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The economic impact of chronic fatigue syndrome

The economic impact of chronic fatigue syndrome Background: Chronic fatigue syndrome (CFS) is a chronic incapacitating illness that affects between 400,000 and 800,000 Americans. Despite the disabling nature of this illness, scant research has addressed the economic impact of CFS either on those affected or on the national economy. Methods: We used microsimulation methods to analyze data from a surveillance study of CFS in Wichita, Kansas, and derive estimates of productivity losses due to CFS. Results: We estimated a 37% decline in household productivity and a 54% reduction in labor force productivity among people with CFS. The annual total value of lost productivity in the United States was $9.1 billion, which represents about $20,000 per person with CFS or approximately one-half of the household and labor force productivity of the average person with this syndrome. Conclusion: Lost productivity due to CFS was substantial both on an individual basis and relative to national estimates for other major illnesses. CFS resulted in a national productivity loss comparable to such losses from diseases of the digestive, immune and nervous systems, and from skin disorders. The extent of the burden indicates that continued research to determine the cause and potential therapies for CFS could provide substantial benefit both for individual patients and for the nation. being of those affected, on the health care system, or on Background Chronic fatigue syndrome (CFS) is an illness defined by society as a whole. disabling physical and mental fatigue and physical and mental symptoms that are not explained by conventional The burden of CFS is poorly recognized, and the illness medical and psychiatric diagnoses [1]. CFS affects remains an inadequately managed health problem. Two between 400,000 and 800,000 people in the United States population-based studies of CFS have been conducted in [2,3] and has an average duration of 5 years, but symp- the United States, and both found that CFS is one of the toms can persist as long as 20 years [4]. The prognosis for more common chronic illnesses among women across all recovery of severely ill CFS patients is poor [5,6]. Despite racial/ethnic groups and that less than 20% of those who CFS's disabling, enduring, and prevalent nature, scant suffer from CFS have been diagnosed by a health care pro- studies have quantified its impact on the health and well- vider [2,3]. Only three studies, all of which were clinic Page 1 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 based, have attempted to quantify the impact of CFS, and classified into the CFS subgroup if clinical evaluation con- each showed that people with the syndrome were likely to firmed a diagnosis of CFS. have lost their job or to be unemployed [6-8]. In addition, it was shown that persons with CFS pose a disproportion- To estimate lost productivity due to CFS, data were ate burden on the health care system and their families obtained from individual responses to the detailed inter- since they are sick for long periods of time and since there view and clinical evaluation. The detailed interview and is no known cure for the illness [9]. subsequent analysis provided individual responses and classifications for current employment status, categorical The study reported herein is the first attempt to develop household income, age, sex, ethnicity, level of education, more generalizable results concerning the impact of CFS. duration and classification of fatigue, and occupation. In To this end, we derived quantitative measures of lost pro- addition, responses and analysis detailed household ductivity by interviewing persons identified as having CFS chores prior to and during fatigue and medical and psy- and non-fatigued people who were representative of the chiatric conditions. The clinical evaluation and subse- general population of Wichita, Kansas. We assessed the quent independent review determined those individuals economic burden of CFS on afflicted individuals and on with CFS and those with exclusionary medical conditions society as a whole and found that lost productivity in the from the suspected CFS group. Table 1 summarizes the United States amounted to an annual loss of $9.1 billion descriptive statistics for the study groups. or about $20,000 per afflicted person. Analysis The economic theory of human capital is the basis of the Methods Study Design simulation model we used to estimate the impact of CFS. This study adhered to human experimentation guidelines The human capital approach models an individual's pro- of the U.S. Department of Health and Human Services. All ductivity (in terms of employment and earnings) as a participants were volunteers who gave informed consent. function of human capital characteristic's such as age, The Centers for Disease Control and Prevention (CDC) education, occupation, and health status [10-13], and it Human Subjects Committee approved study protocols. hypothesizes that specific attributes of workers are valued Details of the population-based study to estimate the in the marketplace; thus, it recognizes differences among prevalence and incidence of CFS in the adult population individuals in terms of their experience, training, educa- of Wichita, Kansas, have been published [2]. In brief, the tion, and other characteristics that are valued in labor study used random-digit dialing to screen about 56,000 markets. Just as machines or other productive capital persons between 18 and 69 years of age. Those reporting involve investment that lead to future returns, human fatigue of at least one-month duration and randomly capital requires investments in schooling, health, appren- selected non-fatigued respondents were interviewed in ticeships, and other skill-building that may pay off in detail on the telephone to ascertain demographic charac- higher future wages. We treat illnesses, such as CFS, as a teristics, previous diagnosis of medical or psychiatric con- negative shock, which may potentially negatively affect an ditions that excluded classification as CFS, symptoms, individuals' ability to achieve returns on their human cap- occupation, and household income. People who were ital, given the severity of the illness. Therefore, the human suspected to have CFS on the basis of the detailed inter- capital framework enables us to examine the impact of view were invited to participate in a clinical evaluation to CFS on the ability to work and, given work, on pay. determine if they did indeed have CFS or some other illness. To estimate productivity loss, we employed methods developed as part of the RAND Health Insurance Experi- For analysis, subjects were classified into the "non-fatigue ment microsimulation [14-16]. Table 2 explains the two- group" (n = 3,634) if they did not report fatigue during step microsimulation approach that first used logistical the telephone interview or into the "fatigue group" (n = regression to predict employment and then ordinary least 3,528) if they reported fatigue lasting ≥1 month. The squares regression to estimate expected income, condi- fatigue group was further divided into 3 subgroups: those tional on employment, for the fatigue and non-fatigue with "prolonged fatigue" (n = 2973), those with "sus- groups. The expected decline in employment and income, pected CFS" (n = 555), and those with "CFS" (n = 43). given employment, would most likely stem from the Fatigue group respondents were classified into the pro- change in health status that resulted from the CFS diagno- longed fatigue subgroup if they reported fatigue lasting ≥1 sis. The two-step model provided consistent and efficient month but did not fulfill criteria for CFS. Fatigue group estimates through better exploitation of the sample char- respondents were classified into the suspected CFS sub- acteristics of the household income distribution group if they met the CFS case definition based on self- [15,17,18]. reported telephone interview responses, and they were Page 2 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 1: Demographic Characteristics of the U.S. population and the Wichita Sample by Fatigue and Non-Fatigued Group Demographic U.S. Population* Wichita Surveillance Groups Characteristics ‡ § Non-Fatigue Group Prolonged Fatigue Suspect CFS CFS (n = 43) (n = 3,634) (n = 2,973) (n = 512) Age, Mean (years) 41.0 40.5 42.7 44.1 47.8 <35 years (%) 35.6 37.3 29.1 18.9 8.3 35–49 years (%) 35.2 35.5 38.6 50.6 46.8 50–69 years (%) 29.2 27.2 32.3 30.6 45.0 Male (%) 49.1 49.3 34.7 29.1 14.6 Female (%) 50.9 50.7 65.3 70.9 85.4 Black (%) 12.1 8.5 11.2 5.5 2.8 Latino (%) 12.6 4.8 5.0 5.7 1.9 Employed (%) 78.4 76.5 64.1 68.4 52.8 Mean Household NA $33,477 $39,027 $44,143 $40,802 Income <$20,000 (%) 26.8 17.7 33.6 24.3 16.8 $20,000–$49,999 38.1 40.7 40.0 41.7 46.2 (%) $50,000–$74,999 17.8 18.1 12.6 19.1 23.6 (%) ≥$75,000 (%) 17.4 12.7 6.6 8.2 5.4 Not Reporting (%) 0.0 10.8 7.1 6.6 8.1 Education (%) <12 Years 14.9 7.8 14.8 8.5 2.7 High School 31.5 28.4 32.6 33.2 35.6 Graduate Some College 28.2 36.4 35.9 40.0 51.9 College Graduate 17.3 16.7 8.5 11.1 6.0 (4-Year) Post Graduate 8.2 8.9 5.8 4.7 3.7 Education Not Reporting 0.0 1.7 2.4 2.5 0.0 Occupation (%) Management or 23.4 27.1 20.3 25.9 16.8 Professional Clerical Worker 10.3 10.2 12.1 12.8 22.8 Service Worker 10.6 6.9 9.4 7.4 10.7 Sales Professional 8.7 6.6 5.2 5.1 7.5 Technician 2.5 6.6 6.3 5.8 17.0 Skilled Craftsman 8.3 4.3 5.0 6.3 4.6 Homemaker NA 1.9 2.4 2.3 3.5 Other/Not 36.2 36.2 39.3 34.4 17.2 Reporting * Based on analysis of the March Supplement to the Current Population Survey, 2002 conducted by SRA International, Inc. Columns may not add to † ‡ 100% due to rounding. Weighted to reflect population of Wichita, Kansas. Columns may not add to 100% due to rounding. Excludes the 555 CFS-like observations. Excludes the 43 CFS observations. The dependent variables for our analysis are an indicator point of $100,000. Since we used household income as variable for employment and a continuous measure of the dependent variable in the income regression, we ide- household income. Individuals were coded as participat- ally would control for marital status and household com- ing in the labor force if they reported that they were cur- position in the regression. Unfortunately, this rently employed. Household income was collected as a information was not included in the Wichita survey. categorical variable. We defined household income at the mid-point for each category to develop a continuous Ideally, personal income per household member is the measure, and the top category was coded at the truncation desired proxy to more accurately estimate individual and Page 3 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 2: Microsimulation steps for estimating the cost of productivity losses due to CFS. Step Equation 1. Divide the study sample data into two groups: Fatigued (F), N = 3,528, and Non-Fatigued (NF), N = 3,634. Estimate logistic regressions to obtain the probability that an individual is employed for F, P[W = Y/F], and NF, P[W = Y/NF], subsamples, as a function of human capital characteristics, as displayed in equations 1a and 1b. a. P[W = Y/F] = f(age, sex, ethnicity, education, suspected CFS, CFS, history of diseases that exclude respondent from CFS diagnosis) b. P[W = Y/NF] = f(age, sex, ethnicity, education, history of diseases that exclude respondent from CFS diagnosis) 2. Estimate ordinary least squares regressions to predict the natural log of income (I) given employment for the F, E[I/W = Y,F], and NF, E[I/W = Y,NF], subsamples, as a function of human capital characteristics, as displayed in equations 2a and 2b. a. E[I/W = Y,F] = f(age, sex, ethnicity, education, suspected CFS, CFS, history of diseases that exclude respondent from CFS diagnosis, occupation) b. E[I/W = Y,NF] = f(age, sex, ethnicity, education, history of diseases that exclude respondent from CFS diagnosis, occupation) Note: Once these regressions are estimated, only the sample of 43 individuals with CFS are used for the remainder of the microsimulation to estimate mean labor force and household productivity by age and sex in the presence and absence of CFS. 3. Calculate predicted mean F and NF employment rates by age and sex categories, weighting by sampling weights. Multiply the coefficient estimates from the F (Blf) and NF (Blnf) Logit regressions by the human capital characteristics of the 43 individuals with CFS (X) to obtain their F (P[W = Y/ F]) and NF (P[W = Y/NF) employment rates, respectively, as shown in equations 3a and 3b. Then, calculate the mean employment rate across the 43 individuals with CFS for each age and sex category weighting these means to reflect the survey sampling rates. a. P[W = Y/F] = {exp(X*Blf)/1+exp(X*Blf) b. P[W = Y/NF] = {exp(X*Blnf)/1+exp(X*Blnf) 4. Calculate predicted mean F and NF income given employment by age and sex categories weighting by sampling weight. Multiply the coefficient estimates from the F (Bolsf) and NF (Bolsnf) OLS income regressions by the human capital characteristics of the 43 individuals with CFS (X) to obtain their F (E[I/W = Y,F]) and NF (E[I/W = Y,NF) income given employment, respectively. Then, apply the smearing adjustment to the exponent of these F and NF products, as shown in equations 4a and 4b, to correct for the "retransformation" bias that arises from estimating impacts using loglinear models and to protect against data issues such as heteroskedasticity . The smearing factors for the regressions among individuals with F (Sf) and in the absence of F (Snf) are equal to the means of the anti-logs of the residuals of the respective income regressions. Calculate predicted F and NF income given employment for each age and sex category weighting by the survey sampling weights, and adjust these means from 1997 to 2002 dollars to account for inflation using the Department of Labor, Bureau of Labor Statistics Consumer Price Index from 1997 to 2002 . Apply an adjustment factor for the difference between mean income in Wichita and the nation based on analysis by the U.S. Department of Commerce increasing the estimated losses by 1.3 percent. In addition, to account for fringe benefits, multiply predicted income by a factor of 1.338, which is obtained from the Bureau of Labor Statistics Report on Employer Costs for Employee Compensation – June 2002 . a. E[I/W = Y,F] = {exp(X*Bolsf)*Sf}*1.114*1.013*1.338 b. E[I/W = Y,NF] = {exp(X*Bolsnf)*Snf}*1.114*1.013*1.338 5. Calculate predicted household productivity given employment and no employment in absence of F. The value of household productivity by sex, age, and employment status absent F is calculated on the basis of data on the number of hours spent on household chores for the NF sample, given employment (HH hours/W = Y,NF) and no employment (hours/W = N, NF). Value these hours at the average hourly wage for a service industry worker as estimated on the basis of the March Supplement of the Current Population Survey 2002 or $9.20. Similar to employment income, increase the value of the service industry worker wage by a factor of 1.338 to account for the value fringe benefits. This equation is displayed in 5a and 5b. a. E[HH/W = Y,NF] = E[HH Hours/W = Y, NF]*$9.20*1.338 b. E[HH/W = N,NF] = E[HH Hours/W = N, NF]*$9.20*1.338 6. Calculate predicted household productivity given F. Assume that the percentage reduction in employment related income, given work, is equal to the percentage reduction in household productivity. Apply a reduction factor representing the estimated reduction in employment-related income, given work, resulting from CFS to the predicted values of household productivity, given employment and no employment, as displayed in 6a and 6b. Calculate reduction factors separately for males and females. a. E[HH/W = Y,F] = E[HH/W = Y,NF] * E[I/W = Y,F]/E[I/W = Y,NF] b. E[HH/W = N,F] = E[HH/W = N,NF] * E[I/W = Y,F]/E[I/W = Y,NF] 7. Calculate predicted mean F and NF total productivity for each CFS individual. Overall, each CFS individual's expected total productivity in the presence or absence of F, E[Y/F] or E[Y/NF] respectively, is equal to the probability that they participate in the labor force, P[W = Y/F] or P[W = Y/NF], times the expected value of their total labor force and household productivity if they participate in the labor force plus the probability they choose not to participate in the labor force, P[W = N/F] or P[W = N/NF], times the expected value of their household productivity when they do not participate in the labor force. Equations 7a and 7b display expected productivity. a. E[Y/F] = P[W = Y/F]{E[I/W = Y,F] + E[HH/W = Y,F]} + P(W = N/F) {E[I/W = N,F] + E[HH/W = N,F]} b. E[Y/NF] = P[W = Y/NF]{E[I/W = Y,NF] + E[HH/W = Y,NF]} + P(W = N/NF) {E[I/W = N,NF] + E[HH/W = N,NF]} 8. Calculate estimated number of individuals with CFS nationally by age and sex. Using the Wichita Prevalence Study data, calculate the prevalence of CFS per 100,000 by age and sex cells and then use national population data from the Current Population Survey to calculate the number of individuals in each age and sex category with CFS. 9. Calculate individual and societal productivity losses due to CFS. Compute the difference between predicted mean total productivity without and with F, (E[Y/NF]-E[Y/F]), by age and sex category to estimate the individual loss for each age and sex cell and then multiply these differences for each sex and age cell by the estimated by number of individuals with CFS nationally in each cell and sum across the cells to estimate the total societal cost of lost productivity due to CFS. Page 4 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 2: Microsimulation steps for estimating the cost of productivity losses due to CFS. (Continued) 1 2 Duan N. Smearing Estimates: A non-parametric retransformation technique. J Am Stat Assoc 1983,383:605–10. Consumer Price Index from 1997 to 2002. Department of Labor, Bureau of Labor Statistics. (http://data.bls.gov/cgi-bin/surveymost?cu, then select U.S. All items, 1982-84=100 Per capita net earnings ($) Metro Comparisons. Department of Economics, Iowa State University, Midwest Profiles, Public CUUR0000SA0) Resources Online. (http://www.bea.doc.gov/bea/regional/reis/, then select Personal income and population summary estimates (CA1-3) plus per capita personal income plus Metropolitan Statistical Areas*) Employer Costs for Employee Compensation – June 2002. Bureau of Labor Statistics, September 2002. (http://http//data.bls.gov/cgi-bin/surveymost?cc, then click on Civilian, All workers, Total compensation - CCU110000100000D) national productivity loss. Personal income per house- Bootstrap standard errors were calculated for the esti- hold individual allows one to distinguish between pro- mated declines in employment, income given employ- ductivity losses resulting from CFS affliction versus ment, and total productivity derived from the productivity losses stemming from household members microsimulation model by age and sex cell. Bootstrap assuming caregiver roles at the expense of their employ- errors were calculated to test the sensitivity of the micro- ment productivity. Ideally, when estimating individual simulation to sampling error. Employment declines were productivity loss from CFS, one should distinguish all significant at the 95 percent confidence level. For between the productivity loss associated with CFS afflic- female age cells, income declines were significant at the 95 tion and that associated with the assumption of caregiver percent level for the 18 to 34 and 50 to 69 age cells and at roles at the expense of employment productivity. the 90 percent level for the 35 to 49 year age cell. Income declines estimated for males were not significant. This The national productivity loss estimate should include may result because low earning males exit the labor force both to reflect accurately the total national reduction in and higher earning males retain employment, causing the employment productivity stemming from CFS. However, mean earnings of those with employment to rise. Overall, given the structure of the Wichita Study questionnaire, a the total declines in productivity estimated under the recorded change in household income stems from an model were significant at the 99 percent confidence level individual within that household acquiring CFS; thus it with the exception of males 18 to 34 and 35 to 49 years of captures productivity losses that result directly from CFS age, which were significant at the 90 percent level. affliction and indirectly from the assumption of caregiver roles by non-afflicted household members. To date, CFS We conducted sensitivity tests on key assumptions of the research reports a clear reduction in hours worked by simulation model. We examined how the decision to those afflicted directly with CFS; thus, we believe that the model male and female productivity separately impacted annual productivity loss due to assuming caregiver roles is estimated productivity losses, and we examined the sensi- small. Therefore, using household income from the tivity of the model to the demographic characteristics of Wichita Study to estimate annual, national productivity the sample of individuals with CFS. Aggregate productiv- loss should realize an accurate estimate, but the reported ity loss varied by less than 17 percent. average individual productivity loss may be somewhat biased because of the inability to distinguish productivity Results CFS Prevalence losses associated with individuals afflicted with CFS ver- sus productivity losses associated with household mem- Based on the prevalence of CFS in Wichita, Kansas, we bers assuming a caregiver role. estimated that 454,439 individuals nationwide suffered from CFS. Women aged 18 to 69 represented 82% The independent variables include an indicator variable (373,891) of those afflicted with CFS and men aged 18 to for female and continuous variables for age and age- 69 represented the remaining 18% (80,548). squared to capture any non-linear effect of age on income. This effort used indicator variables for black and Latino on the basis of self-reported race and ethnicity, for education Productivity Loss on the basis of self-reports of the highest level of educa- We hypothesized that persons with CFS have lower tion completed, for occupation on the basis of self-reports employment rates and income relative to those with sim- of current or most recent occupation, and for the presence ilar characteristics without CFS. The microsimulation first of select health conditions and illnesses on the basis of applied logistic and ordinary least squares regressions to self-reports of whether the individual had ever been diag- estimate expected employment and income, respectively, nosed or treated by a physician for the conditions or for individuals in the fatigue and non-fatigue groups illnesses. (Table 3). The sign and magnitude of the coefficient esti- mates for the independent variables in the regressions are Page 5 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 3: Employment and Income Regression Results Employment Regression Income Regression NF* Coefficient Fatigue Coefficient NF Coefficient Esti- Fatigue Coefficient † † † † Estimate (95% CI ) Estimate (95% CI ) mate(95% CI ) Estimate (95% CI ) Intercept -1.744 (-2.471, -1.017) -0.628 (-1.446, 0.190) 8.853 (8.598, 9.108) 8.580 (8.237, 8.923) CFS NA -0.699 (-1.345, -0.052) NA -0.081 (-0.382, 0.219) Suspected CFS NA -0.006 (-0.234, 0.223) NA -0.03 (-0.118, 0.049) Education ≤12 Years -0.7 (-1.021, -0387) -0.583 (-828, -0.338) -0.308 (-0.430, -0.185) -0.218 (-0.328, -0.109) Some College 0.228 (0.019, 0.438) 0.099 (-0.083, 0.281) 0.080 (0.013, 0.146) 0.074 (0.001, 0.146) College Graduate (4-Year) 0.347 (0.066, 0.628) 0.410 (0.106, 0.714) 0.279 (0.197, 0.360) 0.263 (0.156, 0.370) Post Graduate Education 0.615 (0.266, 0.963) 0.768 (0.384, 1.153) 0.250 (0.151, 0.350) 0.328 (0.197, 0.459) Not Reporting 0.490 (-0.210, 1.189) 0.550 (0.020, 1.079) 0.155 (-0.041, 0.351) 0.022 (-0.162, 0.206) Age 0.235 (0.198, 0.272) 0.144 (0.105, 0.184) 0.074 (0.061, 0.087) 0.084 (0.067, 0.101) Age Squared -0.003 (-0.004, -0.003) -0.002 (-0.003, -0.002 -0.001 (-0.001, -0.001) -0.001 (-0.001, -0.001) Race/ethnicity Black -0.265 (-0.619, 0.089) -0.233 (-0.528, 0.063) -0.342 (-0.460, -0.225) -0.288 (-0.407, -0.168) Race/ethnicity Latino -0.135 (-0.554, 0.284) 0.089 (-0.279, 0.457) -0.309 (-0.437, -0.181) -0.108 (-0.244, 0.028) Female -0.953 (-1.143, -0.762) -0.380 (-0.568, -0.191) -0.065 (-0.122, -0.008) -0.092 (-0.163, -0.020) Ever Diagnosed or Treated For Alcohol and Drug Dependency -0.210 (-0.747, 0.327) -0.203 (-0494, 0.088) -0.315 (-0.475, -0.155) -0.180 (-0.296, -0.063) Anemia with Blood Transfusion 0.325 (-0.303, 0.954) -0.278 (-0.593, 0.038) -0.112 (-0.314, 0.089 -0.191 (-0.334, -0.047) Anorexia Nervosa or Bulimia -0.859 (-1.814,0.096) -0.031 (-0.601, 0.539) -0.123 (-0.493, 0.247) 0.102 (-0.117, 0.321) Cancer -0.411 (-0.811, -0.011) -0.151 (-0.423, 0.122) 0.129 (-0.024, 0.282) -0.040 (-0.162, 0.082) Chronic Bronchitis or Emphysema 0.230 (-0.266, 0.727) -0.188 (-0.420, 0.045) -0.057 (-0.215, 0.102) -0.148 (-0.249, -0.047) Chronic Hepatitis or Cirrhosis 0.536 (-0.514, 1.586) -0.416 (-0.875, 0.043) -0.074 (-0.372, 0.224) -0.166 (-0.362, 0.030) Depression -0.192 (-0.511, 0.128) -0.385 (-0.556, -0.213) -0.038 (-0.143, 0.066) -0.030 (-0.099, 0.038 Diabetes -0.318 (-0.720, 0.085) -0.319 (-0.568, -0.070) -0.020 (-0.174, 0.133) -0.030 (-0.143, 0.084) Heart Attack -0.233 (-0.795, 0.330) -0.288 (-0.668, 0.092) 0.024 (-0.226, 0.274) -0.059 (-0.242, 0.124) Heart Condition Limiting Ability to Walk -0.578 (-1.402, 0.247) -0.498 (-0.876, -0.120) 0.251 (-0.094, 0.597) 0.052 (-0.139, 0.242) Heart Failure or Fluid in Lungs -0.513 (-1.271, 0.245) -0.312 (-0.636, 0.012) 0.003 (-0.291, 0.298) -0.038 (-0.192, 0.117) High Blood Pressure -0.243 (-0.485, -0.001) -0.013 (-0.201, 0.176) 0.022 (-0.063, 0.106) -0.090 (-0.168, -0.011) Hypothyroidism 0.298 (-0.056, 0.652) -0.103 (-0.324, 0.118) 0.070 (-0.049, 0.189) 0.072 (-0.022, 0.166) AIDS 0.227 (-1.816, 2.270) -1.520 (-2.266, -0.773) 0.471 (-0.299, 1.242) 0.142 (-0.257, 0.540) Lupus or Sjogren's Syndrome 0.498 (-0.963, 1.959) -0.449 (-0.931, 0.033) -0.041 (-0.633, 0.551) 0.043 (-0.187, 0.273) Manic Depressive or Bipolar Disorder -0.611 (-1.501, 0.279) -0.618 (-1.001, -0.236) -0.025 (-0.319, 0.269) -0.127 (-0.304, 0.050) Multiple Sclerosis -0.797 (-2.615, 1.020) -1.259 (-1.773, -0.745) -0.096 (-0.776, 0.584) -0.245 (-0.505, 0.014) Organ Transplant -1.133 (-2.628, 0.363) -1.043 (-1.996, -0.090) -0.029 (-0.667, 0.609) -0.134 (-0.616, 0.347) Rheumatoid Arthritis -0.586 (-1.050, -0.121) -0.495 (-0.738, -0.251) -0.169 (-0.353, 0.015) -0.057 (-0.172, 0.058) Schizophrenia -3.095 (-5.463, -0.727) -0.924 (-1.976, 0.128) -2.247 (-3.521, -0.973) -0.269 (-0.832, 0.294) Stroke -0.096 (-1.057, 0.865) -0.732 (-1.232, -0.232) -0.315 (-0.725, 0.095) 0.091 (-0.182, 0.364) Occupation Management or Professional NA NA 0.188 (0.116, 0.260) 0.158 (0.071, 0.244) Self-employed NA NA 0.006 (-0.124, 0.137) 0.062 (-0.067, 0.190) Technician NA NA 0.093 (-0.020, 0.207) 0.082 (-0.056, 0.220) Clerical Worker NA NA 0.022 (-0.076, 0.119) -0.040 (-0.143, 0.063) Sales Professional NA NA 0.082 (-0.031, 0.195) -0.081 (-0.223, 0.062) Skilled Craftsman NA NA 0.009 (-0.130, 0.147) 0.041 (-0.106, 0.188) Machine Operator NA NA 0.130 (-0.041, 0.302) -0.098 (-0.263, 0.067) Transportation Operator NA NA -0.114 (-0.355, 0.126) -0.212 (-0.485, 0.062) Private Household Workers NA NA -0.133 (-0.562, 0.296) -0.672 (-1.054, -0.290) Protection Services NA NA -0.195 (-0.517, 0.128) -0.155 (-0.522, 0.212) Service Worker NA NA -0.154 (-0.281, -0.026) -0.399 (-0.537, -0.261) Farmer, Farm Worker NA NA 0.233 (-0.340, 0.807) 0.017 (-0.770, 0.804) Unskilled Laborer NA NA -0.168 (-0.337, 0.000) -0.252 (-0.427, -0.077) Military Service NA NA 0.020 (-0.238, 0.278) 0.103 (-0.452, 0.657) Not Reported NA NA -0.651 (-1.287, -0.014) -0.503 (-1.300, 0.295) Number of Observations 3,634 (NA) 3,528 (NA) 2,493 (NA) 2,129 (NA) Page 6 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 in-line with human capital theory and with the results of and 63%, respectively) than men (4 and 32%, respec- similar models in the employment literature. For exam- tively). Table 3 also displays the estimated annual dollar ple, coefficient estimates show that relative to high school loss per individual and the annual productivity loss for graduates, individuals with less than 12 years of education the nation due to CFS. The microsimulation estimated have lower employment rates and income and those with that individuals with CFS lost approximately $20,000 education beyond high school have greater employment annually, which implies a total societal loss in 2002 of rates and income in both the fatigue and non-fatigue $9.1 billion. Twenty-five percent ($2.3 billion) resulted regressions. In addition, in fatigue and non-fatigue regres- from lost household productivity, and the remaining 75% sions, employment rates and income increase with age, ($6.8 billion) from lost labor force productivity. Women but at a declining rate, and being female has a negative represented 82% of those with CFS and 87% of the pro- effect on both employment and income. The regression ductivity losses. The total loss per woman was slightly results indicate that individuals who are black have lower higher than the loss per man, about $21,000 compared employment rates and income. The results for Latinos are with about $15,000. not significant except for a negative impact on income in the non-fatigue regression. Having been diagnosed or The individual and national annual estimated loss of treated for a medical condition included in the regressions $20,000 and $9.1 billion respectively stems from a point generally resulted in lower employment rates and income. prevalence of 235 per 100,000 for the Wichita Study. The When the opposite signs were observed, the results were confidence interval surrounding the point prevalence esti- not significant and thus may have been the result of small mate is 142 to 327 per 100,000, which yields an individ- sample size. ual and national estimate range of $12,000 to $28,000 and $5.5 billion to $12.7 billion, respectively. Although the regression results for the fatigue and non- fatigue groups are both consistent with the human capital Additionally, this research valued household productivity approach, there are some differences. First, the intercept in at the average hourly wage for a service industry worker as the fatigue regression is lower than that in the non-fatigue estimated on the basis of the March Supplement of the regression for the employment and income model, gener- Current Population Survey 2002, which is $9.20. This was ally indicating fatigued individuals are less likely to work because CFS mostly affects females. Using average service and have lower income when working than non-fatigue industry worker wage rates by age and sex is plausible if individuals. Also, the CFS coefficient in the fatigue regres- incidence amongst males and females was similar. sion is negative in the employment and income model. Because the incidence of CFS amongst males was much The income effect is small and not significant; however, lower than females, the additional burden of obtaining the employment impact is substantial and significant. and using average service industry worker wage rates by Given the confidence intervals, the other coefficient esti- age and sex to estimate annual household productivity mates are generally similar in the fatigue and non-fatigue loss from CFS did not justify their use. regressions. One exception is the female coefficient in the employment model. Being female has less of a reduction Discussion on employment for individuals who are in the fatigue The magnitude of the economic impact imposed on the group than for the non-fatigue group. Another exception individual and on society by CFS is substantial. Approxi- is the age coefficient in the employment model, which mately one-quarter of persons with CFS, who would oth- indicates that employment does not increase as quickly erwise have participated in the labor force, ceased with age for individuals who are in the fatigue group com- working. For those who continued to work, average pared with the non-fatigue group. income declined by one-third. This represents an esti- mated annual loss of almost $20,000 for the individual The differences in the coefficients in the fatigue and non- suffering from CFS. This magnitude of loss approximates fatigue regressions translate into substantial declines in half of their labor force and household productivity in a employment resulting from CFS for individuals of all age given year. The $9.1 billion national loss is comparable to and sex groups (Table 4). For women and men, we esti- that estimated for other illnesses, such as digestive system mated about a 27% reduction in employment attributable illnesses ($8.4 B) and infectious and parasitic diseases to CFS. Overall, employment declined from 72.5 to ($10.0 B) [19] and is greater than the estimated productiv- 54.8% for women and from 86.1 to 63.3% for men. These ity losses from immunity disorders ($5.5 B), nervous sys- reductions in employment combined with reductions in tem disorders ($6.4 B), or skin disorders ($1.3) [23]. This hours worked and in productivity per hour resulted in estimate does not include health care costs, which are reductions in household and labor force productivity of likely to be substantial and does not address reductions in 37% and 54%, respectively. Women suffered substantially quality of life, which are likely to be large due to the debil- greater household and labor force productivity losses (42 itating fatigue. Page 7 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 4: Individual and Societal Productivity Losses* Women (years) Men (years) Total 18–34 35–49 50–69 Total 18–34 35–49 50–69 Total Predicted Employment Rate (%) CFS 69.8 56.5 43.1 54.8 63.6 74.0 49.6 63.3 56.3 Non-fatigue 83.9 79.1 60.5 72.5 85.9 94.2 76.2 86.1 74.9 U.S. Employment Rate (%) 76.9 79.5 59.1 72.5 87.6 91.8 72.0 84.6 78.4 Household Productivity CFS $8,502 $9,703 $7,764 $8,495 $8,536 $9,629 $7,100 $8,513 $8,498 Non-fatigue $14,403 $15,986 $13,852 $14,577 $9,208 $9,853 $7,285 $8,907 $13,572 Labor Force Productivity** CFS $3,891 $13,999 $9,442 $8,932 $19,179 $45,016 $30,862 $30,828 $12,813 Non-fatigue $20,140 $31,664 $22,121 $24,001 $26,973 $64,440 $50,429 $45,607 $27,831 Overall Productivity CFS $12,394 $23,702 $17,207 $17,427 $27,715 $54,645 $37,962 $39,341 $21,311 Non-fatigue $34,543 $47,649 $35,974 $38,578 $36,181 $74,292 $57,714 $54,513 $41,403 †† Individual Loss Household Productivity $5,901 $6,283 $6,088 $6,081 $672 $224 $185 $394 $5,073 Labor Force Productivity $16,249 $17,664 $12,679 $15,070 $7,794 $19,424 $19,566 $14,779 $15,018 Total Loss $22,149 $23,947 $18,767 $21,151 $8,466 $19,648 $19,752 $15,173 $20,092 Number of Individuals with CFS 114,373 97,416 162,101 373,891 32,436 26,579 21,533 80,548 454,439 Total Societal Loss (Millions) $2,533 $2,333 $3,042 $7,908 $275 $522 $425 $1,222 $9,130 * Numbers may not sum exactly due to rounding. The microsimulation estimated Employment rates by age and sex based on data from Wichita, Kansas. These means were then weighted to reflect the age and sex distribution of the U.S. population using population estimates from the March ‡ § Supplement to the Current Population Survey, 2002. Based on the March Supplement to the Current Population Survey, 2002. Hours of household productivity valued at the mean hourly earnings of service industry worker, and estimate based in 2002 dollars and increased by 33.8 percent to reflect the value of fringe benefits. ** Estimated personal earnings in 2002 dollars increased by 33.8 percent to reflect the value of fringe †† The individual losses represent the difference between mean productivity with CFS and in absence of CFS. benefits. We estimated annual lost productivity. However, CFS is a half those estimated for a study of CFS in a Chicago pop- chronic illness. The average duration of CFS identified in ulation [3]. To the extent that the Wichita Study underes- population studies is 5 years and most patients with CFS timated prevalence, the productivity loss estimates seen by health care providers have been ill for more than derived in this study are likely to be proportionally under- 6 years [20]. Thus, productivity losses, health care stated. Thus, we believe that the productivity loss esti- expenses, and reductions in quality of life continue for mates presented here are a lower bound on the losses many years for most affected individuals and thus would related to CFS. In addition, as patients with CFS recover have a substantial long-term impact on the standard of they may no longer fulfill all case-defining criteria but living of individuals with CFS and their family members. may still have reductions in income because they lost job tenure and experience at the time of their illness. Thus, Some limitations should be considered when interpreting these individuals should be included in productivity loss our results and considering future studies. The prevalence estimates. estimates we used are likely to understate the number of individuals affected by CFS since the Wichita study was We used the human capital approach to estimate lost pro- designed to estimate point prevalence. Forty-three partici- ductivity rather then the friction cost method. Several pants were classified as having CFS at baseline because studies that have compared indirect costs of illness by they fulfilled all criteria of the case definition at the time both methods show that the human capital approach of clinical evaluation. The study continued an additional potentially overestimates indirect costs related to illness 3 years, during which the cohort was interviewed annu- because it does not account for labor scarcity. We take the ally, and over the entire study, 90 persons were identified view that labor markets clear relatively quickly, and that as having CFS. Incident CFS was extremely rare, most of the hypothetical unemployed worker who takes the job the 47 cases identified during subsequent years reported vacated by the CFS victim would have soon found they had been ill with CFS for many years but were in par- employment at about the same wage anyway. For individ- tial remission during previous interviews and so had not uals with CFS, we reduced the value of household produc- acknowledged symptoms at that instant in time. Preva- tivity by the same percentage as the reduction in their lence estimates from the CDC Wichita Study are about labor force income due to the presence of CFS. This con- Page 8 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 6. Hill NF, Tiersky LA, Scavalla VR, Lavietes M, Natelson BH: Natural servative approach also based its estimate on reductions history of severe chronic fatigue syndrome. Arch Phys Med in labor productivity among those individuals with CFS Rehabil 1999, 80:1090-4. who remained in the labor force after the onset of their ill- 7. Lloyd AR, Pender H: The economic impact of chronic fatigue syndrome. Med J Aust 1992, 157:599-601. ness. The severity of the illness for these individuals was 8. Bombardier CH, Buchwald D: Chronic fatigue, chronic fatigue likely to be much less than that of individuals with the ill- syndrome, and fibromyalgia: disability and health-care use. Med Care 1996, 34:924-30. ness who exited the labor force. While there are many dif- 9. McCrone P, Darbishire L, Ridsdale L, Seed P: The economic cost ficulties in precisely estimating the costs of illnesses such of chronic fatigue and chronic fatigue syndrome in UK pri- as CFS because of human factors that are difficult or mary care. Psychol Med 2003, 33:253-61. 10. Rice DP: Estimating the cost of illness. Health Economics Series 6 impossible to quantify, this estimate documents the Washington, DC: US Department of Health, Education, and Welfare; dimension and magnitude of the stark economic impact 1966. Publication 947-6 that CFS has on individuals, households and on the 11. Rice DP: Estimating the cost of illness. Am J Public Health Nations Health 1967, 57:424-40. nation. 12. Rice DP, Cooper BS: The economic value of human life. Am J Public Health Nations Health 1967, 57:1954-66. 13. Rice DP, Hodgson TA, Kopstein AN: The economic costs of ill- Conclusions ness, a replication and update. Health Care Financ Rev 1985, Lost productivity due to CFS was substantial both on an 7:61-80. individual basis and relative to national estimates for 14. Newhouse JP: The Health Insurance Group. Free-for-all: health insurance, medical costs, and health outcomes: the results of the health other major illnesses. CFS resulted in a national produc- insurance experiment Cambridge, MA: Harvard University Press; 1993. tivity loss comparable to such losses from diseases of the 15. Manning WG, Newhouse JP, Duan N, Keeler EB, Leibowitz A, Mar- quis MS: Health insurance and the demand for medical care: digestive, immune and nervous systems, and from skin evidence from a randomized experiment. Am Econ Rev 1987, disorders. The extent of the burden indicates that contin- 77:251-77. ued research to determine the cause and potential thera- 16. Duan N, Manning WG, Morris C, Newhouse JP: A comparison of alternative models for the demand for medical care. J Bus Stat pies for CFS could provide substantial benefit both for 1983, 1:115-26. individual patients and for the nation. 17. Duan N, Manning WG, Morris C, Newhouse JP: Choosing between the sample-selection model and the multi-part model. J Busi- ness Econ Stat 1984, 2:283-9. Competing interests 18. Manning WG, Duan N, Rogers W: Monte Carlo evidence on the None declared. choice between sample selection and two-part models. J Econometrics 1987, 35:59-82. 19. Rizzo JA, Abbott TA 3rd, Berger ML: The labor productivity Authors' contributions effects of chronic backache in the United States. Med Care KJR had primary responsibility for data analysis strategies 1998, 36:1471-88. 20. Reyes M, Gary HE Jr, Dobbins JG, Randall B, Steele L, Fukuda K, Hol- and interpretation of economic data, and drafted the mes GP, et al.: Surveillance for chronic fatigue syndrome – four manuscript. SDV conceived the idea to assess the eco- U.S. cities, September 1989 through August 1993. MMWR nomic impact of CFS presented in this manuscript, partic- CDC Surveill Summ 1997, 46:1-13. ipated in analysis strategies, collaborated in interpretation of the data and drafting the manuscript. EB was responsi- ble for data analysis and collaborated in interpretation and drafting the manuscript. WCR conceived of the study from which the data was derived, led its design implemen- tation and conduct, collaborated in conception of this analysis, collaborated in interpretation of the results and drafting the manuscript. All authors read and approved the final manuscript. References 1. Fukuda K, Straus SE, Hickie I, Sharpe MC, Dobbins JG, Komaroff A: Publish with Bio Med Central and every The chronic fatigue syndrome: a comprehensive approach to its definition and study. Ann Intern Med 1994, 121:953-9. scientist can read your work free of charge 2. Reyes M, Nisenbaum R, Hoaglin DC, Unger ER, Emmons C, Randall "BioMed Central will be the most significant development for B, et al.: Prevalence of chronic fatigue syndrome in Wichita, disseminating the results of biomedical researc h in our lifetime." Kansas. Arch Intern Med 2003, 163:1530-6. 3. Jason LA, Richman JA, Rademaker AW, Jordan KM, Plioplys AV, Tay- Sir Paul Nurse, Cancer Research UK lor RR, et al.: A community-based study of chronic fatigue syndrome. Arch Intern Med 1999, 159:2129-37. Your research papers will be: 4. Nisenbaum R, Jones A, Jones J, Reeves W: Longitudinal analysis of available free of charge to the entire biomedical community symptoms reported by patients with chronic fatigue peer reviewed and published immediately upon acceptance syndrome. Ann Epidemiol 2000, 10:458. 5. Reyes M, Dobbins JG, Nisenbaum R, Subedar NS, Randall B, Reeves cited in PubMed and archived on PubMed Central WC: Chronic fatigue syndrome progression and self-defined yours — you keep the copyright recovery: evidence from the CDC surveillance system. J CFS 1999, 5:17-27. BioMedcentral Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.asp Page 9 of 9 (page number not for citation purposes) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cost Effectiveness and Resource Allocation Springer Journals

The economic impact of chronic fatigue syndrome

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Springer Journals
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Copyright © 2004 by Reynolds et al; licensee BioMed Central Ltd.
Subject
Medicine & Public Health; Health Administration; Social Policy; Quality of Life Research
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1478-7547
DOI
10.1186/1478-7547-2-4
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Abstract

Background: Chronic fatigue syndrome (CFS) is a chronic incapacitating illness that affects between 400,000 and 800,000 Americans. Despite the disabling nature of this illness, scant research has addressed the economic impact of CFS either on those affected or on the national economy. Methods: We used microsimulation methods to analyze data from a surveillance study of CFS in Wichita, Kansas, and derive estimates of productivity losses due to CFS. Results: We estimated a 37% decline in household productivity and a 54% reduction in labor force productivity among people with CFS. The annual total value of lost productivity in the United States was $9.1 billion, which represents about $20,000 per person with CFS or approximately one-half of the household and labor force productivity of the average person with this syndrome. Conclusion: Lost productivity due to CFS was substantial both on an individual basis and relative to national estimates for other major illnesses. CFS resulted in a national productivity loss comparable to such losses from diseases of the digestive, immune and nervous systems, and from skin disorders. The extent of the burden indicates that continued research to determine the cause and potential therapies for CFS could provide substantial benefit both for individual patients and for the nation. being of those affected, on the health care system, or on Background Chronic fatigue syndrome (CFS) is an illness defined by society as a whole. disabling physical and mental fatigue and physical and mental symptoms that are not explained by conventional The burden of CFS is poorly recognized, and the illness medical and psychiatric diagnoses [1]. CFS affects remains an inadequately managed health problem. Two between 400,000 and 800,000 people in the United States population-based studies of CFS have been conducted in [2,3] and has an average duration of 5 years, but symp- the United States, and both found that CFS is one of the toms can persist as long as 20 years [4]. The prognosis for more common chronic illnesses among women across all recovery of severely ill CFS patients is poor [5,6]. Despite racial/ethnic groups and that less than 20% of those who CFS's disabling, enduring, and prevalent nature, scant suffer from CFS have been diagnosed by a health care pro- studies have quantified its impact on the health and well- vider [2,3]. Only three studies, all of which were clinic Page 1 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 based, have attempted to quantify the impact of CFS, and classified into the CFS subgroup if clinical evaluation con- each showed that people with the syndrome were likely to firmed a diagnosis of CFS. have lost their job or to be unemployed [6-8]. In addition, it was shown that persons with CFS pose a disproportion- To estimate lost productivity due to CFS, data were ate burden on the health care system and their families obtained from individual responses to the detailed inter- since they are sick for long periods of time and since there view and clinical evaluation. The detailed interview and is no known cure for the illness [9]. subsequent analysis provided individual responses and classifications for current employment status, categorical The study reported herein is the first attempt to develop household income, age, sex, ethnicity, level of education, more generalizable results concerning the impact of CFS. duration and classification of fatigue, and occupation. In To this end, we derived quantitative measures of lost pro- addition, responses and analysis detailed household ductivity by interviewing persons identified as having CFS chores prior to and during fatigue and medical and psy- and non-fatigued people who were representative of the chiatric conditions. The clinical evaluation and subse- general population of Wichita, Kansas. We assessed the quent independent review determined those individuals economic burden of CFS on afflicted individuals and on with CFS and those with exclusionary medical conditions society as a whole and found that lost productivity in the from the suspected CFS group. Table 1 summarizes the United States amounted to an annual loss of $9.1 billion descriptive statistics for the study groups. or about $20,000 per afflicted person. Analysis The economic theory of human capital is the basis of the Methods Study Design simulation model we used to estimate the impact of CFS. This study adhered to human experimentation guidelines The human capital approach models an individual's pro- of the U.S. Department of Health and Human Services. All ductivity (in terms of employment and earnings) as a participants were volunteers who gave informed consent. function of human capital characteristic's such as age, The Centers for Disease Control and Prevention (CDC) education, occupation, and health status [10-13], and it Human Subjects Committee approved study protocols. hypothesizes that specific attributes of workers are valued Details of the population-based study to estimate the in the marketplace; thus, it recognizes differences among prevalence and incidence of CFS in the adult population individuals in terms of their experience, training, educa- of Wichita, Kansas, have been published [2]. In brief, the tion, and other characteristics that are valued in labor study used random-digit dialing to screen about 56,000 markets. Just as machines or other productive capital persons between 18 and 69 years of age. Those reporting involve investment that lead to future returns, human fatigue of at least one-month duration and randomly capital requires investments in schooling, health, appren- selected non-fatigued respondents were interviewed in ticeships, and other skill-building that may pay off in detail on the telephone to ascertain demographic charac- higher future wages. We treat illnesses, such as CFS, as a teristics, previous diagnosis of medical or psychiatric con- negative shock, which may potentially negatively affect an ditions that excluded classification as CFS, symptoms, individuals' ability to achieve returns on their human cap- occupation, and household income. People who were ital, given the severity of the illness. Therefore, the human suspected to have CFS on the basis of the detailed inter- capital framework enables us to examine the impact of view were invited to participate in a clinical evaluation to CFS on the ability to work and, given work, on pay. determine if they did indeed have CFS or some other illness. To estimate productivity loss, we employed methods developed as part of the RAND Health Insurance Experi- For analysis, subjects were classified into the "non-fatigue ment microsimulation [14-16]. Table 2 explains the two- group" (n = 3,634) if they did not report fatigue during step microsimulation approach that first used logistical the telephone interview or into the "fatigue group" (n = regression to predict employment and then ordinary least 3,528) if they reported fatigue lasting ≥1 month. The squares regression to estimate expected income, condi- fatigue group was further divided into 3 subgroups: those tional on employment, for the fatigue and non-fatigue with "prolonged fatigue" (n = 2973), those with "sus- groups. The expected decline in employment and income, pected CFS" (n = 555), and those with "CFS" (n = 43). given employment, would most likely stem from the Fatigue group respondents were classified into the pro- change in health status that resulted from the CFS diagno- longed fatigue subgroup if they reported fatigue lasting ≥1 sis. The two-step model provided consistent and efficient month but did not fulfill criteria for CFS. Fatigue group estimates through better exploitation of the sample char- respondents were classified into the suspected CFS sub- acteristics of the household income distribution group if they met the CFS case definition based on self- [15,17,18]. reported telephone interview responses, and they were Page 2 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 1: Demographic Characteristics of the U.S. population and the Wichita Sample by Fatigue and Non-Fatigued Group Demographic U.S. Population* Wichita Surveillance Groups Characteristics ‡ § Non-Fatigue Group Prolonged Fatigue Suspect CFS CFS (n = 43) (n = 3,634) (n = 2,973) (n = 512) Age, Mean (years) 41.0 40.5 42.7 44.1 47.8 <35 years (%) 35.6 37.3 29.1 18.9 8.3 35–49 years (%) 35.2 35.5 38.6 50.6 46.8 50–69 years (%) 29.2 27.2 32.3 30.6 45.0 Male (%) 49.1 49.3 34.7 29.1 14.6 Female (%) 50.9 50.7 65.3 70.9 85.4 Black (%) 12.1 8.5 11.2 5.5 2.8 Latino (%) 12.6 4.8 5.0 5.7 1.9 Employed (%) 78.4 76.5 64.1 68.4 52.8 Mean Household NA $33,477 $39,027 $44,143 $40,802 Income <$20,000 (%) 26.8 17.7 33.6 24.3 16.8 $20,000–$49,999 38.1 40.7 40.0 41.7 46.2 (%) $50,000–$74,999 17.8 18.1 12.6 19.1 23.6 (%) ≥$75,000 (%) 17.4 12.7 6.6 8.2 5.4 Not Reporting (%) 0.0 10.8 7.1 6.6 8.1 Education (%) <12 Years 14.9 7.8 14.8 8.5 2.7 High School 31.5 28.4 32.6 33.2 35.6 Graduate Some College 28.2 36.4 35.9 40.0 51.9 College Graduate 17.3 16.7 8.5 11.1 6.0 (4-Year) Post Graduate 8.2 8.9 5.8 4.7 3.7 Education Not Reporting 0.0 1.7 2.4 2.5 0.0 Occupation (%) Management or 23.4 27.1 20.3 25.9 16.8 Professional Clerical Worker 10.3 10.2 12.1 12.8 22.8 Service Worker 10.6 6.9 9.4 7.4 10.7 Sales Professional 8.7 6.6 5.2 5.1 7.5 Technician 2.5 6.6 6.3 5.8 17.0 Skilled Craftsman 8.3 4.3 5.0 6.3 4.6 Homemaker NA 1.9 2.4 2.3 3.5 Other/Not 36.2 36.2 39.3 34.4 17.2 Reporting * Based on analysis of the March Supplement to the Current Population Survey, 2002 conducted by SRA International, Inc. Columns may not add to † ‡ 100% due to rounding. Weighted to reflect population of Wichita, Kansas. Columns may not add to 100% due to rounding. Excludes the 555 CFS-like observations. Excludes the 43 CFS observations. The dependent variables for our analysis are an indicator point of $100,000. Since we used household income as variable for employment and a continuous measure of the dependent variable in the income regression, we ide- household income. Individuals were coded as participat- ally would control for marital status and household com- ing in the labor force if they reported that they were cur- position in the regression. Unfortunately, this rently employed. Household income was collected as a information was not included in the Wichita survey. categorical variable. We defined household income at the mid-point for each category to develop a continuous Ideally, personal income per household member is the measure, and the top category was coded at the truncation desired proxy to more accurately estimate individual and Page 3 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 2: Microsimulation steps for estimating the cost of productivity losses due to CFS. Step Equation 1. Divide the study sample data into two groups: Fatigued (F), N = 3,528, and Non-Fatigued (NF), N = 3,634. Estimate logistic regressions to obtain the probability that an individual is employed for F, P[W = Y/F], and NF, P[W = Y/NF], subsamples, as a function of human capital characteristics, as displayed in equations 1a and 1b. a. P[W = Y/F] = f(age, sex, ethnicity, education, suspected CFS, CFS, history of diseases that exclude respondent from CFS diagnosis) b. P[W = Y/NF] = f(age, sex, ethnicity, education, history of diseases that exclude respondent from CFS diagnosis) 2. Estimate ordinary least squares regressions to predict the natural log of income (I) given employment for the F, E[I/W = Y,F], and NF, E[I/W = Y,NF], subsamples, as a function of human capital characteristics, as displayed in equations 2a and 2b. a. E[I/W = Y,F] = f(age, sex, ethnicity, education, suspected CFS, CFS, history of diseases that exclude respondent from CFS diagnosis, occupation) b. E[I/W = Y,NF] = f(age, sex, ethnicity, education, history of diseases that exclude respondent from CFS diagnosis, occupation) Note: Once these regressions are estimated, only the sample of 43 individuals with CFS are used for the remainder of the microsimulation to estimate mean labor force and household productivity by age and sex in the presence and absence of CFS. 3. Calculate predicted mean F and NF employment rates by age and sex categories, weighting by sampling weights. Multiply the coefficient estimates from the F (Blf) and NF (Blnf) Logit regressions by the human capital characteristics of the 43 individuals with CFS (X) to obtain their F (P[W = Y/ F]) and NF (P[W = Y/NF) employment rates, respectively, as shown in equations 3a and 3b. Then, calculate the mean employment rate across the 43 individuals with CFS for each age and sex category weighting these means to reflect the survey sampling rates. a. P[W = Y/F] = {exp(X*Blf)/1+exp(X*Blf) b. P[W = Y/NF] = {exp(X*Blnf)/1+exp(X*Blnf) 4. Calculate predicted mean F and NF income given employment by age and sex categories weighting by sampling weight. Multiply the coefficient estimates from the F (Bolsf) and NF (Bolsnf) OLS income regressions by the human capital characteristics of the 43 individuals with CFS (X) to obtain their F (E[I/W = Y,F]) and NF (E[I/W = Y,NF) income given employment, respectively. Then, apply the smearing adjustment to the exponent of these F and NF products, as shown in equations 4a and 4b, to correct for the "retransformation" bias that arises from estimating impacts using loglinear models and to protect against data issues such as heteroskedasticity . The smearing factors for the regressions among individuals with F (Sf) and in the absence of F (Snf) are equal to the means of the anti-logs of the residuals of the respective income regressions. Calculate predicted F and NF income given employment for each age and sex category weighting by the survey sampling weights, and adjust these means from 1997 to 2002 dollars to account for inflation using the Department of Labor, Bureau of Labor Statistics Consumer Price Index from 1997 to 2002 . Apply an adjustment factor for the difference between mean income in Wichita and the nation based on analysis by the U.S. Department of Commerce increasing the estimated losses by 1.3 percent. In addition, to account for fringe benefits, multiply predicted income by a factor of 1.338, which is obtained from the Bureau of Labor Statistics Report on Employer Costs for Employee Compensation – June 2002 . a. E[I/W = Y,F] = {exp(X*Bolsf)*Sf}*1.114*1.013*1.338 b. E[I/W = Y,NF] = {exp(X*Bolsnf)*Snf}*1.114*1.013*1.338 5. Calculate predicted household productivity given employment and no employment in absence of F. The value of household productivity by sex, age, and employment status absent F is calculated on the basis of data on the number of hours spent on household chores for the NF sample, given employment (HH hours/W = Y,NF) and no employment (hours/W = N, NF). Value these hours at the average hourly wage for a service industry worker as estimated on the basis of the March Supplement of the Current Population Survey 2002 or $9.20. Similar to employment income, increase the value of the service industry worker wage by a factor of 1.338 to account for the value fringe benefits. This equation is displayed in 5a and 5b. a. E[HH/W = Y,NF] = E[HH Hours/W = Y, NF]*$9.20*1.338 b. E[HH/W = N,NF] = E[HH Hours/W = N, NF]*$9.20*1.338 6. Calculate predicted household productivity given F. Assume that the percentage reduction in employment related income, given work, is equal to the percentage reduction in household productivity. Apply a reduction factor representing the estimated reduction in employment-related income, given work, resulting from CFS to the predicted values of household productivity, given employment and no employment, as displayed in 6a and 6b. Calculate reduction factors separately for males and females. a. E[HH/W = Y,F] = E[HH/W = Y,NF] * E[I/W = Y,F]/E[I/W = Y,NF] b. E[HH/W = N,F] = E[HH/W = N,NF] * E[I/W = Y,F]/E[I/W = Y,NF] 7. Calculate predicted mean F and NF total productivity for each CFS individual. Overall, each CFS individual's expected total productivity in the presence or absence of F, E[Y/F] or E[Y/NF] respectively, is equal to the probability that they participate in the labor force, P[W = Y/F] or P[W = Y/NF], times the expected value of their total labor force and household productivity if they participate in the labor force plus the probability they choose not to participate in the labor force, P[W = N/F] or P[W = N/NF], times the expected value of their household productivity when they do not participate in the labor force. Equations 7a and 7b display expected productivity. a. E[Y/F] = P[W = Y/F]{E[I/W = Y,F] + E[HH/W = Y,F]} + P(W = N/F) {E[I/W = N,F] + E[HH/W = N,F]} b. E[Y/NF] = P[W = Y/NF]{E[I/W = Y,NF] + E[HH/W = Y,NF]} + P(W = N/NF) {E[I/W = N,NF] + E[HH/W = N,NF]} 8. Calculate estimated number of individuals with CFS nationally by age and sex. Using the Wichita Prevalence Study data, calculate the prevalence of CFS per 100,000 by age and sex cells and then use national population data from the Current Population Survey to calculate the number of individuals in each age and sex category with CFS. 9. Calculate individual and societal productivity losses due to CFS. Compute the difference between predicted mean total productivity without and with F, (E[Y/NF]-E[Y/F]), by age and sex category to estimate the individual loss for each age and sex cell and then multiply these differences for each sex and age cell by the estimated by number of individuals with CFS nationally in each cell and sum across the cells to estimate the total societal cost of lost productivity due to CFS. Page 4 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 2: Microsimulation steps for estimating the cost of productivity losses due to CFS. (Continued) 1 2 Duan N. Smearing Estimates: A non-parametric retransformation technique. J Am Stat Assoc 1983,383:605–10. Consumer Price Index from 1997 to 2002. Department of Labor, Bureau of Labor Statistics. (http://data.bls.gov/cgi-bin/surveymost?cu, then select U.S. All items, 1982-84=100 Per capita net earnings ($) Metro Comparisons. Department of Economics, Iowa State University, Midwest Profiles, Public CUUR0000SA0) Resources Online. (http://www.bea.doc.gov/bea/regional/reis/, then select Personal income and population summary estimates (CA1-3) plus per capita personal income plus Metropolitan Statistical Areas*) Employer Costs for Employee Compensation – June 2002. Bureau of Labor Statistics, September 2002. (http://http//data.bls.gov/cgi-bin/surveymost?cc, then click on Civilian, All workers, Total compensation - CCU110000100000D) national productivity loss. Personal income per house- Bootstrap standard errors were calculated for the esti- hold individual allows one to distinguish between pro- mated declines in employment, income given employ- ductivity losses resulting from CFS affliction versus ment, and total productivity derived from the productivity losses stemming from household members microsimulation model by age and sex cell. Bootstrap assuming caregiver roles at the expense of their employ- errors were calculated to test the sensitivity of the micro- ment productivity. Ideally, when estimating individual simulation to sampling error. Employment declines were productivity loss from CFS, one should distinguish all significant at the 95 percent confidence level. For between the productivity loss associated with CFS afflic- female age cells, income declines were significant at the 95 tion and that associated with the assumption of caregiver percent level for the 18 to 34 and 50 to 69 age cells and at roles at the expense of employment productivity. the 90 percent level for the 35 to 49 year age cell. Income declines estimated for males were not significant. This The national productivity loss estimate should include may result because low earning males exit the labor force both to reflect accurately the total national reduction in and higher earning males retain employment, causing the employment productivity stemming from CFS. However, mean earnings of those with employment to rise. Overall, given the structure of the Wichita Study questionnaire, a the total declines in productivity estimated under the recorded change in household income stems from an model were significant at the 99 percent confidence level individual within that household acquiring CFS; thus it with the exception of males 18 to 34 and 35 to 49 years of captures productivity losses that result directly from CFS age, which were significant at the 90 percent level. affliction and indirectly from the assumption of caregiver roles by non-afflicted household members. To date, CFS We conducted sensitivity tests on key assumptions of the research reports a clear reduction in hours worked by simulation model. We examined how the decision to those afflicted directly with CFS; thus, we believe that the model male and female productivity separately impacted annual productivity loss due to assuming caregiver roles is estimated productivity losses, and we examined the sensi- small. Therefore, using household income from the tivity of the model to the demographic characteristics of Wichita Study to estimate annual, national productivity the sample of individuals with CFS. Aggregate productiv- loss should realize an accurate estimate, but the reported ity loss varied by less than 17 percent. average individual productivity loss may be somewhat biased because of the inability to distinguish productivity Results CFS Prevalence losses associated with individuals afflicted with CFS ver- sus productivity losses associated with household mem- Based on the prevalence of CFS in Wichita, Kansas, we bers assuming a caregiver role. estimated that 454,439 individuals nationwide suffered from CFS. Women aged 18 to 69 represented 82% The independent variables include an indicator variable (373,891) of those afflicted with CFS and men aged 18 to for female and continuous variables for age and age- 69 represented the remaining 18% (80,548). squared to capture any non-linear effect of age on income. This effort used indicator variables for black and Latino on the basis of self-reported race and ethnicity, for education Productivity Loss on the basis of self-reports of the highest level of educa- We hypothesized that persons with CFS have lower tion completed, for occupation on the basis of self-reports employment rates and income relative to those with sim- of current or most recent occupation, and for the presence ilar characteristics without CFS. The microsimulation first of select health conditions and illnesses on the basis of applied logistic and ordinary least squares regressions to self-reports of whether the individual had ever been diag- estimate expected employment and income, respectively, nosed or treated by a physician for the conditions or for individuals in the fatigue and non-fatigue groups illnesses. (Table 3). The sign and magnitude of the coefficient esti- mates for the independent variables in the regressions are Page 5 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 3: Employment and Income Regression Results Employment Regression Income Regression NF* Coefficient Fatigue Coefficient NF Coefficient Esti- Fatigue Coefficient † † † † Estimate (95% CI ) Estimate (95% CI ) mate(95% CI ) Estimate (95% CI ) Intercept -1.744 (-2.471, -1.017) -0.628 (-1.446, 0.190) 8.853 (8.598, 9.108) 8.580 (8.237, 8.923) CFS NA -0.699 (-1.345, -0.052) NA -0.081 (-0.382, 0.219) Suspected CFS NA -0.006 (-0.234, 0.223) NA -0.03 (-0.118, 0.049) Education ≤12 Years -0.7 (-1.021, -0387) -0.583 (-828, -0.338) -0.308 (-0.430, -0.185) -0.218 (-0.328, -0.109) Some College 0.228 (0.019, 0.438) 0.099 (-0.083, 0.281) 0.080 (0.013, 0.146) 0.074 (0.001, 0.146) College Graduate (4-Year) 0.347 (0.066, 0.628) 0.410 (0.106, 0.714) 0.279 (0.197, 0.360) 0.263 (0.156, 0.370) Post Graduate Education 0.615 (0.266, 0.963) 0.768 (0.384, 1.153) 0.250 (0.151, 0.350) 0.328 (0.197, 0.459) Not Reporting 0.490 (-0.210, 1.189) 0.550 (0.020, 1.079) 0.155 (-0.041, 0.351) 0.022 (-0.162, 0.206) Age 0.235 (0.198, 0.272) 0.144 (0.105, 0.184) 0.074 (0.061, 0.087) 0.084 (0.067, 0.101) Age Squared -0.003 (-0.004, -0.003) -0.002 (-0.003, -0.002 -0.001 (-0.001, -0.001) -0.001 (-0.001, -0.001) Race/ethnicity Black -0.265 (-0.619, 0.089) -0.233 (-0.528, 0.063) -0.342 (-0.460, -0.225) -0.288 (-0.407, -0.168) Race/ethnicity Latino -0.135 (-0.554, 0.284) 0.089 (-0.279, 0.457) -0.309 (-0.437, -0.181) -0.108 (-0.244, 0.028) Female -0.953 (-1.143, -0.762) -0.380 (-0.568, -0.191) -0.065 (-0.122, -0.008) -0.092 (-0.163, -0.020) Ever Diagnosed or Treated For Alcohol and Drug Dependency -0.210 (-0.747, 0.327) -0.203 (-0494, 0.088) -0.315 (-0.475, -0.155) -0.180 (-0.296, -0.063) Anemia with Blood Transfusion 0.325 (-0.303, 0.954) -0.278 (-0.593, 0.038) -0.112 (-0.314, 0.089 -0.191 (-0.334, -0.047) Anorexia Nervosa or Bulimia -0.859 (-1.814,0.096) -0.031 (-0.601, 0.539) -0.123 (-0.493, 0.247) 0.102 (-0.117, 0.321) Cancer -0.411 (-0.811, -0.011) -0.151 (-0.423, 0.122) 0.129 (-0.024, 0.282) -0.040 (-0.162, 0.082) Chronic Bronchitis or Emphysema 0.230 (-0.266, 0.727) -0.188 (-0.420, 0.045) -0.057 (-0.215, 0.102) -0.148 (-0.249, -0.047) Chronic Hepatitis or Cirrhosis 0.536 (-0.514, 1.586) -0.416 (-0.875, 0.043) -0.074 (-0.372, 0.224) -0.166 (-0.362, 0.030) Depression -0.192 (-0.511, 0.128) -0.385 (-0.556, -0.213) -0.038 (-0.143, 0.066) -0.030 (-0.099, 0.038 Diabetes -0.318 (-0.720, 0.085) -0.319 (-0.568, -0.070) -0.020 (-0.174, 0.133) -0.030 (-0.143, 0.084) Heart Attack -0.233 (-0.795, 0.330) -0.288 (-0.668, 0.092) 0.024 (-0.226, 0.274) -0.059 (-0.242, 0.124) Heart Condition Limiting Ability to Walk -0.578 (-1.402, 0.247) -0.498 (-0.876, -0.120) 0.251 (-0.094, 0.597) 0.052 (-0.139, 0.242) Heart Failure or Fluid in Lungs -0.513 (-1.271, 0.245) -0.312 (-0.636, 0.012) 0.003 (-0.291, 0.298) -0.038 (-0.192, 0.117) High Blood Pressure -0.243 (-0.485, -0.001) -0.013 (-0.201, 0.176) 0.022 (-0.063, 0.106) -0.090 (-0.168, -0.011) Hypothyroidism 0.298 (-0.056, 0.652) -0.103 (-0.324, 0.118) 0.070 (-0.049, 0.189) 0.072 (-0.022, 0.166) AIDS 0.227 (-1.816, 2.270) -1.520 (-2.266, -0.773) 0.471 (-0.299, 1.242) 0.142 (-0.257, 0.540) Lupus or Sjogren's Syndrome 0.498 (-0.963, 1.959) -0.449 (-0.931, 0.033) -0.041 (-0.633, 0.551) 0.043 (-0.187, 0.273) Manic Depressive or Bipolar Disorder -0.611 (-1.501, 0.279) -0.618 (-1.001, -0.236) -0.025 (-0.319, 0.269) -0.127 (-0.304, 0.050) Multiple Sclerosis -0.797 (-2.615, 1.020) -1.259 (-1.773, -0.745) -0.096 (-0.776, 0.584) -0.245 (-0.505, 0.014) Organ Transplant -1.133 (-2.628, 0.363) -1.043 (-1.996, -0.090) -0.029 (-0.667, 0.609) -0.134 (-0.616, 0.347) Rheumatoid Arthritis -0.586 (-1.050, -0.121) -0.495 (-0.738, -0.251) -0.169 (-0.353, 0.015) -0.057 (-0.172, 0.058) Schizophrenia -3.095 (-5.463, -0.727) -0.924 (-1.976, 0.128) -2.247 (-3.521, -0.973) -0.269 (-0.832, 0.294) Stroke -0.096 (-1.057, 0.865) -0.732 (-1.232, -0.232) -0.315 (-0.725, 0.095) 0.091 (-0.182, 0.364) Occupation Management or Professional NA NA 0.188 (0.116, 0.260) 0.158 (0.071, 0.244) Self-employed NA NA 0.006 (-0.124, 0.137) 0.062 (-0.067, 0.190) Technician NA NA 0.093 (-0.020, 0.207) 0.082 (-0.056, 0.220) Clerical Worker NA NA 0.022 (-0.076, 0.119) -0.040 (-0.143, 0.063) Sales Professional NA NA 0.082 (-0.031, 0.195) -0.081 (-0.223, 0.062) Skilled Craftsman NA NA 0.009 (-0.130, 0.147) 0.041 (-0.106, 0.188) Machine Operator NA NA 0.130 (-0.041, 0.302) -0.098 (-0.263, 0.067) Transportation Operator NA NA -0.114 (-0.355, 0.126) -0.212 (-0.485, 0.062) Private Household Workers NA NA -0.133 (-0.562, 0.296) -0.672 (-1.054, -0.290) Protection Services NA NA -0.195 (-0.517, 0.128) -0.155 (-0.522, 0.212) Service Worker NA NA -0.154 (-0.281, -0.026) -0.399 (-0.537, -0.261) Farmer, Farm Worker NA NA 0.233 (-0.340, 0.807) 0.017 (-0.770, 0.804) Unskilled Laborer NA NA -0.168 (-0.337, 0.000) -0.252 (-0.427, -0.077) Military Service NA NA 0.020 (-0.238, 0.278) 0.103 (-0.452, 0.657) Not Reported NA NA -0.651 (-1.287, -0.014) -0.503 (-1.300, 0.295) Number of Observations 3,634 (NA) 3,528 (NA) 2,493 (NA) 2,129 (NA) Page 6 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 in-line with human capital theory and with the results of and 63%, respectively) than men (4 and 32%, respec- similar models in the employment literature. For exam- tively). Table 3 also displays the estimated annual dollar ple, coefficient estimates show that relative to high school loss per individual and the annual productivity loss for graduates, individuals with less than 12 years of education the nation due to CFS. The microsimulation estimated have lower employment rates and income and those with that individuals with CFS lost approximately $20,000 education beyond high school have greater employment annually, which implies a total societal loss in 2002 of rates and income in both the fatigue and non-fatigue $9.1 billion. Twenty-five percent ($2.3 billion) resulted regressions. In addition, in fatigue and non-fatigue regres- from lost household productivity, and the remaining 75% sions, employment rates and income increase with age, ($6.8 billion) from lost labor force productivity. Women but at a declining rate, and being female has a negative represented 82% of those with CFS and 87% of the pro- effect on both employment and income. The regression ductivity losses. The total loss per woman was slightly results indicate that individuals who are black have lower higher than the loss per man, about $21,000 compared employment rates and income. The results for Latinos are with about $15,000. not significant except for a negative impact on income in the non-fatigue regression. Having been diagnosed or The individual and national annual estimated loss of treated for a medical condition included in the regressions $20,000 and $9.1 billion respectively stems from a point generally resulted in lower employment rates and income. prevalence of 235 per 100,000 for the Wichita Study. The When the opposite signs were observed, the results were confidence interval surrounding the point prevalence esti- not significant and thus may have been the result of small mate is 142 to 327 per 100,000, which yields an individ- sample size. ual and national estimate range of $12,000 to $28,000 and $5.5 billion to $12.7 billion, respectively. Although the regression results for the fatigue and non- fatigue groups are both consistent with the human capital Additionally, this research valued household productivity approach, there are some differences. First, the intercept in at the average hourly wage for a service industry worker as the fatigue regression is lower than that in the non-fatigue estimated on the basis of the March Supplement of the regression for the employment and income model, gener- Current Population Survey 2002, which is $9.20. This was ally indicating fatigued individuals are less likely to work because CFS mostly affects females. Using average service and have lower income when working than non-fatigue industry worker wage rates by age and sex is plausible if individuals. Also, the CFS coefficient in the fatigue regres- incidence amongst males and females was similar. sion is negative in the employment and income model. Because the incidence of CFS amongst males was much The income effect is small and not significant; however, lower than females, the additional burden of obtaining the employment impact is substantial and significant. and using average service industry worker wage rates by Given the confidence intervals, the other coefficient esti- age and sex to estimate annual household productivity mates are generally similar in the fatigue and non-fatigue loss from CFS did not justify their use. regressions. One exception is the female coefficient in the employment model. Being female has less of a reduction Discussion on employment for individuals who are in the fatigue The magnitude of the economic impact imposed on the group than for the non-fatigue group. Another exception individual and on society by CFS is substantial. Approxi- is the age coefficient in the employment model, which mately one-quarter of persons with CFS, who would oth- indicates that employment does not increase as quickly erwise have participated in the labor force, ceased with age for individuals who are in the fatigue group com- working. For those who continued to work, average pared with the non-fatigue group. income declined by one-third. This represents an esti- mated annual loss of almost $20,000 for the individual The differences in the coefficients in the fatigue and non- suffering from CFS. This magnitude of loss approximates fatigue regressions translate into substantial declines in half of their labor force and household productivity in a employment resulting from CFS for individuals of all age given year. The $9.1 billion national loss is comparable to and sex groups (Table 4). For women and men, we esti- that estimated for other illnesses, such as digestive system mated about a 27% reduction in employment attributable illnesses ($8.4 B) and infectious and parasitic diseases to CFS. Overall, employment declined from 72.5 to ($10.0 B) [19] and is greater than the estimated productiv- 54.8% for women and from 86.1 to 63.3% for men. These ity losses from immunity disorders ($5.5 B), nervous sys- reductions in employment combined with reductions in tem disorders ($6.4 B), or skin disorders ($1.3) [23]. This hours worked and in productivity per hour resulted in estimate does not include health care costs, which are reductions in household and labor force productivity of likely to be substantial and does not address reductions in 37% and 54%, respectively. Women suffered substantially quality of life, which are likely to be large due to the debil- greater household and labor force productivity losses (42 itating fatigue. Page 7 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 Table 4: Individual and Societal Productivity Losses* Women (years) Men (years) Total 18–34 35–49 50–69 Total 18–34 35–49 50–69 Total Predicted Employment Rate (%) CFS 69.8 56.5 43.1 54.8 63.6 74.0 49.6 63.3 56.3 Non-fatigue 83.9 79.1 60.5 72.5 85.9 94.2 76.2 86.1 74.9 U.S. Employment Rate (%) 76.9 79.5 59.1 72.5 87.6 91.8 72.0 84.6 78.4 Household Productivity CFS $8,502 $9,703 $7,764 $8,495 $8,536 $9,629 $7,100 $8,513 $8,498 Non-fatigue $14,403 $15,986 $13,852 $14,577 $9,208 $9,853 $7,285 $8,907 $13,572 Labor Force Productivity** CFS $3,891 $13,999 $9,442 $8,932 $19,179 $45,016 $30,862 $30,828 $12,813 Non-fatigue $20,140 $31,664 $22,121 $24,001 $26,973 $64,440 $50,429 $45,607 $27,831 Overall Productivity CFS $12,394 $23,702 $17,207 $17,427 $27,715 $54,645 $37,962 $39,341 $21,311 Non-fatigue $34,543 $47,649 $35,974 $38,578 $36,181 $74,292 $57,714 $54,513 $41,403 †† Individual Loss Household Productivity $5,901 $6,283 $6,088 $6,081 $672 $224 $185 $394 $5,073 Labor Force Productivity $16,249 $17,664 $12,679 $15,070 $7,794 $19,424 $19,566 $14,779 $15,018 Total Loss $22,149 $23,947 $18,767 $21,151 $8,466 $19,648 $19,752 $15,173 $20,092 Number of Individuals with CFS 114,373 97,416 162,101 373,891 32,436 26,579 21,533 80,548 454,439 Total Societal Loss (Millions) $2,533 $2,333 $3,042 $7,908 $275 $522 $425 $1,222 $9,130 * Numbers may not sum exactly due to rounding. The microsimulation estimated Employment rates by age and sex based on data from Wichita, Kansas. These means were then weighted to reflect the age and sex distribution of the U.S. population using population estimates from the March ‡ § Supplement to the Current Population Survey, 2002. Based on the March Supplement to the Current Population Survey, 2002. Hours of household productivity valued at the mean hourly earnings of service industry worker, and estimate based in 2002 dollars and increased by 33.8 percent to reflect the value of fringe benefits. ** Estimated personal earnings in 2002 dollars increased by 33.8 percent to reflect the value of fringe †† The individual losses represent the difference between mean productivity with CFS and in absence of CFS. benefits. We estimated annual lost productivity. However, CFS is a half those estimated for a study of CFS in a Chicago pop- chronic illness. The average duration of CFS identified in ulation [3]. To the extent that the Wichita Study underes- population studies is 5 years and most patients with CFS timated prevalence, the productivity loss estimates seen by health care providers have been ill for more than derived in this study are likely to be proportionally under- 6 years [20]. Thus, productivity losses, health care stated. Thus, we believe that the productivity loss esti- expenses, and reductions in quality of life continue for mates presented here are a lower bound on the losses many years for most affected individuals and thus would related to CFS. In addition, as patients with CFS recover have a substantial long-term impact on the standard of they may no longer fulfill all case-defining criteria but living of individuals with CFS and their family members. may still have reductions in income because they lost job tenure and experience at the time of their illness. Thus, Some limitations should be considered when interpreting these individuals should be included in productivity loss our results and considering future studies. The prevalence estimates. estimates we used are likely to understate the number of individuals affected by CFS since the Wichita study was We used the human capital approach to estimate lost pro- designed to estimate point prevalence. Forty-three partici- ductivity rather then the friction cost method. Several pants were classified as having CFS at baseline because studies that have compared indirect costs of illness by they fulfilled all criteria of the case definition at the time both methods show that the human capital approach of clinical evaluation. The study continued an additional potentially overestimates indirect costs related to illness 3 years, during which the cohort was interviewed annu- because it does not account for labor scarcity. We take the ally, and over the entire study, 90 persons were identified view that labor markets clear relatively quickly, and that as having CFS. Incident CFS was extremely rare, most of the hypothetical unemployed worker who takes the job the 47 cases identified during subsequent years reported vacated by the CFS victim would have soon found they had been ill with CFS for many years but were in par- employment at about the same wage anyway. For individ- tial remission during previous interviews and so had not uals with CFS, we reduced the value of household produc- acknowledged symptoms at that instant in time. Preva- tivity by the same percentage as the reduction in their lence estimates from the CDC Wichita Study are about labor force income due to the presence of CFS. This con- Page 8 of 9 (page number not for citation purposes) Cost Effectiveness and Resource Allocation 2004, 2 http://www.resource-allocation.com/content/2/1/4 6. Hill NF, Tiersky LA, Scavalla VR, Lavietes M, Natelson BH: Natural servative approach also based its estimate on reductions history of severe chronic fatigue syndrome. Arch Phys Med in labor productivity among those individuals with CFS Rehabil 1999, 80:1090-4. who remained in the labor force after the onset of their ill- 7. Lloyd AR, Pender H: The economic impact of chronic fatigue syndrome. Med J Aust 1992, 157:599-601. ness. The severity of the illness for these individuals was 8. Bombardier CH, Buchwald D: Chronic fatigue, chronic fatigue likely to be much less than that of individuals with the ill- syndrome, and fibromyalgia: disability and health-care use. Med Care 1996, 34:924-30. ness who exited the labor force. While there are many dif- 9. 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Health Care Financ Rev 1985, Lost productivity due to CFS was substantial both on an 7:61-80. individual basis and relative to national estimates for 14. Newhouse JP: The Health Insurance Group. Free-for-all: health insurance, medical costs, and health outcomes: the results of the health other major illnesses. CFS resulted in a national produc- insurance experiment Cambridge, MA: Harvard University Press; 1993. tivity loss comparable to such losses from diseases of the 15. Manning WG, Newhouse JP, Duan N, Keeler EB, Leibowitz A, Mar- quis MS: Health insurance and the demand for medical care: digestive, immune and nervous systems, and from skin evidence from a randomized experiment. Am Econ Rev 1987, disorders. The extent of the burden indicates that contin- 77:251-77. ued research to determine the cause and potential thera- 16. Duan N, Manning WG, Morris C, Newhouse JP: A comparison of alternative models for the demand for medical care. J Bus Stat pies for CFS could provide substantial benefit both for 1983, 1:115-26. individual patients and for the nation. 17. Duan N, Manning WG, Morris C, Newhouse JP: Choosing between the sample-selection model and the multi-part model. J Busi- ness Econ Stat 1984, 2:283-9. Competing interests 18. Manning WG, Duan N, Rogers W: Monte Carlo evidence on the None declared. choice between sample selection and two-part models. J Econometrics 1987, 35:59-82. 19. Rizzo JA, Abbott TA 3rd, Berger ML: The labor productivity Authors' contributions effects of chronic backache in the United States. Med Care KJR had primary responsibility for data analysis strategies 1998, 36:1471-88. 20. Reyes M, Gary HE Jr, Dobbins JG, Randall B, Steele L, Fukuda K, Hol- and interpretation of economic data, and drafted the mes GP, et al.: Surveillance for chronic fatigue syndrome – four manuscript. SDV conceived the idea to assess the eco- U.S. cities, September 1989 through August 1993. 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Reyes M, Dobbins JG, Nisenbaum R, Subedar NS, Randall B, Reeves cited in PubMed and archived on PubMed Central WC: Chronic fatigue syndrome progression and self-defined yours — you keep the copyright recovery: evidence from the CDC surveillance system. J CFS 1999, 5:17-27. BioMedcentral Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.asp Page 9 of 9 (page number not for citation purposes)

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