health of populations. Second, Westreich et al. make the case to treat population intervention effects as a means to making population health science findings more accessible.5 This provides an equally important reminder of the need for accessible analytic representations that help us realize the promise of translation of population health scholarship. With Westreich et al’s words as framework, we highlight two articles in this month’s AJPH that address the two central points made in this framework; first we discuss an article that uses a populationbased sample to conduct informative science that can guide population-level intervention, and second, an article that presents interesting modeling data, accessibly, in a way that can plausibly inform, inflect, and influence an important public conversation. First, Keyes et al.6 demonstrate a link between early adolescent use of tobacco and marijuana and cocaine use by 12th grade. They suggest that each percentagepoint decrease in the prevalence of smoking in the 8th and 10th grade is associated with an 8% decrease in the prevalence of later marijuana use and a 14% to 23% decrease in later cocaine use. The Monitoring the Future data are nationally representative data, and the sheer number of people to whom this applies puts the
974
Editorial
Galea and Vaughan
importance of these findings in context. Nearly 10 million adolescents will have tried marijuana by the 12th grade, and one million will have tried cocaine. Importantly therefore, this article suggests a logical way forward for any effort by public health to reduce marijuana and cocaine use by millions later in life. As the authors note, [P]ublic health campaigns to reduce the burden of drug use among adolescents should focus on the early stages of adolescence when drug use habits are forming, and that prevention of cigarette smoking, and use of tobacco products more generally, may be a crucial component of a public health strategy.6(p1148)
This article provides us with an elegant example of how a population-based sample can illuminate the tremendous potential of interventions that target ubiquitous factors (like early adolescent smoking) toward the creation of healthier populations. It seems to us that the logical next step in this thinking would be inquiry about the most effective population-based approaches that successfully reduce smoking in these target populations, and that do so without widening intergroup differences and introducing health inequities. As a start, Cobb et al.7 present early evidence of the potential utility of a Facebook-based smoking cessation intervention, showing that those initially enrolled in an online smoking cessation program can act as viral agents and enroll others to participate at no additional cost. Whether this is effective in reducing smoking among adolescents remains to be seen. Second, Tsao et al.8 make an excellent contribution to the literature through an article that models the potential impact of
a $15 per hour minimum wage on preventable mortality in the New York City population. Their analyses suggest that this minimum wage could have prevented 2800 to 5500 premature deaths in New York City, principally among populations of color living in low-income neighborhoods. Any such modeling exercise must always be approached with caution, recognizing that findings from this work are only as robust as the assumptions that were used to inform and parametrize the model; to this end Tsao et al. do an admirable job of presenting, comprehensively, the limitations to their work. However, notwithstanding these limitations, their presentation of these findings, as the authors note, “adds to a growing body of work by health departments to resurrect the centrality of minimum wages to population health.”8(p1039) Importantly, it both does so and presents important data that are accessible to the nonspecialist and can contribute meaningfully to the public discussion about the minimum wage. As this article was going to press, Governor Cuomo had proposed a $15 per hour minimum wage in New York State.9 Further analyses demonstrating how this may influence premature death in the whole state would be welcome. It is in part the premise behind this monthly commentary that the lenses we adopt to inform our work are important: they shape how we think, the questions we ask, the studies we design, and how we analyze our data. An approach to population health science that prioritizes the potential public health consequences of our work will definitionally ask questions that matter to populations, draw inference that can inform how we might intervene to improve these
populations’ health, and present results in such a way as to inform those who can make change happen, locally and globally. These articles in this month’s issue provide a compelling illustration of these principles. Sandro Galea, MD, DrPH Roger Vaughan, DrPH, MS CONTRIBUTORS Both authors contributed equally to this editorial.
REFERENCES 1. Westreich D, Edwards JK, Rogawski ET, Hudgens MG, Stuart EA, Cole SR. Causal impact: epidemiologic approaches for a public health of consequence. Am J Public Health. 2016;106(6):1011–1012. 2. Rothman KJ, Gallacher JEJ, Hatch EE. Why representativeness should be avoided. Int J Epidemiol. 2013;42: 1012–1014. 3. DeKosky ST, Williamson JD, Fitzpatrick AL, et al.; Ginkgo Evaluation of Memory (GEM) Study Investigators. Ginkgo biloba for prevention of dementia: a randomized controlled trial. JAMA. 2008;300(19):2253–2262. 4. Andrieu S, Gillette S, Amouyal K, et al. Association of Alzheimer’s disease onset with ginkgo biloba and other symptomatic cognitive treatments in a population of women aged 75 years and older from the EPIDOS study. J Gerontol A Biol Sci Med Sci. 2003;58(4):M372–M377. 5. Ahern J, Hubbard A, Galea S. Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. Am J Epidemiol. 2009;169(9):1440–1147. 6. Keyes K, Hamilton A, Kandel DB. Birth cohorts analysis of adolescent cigarette smoking and subsequent marijuana and cocaine use. Am J Public Health. 2016; 106(6):1143–1149. 7. Cobb NK, Jacobs MA, Wileyto P, Valente T, Graham AL. Diffusion of an evidence-based smoking cessation intervention through Facebook: a randomized controlled trial. Am J Public Health. 2016;106(6):1099–1100. 8. Tsao TY, Konty KJ, Van Wye G, et al. Estimating potential reductions in premature mortality in New York City from raising the minimum wage to $15. Am J Public Health. 2016;106(6):1036–1041. 9. New York Gov. Cuomo signs $15 minimum wage law. Associated Press. April 4, 2016. Available at: http://pix11. com/2016/04/04/new-york-govcuomo-signs-minimum-wage-law. Accessed April 11, 2016.
AJPH
June 2016, Vol 106, No. 6
