An Unhealthy America: The Economic Burden of ... - Milken Institute

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An Unhealthy AmericaMilken InstituteThe highlights of each regression model follow:BREAST CANCERThe female 65-and-over population and the percentage of female obesity significantly explain incidence.As expected, the older females and those with a BMI above 30 are likely to have a higher incidence. Coefficientsare highly significant, as indicated by their respective high t-values. (Again, a t-statistic over 2.0 is significant.A coefficient with an associated t-statistic over 3.0 is considered highly significant.) Aging, exhibited by thepopulation 65 and over, yields the largest coefficient of 0.94. This would indicate that for 1.0 percent increasein the female population 65 and over, breast cancer incidence would increase by nearly 1.0 percent, holdingall other factors constant. A 1.0 percent increase in the percentage of the obese adult female populationindicates that incidence rises by 0.34 percent.COLON CANCERSmoking represents the most significant risk factor. The population 65 and over is also significant. Obesity anda higher percentage of “at risk” smokers are likely to increase incidence. A 1.0 percent change in smokingprevalence results in a 0.5 percent incidence change in the same direction. Since exercise is significant, we mayconclude that incidence decreases with moderate exercise.LUNG CANCERSmoking and population 65 and over both exhibit high significance. A 1.0 percent change in smokingprevalence leads to a roughly 1.0 percent incidence change in the same direction. Lung cancer probabilityincreases with age, reflecting the cumulative effect of a lifetime of unhealthy behaviors.PROSTATE CANCERProstate cancer tends to occur more often in African Americans and men 65 and over. Male obesity is also asignificant determinant. A 1.0 percent change in obesity prevalence leads to a 0.5 percent incidence change inthe same direction.OTHER CANCERSSince “other cancers” are not specific to one type, we test against various behavioral and demographic factors.Obesity, smoking, and cholesterol display high significance in “other cancer” incidence, but demographicfactors, particularly aging, also yield high correlation. A 1.0 percent change in obesity prevalence leads to 0.3percent incidence change in the same direction.ASTHMAThe onset of asthma 43 typically occurs to individuals under 40. Thus, we do not include age as a variable.Asthma is likely to be more prevalent among the Hispanic population, but the disease impact in that populationis not large, as indicated by the small coefficient. Smoking and air quality appear to be major risk factors. A 1.0percent change in smoking prevalence results in a 0.6 percent asthma prevalence change in the same direction.Air quality also seems to have a fairly significant impact.43. BRFSS provides data only on asthma, as opposed to pulmonary conditions.[ 85 ]
An Unhealthy AmericaMilken InstituteDIABETESThe population 65 and over appears to be the most significant factor increasing the prevalence of diabetes,which shows a cumulative impact over the life cycle. A 1.0 percent change in population 65 and over leads toalmost 0.8 percent prevalence change in the same direction. Among behavioral risk factors, obesity has thestrongest relationship with diabetes, apparent from the highly significant coefficient. Diet and exercise werenot found to have independent impacts on diabetes separate from their influence on obesity.HYPERTENSIONWe use prevalence as the dependent variable. Age and obesity seem to be positively and significantlycorrelated. Exercise appears to significantly reduce occurrence of hypertension. Exercise was found to exhibita notable and separate impact on hypertension from its associated link to obesity. A 1.0 percent increase inphysical activity prevalence leads to a 0.3 percent decrease in hypertension prevalence.HEART DISEASEDue to the lack of state-level prevalence/incidence data, we use death rates as a proxy for the dependentvariable. Age and obesity are the most significant factors, followed by smoking. Exercise appears to decreasethe risk significantly. A 1.0 percent increase in physical activity prevalence leads to a 1.2 percent decrease inheart disease death rates.STROKEWe use the death rates as the dependent variable due to the limitation of state prevalence data. Smokingappears to be the most significant behavioral risk factor, as indicated by its highly significant and largecoefficient. A 1.0 percent change in the number of smokers results in over a 0.6 percent death rate change inthe same direction.MENTAL DISORDERSWe use death rates as the dependent variable due to limited data on incidence or prevalence rates. Heavydrinking and illicit drug use appear to increase mental disorders. Age is also a significant factor, with statisticalsignificance at around 10.0 percent. This is partly attributable to the higher prevalence of other chronic diseasesas we age. It is also attributable to death of a spouse. The widowed spouse can be highly susceptible todepression and other mental disorders. A 1.0 percent change in the population 65 and over leads to over a0.2 percent change in the death rate.These coefficients are applied to the baseline and optimistic assumptions. In this way it is possible to adjust the projectincidence/prevalence rates based solely upon age for behavioral and other demographic risk factors.Model 3: The Path of Screening and Treatment InnovationModel 3 builds on Model 2, which calculated assumptions of risk factor trends into the aging demographic projectionsof Model 1. Now we estimate the positive values of improvements in screening, early intervention, and treatment. Theseimpacts can be estimated into baseline and optimistic projections of prevalence and incidence.Because state-level data is limited, we rely here on national-level date to build time-series regression models. Availabledata exist for just six of the disease categories under study—colon and prostate cancer, heart disease, hypertension,mental disorders, and stroke.[ 86 ]
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