Uncertainty in Disability Weights
Although health state valuations are often treated as value parameters without uncertainty, we argue that unlike social choices such as the discount rate, no clear normative basis is available on which to assign relative values to the different dimensions of health that collectively define the universe of health states. Ideally, these values should be derived from empirical data among representative populations (Salomon and others 2003). Numerous challenges are associated with population-based data collection for the purpose of health state valuations, particularly given the broad scope of valuations required for a comprehensive assessment of disease burden. As a result, the current empirical base for disability weights remains well short of this ideal. Given the limitations in currently available information, an examination of the contribution of uncertainty around health state valuations to overall uncertainty in burden of disease estimates measured using YLD or DALYs is useful.
Conceptually, the basis for assigning disability weights to specific sequelae requires an understanding of (a) the distribution of health states among those living with the particular sequelae, where a health state is defined by the levels on the various dimensions that constitute health; and (b) a valuation function that provides a systematic way to aggregate across multiple dimensions of health in order to arrive at a single index value that captures the overall level of health associated with a given health state (Salomon and others 2003). While disability weights may vary across regions because of variation in either component, we have proposed elsewhere that for purposes of standardization and global comparisons, computing disability weights based on an average global valuation function is the most appropriate approach (Murray and others 2002). The need to understand variation in the distribution of health states among people living with given sequelae highlights the critical link between the epidemiological inputs of burden and the estimated disability weights.
In this section, we undertake a first analysis of the contribution of uncertainty in disability weights to uncertainty in the GBD DALY estimates. Given that the current set of disability weights reflects the accumulation of a wide array of different empirical inputs rather than the result of the comprehensive and standardized approach defined earlier as the ideal, we operationalize our analysis of uncertainty in terms of error around the disability weights by sequelae rather than in terms of the uncertainty arising from the constituent components, that is, the health state distributions and the valuation function. Based on this approach, the results offer guidance on the sensitivity of burden estimates to a certain degree of uncertainty around disability weights, but do not necessarily capture all sources of uncertainty and their covariance. As noted earlier, certain specific measurement methods for eliciting health state valuations, for example, the person trade-off or standard gamble, may have important normative implications that are orthogonal to the assessment of health levels. However, undertaking a sensitivity analysis that focuses on a specific measurement approach is not appropriate here, because the weights currently used in the GBD estimates have been derived from the synthesis of multiple data sources rather than from a single measurement method.
Because of the natural constraints on the range of values that disability weights may assume, we have incorporated normal distributions with constant variance in the space of the logit of disability weights. The logit transformation is given by logit(x) = ln[x/(1 - x)]. By allowing for normally distributed error in logit space, ranges in the natural space of valuations are constrained to fall between 0 and 1. We chose a value of 0.6 for the standard deviation of the logits, based on the standard deviations observed across the mean valuations by country for an array of conditions included in the WHO multicountry survey study from 2000-1 (with valuation modules implemented in 14 countries) (Salomon and others 2003; Ustun and others 2003). Although the variability in country means may reflect a range of different factors, including the possibility of real valuation heterogeneity, we use this value to approximate the average level of uncertainty around the set of disability weights used in the GBD study. A constant value in logit space yields absolute ranges that widen at the midpoint of the interval and narrow as the disability weight approaches 0 or 1 (figure 5.15). In relative terms, the uncertainty is greatest for the smallest disability weights and narrows as more severe weights are attained (figure 5.16).
[Figure
5.15]
[Figure
5.16]
To trace the implications of this uncertainty through to the calculation of DALYs(3,0) used in the DCPP, we took 100 draws from each of 622 independent normal distributions with a mean of 0 and a standard deviation of 0.6 (for the 622 sequelae included in the calculations). For each of the sequelae we applied a given sampled value as a perturbation of all age, sex, and region estimates of logit-transformed disability weights pertaining to that sequela, and recomputed YLD(3,0) based on the disability weight plus the random perturbation (after reversing the transformation for the sum). We estimated uncertainty ranges by taking the 2.5th and 97.5th percentiles across the 100 values of the various quantities of interest based on the random draws of error around the disability weights. This method implies the simplifying assumption that errors are uncorrelated between sequelae but perfectly correlated for all estimates within a sequela. In addition to YLD(3,0) numbers, we recomputed YLD ranks resulting from each set of sampled values, and also calculated DALY numbers and ranks by adding each YLD(3,0) draw to constant YLL(3,0) estimates by sequela. Our intent is only to provide an indication of the sensitivity of the YLD and DALY results to disability weight uncertainty. We did not attempt either to carry out a full empirically based analysis of this issue or to combine this source of uncertainty with mortality uncertainty and uncertainty in epidemiological estimates to give a comprehensive uncertainty analysis for the DALY estimates.
Table 5.6 presents the resulting uncertainty ranges for YLD and DALYs by cause for low- and middle-income countries. As would be expected, DALY uncertainty ranges due to disability weight uncertainty are generally largest for those causes dominated by YLD and smallest for those causes dominated by YLL. Uncertainty ranges are also large for those YLD-dominated causes with high prevalence and low disability weight (with high relative uncertainty), such as hearing loss and anemia. Figure 5.17 summarizes in graphical form the uncertainty in total DALYs for low- and middle income countries for the 20 highest-ranked causes.
[Figure
5.17]
[Table .]
Table 5.7 presents the resulting 95 percent uncertainty ranges for the 40 leading causes of the burden of disease in low- and middle-income countries. Taking into account uncertainty in disability weights does not result in significant uncertainty in the ranking of the top four causes, with only the third (ischemic heart disease) and fourth (HIV/AIDS) possibly changing places. The total estimated DALYs for these two causes differ by less than 2 percent, so this is not surprising. Among the other top 10 causes, the disability weight uncertainty could change the rankings of individual causes by up to two ranks, with the exception of depressive disorders, which could change by up to four ranks. This reflects both the high relative uncertainty in the disability weight for mild depression and the fact that YLD are responsible for almost all depression DALYs. Among conditions ranked 20th to 30th in table 5.7, uncertainty ranges for most ranks are relatively narrow with the exception of nonfatal, high-prevalence conditions such as hearing loss and osteoarthritis, where the uncertainty in rank may be as much as 15 places.
[Table .]
This analysis confirms the importance of efforts to improve the measurement of disability weights for health states close to full health, that is, with disability weights close to zero, particularly for health states with high prevalence in many populations, such as mild to moderate sense organ impairment or mild to moderate anemia. Unfortunately, most of the available choice-based or trade-off methods involving comparison in some form with death or survival present greater cognitive challenges to respondents when applied to health states close to full health.
