“The Mismeasure of Association:The Unsoundness of the Rate Ratio and Other Measures That Are Affected by the Prevalence of an Outcome.”Methods Workshop at Minnesota Population Center and Division of Epidemiology and Community Health of the School of Public Health of the University of Minnesota (Sept. 5, 2014) http://jpscanlan.com/images/University_of_Minnesota_Methods_Workshop.pdf
A significant portion of the material listed or maintained on this and the other pages of this site relates to statistical tendencies by which measures of differences between rates at which two groups experience or avoid some outcome are affected by the prevalence of the outcome and the bearing of those tendencies on the evaluation of group differences in the law and the social and medical sciences.Much of the material on these tendencies concerns the measurement of health and healthcare disparities (or inequalities as they are more commonly termed outside the United States).The most notable of those tendencies is that when two groups differ in susceptibility to an outcome, the rarer the outcome the greater tends to be the relative difference between rates of experiencing it and the smaller tends to be the relative difference between rates of avoiding it.Thus, during times of declining mortality and other adverse outcomes, demographic differences in rates of experiencing those outcomes have tended to increase, while demographic differences in rates of avoiding those outcomes have tended to decline.Yet, almost universally, health disparities research has regarded observed increases in relative differences in mortality as reflecting a meaningful worsening of the relative health of disadvantaged groups without consideration of whether the increases have been greater than, or less than, what would be expected to occur solely because of declining mortality, and without consideration of whether relative differences in survival rates have increased.Similarly, much has been made of seemingly large relative differences in mortality within particular settings – e.g., steep social gradients in mortality among British civil servants, large racial differences in infant mortality where mothers have high education, and large socioeconomic differences in mortality within comparatively egalitarian societies like Norway and Sweden.It has gone unrecognized that large relative differences in mortality would be expected in such settings simply because mortality is low in those setting.
But relative differences in experiencing or avoiding an outcome are not the only measures of differences between the rates of two groups that are affected by the prevalence of an outcome.In fact, all other measure of differences between the situation of two groups regarding dichotomous variables (e.g., odds ratios, absolute differences, relative indexes of inequality, Gini coefficients, longevity differences, concentration indexes) also tend to be affected by the prevalence of an outcome.Hence, none of these measures can distinguish between variations that are solely the result of variations in overall prevalence and those that reflect something of greater consequence.Most of the material listed below is skeptical as to the prospects for measuring the size of differences in different settings sufficiently reliably to justify the resources devoted to such efforts.But many items commencing with the January 14, 2008 Comment on Boström and Rosén address what may be the only plausible approach to measuring differences between rates that is unaffected by the prevalence of an outcome.While the approach may have weaknesses of its own, it is plainly superior to the standard practice of appraising the size of differences between rates as if the tendencies discussed above do not exist.The approach is separately discussed on the Solutions sub-page and a downloadable database with which to implement it is made available on the Solutions Database sub-page.As discussed on the Solutions sub-page, in early 2010 I came to recognize that the results achieved mechanically by means of the Solutions Database can be achieved formulaically by means of a probit analysis.
Section A, B, and C, list, respectively, publications, conference presentations, and unpublished papers, usually providing links to the material as it is maintained elsewhere on this site.Section D provides references and links to about (as of the most recent updating of this page) 118 on-line comments on articles in medical or health policy journals addressing health and healthcare disparities where interpretations are affected by the above-referenced statistical tendencies.In each case the comment explains the problems with the article as a result of the failure to consider the extent to which observed patterns are consequences of changes in overall prevalence of an outcome.Most of these that appear on sites of the journal where the article was published are between 400 and 1000 words (though the 2008 PediatricsComment on Morita et al.is over 1800 words).Those appearing on journalreview.org are often four to seven thousand words.Where it seems useful, parenthetical information may be included in the listing, usually with respect to an item’s addressing some issue other than health disparities.Over last few years, increasing numbers of these items have related to the important issues of the interpretation of data on subgroup effects of interventions and the estimation of absolute risk reductions for subgroup (e.g., nos. 88-89, 100, 105, 110-12, 11), which issues, while involving the same questions about measuring association involved in efforts to measure health disparities, are not ostensibly related to the title of this page.Subgroup effects issues are also discussed on the Subgroup Effects sub-page of the Scanlan’s Rule page (discussed several paragraphs below).
The items in sections A through D are generally listed from most recent to least recent (though in Section D follow-up items are listed with the original item).For ease of reference, these items are numbered within sections, but, to facilitate updating, are numbered chronologically (that is, from earliest to most recent).Items in Section D that address statistical issues other than those described in the preceding paragraphs are marked with an asterisk.To facilitate access to these materials, the materials in Section A, Section B, and Section D are maintained on separate subpages that can be accessed by the links indicated in this sentence.
The subparts of Section E briefly summarize the following particular issues and, where applicable, identify the references in Sections A through D addressing those issues:(1) the misinterpretation of health inequalities in the United Kingdom and/or the Whitehall Studies; (2) the misinterpretation of health inequalities in Nordic Countries; (3) absolute differences between rates as a measure of disparities; (4) the approaches to disparities measurement of the National Center for Health Statistics and the Agency for Healthcare Research and Quality; (5) issues regarding health disparities and pay-for-performance; (6) approaches to the measurement of disparities that are unaffected by differences in the overall prevalence of an outcome; (7) scholarly agreement/disagreement with the views expressed in the listed references.Item 5 and aspect of item 4 are also addressed in the Pay for Performance and NHDR Technical Issues sub-pages.To facilitate access to Section E.7 of this page, it is also made available on a separate sub-page.
Apart from the sub-pages already noted, this page has a number of sub-pages addressing particular issues.Among these are sub-pages concerning Irreducible Minimum Issues (addressing, with respect to conventional measures the approach on the Solutions sub-page, the implications of rate’s approaching an irreducible minimum), the Concentration Index (addressing the way the Concentration Index is affected by the overall prevalence of an outcome),and Reporting Heterogeneity (addressing the way extent to which perceptions of reporting heterogeneity fail to consider the extent to which observed patterns are functions of underlying distributions).The most important or the sub-pages may be the Relative Versus Absolute .It attempts to illustrate that there is only one answer to the question of which disparity is larger (albeit an answer they may not always be so easy to divine) and that the notion that value judgments are involved in choosing among health disparities measures is unfounded.Other sub-pages are discussed on the home page to this site.
Three other pages on this site are closely related to this site.After researchers in the United Kingdom referred to the pattern of relative differences described above as “Scanlan’s rule,” the Scanlan’s Rule page was created to discuss the nuances of those and related patterns of correlations between measures and the prevalence of an outcome.It has 19 sub-pages, most of which are described on the home page.The most important of these is probably the Subgroup Effects sub-page mentioned several paragraphs above.
The Mortality and Survivalpage addresses the way that, especially in cancer journals, researchers discuss disparities in mortality and disparities in survival interchangeably without recognizing that the two disparities tend to change in opposite directions.
The Measures of Associationpage briefly explains that the issues addressed with regard to the measures of health disparities and other subjects addressed on various pages of the site are involved in any effort to measure the strength of an association.The page is included as a reminder that fundamentals of epidemiology and every other discipline that is concerned with measures of association, however such matter is characterized, need to be reconsidered with an eye toward the manner in which accepted methods of measuring association are affected by the overall prevalence of an outcome.
17. Race and mortality revisited. Society, _____ 2013 (in press)
(feminization of poverty, racial differences in infant mortality rates)
B.Conferences Presentations and Lectures
29. “Rethinking the Measurement of Demographic Differences in Outcome Rates,” Methods Workshop to be presented to the Maryland Population Research Center of the University of Maryland, Oct. 10, 2014.
28.“The Mismeasure of Association,” Methods Workshop to be presented jointly to the University of Minnesota’s Minnesota Population Center and the Department of Epidemiology and Community Health of the University’s School of Public Health, Sept. 5, 2014.
25. The Mismeasure of Group Differences in the Law and the Social and Medical Sciences, Applied Statistics Workshop at the Institute for Quantitative Social Science at Harvard University, Cambridge, Massachusetts, Oct. 17, 2012:
21.The Emerging European Acceptance of “Scanlan’s Rule” in Health Disparities Research: Will the United States Be Left Behind?, to be presented at American Public Health Association 138th Annual Meeting & Exposition, Denver, Colorado, Nov. 7-10, 2010.
20.Measuring Health Inequalities by an Approach Unaffected by the Overall Prevalence of the Outcomes at Issue, presented at the Royal Statistical Society Conference 2009, Edinburgh, Scotland, Sept. 7-11, 2009.
19.Interpreting Differential Effects in Light of Fundamental Statistical Tendencies, presented at 2009 Joint Statistical Meetings of the American Statistical Association, International Biometric Society, Institute for Mathematical Statistics, and Canadian Statistical Society, Washington, DC, Aug. 1-6, 2009.
18.Approaches to Measuring Health Disparities that are Unaffected by the Prevalence of an Outcome, to be presented at American Public Health Association 136th Annual Meeting & Exposition, San Diego, California, Oct. 25-29, 2008.
17.An Approach to Measuring Differences Between Rates that are not Affected by the Overall Prevalence of an Outcome, presented at the British Society for Populations Studies Conference 2008, Manchester, England, Sept. 10-12, 2008.
16.Evaluating The Sizes Of Differences Between Group Rates In Settings Of Different Overall Prevalence, presented at the 2008 Joint Statistical Meetings of the American Statistical Association, International Biometric Society, Institute for Mathematical Statistics, and Canadian Statistical Society, Denver, Colorado, Aug. 3-7, 2008.
11. Methodological Issues in Comparing the Size of Differences between Rates of Experiencing or Avoiding an Outcome in Different Settings, presented at the British Society for Populations Studies Conference 2007, St. Andrews, Scotland, Sept. 11-13, 2007.
10. Approaches to Measuring Differences in Health That Are Unaffected by the Prevalence of an Outcome, Roundtable coffee at 2006 Joint Statistical Meetings of the American Statistical Association, International Biometric Society, Institute for Mathematical Statistics, and Canadian Statistical Society, Salt Lake City, Utah, July 29 – Aug. 2, 2007.
9. Exploring Methods to Measure Health Inequalities that are Unaffected by the Prevalence of an Outcome, presented at Social, Cultural and Economic Determinants of Health: International Perspectives for Global Action (1st Conference of the Journal Public Health, Journal of the Royal Institute of Public Health), Lisbon, Portugal, May 9-11, 2007.
8. Understanding Variations in Group Differences That are the Results of Variation in the Prevalence of an Outcome,presented at the American Public Health Association 134th Annual Meeting & Exposition, 2006, Boston, MA, Nov. 4-8, 2006.
6. Measuring Health Disparities, Roundtable lunch at 2006 Joint Statistical Meetings of the American Statistical Association, International Biometric Society, Institute for Mathematical Statistics, and Canadian Statistical Society. Seattle, Washington, Aug. 6-10, 2006.
5. The Misinterpretation of Health Inequalities in Nordic Countries, presented at: 5th Nordic Health Promotion Research Conference, Esbjerg, Denmark, June 15-17, 2006.
3. Understanding Increasing Racial Differences in Mortality (and Declining Differences in Survival), presented at the First Annual Health Disparities Conference, Teachers College, Columbia University, New York, New York, Mar. 19, 2006.
2. The Difficulties of Interpreting Changing Racial and Socioeconomic Differences in Health Outcomes," presented at the International Conference on Health Policy Research, Boston, MA, Dec. 9, 2001.
1. The Misunderstood Relationship Between Declining Mortality and Increasing Racial and Socioeconomic Disparities in Mortality Rates, presented at the conference "Making a Difference: Is the Health Gap Widening?" sponsored by the Norwegian National Institute of Public Health, Oslo Norway, May 14, 2001.
A number of the items listed below were initially published online on the websites of The Lancet or Journal Review. Several years ago the Lancet ceased to maintain online response and more recently Journal Review closed its site entirely. The comments from that had been posted on those sites have been, or will be, posted on this site.
141. General reductions in adverse health outcomes tend to increase rather than reduce relative differences in rates of experiencing those outcomes. Health Affairs Oct. 28, 2013 (responding to Qasim M, Andrews RM. Despite Overall Improvement In Surgical Outcomes Since 2000, Income-Related Disparities Persist. Health Affairs. 2013;32(10):1773-1780): http://content.healthaffairs.org/content/32/10/1773/reply#healthaff_el_476813
140. There are important differences between disparities in survival and disparities in mortality. BMC Cancer Oct. 16, 2013 (responding to Yu XQ. Socioeconomic disparities in breast cancer survival: relation to stage at diagnosis, treatment and race. BMC Cancer 2009, 9:364: doi:10.1186/1471-2407-9-364): http://www.biomedcentral.com/1471-2407/9/364/comments#1563696
139. Need for researchers to acknowledge alternative methods. Health Affairs. Apr. 1, 2013 (responding to Trivedi AN, Grebla RC, Wright SM, Washington DL. Despite improved quality of care in the veterans affairs health system, racial disparity persists for important clinical outcomes. Health Affairs, 30, No. 4 (2011):707-715): http://content.healthaffairs.org/content/30/4/707.full/reply#healthaff_el_476354
138. Efforts to quantify the magnitude of inequalities must consider more carefully the implications of the patterns by which measures tend to be affected by the prevalence of an outcome. BMC Public Health March 10, 2013 (responding to Scholes S., Bajekal M, Hande L, et al. Persistent socioeconomic inequalities in cardiovascular risk factors in England over 1994-20000. A time-trend analysis of repeated cross-sectional data. BMC Public Health 2012, 12:129 doi:10.1186/1471-2458-12-129): http://www.biomedcentral.com/1471-2458/12/129/comments#1358696
137. Goodbye to the rate ratio. BMJ Feb. 25, 2013 (responding to Hingorani AD, van der Windt DA, Riley RD, et al. Prognosis research strategy (PROGRESS) 4: Stratified medicine research. BMJ2013;346:e5793): http://www.BMJ.com/content/346/BMJ.e5793/rr/632884
135. The need for new thinking about how to measure disparities. BMJ Feb. 4, 2013 (responding to Epstein K. Persistent health disparities in the US signal for new thinking. BMJ 2012;345:e6204 doi: 10.1136/BMJ.e620): http://www.BMJ.com/content/345/BMJ.e6204/rr/628910
133. Discussions of relative and absolute differences cannot ignore that there are two relative differences. BMJ Nov. 8, 2012 (responding to King NB, Harper S, Young ME. Use of relative and absolute effect measure in reporting health inequalities: structured review. BMJ 2012;345:e544doi: 10.1136/bmj.e5774): http://www.bmj.com/content/345/bmj.e5774/rr/613496
131. Recognizing contrasting patterns of gender differences in mortality and gender differences in survival. PLoS Medicine Sept, 26, 2012 (responding to Cornell M. Schomaker M, Garone db, et al. Gender differences in survival among adult patients starting antiretroviral therapy in South Africa: A Multicentre cohort study. PLoS Med 9(9): e1001304): doi:10.1371/journal.pmed.1001304:
130. Study of effects of increasing coverage on inequalities in use of insecticide–treated bed nets illustrates implications of choices of measure of inequality. PLoS Medicine, July 5, 2012 (responding to Noor AM, Amin AA, Akhwale WS, Snow RW (2007) Increasing Coverage and Decreasing Inequity in Insecticide-Treated Bed Net Use among Rural Kenyan Children. PLoS Med 4(8): e255. doi:10.1371/journal.pmed.0040255):
129. Assumption of constant relative risk reductions across different baseline rates is unsound. CMAJ Mar. 12, 2012(responding to Barratt A, Wyer PC, McGinn T, et al. Tips for learners of evidence-based medicine: 1. Relative risk reduction, absolute risk reduction and number needed to treat. CMAJ 2004;171(4):353-358):
128. Study of changes in the effect of marital status on cancer outcomes overlooks the way standard measures of association tend to be affected by the overall prevalence of an outcome. BMC Public Health March 5, 2012 (responding to Kravdal H, Syse A. Changes over time in the effect of marital status on cancer survival. BMC Public Health 2011;11:804 (doi:10.1186/1471-2458-804): http://www.biomedcentral.com/1471-2458/11/804/comments#730697
127. Studies of effects of health conditions on self-rated health must consider the ways standard measures of health disparities tend to be affected by the prevalence of an outcome. BMJ Public Health Feb. 14, 2012 (responding to Delpierre C, Kelly-Irving M, Munch-Petersen M., et al. SRH and HRQ: does social position impact differently on their with health status. BMC Public Health 2012, 12,19: doi:10.1186/1471-2458-12-19): http://www.biomedcentral.com/1471-2458/12/19/comments#732698
126. Flaws in tools for measuring healthcare disparities can exacerbate those disparities (responding to Blustein J, Weissman JS, Ryan AM. Analysis raised question of whether pay-for-performance in Medicaid can efficiently reduce racial and ethnic disparities. Health Aff (Millwood) 2011;30(6):1165-1175): http://content.healthaffairs.org/content/30/6/1165/reply
125. Understanding contrasting patterns of relative differences in survival and relative differences in mortality. Emerging Themes in Epidemiology. Jan. 12, 2011 (responding to Hockey R, Tooth l, Dobson A. Relative survival: a useful tool to assess generalisability in longitudinal studies of health in older persons. Emerging Themes in Epidemiology 2011, 8,3): http://www.ete-online.com/content/8/1/3/comments
124. Estimation of treatment effects across a range of baseline rates should not be based on assumptions of either constant relative risks or constant odds ratios. Emerging Themes in Epidemiology Jan. 12, 2012 (responding to Wang H, Boissel JP, Nony P. Revisiting the relationship between baseline risk and risk under treatment. Emerging Themes in Epidemiology 2009;6:1): http://www.ete-online.com/content/6/1/1/comments
123. Efforts to appraise changes in inequalities in poor health over the life course must consider the implications of general increases in poor health as the population ages. BMC Public Health Jan. 12, 2012 (responding to Benzeval M, Green MJ, Leyland AH. Do social inequalities in health widen or converge with age. Longitudinal evidence from three cohorts in the West of Scotland. BMC Public Health 2011, 11:947: doi:10.1186/1471-2458-11-947): http://www.biomedcentral.com/1471-2458/11/947/comments
121. Nomogram for calculating number needed to treat is based on an unsound premise. BMJ Dec. 8, 2011 (responding to Chatellier G, Zapletal E, Lemaitre D, et al. The number needed to treat: a clinically useful nomogram in its proper context. BMJ 1996;312:426-429): http://www.bmj.com/content/312/7028/426?tab=responses
120. The crucial priority for research on equity and health is the development of a sound method of measurement. PLoS Med Dec. 3, 2011 (responding to Östlin P, Schrecker T, Sadana R, Bonnefoy J, Gilson L, et al. (2011) Priorities for Research on Equity and Health: Towards an Equity-Focused Health Research Agenda. PLoS Med 8(11); e1001115. Doi:10):
119. Assumption of constant relative risk reductions across different baseline rates is unsound. BMJ Nov. 21, 2011 (responding to Cook RJ, Sackett DL. The number needed to treat: a clinically useful measure of treatment effect. BMJ 1995;310:452-454): http://www.bmj.com/content/310/6977/452?tab=responses
118. Ratio measures are not transportable. BMJ Nov. 11, 2011 (responding to Schwartz LS, Woloshin S, Dvorin EL, Welch HG. Ratio measures in leading medical journals: structured review of underlying absolute risks. BMJ 2006;333:1248-1252): http://www.bmj.com/content/333/7581/1248?tab=responses
117. Studies of trends in the relationship of marital status to mortality must consider the implications of general changes in mortality. BMC Public Health Nov. 7, 2011 (responding to Berntsen KN. Trends in total and cause-specific mortality by marital status among elderly Norwegian men and women. BMC Public Health 2011; 11:537): http://www.biomedcentral.com/1471-2458/11/537/comments#608693
116. Editorial on Holt-Lunstad study raises three data interpretation issues. PLoS Medicine. Oct. 22, 2011 (responding to The PLoS Medicine Editors. Social Relationships Are Key to Health, and to Health Policy. PLoS Med 2010;7(8): e1000334. doi:10.1371/journal.pmed.1000334):
115. As mortality declines relative differences in mortality tend to increase. BMJ Oct. 12, 2011 (responding to Moser KA, Leon Dam Gwatkin DR, How does progress toward the child mortality millennium development goal affect inequalities between the poorest and the least poor? Analysis of Health Survey data. BMJ 2005;331:1180-3):
114a. Study of differences in rates of dying above median age at death raises a number of statistical issues – Part I. BMJ Sept. 7, 2011 (responding to Barr HL, Britton J, Smyth AR, Fogarty AW. Association between socioeconomic status, sex, and age at death from cystic fibrosis in England and Wales (1959 to 2008): Cross sectional study. BMJ 2011:343:d4662 doi:10.1136/bmj.d4662): http://www.bmj.com/content/343/bmj.d4662.full/reply#bmj_el_269661
114b. Study of differences in rates of dying above median age at death raises a number of statistical issues – Part II. BMJ Sept. 7, 2011 (responding to Barr HL, Britton J, Smyth AR, Fogarty AW. Association between socioeconomic status, sex, and age at death from cystic fibrosis in England and Wales (1959 to 2008): Cross sectional study. BMJ 2011:343:d4662 doi:10.1136/bmj.d4662): http://www.bmj.com/content/343/bmj.d4662.full/reply#bmj_el_269663
113. Discussion of patterns of health inequalities must consider measurement issues. BMJ July 12, 2011 (responding to Mackenbach JP. What would happen to health inequalities if smoking were eliminated. BMJ 2011; 342:d3460).
112. Standard measures of differences between outcome rates are problematic for identifying subgroup effects. BMC Medical Research Methodology June 8, 2011 (responding to White IA, Elbourne D. Assessing subgroup effects with binary data: can the use of different effects measures lead to different conclusions? BMC Medical Research Methodology 2005, 5;15):
111. Systematizing the analysis of effect heterogeneity requires rethinking some fundamentals. Trials June 1, 2011 (responding to Gabler NB, Naihua D, Liao D, et al. Dealing with heterogeneity treatments: is the literature up to the challenge. Trials 2009,10:43):
110. Assessing heterogeneity of treatment effects in light of fundamental statistical tendencies. Trials May 26, 2011(responding to Kent DM, Rothwell PM, Ionnadis JPA, et al. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials 2010,11:85): http://www.trialsjournal.com/content/11/1/85/comments#498686
109. Interpreting effects on referral inequalities of policies that increase overall referral rates. BMJ May 6, 2011 (responding to McBride D, Hardoon S, Walters K, et al.. Explaining variation in referral from primary to secondary care: Cohort study. BMJ 2010;341:c6267):
108. Efforts to investigate health inequalities in the United Kingdom have suffered from the failure to address measurement issues. Journal Review Dec. 29, 2010 (responding to Bambra C, Smith KE, Garthwaite K, et al. A labour of Sisyphus? Public policy and health inequalities research from the Black and Acheson Reports to the Marmot Review. J Epidemiol Community Health doi:10.1136/jech.2010.111195):
107. One cannot determine whether high BMI increases mortality more in different age groups based on relative differences in mortality. Journal Review Dec. 29, 2010 (responding to . Berrington de Gonzalez, Hartge P, Cerchan JR, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med 2010;363:2211-19):
105. Subgroup analyses should not be undertaken without consideration of methodological issues. Health Affairs Nov. 27, 2010 (responding toMullins CJ, Onukwugha, Cooke JL, et al. The potential impact of comparative effectiveness research on minority populations. Health Affairs (Millwood) 2010;29(11):2098-2104):
104. Systematic analyses of health disparities cannot ignore measurement issues. Journal Review Nov. 27, 2010 (responding to Koh HK, Oppenheimer SC, Massin-Short SB, et al. Translating research evidence into practice to reduce health disparities: A social determinants approach. Am J Public Health 2010;100:S72-S80.doi:AJPH.2009):
103. Study raises issues concerning nonsignificant findings and implications of large differences in health services utilization between the healthy and the unhealthy. BMJ Nov. 12, 2010 (responding to Saxena S, Eliahoo , Majeed A. Socioeconomic and ethnic differences in self reported health status and use of health services by children and young people in England: cross sectional study. BMJ 2002;325:520-523): http://www.bmj.com/content/325/7363/520.1/reply#bmj_el_244510
102. Effects of health improvements on health inequalities must be examined with other than standard measures of differences between outcome rates. PLoS Med Nov. 2, 2010 (responding to Capewell S, Graham H (2010) Will Cardiovascular Disease Prevention Widen Health Inequalities? PLoS Med 7(8): e1000320. doi:10.1371/journal.pmed.1000320):
98. Caution is warranted in tying policy decisions to perceptions about effects on health inequalities. BMJ Sept. 29, 2010 (responding to Tugwell P, Petticrew M, Kristjansson E., et al. Assessing equity in systematic reviews: realising the recommendations of the Commission on Social Determinants of Health. BMJ 2010; 341:c4739): http://www.bmj.com/content/341/bmj.c4739.full/reply#bmj_el_242311
96. Comparisons of the size of health inequalities must be based on measures that are unaffected by the overall prevalence of an outcome. BMC Medical Research Methodology Sept. 22, 2010 (responding to Jackson AL, Davis CA, Leyland AH. Do differences in the administrative structure of populations confound comparisons of geographic health equalities? BMC Medical Research Methodology 2010: http://www.biomedcentral.com/1471-2288/10/74): http://www.biomedcentral.com/1471-2288/10/74/comments
95. Problems in identifying interaction where groups have different base rates. BMJ Sept. 21, 2010 (responding to Altman DG, Bland JM. Interaction revisited: the difference between two estimates. BMJ 2003;326:219):
94. Recognizing implications of different base rates in measuring improvements in healthcare. Health Aff (Millwood) Aug. 13, 2010 (responding to Guthrie B, Auerbck G, Bindman AB. Health plan competition for Medicaid enrollees based on performance does not improve quality of care. Health Aff (Millwood) 2010;29:1507-1515)
93. Health and healthcare disparities cannot be usefully measured without consideration of overall prevalence. ________ Aug. 6, 2010 (responding to Hoover K, Bohm M, Keppel K. Measuring disparities in the incidence of sexually transmitted disease. Sexually Transmitted Diseases. December Supplement 2008, Vol. 35, No. 12:S40-S44): http://jpscanlan.com/images/Comment_on_Hoover.pdf
92. Resolving measurement issues should be the pressing health disparities research concern. Journal Review August 6, 2010 (responding to Flores G, Committee on Pediatric Research. Technical report – racial and ethnic disparities I the health and healthcare of children. Pediatrics 201; 1253979-1020): http://jpscanlan.com/images/Flores_Pediatrics_2010.pdf
91. To be of value health inequalities research must address measurement issues. BMJ July 23, 2010 (responding to Thomas B, Dorling D, Davey Smith G. Inequalities in premature mortality in Britain: observational study from 1921 to 2007. BMJ 2010;341:c3639): http://www.bmj.com/cgi/eletters/341/jul22_1/c3639
90. Appraising mortality inequalities among different age groups. BMC Public Health July 19, 2010 (responding to Menvielle G, Leclerc A, Chastang, J-F, Luce D. Socioeconomic inequality in cause specific morality among older people in France. BMC Public Health 2010, 10:260): http://www.biomedcentral.com/1471-2458/10/260/comments#413678
89. Rethinking the premises of subgroup analyses. BMJ June 7, 2010 (responding to Sun X, Briel M. Walter SD, and Guyatt GH. Is as subgroup effect believable? Updating criteria to evaluated the credibility of subgroup analyses. BMJ 2010;340:850-854): http://www.bmj.com/cgi/eletters/340/mar30_3/c117
88. Relative differences cannot effectively identify reporting heterogeneity. Journal Review May 21, 2010 (responding to Huisman M, van Lenthe F, Mackenbach JP. The predictive ability of self assessed health for mortality in different educational groups. Int J Epidemiol 2007;36:1207–1213):http://jpscanlan.com/images/Huisman_IJE_2007.pdf
87. Research into effects of interventions on inequalities must first address measurement issues. Journal Review Apr. 28, 2020 (responding to Bambra C, Gibson M, Sowden A, et al. Tackling the wider social determinants of health and health inequalities: evidence from systematic review. J Epidemiol Community Health 2010;64:284-291):
86. Interpreting racial differences in hypertension control. Journal Review Apr. 28, 2020 responding to Rehman SU, Hutchison FN, Hendrix K, et al. Ethnic differences in blood pressure control among men at Veterans Affairs clinic and other health care sites. Arch Intern Med 2005;165:1041-104): http://jpscanlan.com/images/Rehman_Arch_Int_Med_2005.pdf
85. Additional Issues to be considered by a World Council of Epidemiology and Causality. Emerging Themes in Epidemiology April 8, 2010 (responding to Bhopal R. Seven mistakes and potential solutions in epidemiology, including a call for a World Council of Epidemiology and Causality. Emerging Themes in Epidemiology 2009,6:6):
84. Incentive programs to reduce healthcare disparities should await better understanding of how to measure those disparities. Journal Review March 2, 2010 (responding to Siegel B, Nolan L. Leveling the field – ensuring equity through National Health Care Reform. N Engl J Med 2009;361:2401-2403):
83. Interpreting data on comparative efficacy of an intervention in settings with different base rates. Journal Review Feb. 28, 2010 (responding to Madhi SA, Cunliffe NA, Steele D, et al. Effect of human rotavirus vaccine on severe diarrhea in African infants. N Engl J Med 2010;362:289-98: http://jpscanlan.com/images/Madhi_NEJM_2010.pdf
82. Importance of distinguishing disparities in survival from disparities in mortality. ______ Feb. 17, 2010 (responding to Keegan, THM, Clarke CA, Chang ET, et al. Disparities in survival after Hodgkin lymphoma: a population based study. Cancer Causes Control 2009;20:1881-1892: http://www.jpscanlan.com/images/Comment_on_Keegan.pdf
81. The importance of distinguishing mortality inequalities from survival inequalities. Journal Review Feb. 17, 2010 (responding to Hill S, Sarfati D, Blakely t, et al. Survival disparities in indigenous and non-indigenous New Zealanders with colon cancer: the role of patient comorbidity, treatment, and health service factors. J Epidemiol Community Health 2010;64:117-123):http://jpscanlan.com/images/Hill_JECH_2010.pdf
80. Health disparities cannot be measured without consideration of the overall prevalence of an outcome. Journal Review Feb.10, 2010 (responding to Orsi JM, Margellos-Anast H., Whitman S. Black-white health disparities in the United States and Chicago: A 15-Year Progress Analysis. Am J Public Health. 201;100:349-356:http://jpscanlan.com/images/Orsi_AJPH_2010.pdf
79. Understanding the forces driving cross-national variations in relative differences in outcome rates. Eur J Pub Health Jan. 25, 2009 (responding to Huijts T, Eikemo TA. Causality, social selectivity or artefacts? Why socioeconomic inequalities in health are not smallest in the Nordic countries. Eur J Pub Health 2009;19:452-53): http://eurpub.oxfordjournals.org/cgi/eletters/19/5/452
77. The effect of changes in the overall prevalence of an outcome on relative differences in experiencing and avoiding it. BMJ Dec. 28, 2009 (responding to Gregory IN. Comparison between geographies of mortality and deprivation from the 100s and 2001: spatial analysis of census and mortality statistics. BMJ 2009;339:b3454):
76. Disparities quality index is flawed in several respects. Journal Review Dec. 19, 2009 (responding to Siegel B, Bear D, Andres E, Mead H. Measuring equity: An index of health care disparities. Q Manage Health Care 2009;18(2):84-90): http://jpscanlan.com/images/Siegel_QMHC_2009.pdf
75. Mastering tools to monitor health disparities should precede expansion of monitoring. Journal Review Dec. 5, 2009 (responding to Rosenthal MB, Landon Bruce E., Normand ST, et al. Engagement of health plans and employers in addressing racial and ethnic disparities in healthcare. Med Care Res Rev 2009;66(2):219-231): http://jpscanlan.com/images/Rosenthal_MCRR_2009.pdf
74. Article on disparities in control of cardiovascular disease and diabetes raises several measurement issues. Ann Int Med Nov. 30, 2009 (responding to McWilliams JM, Meara E., Zaslavsky AM, Ayanian JZ. Differences in control of cardiovascular disease and diabetes by race, ethnicity, and education, U.S. trends from 1999 to 2006 and effects of Medicare coverage. Ann Int Med 2009;150:505-515):
73. Measuring disparities in risk factors by means of absolute differences between rates. Journal Review Nov. 28, 2009 (responding to Kanjilat S, Gregg EW, Cheng YJ, et al. Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adjust, 1971-2002. Arch Intern Med 2006;166:2348-2355): http://jpscanlan.com/images/Kanjilat_Arch_Int_Med_2006.pdf
72. The relationship between overall prevalence and measures of differences between outcome rates. International Journal for Equity in Health __________ (responding to Eikemo TA, Skalicka V, Avendano M. Variations in health inequalities: are they a mathematical artifact? International Journal for Equity in Health 2009;8:32: http://www.equityhealthj.com/content/pdf/1475-9276-8-32.pdf): http://jpscanlan.com/images/Comment_on_Eikemo_et_al..pdf
(This comment was submitted to the referenced journal on October 3, 2009. The journal originally planned to it after receiving replies from the authors of the articles it addresses, but subsequently indicated it would publish only a much shorter version, which I have not yet submitted. Meanwhile a version of the comment may be accessed by the indicated link.)
71. Effects of standard adjustment approaches on relative and absolute inequalities. J Epidemiol and Community Health Nov. 2, 2009 (responding to Lynch J, Davey Smith G, Harper S, Bainbridge K. Explaining the social gradient in coronary heart disease: comparing relative and absolute risk approaches. J Epidemiol Community Health 2006:60:436-441): http://jech.bmj.com/content/60/5/436.abstract/reply#jech_el_2400
(Item 71 comment is a follow-up to item 11. It also addresses issues raised in Singh-Manoux A, Nabi H, Shipley M, et al. The role of conventional risk factors in explaining social inequalities in coronary heart disease – the relative and absolute approaches. Epidemiology 2008;19:599-605.)
70. Measurement lessons learned, then forgotten. Journal Review May 6, 2009 (responding to Mackenbach JP, Stirbu I, Roskam AJ, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med 2008;358:2468-2481: http://jpscanlan.com/images/Comment_on_Mackenbach.pdf
69. Study raises a number of issues about analyzing disparities between and among demographic groups. Journal Review March 23, 2009 (responding to Harper S, Lynch J, Meersman SC, et al. Trends in area-socioeconomic disparities in breast cancer screening, mortality, and survival among women ages 50 years and over (1987-2005). Cancer Epidemiol Biomarkers Prev 2009;18(1):121-131):
68. Relative differences in survival and relative differences in mortality are different things. Journal Review Mar. 3, 2009 (Woldemichael G, Christiansen D, Thomas S, Benbow N. Demographic characteristics and survival with AIDS in Chicago, 1993-2001) Am J Public Health 2009;XXX-XXX.doi:10.2105/AJPH.2007.124750: http://jpscanlan.com/images/Woldemichael_AJPH_2009.pdf
67. Recommendations to incorporate reductions in disparities in P4P programs cannot ignore measurement issues. Journal Review Feb. 21, 2009 (responding to Chien AT, Chin MH. Incorporating disparity reduction into pay-for-performance. J Gen Intern Med 2008;24(1):135-136): http://jpscanlan.com/images/Chien_JGIM_2008.pdf
66. Tying pay-for-performance to healthcare disparities should await mastery of measurement issues. BMJ Feb. 8, 2009 (responding to Bierman AS, Clark JP. Performance measure and equity. BMJ 2007;334:1333-1334): http://www.bmj.com/cgi/eletters/334/7608/1333
65. Measuring racial disparities in hypertension control. Ann Fam Med Jan. 25, 2009 (responding to Satcher D. Examining racial and ethnic disparities in health and hypertension control. Ann Fam Med 2008;6:483-485): http://www.annfammed.org/cgi/eletters/6/6/483
64. Interpreting patterns of changes in absolute differences between rates when common outcomes become even more common. BMJ Dec. 7, 2008 (responding to Ashworth M, Medina J, Morgan M. Effect of social deprivation on blood pressure monitoring and control in England: a survey of data from the quality and outcomes framework. BMJ 2008;337:a2030): http://www.bmj.com/cgi/eletters/337/oct28_2/a203063
63. “Inverse equity hypothesis” overlooks important statistical tendencies. Journal Review Dec. 2, 2008 (responding to Victora CG, Vaughan JP, Barros FC, et al. Explaining trends in inequities: evidence from Brazilian child health studies. Lancet 2000;356:1093-1098): http://jpscanlan.com/images/Comment_on_Victora.pdf
62. Interpreting patterns of changes in measures of demographic differences in folate status in light of overall improvements in folate status. Journal Review Dec. 2, 2008 (responding to Dowd JB, Aiello AE. Did national folic acid fortification reduce socioeconomic and racial disparities in folate status in the US. Int J Epidemiol 2008:37:1059-1066): http://jpscanlan.com/images/Comment_on_Dowd_and_Aiello.pdf
61. Measures of health and healthcare disparities that are unaffected by the overall prevalence of an outcome (responding to Low A, Low A. Importance of relative measures in policy on health inequalities. BMJ. 2006;332:967-969. BMJ Nov. 29, 2008): http://www.bmj.com/cgi/eletters/332/7547/967
60. Illustrating whether the relationship between race and allostatic load scores increases with age. Journal Review July 24, 2008 (responding to Geronimus A, Hicken M, Keene D, and Bound J. Weathering and age patterns of allostatic load scores among blacks and whites in the United States. Am J Pub Health 2006;96:826-833): http://jpscanlan.com/images/Geronimus_AJPH_2005.pdf
59. Relative differences in outcome rates tend to be large where outcomes are rare. Journal Review May 31, 2008 (responding to Kawachi I, Daniels N, Robinson DE. Health disparities by race and class: why both matter. Health Affairs 2005;24(2):343-352): http://jpscanlan.com/images/Kawachi_Health_Affairs_2005.pdf
58. Identifying meaningful differences in inequalities in revascularization rates in different settings. Journal Review May 9, 2008 (responding to Hetemaa T, Keskimäki I, Manderbacka, et al. How did the recent increase in the supply or coronary operations in Finland affect socioeconomic and gender equity in their use? J Epidemiol Community Health 2003;57:178-185):http://jpscanlan.com/images/Second_Hetemaa_Comment.pdf
57. Health disparities curricula must address measurement issues. Ann Intern Med May 12, 2008 (responding to Smith WR, Betancourt JR, Wynia MK. Recommendations for teaching about racial and ethnic differences in health and health care. Ann Intern Med 2007;147:654-665): http://www.annals.org/cgi/eletters/147/9/654
56. Study shows different adjustment approaches rather than different relative and absolute perspectives. Journal Review May 1, 2008 (responding to Khang YH, Lynch JW, Jung-Choi K, Cho HJ. Explaining age-specific inequalities in mortality from all causes, cardiovascular disease and ischaemic heart disease among South Korean public servants: relative and absolute perspectives. Heart 2008;94:75-82):http://jpscanlan.com/images/Khang_Heart_2009.pdf
55. Understanding patterns of absolute differences in vaccination rates in different settings. Journal Review Apr. 22, 2008 (responding to Schneider EC, Cleary PD, Zaslavsky AM, Epstein AM. Racial disparity in influenza vaccination: Does managed care narrow the gap between blacks and whites? JAMA 2001;286:1455-1460):
54. Misinterpreting patterns of relative differences in mortality. Am J Public Health Apr. 13, 2008 (responding to Wilkinson RG, Pickett KE. Income inequality and socioeconomic gradients in mortality. Am J Public Health 2008;98:699-704): http://www.ajph.org/cgi/eletters/98/4/699
53. Comparisons of the sizes of differences between black and white rates for different procedures are not informative without consideration of the overall levels for each procedure. Journal Review Mar. 28, 2008 (responding to Baicker K, Chandra A, Skinner JS, Wennberg JE. Who you are and where you live: how race and geography affect the treatment of Medicare beneficiaries. Health Affairs 2004:Var-33-Var-44):
52. Study illustrates ways in which the direction of a change in disparity turns on the measure chosen. Pediatrics Mar. 27, 2008 (responding to Morita JY, Ramirez E, Trick WE. Effect of school-entry vaccination requirements on racial and ethnic disparities in Hepatitis B immunization coverage among public high school students. Pediatrics 2008;121:e547-e552): http://pediatrics.aappublications.org/cgi/eletters/121/3/e547
51. First learn to measure healthcare disparities. Health Affairs Mar. 12, 2008 (responding to Casalino LP, Elster A, Eisenberg A, et al. Will pay-for-performance and quality reporting affect health care disparities? Health Affairs 2007;26(3):405-414): http://content.healthaffairs.org/cgi/eletters/26/3/w405
50. Reconsidering a landmark study. Lancet Feb. 25, 2008 (responding to Mackenbach JP, Kunst AE, Cavelaars, et al. Socioeconomic inequalities in morbidity and mortality in western Europe, Lancet 1997; 349: 1655-59):
49. Inclusion of healthcare disparities issues in pay-for-performance programs should await development of reliable means of measuring changes in disparities over time. Journal Review Feb. 16, 2008 (responding to Casalino LP, Elster A, Eisenberg A, et al. Will pay-for-performance and quality reporting affect health care disparities? Health Affairs 2007;26(3):405-414): http://jpscanlan.com/images/Casalino_Health_Affairs_2007.pdf
48. Perceptions of changes in healthcare disparities among the elderly dependant on choice of measure, Journal Review 2/12/08 (responding to Escarce JJ, McGuire TG. Changes in racial differences in use of medical procedures and diagnostic tests among elderly persons: 1986-1997. Am J Public Health 2004;94:795-1799):http://jpscanlan.com/images/Escarce_McGuire_2004.pdf
47. Pay-for-performance and the measurement of healthcare disparities. Journal Review Feb. 10, 2008 (responding to Chien AT, Chin MH, Davis AM, Casalino LP. Pay for performance, public reporting, and racial disparities in health care: how are programs being designed. Med Car Res Rev 2007;64:283S-304S): http://jpscanlan.com/images/Chien_MCRR_2007.pdf
46a. Implications of the focus on racial/ethnic disparities in control rather than processes in the context of pay-for-performance . Journal Review Feb. 10, 2008 (responding to Werner, RM, Asch DA, Polsky D. Racial profiling: The unintended consequences of coronary artery bypass graft report cards. Circulation 2005;111:1257–63):
46. Pay-for-performance implications of the failure to recognize the way changes in prevalence of an outcome affect measures of racial disparities in experiencing the outcome. Journal Review Feb. 8, 2008 (responding to Werner, RM, Asch DA, Polsky D. Racial profiling: The unintended consequences of coronary artery bypass graft report cards. Circulation 2005;111:1257–63): http://jpscanlan.com/images/Werner_Circulation_2005.pdf
45. Comparing health inequalities across time and place with an understanding of the usual correlations between various measures of difference and overall prevalences. Journal Review Jan. 30, 2008 (responding to Moser K, Frost C, Leon D. Comparing health inequalities across time and place—rate ratios and rate differences lead to different conclusions: analysis of cross-sectional data from 22 countries 1991–200. Int J Epidemiol 2007;36:1285-1291: http://jpscanlan.com/images/Moser_IJE_2007.pdf
44. Increases in relative differences in adverse health outcomes do not necessarily reflect increasing health inequality. Am J Public Health Jan. 24, 2008 (responding to Frohlich KL, Potvin L. Transcending the Known in Public Health Practice: The inequality paradox: The population approach and vulnerable populations. Am J Pub Health 2008;98:216-221): http://www.ajph.org/cgi/eletters/98/2/216
D43. Comparing the size of inequalities in dichotomous measures in light of the standard correlations between such measures and the prevalence of an outcome. Journal Review Jan. 14, 2008 (responding to Boström G, Rosén M. Measuring social inequalities in health – politics or science? Scan J Public Health 2003;31:211-215):
42. Recognizing the way correlations between improvements in healthcare and reductions in healthcare disparities tend to turn on the choice of disparities measure. Journal Review Nov. 9, 2007 (responding to Aaron KF, Clancy CM. Improving quality and reducing disparities. JAMA 2003;289:1033-34):
41. Understanding patterns of correlations between plan quality and different measures of healthcare disparities. Journal Review Aug. 30, 2007 (responding to Trivedi AN, Zaslavsky AM, Schneider EC, Ayanian JZ. Relationship between quality of care and racial disparities in Medicare health plans. JAMA 2006;296:1998-2004):
40a. Correction to statements concerning the measurement of healthcare disparities by the Agency for Healthcare Research and Quality in earlier comment on Sequist et al. Journal Review Nov. 15, 2007: http://jpscanlan.com/images/Sequist_Correction.pdf
D40. Understanding the ways improvements in quality affect different measures of disparities in healthcare outcomes regardless of meaningful changes in the relationships between two groups’ distributions of factors associated with the outcome. Journal Review Aug. 30, 2007 (responding to Sequist TD, Adams AS, Zhang F, Ross-Degnan D, Ayanian JZ. The effect of quality improvement on racial disparities in diabetes care. Arch Intern Med 2006;166:675-681): http://jpscanlan.com/images/Sequist_Archives_Int_Med_2006.pdf
39. Understanding widening socioeconomic differences in child mortality. Journal Review Aug. 27, 2007 (responding to Singh GK, Kogan MD. Widening socioeconomic disparities in US childhood mortality, 1969-2000. Am J Public Health 2007:97:1658-1665): http://jpscanlan.com/images/Singh_AJPH_2007.pdf
38. Interpreting patterns of differing effects of chronic conditions on self-assessed health. Journal Review June 30, 2007 (responding to Brown AF, Ang A, Pebley AR. The relationship between neighborhood characteristics and self-rated health for adults with chronic conditions. Am J Public Health 2007;97:926-932): http://jpscanlan.com/images/Brown_AJPH_2007.pdf
37. Recognizing expected patterns of relative differences in the Whitehall cohort. Journal Review June 25, 2007 (responding to van Rossum CTM, Shipley MJ, van de Mheen H, et al. Employment grade differences in cause specific mortality. A 25-year follow up of civil servants from the first Whitehall study. J Epidemiol Community Health 2000;54:178-84): http://jpscanlan.com/images/Van_Rossum_JECH_2000.pdf
36. Understanding the way choice of measure tends to dictate the results of studies of the way improvements in healthcare affect disparities. Journal Review June 19, 2007 (responding to James PD, Wilkins R, Detsky AS, et al. Avoidable mortality by neighborhood income in Canada: 25 years after the establishment of universal health insurance. J Epidemiol Community Health 2007;61:287-296): http://jpscanlan.com/images/James_JECH_2007.pdf
35. Problems with the measurement of changes in health inequalities over time using dichotomous variables and possibilities using continuous variables. Journal Review June 19, 2007 (responding to Ferrie JE, Shipley MJ, Davey Smith GD. Change in health inequalities among British civil servants: the Whitehall II study. J Epidemiol Community Health 2002:56:922-926): http://jpscanlan.com/images/Ferrie_JECH_2002.pdf
34. Recognizing why dichotomous and continuous measures may yield contrary results. BMJ June 11, 2007 (responding to Chandola T, Ferrie J, Sacker A, Marmot M. Social inequalities in self reported health in early old age: follow-up of prospective cohort study. BMJ 2007:334:990-996): http://www.BMJ.com/cgi/eletters/334/7601/990
33. Recognizing the statistical basis for advances in health care to cause larger relative reductions in mortality in groups with lower base rates. Journal Review June 9, 2007 (responding to Korda RJ, Butler JRG, Clements MS, Kunitz SJ. Differential impacts of health care in Australia: trend analysis of socioeconomic inequalities in avoidable mortality. Int J Epidemiol 2007;36:157-165): http://jpscanlan.com/images/Korda_IJE_2007.pdf
32. Understanding the ways factors tend to increase outcome rates proportionately more in groups with lower base rates. Journal Review June 7, 2007 (responding to Thurston RC, Kubzansky LD, Kawachi I, Berkman LF. Is the association between socioeconomic position and coronary heart disease stronger in women than in men. Am J Epidemiol 2005;162:57-64): http://jpscanlan.com/images/Thurston_AJE_2005.pdf
31. Role of the prevalence of an outcome in the size of rate differences. J Epidemiol Community Health June 4, 2007 (responding to Martikainen P, Blomgren J, Valkonen T. Change in the total and independent effects of education and occupational social class on mortality: analyses of all Finnish men and women the period 1971-2000. J Epidemiol Community Health 2007;61:499-505): http://jech.bmj.com/content/61/6/499.abstract/reply#jech_el_1338
30. Interpreting departures from expected patterns of relative differences. J Epidemiol Community Health June 4, 2007 (responding to Mustard CA, Etches J. Gender differences in socioeconomic inequality in mortality. J Epidemiol Community Health 2003;57:974-980): http://jech.bmj.com/content/57/12/974.abstract/reply#jech_el_1340
29. A study with a variety of problems. Journal Review June 2, 2007 (responding to Schulman KA, Berlin JA, Harless, et al. The effect of race and sex on physicians’ recommendations for cardiac catheterization. N Engl J Med 1999;340:618-26):
28. Understanding why the accomplishments of the welfare state generally will not reduce health inequalities as they are typically measured. Journal Review June 2, 2007 (responding to Lawlor DA, Ronalds G, Macintyre S, et al. Family socioeconomic position at birth and future cardiovascular disease risk: findings from the Aberdeen children of the 1950s cohort study. Am J Public Health 2006;96:1271-1277): http://jpscanlan.com/images/Lawlor_AJPH_2006.pdf
27. Understanding when general increases in an outcome tend to result in increasing absolute differences between the rates of two groups. Journal Review June 1, 2007 (responding to Trivedi AN, Zaslavsky AM, Schneider EC, Ayanian JZ. Trends in the quality of care and racial disparities in Medicare managed care. N Engl J Med 2005;353:692-700)(included in item 23):
26. Understanding when general increases in an outcome tend to result in increasing absolute differences between the rates of two groups. Journal Review June 1, 2007 (responding to Jha AK, Fisher ES, Li Z, Orav EJ, Epstein AM. Racial trends in the use of major procedures among the elderly. N Engl J Med 2005;353:683-691) (included in item 23): http://jpscanlan.com/images/Vaccarino_NEJM_2005.pdf
25. Understanding expected patterns of changes in absolute differences between the rates at which racial or gender groups receive adequate care. Journal Review May 1, 2007 )Sehgal AR. Impact of quality improvement efforts on race and sex disparities in hemodialysis. JAMA 2003;289:996-1000): http://jpscanlan.com/images/Sehgal_JAMA_2003.pdf
24. Recognizing the role of the prevalence of an outcome in comparing the size of relative differences in experiencing or failing to experience the outcome. Journal Review May 31, 2007 (responding to Gan SC, Beaver SK, Houck PM, et al. Treatment of acute myocardial infarction and 30-day mortality among women and men. N Engl J Med 2000;343:8-15): http://jpscanlan.com/images/Gan_NEJM_2000.pdf
23. Effects of choice measure on determination of whether health care disparities are increasing or decreasing. Journal Review May 1, 2007 (responding to Vaccarino V, Rathore SS, Wenger NK, et al. Sex and racial differences in the management of acute myocardial infarction, 1994 through 2002. N Engl J Med 2005;353:671-682):
22. Understanding why reductions in injury rates will tend to increase relative differences in injury rates. Lancet Jan. 31, 2007 (responding to Sethi D, Racioppi F, Baumgarten I, Bertollini R. Reducing inequalities from injuries in Europe. Lancet 2006:368:2243-50): http://jpscanlan.com/images/Sethi_Lancet_2006.pdf
21. Interpreting changes in relative inequalities in receipt of procedures. J Epidemiol Community Health Jan 25, 2007 (responding to Hetemaa T, Keskimäki I, Manderbacka, et al. How did the recent increase in the supply or coronary operations in Finland affect socioeconomic and gender equity in their use? J Epidemiol Community Health 2003;57:178-185): http://jech.bmj.com/content/57/3/178.abstract/reply#jech_el_1305
20. Interpreting patterns of inequalities in perinatal outcomes. J Epidemiol Community Health Jan 18, 2007 (responding to Fairley L, Leyland AH. Social class inequalities in perinatal outcomes: Scotland 1980-2000. J Epidemiol Community Health 2006;601:31- 36: http://jech.bmj.com/content/60/1/31.full/reply#jech_el_1282
19. The relationship between the prevalence of an outcome and the size of the relative disparity in experiencing it. BMJ Dec 14, 2006 (responding to Kristensen P. Review of Social Inequalities in Health: New Evidence and Policy Implications. BMJ 2006;333:1167): http://www.BMJ.com/cgi/eletters/333/7579/117
18. Issue in the interpretation of health inequalities in New York. J Epidemiol Community Health Dec. 14, 2006 (responding to Karpati AM, Bassett MT, McCord C. Neighborhood mortality inequalities in New York City, 1989-1991 and 1999-2001. J Epidemiol Community Health 2006;60:1060-1064): http://jech.bmj.com/content/60/12/1060.abstract/reply#jech_el_1277
16. Explanation for large health inequalities in Nordic countries. Eur J Public Health Nov. 1, 2006 (responding to Hemmingsson T, Lundberg I. Can large relative mortality differences between socioeconomic groups among Swedish men be explained by risk indicator-associated social mobility? Eur J Public Health 200515:518 -522): http://eurpub.oxfordjournals.org/cgi/eletters/15/5/518#22
15. Difficulties in comparing relative differences across subgroups. J Epidemiol Community Health Dec. 4, 2006 (responding to Kaplan RM, Kronick RG. Marital status and longevity in the United States Population. J Epidemiol Community Health 2006;60:760-765): http://jech.bmj.com/content/60/9/760.abstract/reply#jech_el_853
14. Differences in average hospital stay as a measure of inequality. Am J Public Health Aug. 18, 2006 (responding to Icks A, Haastert B, Rathmann W, et al. Trends in hospitalization and sociodemographic factors in diabetic and nondiabetic populations in Germany: National Health Survey, 1990-1992 and 1998. Am J Public Health 2006;96:1656-1661): http://www.ajph.org/cgi/eletters/AJPH.2005.063339v1
13. Understanding inequalities in injury deaths BMJ July 19, 2006 (responding to Edwards P, Green J, Roberts I, Lutchmun S. Deaths from injury in children and employment status in family: analysis of trends in class specific death rates. BMJ 2006;333:119-121):
12. Understanding how changes in prevalence of adverse health outcomes affect health inequalities. Lancet May 23, 2006 (responding to Wilkinson R, Pickett K. Health inequalities and the UK Presidency of the EU. Lancet 2006;376:1126-1128:
11. Understanding social gradients in adverse health outcomes within high and low risk populations. J Epidemiol Community Health May 18, 2006 (responding to Lynch J, Davey Smith G, Harper S, Bainbridge K. Explaining the social gradient in coronary heart disease: comparing relative and absolute risk approaches. J Epidemiol Community Health 2006:60:436-441): http://jech.bmj.com/content/60/5/436.abstract/reply#jech_el_549
10. Changing inequalities in morbidity. J Epidemiol Community Health May 16, 2006 (responding to Adams J, Holland L, White M. Changes in socioeconomic inequalities in census measures of health in England and Wales, 1991-2001. J Epidemiol Community Health 2006;60:218-222): http://jech.BMJjournals.com/cgi/eletters/60/3/218
7. Interpreting increasing health inequalities in Spain. Am J Public Health Apr. 24, 2006 (responding to Regidor E, Ronda E, Pascual C, Martinez D, Calle ME, Dominguez V. Decreasing socioeconomic inequalities and increasing health inequalities in Spain: A case study. Am J Public Health 2006;96:102-108): http://www.ajph.org/cgi/eletters/96/1/102
6. Measuring health disparities. J Public Health Manag Pract 2006;12(3):293-296 (responding to Keppel KG, Pearcy JN. Measuring relative disparities in terms of adverse events. J Public Health Manag Pract 2005;11(6):479–483):
5. Difficulties in the interpretation of patterns of health racial differences in allostatic load. Am J Public Health Feb. 26, 2006 (responding to Geronimus A, Hicken M, Keene D, and Bound J. Weathering and Age Patterns of Allostatic Load Scores Among Blacks and Whites in the United States. Am J Public Health 2006;96:826-833): http://www.ajph.org/cgi/eletters/96/5/826
4. Interpreting trends in racial disparities in stillbirth. Am J Public Health Feb. 26, 2006 (responding to Ananth CV, Shiliang L, Kinzler WL, Kramer MS. Stillbirths in the United States, 1981-2000: An Age, Period and Cohort Analysis. Am J Public Health 2005;95:2213-2217): http://www.ajph.org/cgi/eletters/95/12/2213
3. Changing social inequalities in SIDS. Am J Public Health Dec. 11, 2005 (responding to Pickett KE, Luo Y, Lauderdale DB. Widening social inequalities in risk for sudden infant death syndrome. Am J Public Health 2005;95:97-81): http://www.ajph.org/cgi/eletters/95/11/1976
2. Interpreting changes in mortality differences. J Epidemiol Community Health Sep. 8, 2005 (responding to Shaw C, Blakely T, Atkinson J, Crampton P. Do social and economic reforms change socioeconomic inequalities in child mortality? A case study: New Zealand, 1981-1999. J Epidemiol Community Health 2005;59:638-644): http://jech.BMJ.com/cgi/eletters/59/8/638
1. Appraising the size of racial differences in mortality. Health Affairs Aug. 23, 2005 (responding to Satcher D, Fryer GE, McCann J, et al. What if we were equal? A comparison of the black-white mortality gap in 1960 and 2000. Health Affairs 2005;24(2):459-564): http://content.healthaffairs.org/cgi/eletters/24/2/459
E.Summaries of Particular Issues
1. Health Inequalities in the United Kingdom
For three decades, the United Kingdom has been a leader in health inequalities research, and the Whitehall Studies have played a significant role in such research.In the main, such research has relied on relative differences in adverse outcomes and has interpreted increasing relative differences in mortality as reflecting increasing health inequality without regard to the extent to which increases in relative differences in mortality are a statistical function of declining mortality or whether relative differences in survival rates have declined.That relative differences in mortality are larger among British civil servants than in UK society at large has been the basis for a number of inferences about the causes or nature of health inequalities.For example, such fact has been interpreted to suggest that health inequalities in the UK population at large are greater than they seem.It has also been interpreted to suggest that psycho-social factors play as large a role in health inequalities as material circumstances.But such interpretations have been reached without consideration of the extent to which large relative inequalities in mortality (or small relative differences in survival) among British civil servants are a function of the low mortality in that population. These and related issues are addressed in references B3, D8-D10, D12,D13, D20, D28, D32, D34, D35, D37, D61, D64, D77.
2. Seemingly Large Health Inequalities in Nordic Countries
In 1997 a landmark study in The Lancet surprised many by finding that the comparatively egalitarian countries of Sweden and Norway had larger than average relative differences in mortality.Such finding would be repeatedly noted in ensuing years and may well have increased within Nordic countries interest in the study of health inequalities.But the Lancet article overlooked the role of low mortality in large relative differences in mortality in countries like Norway and Sweden.These issues are discussed generally in references A12, B3, B5, D16, D17, D19, D50, and D54.References D50 and D54 also discuss a 2007 article co-authored by two of the principal authors of the Lancet study. The 2007 article reaches conclusions about the systematic relationship between relative differences in an outcome and the prevalence of an outcome that also call into question the conclusions of the Lancet article.See also Section E.7.
Reference B3 (at 13-14) discusses the implications of absolute minimums in the context of a situation, such as that in Sweden, with extremely low infant mortality.References D20 and D31 also discuss certain health inequalities issues involving Finland, but not with regard to issues that are peculiarly germane to Nordic countries,
3. Absolute Differences and Odds Ratio
Most of the earlier references listed on this page principally or exclusive discuss the correlations between overall prevalence of an outcome and relative differences in experiencing or avoiding it.The reasoning of those references, however, would also lead to certain conclusions about the way absolute differences and odds ratios are correlated with overall prevalence, as discussed in a couple of earlier works (A3 (1991), C1 (1992)).Such pattern, roughly, is that absolute differences tend to be small when an outcome is rare, grow larger as it becomes more common, and then grow small again as it becomes nearly universal; differences measured in odds ratios behave in the opposite manner.Beginning with B2 (2001) and B3 (2001), the listed references give increasing attention to absolute differences and odds ratios, including A12, B4-B14, and D2, D6-D9, D15, D17, D18, D20, D23, D25-D27, D30, D31, D33-D36, D38, D40-D42, D 47, D53, D55.The more recent references, particular the conference presentations from B11 on and D23, D40, D41 give a good deal of attention to the point at which the direction of changes in absolute differences reverse as an outcome becomes more common and the implications of that point with respect to the interpretation of changes in healthcare disparities over time and the correlation of the quality of health care with absolute differences.
4. Health and Healthcare Disparities Measurement Approaches of the National Center for Health Statistics and Agency for Healthcare Research and Quality
The 2000 article Race and Mortality (item A12) principally discussed the way relative differences in adverse health outcomes tend to be larger (and relative differences in the opposite outcome tend to be smaller) where the adverse health outcomes are rarer.But the article also pointed out that, solely as a matter of convention, disparities in things like beneficial healthcare procedures were typically measured in terms of relative differences in rates of receiving such procedures. Thus, it noted, since such procedures were becoming more widespread, racial disparities in those outcomes were perceived to be declining.Responding to Race and Mortality (and A5), in 2004 and 2005 statisticians at the National Center for Health Statistics (NCHS) published a number of reports or articles recommending that all disparities (both with regard to health and healthcare) be measured in terms of relative differences in adverse outcomes (in the case of healthcare, the failure to receive appropriate healthcare).Such is the approach used to measure progress in eliminating disparities for purposes of Health People 2010.The Agency for Healthcare Research and Quality (AHRQ), which issues the yearly National Healthcare Disparities Report, measures disparities in terms of the larger of the relative difference in the favorable or the adverse outcome. Since the latter relative difference is almost always larger than the former for the things that AHRQ examines, its approach is usually consistent with that of NCHS.
A number of the references on this page criticize NCHS and AHRQ for the failure to recognize or address the correlation between prevalence of an outcome and relative differences in experiencing or avoiding it.These references also point out that the consequence of the usual approach of NCHS and AHRQ is to find improvement of healthcare to be correlated with increasing healthcare disparities as NCHS (always) and AHRQ (usually) measure them.See A12, B3, B12, B13, D12, D23a, D40, D40a, D41, D41a, D52 (Comment on Morita), D53, D55.The Addendum to B12 and item D52 also discuss the situations where the AHRQ approach would lead to reliance on the relative differences in the favorable outcome at one point in time and the relative difference in the adverse outcome at another point in time.See also Section E.7 below, the NHDR Technical Issues sub-page to this page, and Section A.6 of the Scanlan’s Rule page.
5. Pay-for-Performance Issues
Relying on absolute differences between rates, the 2005 article to which reference D46 responds found that coronary artery bypass report cards tended to increase racial disparities in bypass grafts, measured in terms of absolute differences between rates.Such finding has been interpreted as suggesting that pay-for-performance programs may increase racial disparities in healthcare and have led to suggestions that effects on healthcare disparities be part of pay-for-performance programs, something now being implemented in Massachusetts.A number of the references listed in Section D argue that the finding that coronary artery bypass report card grafts increase racial disparities was flawed for failure to recognize that, solely for statistical reasons, increases in availability of coronary artery bypasses would tend to increase absolute differences between rates bypass rates.Apart from questioning this finding, references D46, D46a, D47, D49, D51, D64, D66, D67, D75, D78, and D84 raise questions about including effects on healthcare disparities in pay-for-performance programs, given that near universal lack of understanding of the relationship of the prevalence of an outcome to each measure of health disparities and the difficulty, even with such an understanding, of reliably measuring changes in disparities over time.The matter is addressed more fully on the Pay for Performance sub-page to this page.
6. Measures of Health and Healthcare Disparities that are Unaffected by the Prevalence of an Outcome
A number of the references on this page (e.g., A12, B7), in addition to describing the problems with the standard measure of health disparities, raise issues about whether, with regard to the crucial appraisal of the comparative size of disparities in different settings (particularly at different points in time), disparities can be measured reliably enough to justify the amount of research conducted in this area.Several items (e.g., B3, B9-B11)) explore the possibility of using genuinely continuous measures, assuming that they do not raise the same issues as binary measures, and in doing so, explain why many seemingly continuous measures are in fact functions of dichotomies and hence implicate the same interpretive issues as binary measures.A number of more recent works (B13-B18, D43, D45, D46, D46a, D48, D52, D53, D55, D56, D60), however, explore the possibility of measuring the size of disparities in particular settings by deriving, from the rates of the advantaged and disadvantaged groups being compared in each setting, the size of the difference between means of hypothesized underlying distributions (measured in terms of percentage of a standard deviation).As repeatedly noted in those works (and as explored at greatest length in Section B of reference 43), this approach involves some speculation, given that we do not know what the underlying distributions actually look like.And, as discussed in references 43 and D46a, the approach appears not to be even theoretically sound as to situations where we know the distributions are not normal because they are in fact truncated portions or larger distributions.Nevertheless, an approach along these lines is superior to anything else currently employed and, at a minimum, provides a basis for appraising the plausibility of conclusions reached through other methods.The approach is separately discussed on the Solutions sub-page and a downloadable database with which to implement it is made available on the Solutions Database sub-page.
Inasmuch as most of the D references contain a good deal of narrative material that, while not without value, may discourage some readers, links to the tables illustrating the approach in most of the D references are set out below:
7.Consensus with Views Expressed on the Measuring Health Disparities Page
The references listed in Section A to D of this page take for granted that the views they express as to the correlations between prevalence of an outcome and binary measures of difference are correct.I expect that there will eventually be universal acceptance, if not of the precise views I have been expressing since 1987, at least of the fact that the standard measures of differences between outcome rates are in some manner affected by overall prevalence and, hence, that none of those measures can provide useful information regarding the comparative situation of two groups without taking overall prevalence into account.But even in the countries where issues of the relationships between overall prevalence of an outcome and various measures of experiencing or avoiding it have lately been given a good deal more attention than in the United States, it may be some time before there will be widespread, much less universal, understanding of these issues.
Set out below is the discussion of the scholarly work that has in responded somewhat directly to the issues I have raised about the way standard measures of differences between outcome rates tend to be affected by the prevalence of an outcome.
(a) National Center for Health Statistics:In a 2005 monograph “Methodological Issues in Measuring Health Disparities (item a1 below), citing “Race and Mortality” (Society 2000), the National Center for Health Statistics observed: “Conclusions about changes in disparity over time also depend on whether an indicator is expressed in terms of favorable or adverse events.”The reliance on “Race and Mortality” (and the examples the monograph used) would seem to reflect recognition that relative differences in favorable and adverse events tend to change in opposite directions because of the pattern whereby the rarer an outcome the greater tends to be the relative difference in experiencing it and the smaller tends to be the relative difference in avoiding it.But the NCHS decision to deal with the matter simply by recommending that all health and healthcare disparities be measured in terms of relative differences in adverse outcomes is utterly inconsistent the point of “Race and Mortality” that, because the two relative differences tend to be affected by the prevalence of an outcome changes, neither relative difference is a useful indicator of whether a health disparity in increasing or decreasing without taking implications of overall prevalence into account (a point I repeatedly made to the lead author of the monograph, Kenneth Keppel, as NCHS was developing that position and as Keppel and Jeffrey Pearcy were drafting an article as a more direct response to “Race and Mortality” and “Divining Difference” (Chance 1994).
When Keppel and Pearcy published their article (item a2 below), it appeared to regard the patterns I described as if they were phenomena I had observed regarding particular outcomes rather than as systematic patterns stemming from properties of the underlying risk distributions.See my Comment on Keppel and Pearcy (item D.6 above). Keppel and Pearcy’s reply to this comment (item a3 below) is somewhat cryptic (though it does reflect some appreciation that the crucial issue involves what is occurring with the underlying distributions, something that, as discussed below, so far only Carr-Hill/Chalmers-Dixon, Gallestey, Gansky, and Lambert/Subramanian seem to have recognized).On the panel at the 7th International Conference on Health Policy Statistics at which I presented reference B13 (ICHPS 2008), Keppel (in presenting item a4 below) expressed the view that the points I have expressed in B13 and elsewhere, while correct with respect to cross-sectional data, are not correct with respect to longitudinal data, stating that patterns observed over time are not the same as those observed when a cutoff is raised or lowered.But, while patterns observed over time will rarely if ever be precisely those illustrated by the lowering or raising of a cutoff, they will almost invariably exhibit similar tendencies.And it makes no sense to make comparisons over time without consideration of such tendencies.
Keppel and Pearcy addressed this matter further in a 2009 Chance article, simply observing:
“James Scanlan, in his 1994 CHANCE article "Divining Difference," pointed out that the relative difference in rates of survival between black and white infants decreases as the relative difference in rates of infant mortality increases. When disparities are measured in HP2010, indicators are usually expressed in terms of adverse events so meaningful comparisons can be made across indicators.”
But Keppel and Pearcy offered no justification for the approach and ignored entirely the criticisms I have repeatedly made of the NCHS approach, including in the 2006 Chance editorial “Can We Actually Measure Health Disparities.”
Kenneth Keppel also circulated within NCHS a document titled “Response to Jim Scanlan,” which he shared with me by email of May 22, 2008.My May 22, 2008 email responding to Keppel may be found here.The internal document appears to challenge my contention that the prevalence of an outcome affects relative difference in experiencing it, asserting that changes in relative differences over time, rather than being a function of changes in prevalence, are in fact functions of the comparative proportionate changes in each group’s rate.But I had made clear both in “Divining Difference” and “Race and Mortality,” and it should be evident in any case, that (a) a certain pattern of proportionate changes in different baseline rates as prevalence change (i.e., the group with the lower baseline rate for the outcome will tend to experience a larger proportionate change in the outcome while the other group will tend to experience a larger proportionate change in the opposite outcome) and (b) the patterns I describe whereby the two relative differences tend to change in opposite directions as the prevalence of an outcome changes are simply corollaries to one another.Keppel’s positing a situation where the patterns of proportionate changes operate contrary to the patterns I described, with corresponding changes in relative differences contrary to the patterns I have described, is simply a demonstration that the patterns I describe do not have to occur.But that they do not have to occur – and indeed that many times they in fact do not occur – is something I have pointed out numerous times.That such patterns may not be observed in a particular situation does not provide a reason to ignore the implications of the patterns or, in situations where they are observed, provide a basis to maintain that some meaningful change in the comparative situation of two groups has occurred and that it has occurred in the direction indicated by the particular relative difference the observer happens to prefer.
If NCHS is to provide useful guidance in this area, it must directly address these issues.Specifically, it must address whether relative differences in adverse and favorable outcomes (as well as other measures) tend to be affected by the overall prevalence of an outcome and, if so, whether those measures can provide useful information about whether disparities are changing over time or are otherwise larger in one setting than another if prevalence is not in some manner taken into account.And the longer it takes for NCHS to address the issues, the greater will be the amount of flawed research for which NCHS will be in part responsible.
(Subsequent to the last material modification of the consensus materials, I further criticized the NCHS failure to responsibly address health and healthcare disparities measurement issues in quite a few places, including an October 9, 2012 letter to Harvard University (at __), a Federal Committee on Statistical Methodology 2013 Research Conference paper “Measuring Health and Healthcare Disparities” (especially at 11-12 and Sections C.1.a-c, at 26-28), and my July/Augst 2014 Society article “Race and Mortality Revisited” (especially the section titled “Response of the National Center for Health Statistics to “Race and Mortality”)).
(b) Carr-Hill and Chalmers-Dixon.In the 2005 Southeast Public Health Observatory Handbook of Health Inequalities Measurement, relying on my 2001 presentation on these issues in Oslo (B1), Carr-Hill and Chalmers-Dixon (item b below) (at 171-72), explicitly accepted the reasoning of that presentation with regard to relative differences, including a recognition of the way the statistical patterns are functions of the properties of the underlying distributions. They noted:
“[I]f inequalities in health were to be measured in terms of the numbers and proportion who survive rather than the numbers and rates of death, the picture is very different (Part (b) of Table 11.4). The point is that as a negative outcome becomes more rare, it is more and more likely to occur disproportionately among the less advantaged groups. Conversely, as a valued outcome becomes relatively rarer, it is likely to be concentrated among the elite. This is a simple consequence of the statistical distributions [citing B1], rather than another example of inequalities.”
But the lengthy document, which discusses a variety of health disparities issues and measurement techniques, seems not to recognize the implications of such acceptance as to other measures it discusses.In my view, the acceptance calls into question much of the reasoning in the remainder of the document.For the patterns described here affect each of the measures discussed in the Carr-Hill/Chalmers-Dixon document.
(c)Houweling et al.A 2007 article by Houweling et al. (item c below) is the article mentioned in Section E.2 as one co-authored by two of the authors of the 1997 Lancet article that calls the conclusions of the Lancet article into question.See also B51a (Comment on Mackenbach Lancet 1997).The Houweling article is in part a response to Race and Mortality (A10) and questions Race and Mortality for overstating the force of the tendencies it describes.The Houweling article ignores entirely Race and Mortality’s discussion as to why certain patterns will tend to be observed and why in some cases they will tend not to be observed – in particular the explanation that differences between rates will be a function of (a) the prevalence of the outcome and (b) the size of the difference between the underlying distributions.But the Houweling article nevertheless finds systematic correlations between the prevalence of an outcome and relative differences in experiencing it and avoiding it that are the same as those described in Race and Mortality.Houweling et al. were unaware of the 2006 Chance editorial, Can We Actually Measure Health Disparities? (A12) and other treatments of absolute differences between 2005 and 2007 (see Section E.3 of MHD), but their study found systematic correlations between absolute differences and the prevalence of an outcome according to the same reverse U-shaped pattern illustrated or described in the references listed in Section E.3.However, the Houweling article suggested that the odds ratio would avoid the problems arising from the correlations it describes.In my view, as discussed in the Section E.3 references, the odds ratio does not avoid such problems, because differences measured in odds ratios tend also to be correlated with the prevalence of an outcome (according to a pattern that is the opposite of that exhibited by absolute differences).Also, in my view, the explanations Houweling et al. offer for the observed patterns are less sound than those described in Race and Mortality and many other places listed on this page.Nevertheless, as with my own treatments of these issues, the Houweling article raises questions as to the validity of the overwhelming majority of health disparities research to date.
Because the Houweling authors included two of the most prominent authorities on health inequalities measurement (Johann P. Mackenbach and Anton E. Kunst), the article is potentially of great significance.But those authors have gone on to do subsequent work in ways that, according to their own reasoning in Houweling et al., is flawed for failure to consider the implications of overall prevalence.See items D70 (Comment on Mackenbach NEJM 2008) and D113 (Comment on Mackenbach BMJ 2011).
(d) Eikemo et al.Whereas the Houweling article responded to Race and Mortality without the authors’ evidencing an awareness ofthe 2006 Chance editorial (A12), a 2009 article by Eikemo et al. (item c2 below) in the same journal responded specificallyto the Chance editorial, while also discussing the Houweling article.Like Houweling et al., Eikemo et al. found correlations between relative differences and prevalence of an outcome and concluded that overall prevalence must be taken into account in interpreting relative differences.Like the Houweling article, however, the Eikemo article offered little guidance on how to do that.Also like the Houweling article, the Eikemo article failed to consider the reasons for the observed patterns that had been explained at some length in Race and Mortality and varied other places.
Item D72 is a comment on Eikemo et al. and somewhat on Houweling et al.It was submitted to the International Journal for Equity in Health at the beginning of October 2009.The journal originally was to publish it upon receiving comments from Eikemo et al. and Houweling et al. but decided it could only publish a much shorter version (and is awaiting my submission of the shorter version).The comment stresses the critical point that, as noted above, observed patterns are functions of (a) the prevalence of the outcome and (b) the differences in the underlying distributions, as had been made clear enough in Race and Mortality.Yet, in contrast to Carr-Hill and Chalmers-Dixon and Gallestey, neither Houweling et al. nor Eikemo et al. seem to recognize these forces.See also D79 (Comment on Huijts and Eikemo EJPH 2009), which discusses an article by Huijts and Eikemo (item c3 below) that alludes to the patterns I have described as a potential explanation for variations in health inequalities across countries.
(e) Bauld et al.A 2008 article by Bauld et al. (item d below) devotes several paragraphs to discussion of my treatments of the pattern whereby the rarer an outcome, the greater tends to be the relative difference in experiencing it and the smaller tends to be the relative difference in rates of avoiding it.They caution that, assuming the correctness of my reasoning, “[i]f governments fail to take account of ‘Scanlan’s rule,’ they run the risk of guaranteeing failure, largely for conceptual and methodological reasons rather than social welfare reasons.”But the authors do not make clear whether they wholly accept the reasoning or not, and it is not clear whether they think that my discussions of these patterns are based solely on observations without regard to the identification of the factors underlying the patterns.Further, they find among “real-world examples that resonate with Scanlan’s arguments” a study by Gisselmann finding that part of the reason for increasing social inequalities in birth outcomes in Sweden “’is likely to be found in the decline in the proportion of women with low education.’”But the pattern identified by Gisselmann raises a compositional issue akin to that addressed in introductory section of D43 (Comment on Boström).(Other issues raised in the Gisselmann article are discussed at pages 13-14 of B7 (BSPS 2006).)The compositional issue addressed by Gisselmann involves a matter quite different from the implications of what Bauld et al. term “Scanlan’s rule,” which implications are present without regard to compositional changes.Finally, the authors conclude by recommending that governmental entities in the United Kingdom continue to establish and monitor health inequalities reductions goals.But if one accepts my reasoning, there is no purpose in creating or monitoring goals until one has effectively addressed the measurement issue.
(f) David Mechanic.In several articles since 2002 (items e1 to e3 below), David Mechanic has discussed or cited A3 (Public Interest 1991) or A12 (Chance 2006).Referencing illustrations of large relative differences where outcomes are rare, and increasing relative differences corresponding with declining absolute differences, he has observed that the former have been overemphasized and the latter provide more valuable information as to the public importance of a disparity.On the basis of what he has written, however, I cannot say whether he fully agrees with my reasoning regarding the failure of either measure to provide useful information as to whether a disparity is increasing or decreasing in a meaningful sense.
(g) Jorge Bacallao Gallestey.In two 2007 articles (items f1, f2)Jorge Bacallao Gallestey cited A12 (Chance 2006) and, like Carr-Hill and Chalmers-Dixon, recognized the distributional basis for the patterns whereby relative differences in one outcome and its opposite would change systematically in opposite directions as the outcome changed in prevalence.See especially Table 2 in item f1.But, as was also the case with Carr-Hill and Chalmers-Dixon, other parts of these articles seem not to reflect recognition of the implications of that recognition as to other measures the articles describe.
(h) Stuart Gansky.In a 2009 presentation at the Joint Statistical Meetings (JSM) (item g below) Stuart Gansky cited A12 (Chance 2006) for the proposition that a useful measure of health disparities must not change when there occurs a simple overall change in prevalence.He then presented results of study of how health disparities indexes “are affected by underlying factors conditional on prevalence difference (i.e. probit model),” stating (in the abstract)
[Health Disparities Indexes] were estimated for the California Oral Health Needs Assessment 2004, a complex survey, to assess associations with untreated caries. Absolute measure, slope index of inequality, relative index of inequality (RII-mean), and absolute concentration index (ACI) were prevalence invariant with constant prevalence difference in a probit simulation model; relative measures depended on prevalence.
I am unfamiliar with the detail of the analysis.But inasmuch as a probit model appears to achieve the same results as the approach described on the Solutions sub-page of the Measuring Health Disparities (see introductory note to the Solutions sub-page and January 9, 2010 Follow-up Comment on Morita)it may well satisfy the criterion of remaining invariant as overall prevalence changes.Gansky and his colleagues may soon publish a paper based on the JSM presentation and that should shed further light on his technique.
(i) Cristina Masseria.In a 2009 article (item h below) on health disparities measurement issues, Christina Masseria relied on A12 (Chance 2006) for the proposition that “caution is needed when interpreting the results of relative inequalities since they increase (decrease) as a consequence of a decrease (increase) in the overall level of mortality (survival).”That seems almost to capture the point, though failing to recognize that the pattern is a tendency that must be interpreted with a regard for the factors underlying it.In any case, she goes on to state:“Houweling et al. [item c1 here] have shown that low levels of mortality can be achieved together with low levels of health disparities.Indeed, a recent US study shows that there is no clear relationship between level of health and relative inequalities.”Thus, in my view, Masseria, like Houweling et al. and Eikemo et al. (and Huijts and Eikemo and Remes et al. (see below)), fails to recognize the forces that are invariably at work, as discussed above with regard to the Houweling and Eikemo articles, and that the function of health disparities research is to identify patterns of health disparities that are not functions of overall prevalence.
(j) Remes et al.In a 2009 article (item i below), Remes et al., citing Race and Mortality, state:
Scanlan  argues that all measures of differences between binary outcomes are affected by the overall prevalence of an outcome and changes in it. For example, declining mortality tends to be accompanied by increasing relative differentials. Departures from the expected patterns of change are still possible, as demonstrated by Houweling et al [item c1] in a cross-national comparison study.
While not necessarily quarreling with Race and Mortality, this article is similar to several of the other above-discussed items in failing to reflect a recognition of the forces underlying the observed patterns that Race and Mortality described, as reflected by the discussion throughout the remainder of the article.See D13 (Comment on Edwards), D2 (Comment on Shaw), D39 (Comment on Singh) responding, respectively, to references 12, 13, and 14 of Remes et al.
(k)Peter J. Lambert and Subbu Sabramanian.In a 2014 article (item j) Peter J. Lambert and Subbu Sabramanian discuss the pattern of relative differences termed “heuristic rule X in “Can We Actually Measure Health Disparities?”The authors “explore the issue analytically, providing a criterion from stochastic ordering theory under which one of two groups is considered disadvantaged and the other advantaged, and showing that Scanlan’s heuristic obtains as a rigorous finding in such cases.”
(() PRISMA-Equity Bellagio Group.In 2012 the PRISMA-Equity Bellagio Group published guidelines on reporting of systematic review with a focus on health equity in PloS Medicine which editors provided a comment.I posted a comment on the editorial noting, inter alia, that, while the authors stressed the importance of reporting both relative and absolute differences in outcome rates, they showed no awareness that there existed two relative differences or that the two relative differences to change in opposite direction as the prevalence of an outcome changes.Pursuant to the editors’ request, the authors of the guidelines responded to my comment on the editorial.The response appeared to reflect recognition of the importance of the issues I raised and stated that such issues will be addressed in a forthcoming “Elaboration and Explanation document of the PRISMA-Equity 2012.”
a.4.Keppel KG.Measuring Disparities in Health People 2010, presented at the 7th International presented at the 7th International Conference on Health Policy Statistics, Philadelphia, PA, Jan. 17-18, 2008 (invited session).
c.1. Houweling TAJ, Kunst AE, Huisman M, Mackenbach JP.Using relative and absolute measures for monitoring health inequalities: experiences from cross-national analyses on maternal and child health.International Journal for Equity in Health 2007;6:15: http://www.equityhealthj.com/content/6/1/15
f.2.Gallestey JB.Indicadores basados en la noción de entropía para la medición de las desigualdades sociales en salud Indicators based on the notion of entropy for the measurement of social inequalities in health. Rev. Cuban Public Health 2007; 33 (4):http://bvs.sld.cu/revistas/spu/vol33_4_07/spu07407.html