COMMENTARIES
Opinions expressed in these commentaries are those of the author and not necessarily those of the American Academy of Pediatrics or its Committees.
Anabolic Steroids and the Pediatric
Community
T
his past winter was remarkable for many events including a devastating tsunami in southeast Asia, the re-election of the President of the United States, and increasing worries about the avian flu. The winter also saw the elevation of the national consciousness on the issue of anabolic ste-roids, resulting mostly from a weak testing policy implemented by Major League Baseball and, more recently, the release ofJuiced, a book by Jose Canseco that details his steroid use and the widespread use of anabolic steroids in baseball.Anabolic steroids, derivatives of testosterone used to enhance anabolic, muscle-building properties, have important medical uses. For patients with se-vere osteoporosis, AIDS wasting syndrome, and oc-casionally other chronic medical disease states in-cluding hypogonadism, anabolic steroids are important components of treatment. More commonly, however, these compounds are used by athletes look-ing to gain the “extra edge” in competition. Use of steroids has been well described in the medical litera-ture, starting in the 1960s and continuing until the present day. The sanguine approach from the medical community to anabolic steroids in the 1960s and 1970s has been replaced by a slowly developing appreciation for the significant medical problems associated with use, including impotence, cardiovascular disease, mood instability, and hepatic cancer.
In children and teens, anabolic steroid use has been well documented, with various studies show-ing a national user rate between 3% and 9% in high school students. The major reason for the failure to convince young athletes to abstain from anabolic steroid use is that these substances work; they make athletes stronger and faster. This advantage, how-ever, comes at a very dangerous price. More needs to be done to actively discourage steroid use in pediat-ric and adolescent athletes.
From a medical perspective, the complete ramifi-cations of anabolic steroid use in a developing body are not known. Aside from the adult problems asso-ciated with usage, including a significantly increased risk of cardiovascular disease, the issue of mood instability in teens, whose minds are already in a precarious state of flux, seems more acute. The Tay-lor Hooton Foundation (see www.tayTay-lorhooton.org) was started by a father whose son committed suicide
after use of anabolic steroids. Sadly, several more of these steroid-related suicides in teens have been re-ported this year.
Unfortunately, Major League Baseball (who has had the most to say about the issue of steroid use in today’s culture), created a toothless policy on steroid abuse. With a first offense, the guilty player receives a 10-day suspension, which is approximately the same punish-ment that he would get for “bumping” an umpire.
What does this say to the children who emulate professional athletes? Unfortunately, not nearly enough. The pediatric community, therefore, needs to help pick up the ball. The upcoming American Acad-emy of Pediatrics policy statement on performance-enhancing drugs will help, as will the Anabolic Steroid Control Act, which was signed in October 2004 and bans the sale of over-the-counter presteroid supple-ments. These steps will aid the concerted efforts of those who work with young athletes, but the true mo-mentum needs to come from the grass-roots level, team by team and community by community. This type of effort can include medical professionals creating edu-cational seminars for parents and coaches on the issue of performance-enhancing drugs.
Together, we can help raise the tremendously im-portant point that steroid use in children, whose bodies are developing and who are playing sports largely for recreation, is very different from steroid use in professional athletes, who are physiologically mature and paid millions of dollars to compete. Al-though both are problematic, the issues for children, in both mind and body, are simply more significant.
Jordan D. Metzl, MD, FAAP
Sports Medicine Institute for Young Athletes Hospital for Special Surgery
New York, NY 10021
Is Region of Country a Useful
Variable for Child Health Studies?
ABBREVIATIONS. CHI, child health index; MAUP, modifiable areal unit problem.
I
n their article “The Health Status of Southern Children: A Neglected Regional Disparity” (in this month’sPediatrics electronic pages), Goldhagen et al1have raised the intriguing question of whetherAccepted for publication Mar 7, 2005. doi:10.1542/peds.2005-0522 No conflict of interest declared.
Address correspondence to Jordan D. Metzl, MD, FAAP, Sports Medicine Institute for Young Athletes, Hospital for Special Surgery, 535 E 70th St, New York, NY 10021. E-mail: [email protected]
Accepted for publication Jun 28, 2005. doi:10.1542/peds.2005-1568
No conflict of interest declared.
US region is a significant factor for child health out-comes. Although much attention has been given to contextual influences on health,2–4most studies have considered local contextual effects such as neighbor-hood characteristics.5Few have considered region as a contextual level.2,6 Therefore, Goldhagen et al’s conclusion that region is a stronger predictor of poor outcomes than other variables commonly used is quite remarkable. A careful examination of their methods is in order before their recommended re-search agenda is undertaken. We have 2 levels of concern: the general approach to the definition of health regions and a specific concern about the geo-graphic unit of analysis.
DEFINING HEALTH REGIONS
The definition of geographic regions for health studies can be achieved either by an impartial explo-ration of the geographic distribution of health statis-tics or through historical, sociocultural, or policy-relevant considerations. Goldhagen et al frame their article as an examination of the South, which sug-gests a preference for the latter approach. This focus might stem from an interest in the legacies of slavery or in current-day clustering of state policies and funding for public health and health care. Yet their methods are primarily an exploration of health sta-tistics. They mapped the child health index (CHI) and identified the Deep South based on the revealed geographic distribution. Their methods for selecting member states are not precisely explained, but it seems that state selection was influenced somewhat by a de-sire to achieve a contiguous Deep South. Nevertheless, the CHI and its components were the variables used to
define regions. To confirm the validity of the CHI-defined Deep South, they contrasted it with other re-gions to test for differences in CHI. This circularity could have been avoided if the Deep South were de-fined solely by historical or policy considerations. It also could have been avoided by a purely objective exploration of the spatial distribution of CHI.
To illustrate the latter, we analyzed the state CHI values from their first table. A histogram of the val-ues suggested 5 natural categories with breakpoints that we defined by using the Jenks natural-breaks function7in ArcGIS 9.0.8The function seeks to min-imize the squared intraclass deviations from each class mean. The resulting map of the 5 CHI levels is presented in Fig 1. It shows that “South” and “Deep South” can be elusive concepts in terms of child health outcomes. For example, to include Florida and Texas in the general South, one would also have to include 29 states stretching as far as Idaho and Mich-igan. On the other hand, if one sought to obtain the Deep South, it would consist of only 4 noncontigu-ous states (darkest shade) or 11 noncontigunoncontigu-ous states (darkest 2 shades) that are not entirely southern.
If we ignored these objectively defined break points and used contiguity as a criterion for region membership, then we could reassign South Carolina from the worst-outcomes group to second worst group extending from North Carolina to New Mex-ico and Wyoming. This would still leave us with contiguity problems in the rest of the country, but it would yield a tidy Deep Central South region con-sisting of Louisiana, Mississippi, and Alabama. These 3 states lag significantly in child health out-comes, and clearly it would be fruitful to focus child
Fig 1. States grouped by Jenks natural break points of CHI (data are from Goldhagen J, Remo R, Bryant T III, et al. The health status of southern children: a neglected regional disparity.Pediatrics.2005;116(6). Available at: www.pediatrics.org/cgi/content/full/116/6/e746).
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health interventions on the more impoverished areas within these states. However, that has been clear for some time from basic state health statistics.9 That they are contiguous neither increases nor decreases the need within those states. Furthermore, none of the state combinations discussed above is equivalent to the Deep South defined by Goldhagen et al.
The point of this exercise has been to show that defining regions on which to build research pro-grams and policies is challenging and subjective and may have less value than some realize. We also note (and Goldhagen et al would probably agree) that children in low-ranking nonsouthern states such as Wyoming are no less deserving of outcome improve-ments than the children in higher-ranking southern states such as Florida simply because Florida can be construed to belong to a southern region.
GEOGRAPHIC UNIT OF ANALYSIS
Notwithstanding the exercise above, we question the use of large bordered areas such as states to discover and understand causes of child health out-comes. We recognize that states independently set health policies and have varying per-capita resources to maintain public health and health care programs. However, that very independence might partially underlie the discontiguous interstate health patterns revealed in Fig 1. Furthermore, much more localized factors affect health, and most states have heteroge-neous and unevenly distributed populations that make “state” too coarse of a geographic unit to provide nu-anced insight into the contextual influences on health. Spatial analysts refer to this as the modifiable areal unit problem (MAUP).10 MAUP arises from the im-position of artificial units of spatial reporting (eg, states) on continuous or highly localized geographi-cal phenomena, resulting in the generation of artifi-cial and potentially misleading spatial patterns. Al-though rarely considered in the health literature,11 MAUP has been investigated thoroughly in other fields of study. A recent example from political sci-ence, the 2004 presidential election, parallels the Goldhagen et al study in terms of geographic scope and unit of analysis and illustrates the MAUP well. The 2004 electoral map of the lower 48 states shows a vast, contiguous swath of 30 Republican (red) states reaching outward from the South into the Mid-west and West. Democratic (blue) states are confined to 3 contiguous areas: the West Coast, Central North, and Northeast. The map shows that region is a strong predictor of state majority vote. However, state of res-idence is a very poor predictor of an individual’s or community’s vote. This was demonstrated by research-ers at Princeton12and the University of Michigan,13,14 who produced national maps of counties shaded by vote proportions. Changing the geographic unit dra-matically changed the interpretation. Their maps proved that (1) nearly the entire nation was some shade of purple (as opposed to blue or red) in 2004, (2) there was a great deal of variation within nearly all states, and (3) continuous degrees of shading give a more accurate picture than 2 discrete, shaded groups.
If state CHI were reduced to a dichotomous high-low variable such as electoral majority, then region
would probably be as good a predictor of a state’s CHI group as it is of electoral majority. Yet region would be just as poor a predictor of individual or community health as it is of individual or commu-nity vote. Although Goldhagen et al do not claim to have analyzed individual or community health, they repeatedly state that “living in the southern region” is a powerful predictor of children’s health. It takes a careful reading to understand that in this context, “children’s health” refers only to state ranking. The utility of region for understanding individual or community health is not demonstrated. If research-ers are interested in the historical or sociopolitical sequelae of slavery, then a regional study is appro-priate, and relevant theories about the Deep South should be presented and tested explicitly. On the other hand, if researchers are interested in improving child health, then analyses should focus on the proven determinants of health that act at the indi-vidual, local, and state levels until such time as in-dependent region-level effects are demonstrated. Briefly stated, regional differences in health do not prove present-day regional effects, nor do they dem-onstrate the utility of region as a consideration for the development of child health interventions.
SETTLING THE QUESTION
To determine if region is an important factor for individual or community child health, a multilevel analysis such as hierarchical regression15 is an im-portant next step. Such a model would use “individ-ual child” as the unit of study and include factors that influence child health on a number of levels. Examples of individual- and family-level factors are income, insurance, education, and cultural/behav-ioral choices; community-level factors might include access to and availability of clean air and water, healthy affordable foods, health care facilities, and safe neighborhoods for outdoor activities; and state-level factors could be types of public health policies and programs, per-capita dollars allocated to those programs, Medicaid coverage rates, and dominant employment sectors. To test the validity of region as a determinant of health, the analysis then would include region as a clustering variable in a multilevel analysis, and it would have to show significant re-gional health effects that are independent of the other factors in the analysis.
Mark F. Guagliardo, PhD
Department of Prevention and Community Health
Cynthia R. Ronzio, PhD
Department of Epidemiology and Biostatistics George Washington University School of Public
Health and Health Services Washington, DC 20052
Center for Health Services and Community Research Children’s National Medical Center
Washington, DC 20010
REFERENCES
1. Goldhagen J, Remo R, Bryant T III, et al. The health status of southern children: a neglected regional disparity.Pediatrics.2005;116(6). Avail-able at: www.pediatrics.org/cgi/content/full/116/6/e746
2. Hillemeier MM, Lynch J, Harper S, Casper M. Measuring contextual characteristics for community health.Health Serv Res.2003;38:1645–1717 3. Diez Roux AV. Investigating neighborhood and area effects on health.
Am J Public Health.2001;91:1783–1789
4. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeco-nomic context and health outcomes: a critical review.J Epidemiol Com-munity Health.2001;55:111–122
5. Brooks-Gunn J, Duncan GJ, Aber JL.Neighborhood Poverty. New York, NY: Russell Sage Foundation; 1997
6. Andersen RM, Yu H, Wyn R, et al. Access to medical care for low-income persons: how do communities make a difference?Med Care Res Rev.2002;59:384 – 411
7. Jenks GF. The data model concept in statistical mapping. In: Interna-tional Cartographic Association, ed.International Yearbook of Cartography 7.Ulm, Germany: University of Ulm; 1967:186 –190
8. ArcGIS[computer program]. Version 9. Redlands, CA: ESRI, Inc; 2004 9. Kids Count Project, Population Reference Bureau.Children at Risk: State Trends 1990 –2000. Baltimore, MD: Annie E. Casey Foundation; 2002 10. Openshaw S. The Modifiable Areal Unit Problem. Norwich, United
Kingdom: Geo Books; 1984
11. Guagliardo MF. Spatial accessibility of primary care: concepts, methods and challenges.Int J Health Geogr.2004;3:3
12. Vanderbei R. Election 2004 results. Available at: www.princeton.edu/
⬃rvdb/JAVA/election2004. Accessed June 13, 2005
13. Gastner MT, Newman MEJ. Diffusion-based method for producing density-equalizing maps.Proc Natl Acad Sci USA.2004;101:7499 –7504 14. Newman M. Maps and cartograms of the 2004 US presidential election
results. Available at: www-personal.umich.edu/⬃mejn/election. Ac-cessed June 13, 2005
15. Diez-Roux AV. Multilevel analysis in public health research.Annu Rev Public Health.2000;21:171–192
Tax Cuts, Budget Deficits, and
Medicaid Cuts: Does “Starving the
Beast” Mean That Children’s
Health Must Take Two Steps
Backward?
T
he Colorado experience with Medicaid pub-lished in this issue ofPediatrics1demonstrates how attempts to restrain the growth in Medic-aid expenditures can restrict access to primary careand preventive services and ultimately increase per-capita Medicaid expenditures because of higher pre-ventable hospitalization rates. This study deserves the attention of national and state policy makers because of the federal Conference Agreement on the Federal Budget Resolution that includes $10 billion in Medicaid and State Children’s Health Insurance Program reductions. In many states these federal reductions are likely to derail needed increases in Medicaid payments for physician services to levels that at least cover overhead expenses. Do these bud-get reductions mean that we will be taking 2 steps back on the path to improving the lives of America’s children as we address the consequences of our large and growing national deficit and the demographic impact of an aging population? These structural def-icits and projections of future entitlement spending pit the needs of children and future generations against the elderly. This conflict threatens to tear apart our country’s social fabric and sense of equity. Attempts to reduce the large federal budget deficit by containing nonmilitary and nonentitlement spending is disproportionately impacting children and their families. Too many of our nation’s children face substantial challenges and are already vulnera-ble and at high risk for health and social provulnera-blems. Almost 13 million US children live in poverty,2⬎8 million have no health insurance throughout the year,3 many have difficulty getting needed care,4 1 million 2-year-old children are not fully immunized,5 and nearly 4 million (50.7%) 3- and 4-year-olds are not enrolled in nursery school, preschool, or prekin-dergarten education programs.6 In addition to the Medicaid and State Children’s Health Insurance Pro-gram reductions, other budget cuts that will nega-tively impact the well-being of children include sub-stantial cuts to food stamps, the School Lunch and Breakfast Program, Supplemental Nutrition Program for Women, Infants, and Children (WIC), Head Start, and the Child Care and Development Block Grant.
Contrast our national commitment to children with our commitment to the elderly: Poverty among the elderly is now 10.2% compared with 17.6% for children younger than 18 years. In 2000 the average elderly person received⬎7 times the amount of gov-ernmental program benefits as did the average child: $17 688 vs $2491, respectively.2This inequity in allo-cation of federal funds is likely to substantially in-crease in the coming years. Social Security outlays as a share of worker payroll are estimated to rise during the next 35 years from 11.1% to 17.8%, and both parts of Medicare are estimated to rise from 5.6% to 18.2%.2The elderly received the Medicare Prescrip-tion Drug Bill, which is estimated to cost $600 billion between 2004 and 2013, whereas ⬎20 million chil-dren went without health insurance at some point in 2002–2003.3 The number of uninsured children will increase if our nation’s budget deficit continues, bud-get cuts proposed for Medicaid are adopted, and employer-funded insurance declines further.
In his bookRunning on Empty: How the Democratic and Republican Parties are Bankrupting Our Future and What Americans Can Do About It,2Peter G. Peterson, a secretary of Commerce under President Nixon,
Accepted for publication Jun 14, 2005. doi:10.1542/peds.2005-1342
Address correspondence to Steve Berman, MD, Children’s Hospital, 1056 E 19th Ave, B032, Denver, CO 80218. E-mail: [email protected] No conflict of interest declared.
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DOI: 10.1542/peds.2005-1568
2005;116;1542
Pediatrics
Mark F. Guagliardo and Cynthia R. Ronzio
Is Region of Country a Useful Variable for Child Health Studies?
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