Importance of distinguishing inequality in immunization from inequality in failing to be immunized
James Scanlan, James P. Scanlan, Attorney at Law
29 February 2016
In exploring the contribution of various factors to inequality in immunization coverage in India, Lauridsen and Pradhan[1] used the concentration index as a measure of inequality and examined the concentration of failure to be fully immunized rather than the concentration of full immunization. Whether one examines full immunization or failure to be fully immunized would not appear to affect conclusions about the contributions of various factors to inequalities, or at least not in a way that is evident to me at this time. But inasmuch as the authors address their subject in the context of discussions about changing magnitude of inequalities, it warrants note that whether one examines rates of full immunization or rates of failing to be fully immunized has important implications with respect to interpreting changes over time.
As an outcome like immunization generally increases relative differences in experiencing it will tend to decrease while relative differences in failing to experience it will tend to increase.[2.3] An extreme example may be found in a 2008 Pediatrics study by Morita et al.[4] that examined the effects on racial and ethnic vaccination inequalities of a school-entry Hepatitis B vaccination requirement. Relying on relative differences in vaccination rates to measure inequalities, the authors found that the requirement, which dramatically increased overall vaccination rates, also dramatically reduced racial and ethnic vaccination inequalities. But those who examine inequalities in terms of relative differences in failure to be vaccinated would have found dramatic increases in inequalities[5]. In general, studies of immunization inequalities rely on various measures of inequality, but almost universally without recognition of the way the choice of measure, or in the case of relative differences whether one examines immunization rates or rates of failure to be immunized, may affect conclusions about changes in inequalities over time.[6]
The failure to recognize the implications of whether one examines relative differences in favorable outcomes or relative differences in adverse outcomes is almost universal in health inequalities research regardless of the outcome being examined, and even the failure to distinguish between favorable and adverse outcomes is quite common. For example, the authors cite an article by Cleland et al.[7] as examining disparities in child survival. Whereas Cleland et al. referenced disparities in survival chances in their abstract, in fact they examined disparities in mortality. In the Cleland study itself, changing patterns of relative differences in mortality and relative difference in survival show no pattern that can be readily summarized. But very commonly, though researchers will discuss relative differences in mortality and relative differences in survival interchangeably, as overall survival increase, relative difference in survival will tend to decrease while relative differences in mortality will tend to increase.[8] Whether the comparative well being of advantaged and disadvantaged groups changes in a meaningful way requires looking beyond these measures.[9]
As noted, Lauridsen and Pradhan measured inequalities using the concentration index. That complicates a direct comparison with the measures just discussed. But in general, the concentration index tends to behave like relative differences. Thus, those who measure the concentration of the failure to be immunized, as the authors did, will tend to find that general increases in immunization lead to increases in inequality. But those who measure the concentration of immunization will tend to find that general increases in immunization lead to decreases in inequality.[10] A simple illustration of why this is so may be found in Table 1 of reference 2, which shows how reducing poverty (thus increasing the prevalence of the avoidance of poverty) will tend increase both the proportion a disadvantaged group comprises of the poor (hence increasing the concentration of the adverse outcome in the disadvantaged group) and the proportion the disadvantaged group comprises of the non-poor (hence reducing the concentration of the favorable outcome in the advantaged group).
None of this is to say that observed patterns will always comport with the described patterns. . Though the forces underlying the patterns will almost always be present, various factors, including meaningful changes in inequalities, may cause departures from the patterns. But one must understand those patterns in order both to avoid mistakenly inferring that changes in standard measures reflect something meaningful and to identify meaningful changes when they occur.
References:
1. Lauridsen and Pradhan: Socio-economic inequality of immunization coverage in India. Health Economics Review 2011 1:11.
2. Scanlan JP. Can we actually measure health disparities? Chance 2006:19(2):47-51: http://www.jpscanlan.com/images/Can_We_Actually_Measure_Health_Disparities.pdf
3. Scanlan JP. Race and mortality. Society 2000;37(2):19-35: http://www.jpscanlan.com/images/Race_and_Mortality.pdf
4. 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.
5. Scanlan JP. 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
6. Immunization Disparities page of jpscanlan.com: http://jpscanlan.com/immunizationdisparities.html
7. Cleland J, Bicego G, Fegan G: Socioeconomic inequalities in childhood mortality: the 1970s to the 1980s. Health Transit Rev 1992, 2:1-18
8. Mortality and Survival page of jpscanlan.com: http://jpscanlan.com/mortalityandsurvival2.html
9. Scanlan JP. 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: http://www.jpscanlan.com/images/Scanlan_RSS_2009_Presentation.ppt
10. Concentration Index sub-page of Measuring Health Disparities page of jpscanlan.com: http://www.jpscanlan.com/measuringhealthdisp/concentrationindex.html
Importance of distinguishing inequality in immunization from inequality in failing to be immunized
29 February 2016
In exploring the contribution of various factors to inequality in immunization coverage in India, Lauridsen and Pradhan[1] used the concentration index as a measure of inequality and examined the concentration of failure to be fully immunized rather than the concentration of full immunization. Whether one examines full immunization or failure to be fully immunized would not appear to affect conclusions about the contributions of various factors to inequalities, or at least not in a way that is evident to me at this time. But inasmuch as the authors address their subject in the context of discussions about changing magnitude of inequalities, it warrants note that whether one examines rates of full immunization or rates of failing to be fully immunized has important implications with respect to interpreting changes over time.
As an outcome like immunization generally increases relative differences in experiencing it will tend to decrease while relative differences in failing to experience it will tend to increase.[2.3] An extreme example may be found in a 2008 Pediatrics study by Morita et al.[4] that examined the effects on racial and ethnic vaccination inequalities of a school-entry Hepatitis B vaccination requirement. Relying on relative differences in vaccination rates to measure inequalities, the authors found that the requirement, which dramatically increased overall vaccination rates, also dramatically reduced racial and ethnic vaccination inequalities. But those who examine inequalities in terms of relative differences in failure to be vaccinated would have found dramatic increases in inequalities[5]. In general, studies of immunization inequalities rely on various measures of inequality, but almost universally without recognition of the way the choice of measure, or in the case of relative differences whether one examines immunization rates or rates of failure to be immunized, may affect conclusions about changes in inequalities over time.[6]
The failure to recognize the implications of whether one examines relative differences in favorable outcomes or relative differences in adverse outcomes is almost universal in health inequalities research regardless of the outcome being examined, and even the failure to distinguish between favorable and adverse outcomes is quite common. For example, the authors cite an article by Cleland et al.[7] as examining disparities in child survival. Whereas Cleland et al. referenced disparities in survival chances in their abstract, in fact they examined disparities in mortality. In the Cleland study itself, changing patterns of relative differences in mortality and relative difference in survival show no pattern that can be readily summarized. But very commonly, though researchers will discuss relative differences in mortality and relative differences in survival interchangeably, as overall survival increase, relative difference in survival will tend to decrease while relative differences in mortality will tend to increase.[8] Whether the comparative well being of advantaged and disadvantaged groups changes in a meaningful way requires looking beyond these measures.[9]
As noted, Lauridsen and Pradhan measured inequalities using the concentration index. That complicates a direct comparison with the measures just discussed. But in general, the concentration index tends to behave like relative differences. Thus, those who measure the concentration of the failure to be immunized, as the authors did, will tend to find that general increases in immunization lead to increases in inequality. But those who measure the concentration of immunization will tend to find that general increases in immunization lead to decreases in inequality.[10] A simple illustration of why this is so may be found in Table 1 of reference 2, which shows how reducing poverty (thus increasing the prevalence of the avoidance of poverty) will tend increase both the proportion a disadvantaged group comprises of the poor (hence increasing the concentration of the adverse outcome in the disadvantaged group) and the proportion the disadvantaged group comprises of the non-poor (hence reducing the concentration of the favorable outcome in the advantaged group).
None of this is to say that observed patterns will always comport with the described patterns. . Though the forces underlying the patterns will almost always be present, various factors, including meaningful changes in inequalities, may cause departures from the patterns. But one must understand those patterns in order both to avoid mistakenly inferring that changes in standard measures reflect something meaningful and to identify meaningful changes when they occur.
References:
1. Lauridsen and Pradhan: Socio-economic inequality of immunization coverage in India. Health Economics Review 2011 1:11.
2. Scanlan JP. Can we actually measure health disparities? Chance 2006:19(2):47-51: http://www.jpscanlan.com/images/Can_We_Actually_Measure_Health_Disparities.pdf
3. Scanlan JP. Race and mortality. Society 2000;37(2):19-35: http://www.jpscanlan.com/images/Race_and_Mortality.pdf
4. 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.
5. Scanlan JP. 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
6. Immunization Disparities page of jpscanlan.com: http://jpscanlan.com/immunizationdisparities.html
7. Cleland J, Bicego G, Fegan G: Socioeconomic inequalities in childhood mortality: the 1970s to the 1980s. Health Transit Rev 1992, 2:1-18
8. Mortality and Survival page of jpscanlan.com: http://jpscanlan.com/mortalityandsurvival2.html
9. Scanlan JP. 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: http://www.jpscanlan.com/images/Scanlan_RSS_2009_Presentation.ppt
10. Concentration Index sub-page of Measuring Health Disparities page of jpscanlan.com: http://www.jpscanlan.com/measuringhealthdisp/concentrationindex.html
Competing interests
None