Although the two overarching goals of Healthy People are to increase the quality and years of healthy life and eliminate health disparities, Healthy People 2010 did not delineate a mechanism to quantify the degree that progress has been made toward these goals.1,10
Our method would enable specified determinants of health to be examined in a manner amenable to conducting burden of disease and cost-effectiveness analyses (i.e., through an examination of QALYs, LE, and QALE) at multiple levels. This tool follows in the tradition of the Years of Healthy Life measure that was developed for Healthy People 200028
and can fulfill that promise at the local and state levels as well as the national level.
Although burden of disease and cost-effectiveness studies have been conducted with increasing frequency in the USA, such analyses have tended to be cross-sectional, or, when longitudinal, comprised a clinical cohort rather than a population group. Use of the BRFSS enabled us to examine the time trends for the general population and at the local level (New York City) with regard to improving the quality and years of healthy life as well as making comparisons at the national and local levels for two potentially modifiable behavioral risk factors (smoking and overweight/obesity). Because of the availability of trend data that will continue to provide detailed annual and monthly estimates and is able to be modified at the statewide and county level, the BRFSS can measure the progress toward Healthy People at the national, state, and local levels and allow the impact of interventions to be examined at the relevant level in a timely manner.3
Our study indicated similarities and differences between trends over time for the general US population and New York City that might not have been apparent if only cross-sectional data had been obtained a decade apart. For example, overall mean EQ-5D scores decreased from 1995 to 2006 for both groups, but although mean EQ-5D scores for NYC adults initially decreased, more recently, mean scores have increased. Although attribution to any particular public health intervention is difficult, we note that a decline also was observed in nonspecific psychological distress among adults living in New York City between 2002 and 2003.29
In addition, beginning in 2002, the New York City health department began a series of initiatives to address chronic disease prevention and control and help New Yorkers live longer and healthier lives.18,30
The overall worsening trends in HRQOL were observed in all major subpopulations (i.e., age, race/ethnicity, gender; data not shown). Although mean differences in EQ-5D index scores over time were not of a magnitude generally considered to be clinically important for indiviudals,31,32
such differences might be important for monitoring population health, especially among certain population subgroups. Despite mean EQ-5D scores decreasing over time, however, life expectancy and quality-adjusted life expectancy increased for the general US population and NYC adults.
This pattern of HRQOL declining over time is consistent with findings from other investigators in the USA and abroad.33–36
Understanding the deterioration of HRQOL in light of increasing life expectancy is crucial, given the aging of the population and the overarching goals of Healthy People.10
A number of explanations have been provided for these findings, including an increased prevalence of obesity, increases in non-fatal conditions, and technological advances in medicine that offer lifesaving treatments for persons in poor health.34,35
Regarding obesity, the prevalence of overweight or obesity, as assessed in the BRFSS, increased by 29% (i.e., from 48.7% in 1993 to 62.8% in 2007). Additionally, the mean age of the BRFSS sample increased from 43.7 in 1993 to 46.1 in 2007, and such a change would be expected to contribute to the trend of worsened HRQOL.
This study has a number of limitations. With regard to the application of the estimation equation, detailed mortality data only were available through 2005. We were unable to calculate LE and QALE for 2006 or beyond, and because we used a 3-year moving average for NYC, we only have LE/QALE data through 2004. However, since the BRFSS data are released to users in a timely manner, this lag in data still may compare with other surveys that tend to take longer to release data. In addition, given the smaller sample size of the BRFSS for NYC in earlier years, particularly between 1994 and 1996, estimates in these years may be unreliable. However, the BRFSS sample size for NYC is larger than other national data sets. Of note, 2002 estimates also should be interpreted with caution. During this year, 29 states in the BRFSS did not ask all of the Healthy Days questions and had to be excluded from the analysis, thereby potentially producing unmeasured differences in HRQOL, and the Healthy Days measures were moved from the beginning to the end of the BRFSS, thereby leading to a possible order effect.
Other limitations include that this analysis relies on the validity of the EQ-5D estimates from the Healthy Days measures. However, we previously demonstrated that mean scores of the estimated EQ-5D index matched well to the true mean scores; therefore, the estimated QALY and QALE should have acceptable accuracy.6
Furthermore, our calculation of QALY and QALE did not account for the loss to premature death resulting from each modifiable factor due to the lack of data on excess death from each risk factor. However, data from a previous study23
showed that the QALYs lost due to both morbidity and mortality were only 17.6% more than the QALYs lost due to morbidity alone for those below poverty level. Therefore, we do not expect that adding QALY lost due to mortality will change our results significantly, particularly when our results were in proportions.
Because the Healthy Days measures have been included in the BRFSS since 1993, translating Healthy Days Measures to a preference-based measure might be particularly useful as a stop gap method when examining trends over time and the impact of interventions on such trends. Comparisons could be made at multiple levels in order to assess the particular area that was impacted by a given intervention. In addition, different (and more local) interventions might be compared with one another with regard to the impact on those given populations/regions. This is particularly important, as the prevalence of risk factors tends to change over time (i.e., both increasing and decreasing) in different geographic areas and the effect of an intervention might be immediate (and either transient or sustained) or delayed. However, for the long term, the CDC should consider adding an existing validated instrument such as the EQ-5D that has had widespread use in clinical settings and increasing use in measuring population health.