"We suggest that it is long past time to admit an alternative—and arguably more plausible—interpretation of these patterns. The fact that a measure was computed at two different time points does not, by itself, make the difference between them a trend. Imagine if researchers measured the average temperature for the whole of the USA a decade ago, and then for only Alaska this year, and found the former number to be lower than the latter. Would it be appropriate to say that average temperatures had ‘declined’ over the decade? We argue that it would not, and that it is likewise not appropriate to be describing many of the observed differences in subgroup life expectancy or mortality as ‘trends’."
"Our concern is that stable differences between noncomparable subgroups are being mistaken for time trends in a broader group—a phenomenon we term lagged selection bias (LSB). We use the term ‘selection bias’ in a spirit that is common in the population sciences,8 although in epidemiology the phenomenon might also be understood by some as a form of collider bias and by others as a form of confounding.9 Regardless of how it is labelled, LSB generally occurs when: (i) there is a temporal lag between when individuals are selected into their exposure group and when the outcome manifests; and (ii) the dynamics of selection into the exposure group were changing over the period spanned by the lag. Due to this temporal lag, the implications of changing selection dynamics only become manifest long after the changes have happened. When these conditions apply, it is impossible to tell from the data alone whether contemporary differences in the outcome indicate contemporary trends (like increasing social exclusion of high school non-completers), or changes from a long time ago in the exposure selection process, or some complicated combination of these two. A proper interpretation, therefore, must be explicitly based on the history of the selection process itself."
"These ‘trend’ interpretations gloss over important differences between demographic constructs like period age-standardized mortality rates or life expectancy and real-world outcomes like the risk of death or expected length of life. They also give short shrift to two critical facts of the social history of the USA:
- Lag between exposures and outcome. High school completion in the USA has historically been determined by the time a person is in his or her early 20s,14 long before the ages when most mortality is observed.
- Secular change in the dynamics of selection into the exposure group. Access to high school increased dramatically over the 20th century in the USA (Figure 1 ). The average White girl born at the end of World War I stood about a 50% chance of finishing high school by her 20th birthday; born at the end of the Lyndon Johnson administration, she had a 90% chance. Much of this expansion in access was driven by changes in the opportunity costs facing working class families who wanted to send their children to high school;15–17 as access expanded, it likely became more equitable."When one computes mortality rates or life expectancy for the year 1990 for White female high school non-completers, one ascribes to those reaching age 70 the risk of dying that was faced by a woman who was excluded from high school around the end of the Great Depression—a normative experience for her time. By contrast, the same exercise for the year 2010 ascribes mortality risk at age 70 based on the experience of a woman excluded from high school in the late 1950s/early 1960s—which means she was left behind during a period of unprecedented expansion in access to secondary education.The high school completion status of those dying in a given year is like light from a distant star—it reflects social conditions that prevailed decades before. In terms of mortality risk, those excluded from high school in the early part of the 20th century are not comparable with those excluded from high school a generation later, because those left behind by the high school expansions in mid century likely had childhoods that were more disadvantaged along many dimensions, and so were at higher mortality risk all along. Life expectancy among high school non-completers for the year 1990 will largely be determined by the mortality experience of the relatively lower-risk subgroup; for the year 2010, it will be determined by the mortality experience of the higher-risk subgroup. Describing differences between these two subgroups as a ‘decline’ in the life expectancy of high school non-completers simply because they were born at different times almost certainly reflects LSB.""To illustrate the dynamics more clearly, we have used US population data and forecasts to recreate the mortality experience of 141 birth cohorts of women from 1880 to 2020. The overall risk of dying at each age for women in each cohort is based on actual and forecasted data from the US Social Security Administration.19 We also capture the social gradients around these average risks by randomly assigning each simulated person to one of 1001 early life socioeconomic status (EL-SES) categories and assigning those who are more (less) disadvantaged in terms of EL-SES to a higher (lower) than average risk of dying at any age. These disparities are based on patterns observed in the USA.20,21 We also incorporate an EL-SES gradient in access to high school reflecting historical patterns.16 In cohorts for whom high school completion is rare, it is only the most advantaged young adults who achieve it; as access expands, it also becomes more equitable. Our example rules out an increase in actual mortality risk for anyone—every individual stands a lower chance of dying at every age if they are born later, than they would have stood if they had been born earlier but had exactly the same characteristics. For example, in our simulation the average person in the most disadvantaged quintile in 1990 dies at age 69.5 years and in 2010 at age 72.2 years""Life expectancies at age 25 for the bottom quintile of the EL-SES distribution as well as for high school non-completers are shown in Figure 3. When we identify disadvantage based on the stable EL-SES characteristic, period life expectancies at age 25 are 43.8 years in 1980, 44.2 years in 1990, 44.4 in 2000 and 44.7 in 2010. When we rely on people’s access to high school to identify their exposure, the corresponding values are 48 years, 47.3 years, 45.3 years, and 43.4 years. Age-standardized mortality rates, shown in Figure 4, move in parallel with period life expectancies; this reflects the fact that the two demographic constructs are computed in very similar ways.""This difference arises because of a policy success—namely, improvements in equity of access to education over the 20th century. As a result of that policy success, people who were already vulnerable to shorter lifespan because of conditions in their early lives nonetheless had access to high school, whereas those exposed to similar conditions from an earlier birth cohort had no such access. As a result, averages for high school non-completers in earlier years include the less vulnerable, as well as the very vulnerable; in the later years, the less vulnerable were simply reclassified to the high-school completer group, leaving behind only the very vulnerable. It is that reclassification that drives the artefactual ‘decline’ in life expectancy a half-century later, not any change in anyone’s actual risk of dying.More than simply illustrating the important distinction between real-world health outcomes like longevity and demographic constructs like period life expectancy, the exercise underlying Figures 2–4 helps pin down the timing when we would expect to start seeing artefactual ‘trends’ in the demographic constructs, given the history of educational expansion in the USA over the 20th century. The exercise suggests that, if health disparities have been large and stable over the past century, and given trends in overall mortality risk and educational expansion over the past century, one would expect to see an artefactual ‘rise’ in period age-standardized mortality rates among the least educated, starting in the 1990s and continuing for about a generation. This matches the patterns that have been reported."
Friday, January 20, 2017
Is life expectancy really falling for groups of low socio-economic status? Lagged selection bias and artefactual trends in mortality
From the International Journal of Epidemiology. By Jennifer B Dowd and Amar Hamoudi. Excerpts: