Productivity loss in Puerto Rico's labor market due to cancer mortality.
Background: In Puerto Rico (PR), cancer is the second leading cause
of death and the disease that causes most premature deaths, representing
about 15% of them. Thus, premature death due to cancer decreases the
productivity capacity in PR.
Objective: This study aimed to estimate the labor-market productivity loss in PR during 2004 as a result of premature mortality due to overall cancer and cause-specific cancers.
Methods: A model based in the incidence-based approach and in the human capital approach was developed to estimate the labor-market productivity loss. Economic data were obtained from the Public Use Microdata Sample (PUMS) of the PR Community Survey (PRCS). Mortality data were obtained from the Vital Statistics of the PR Department of Health.
Results: The productivity costs of all cancer deaths were estimated to be approximately $64 million (in constant value). The cancer deaths that contributed the most to productivity loss were lung and bronchus, colorectal, breast, and liver and intrahepatic bile duct.
Conclusions: Although these results must be interpreted with caution, this study contributes to show a broader picture that includes the economic dimension of cancer in our society. These estimates imply that productivity cost due to cancer mortality have a great burden in PR. The leading cancer sites that generate most productivity losses are highly preventable or can be diagnosed early or are related to tobacco consumption. This study should be considered within the framework of future cost analyses for the development of health and cancer control policies.
Key words: Cancer mortality, Productivity loss, Human capital approach, Burden of cancer
Trasfondo: En Puerto Rico (PR) el cancer es la segunda causa de muerte y es la enfermedad que mas muertes prematuras ocasiona, representando cerca del 15% de estas. Es importante estimar los costos de cancer para asignar eficientemente los recursos limitados con el fin de reducir la carga del cancer en PR. Objetivo: Estimar la perdida de productividad laboral asociada a la mortalidad por cancer en general y por tipos especificos en PR para el ano 2004. Metodos: Para estimar la perdida de productividad en el mercado laboral se desarrollo un modelo basado en el "enfoque de la incidencia" y en el "enfoque de capital humano". Los datos economicos fueron obtenidos de los Microdatos para el Uso Publico (PUMS) de la Encuesta sobre la Comunidad de PR (PRCS). Resultados: La perdida de productividad laboral de todas las muertes por cancer se estimo en aproximadamente $64 millones (en valor constante). Las muertes que mas contribuyeron a la perdida de la productividad fueron atribuidas a cancer de: pulmon y bronquios, colorrectal, mama, e higado y conducto biliar intrahepatico. Conclusiones: Aunque estos resultados deben ser interpretados con cautela, contribuyen a mostrar un panorama mas amplio que incluye la dimension economica del cancer en nuestra sociedad. Estas estimaciones implican que el costo de la productividad debido a la mortalidad por cancer tiene un gran impacto en PR. Los tipos de cancer que generan la mayor perdida de productividad son altamente prevenibles, se pueden diagnosticar temprano, o estan asociados a consumo de tabaco. Este tipo de estudio se debe considerar dentro del marco del analisis de costos para el desarrollo de politicas de control de la salud y del cancer.
|Article Type:||Perspectiva general de la enfermedad/trastorno|
Cancer (Analisis de casos)
Ortiz-Ortiz, Karen J.
Ortiz, Ana P.
de la Torre-Feliciano, Taina
Calo, William A.
Figueroa-Valles, Nayda R.
|Publication:||Name: Puerto Rico Health Sciences Journal Publisher: Universidad de Puerto Rico, Recinto de Ciencias Medicas Audience: Academic Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2010 Universidad de Puerto Rico, Recinto de Ciencias Medicas ISSN: 0738-0658|
|Issue:||Date: Sept, 2010 Source Volume: 29 Source Issue: 3|
|Geographic:||Geographic Name: Puerto Rico|
Cancer is a leading cause of death worldwide, accounting for 7.4
million deaths (around 13% of all deaths) in 2004 (1). In Puerto Rico
(PR), cancer is the second leading cause of death and the disease that
causes most premature deaths, representing about 15% of them (2).
Besides being a clinical problem, disease and death comprehend other
social issues, including economic aspects that cause a significant
burden to society. Therefore, premature death due to cancer represents
an impairment of labor, a valuable economic resource, that prevents a
person from contributing productively to society in the future,
decreasing its productivity capacity.
Economic theory provides different methods to assess the economic impact of a health condition, as is cancer. The Cost of Illness (COI), developed by Rice (3-6), is the most widely accepted conceptual framework for cost estimates. COI estimates involve three components: direct costs, morbidity costs and mortality costs. Within this framework, several studies with different approaches have been conducted to determine the economic burden of different diseases (7-17). These studies have concluded that the component with the greatest impact lies in the productivity cost, even more than the costs for medical treatment of patients. For example, the National Institutes of Health(18) estimated the cost of illness for different causes of death in United States (US) for 2007. This study estimated the overall cost of cancer at $219.2 billion, of which, $89 billion correspond to direct costs of health expenditure, $18.2 billion in morbidity costs and $112.0 billion in mortality costs (representing more than 52% of total costs). Others studies of the economic burden of cancer in California (14-15) have concluded that the premature mortality cost of breast cancer is 80% of the total costs of the disease. Also, mortality costs of gynecological cancers like ovarian and cervical cancer represent more than 65% of total cost of these cancers. This pattern has also been observed in the state of Texas and in Sweden, Canada, and Spain (10-11, 17, 19-20). Other studies (20-25) have focused on estimating the productivity cost due to cancer mortality. Although these studies show some discrepancies in their methodology, data sources, and the inclusion of indirect costs components, such variations are not necessarily a weakness. Different arenas of application require different approaches and schemes (e.g., economic burden estimates vs. cost-effectiveness analysis) (4-5, 26).
From a societal perspective, estimates of the value of labor productivity loss due to premature mortality are important in determining the economic burden of disease. Previous studies in PR have used the COI approach to estimate the cost of AIDS, schizophrenia and traffic accidents (7, 27-28). For example, cumulative total cost of AIDS in PR from period of 19821989 was estimated to be $ 525.2 million (27). Despite the importance of evaluating the economic impact of cancer in PR, there are no previous studies that have used the COI approach to investigate this issue. In fact, this economic component has been overlooked in cancer investigations in PR. Although the value of a person's life transcends its economic value as a productive unit, cost studies present another dimension of a health problem, providing valuable information for society and for policymakers to decide how to allocate scarce resources more optimally (27). Consequently, the aim of this study is to estimate the labor-market productivity loss in PR, as a result of premature mortality, due to overall cancer and by cause-specific cancers in 2004.
COI studies may consider different timeframes for cost estimation. Two recognized models for establishing a time frame have been used in COI studies: the prevalence-based model and the incidence-based model (5, 29-30). The prevalence-based model quantifies economic costs to society incurred during a period of time (usually a year) as a result of the prevalence of disease. The prevalence approach is functional for measuring the effectiveness of cost control and how well health care expenditure targets are met. This approach measures the value of resources lost during a specified period, irrespective of the time of disease onset. The incidence-based model estimates the lifetime costs of an illness, based on all cases with onset of disease in a given base year. The approach adopted depends on the purpose of the analysis (5, 29). Our study estimated the labor productivity loss due to cancer using an incidence-based approach (the lifetime loss of productivity of those who died of cancer in 2004) instead of a prevalence-based approach (the loss of productivity in 2004 of those who died of cancer in 2004 or in previous years and who otherwise would have been alive in 2004) (22). We selected this model because the incidence approach is better suited for decision making about treatment or research strategies as it more realistically reflects the impact of reduced incidence or improved outcomes in the context of future costs (5).
We also based our study on the human capital approach, that is founded on the assertion that social welfare is reduced by disease, disability, and premature death (4, 6, 31-32). The 'human capital approach' focuses in measuring and valuating production that is lost due to morbidity and mortality in a period. This period is equal to the numbers of years of potential economic contribution of a person to society (27, 31, 33). A person would have continued to be productive for a number of years if he or she had not died prematurely from cancer (13-15). This approach does not measure the value of a life, but instead, it measures only the value of labor, using earnings or imputed earnings as a proxy measure (21). Economists at the Centers for Disease Control and Prevention (CDC) use the human capital approach to value the morbidity and mortality outcomes in cost-benefits analysis (31, 34).
In various studies, productivity is calculated as the present value of the sum of earnings and the imputed value of household production over the lifetime, adjusted for survival, discounting and expected growth (10, 12-17). In the present study, we estimated the value of lost earnings that would have been accrued through the labor market and did not include the non-paid care giving and housekeeping activities like other studies have done. Without reducing importance of all the above items, we focused on those components for which we have enough valid data to report reliable estimates. This will lead us to consider only productivity in the labor market. Our model also considered the earning and employment changes over the life cycle, by summing the expected earnings in each year of forgo ne life over a given life expectancy, accounting for changes in the probability of employment and earning that occur from age group to age group, for each sex (21). The component of earning consisted of money paid directly to individuals in the form of wage, salary income, and self-employment earnings (34-35).
Following a similar nomenclature of another report (10), we used the following formula (Formula 1) to estimate the present value of lifetime earnings (PVLE), that potentially was lost due to premature mortality from cancer.
PVLE = [65.summation over (n=a)] ([Y.sub.ns][W.sub.ns][P.sup.n.sub.as)[(1 + g).sup.na]/(1 + i + [[alpha]).sup.na]
a = midyear age for the given cohort of persons
s = sex
n = age
[Y.sub.ns] = annual average earnings for all persons of a given sex with earnings in an age group where the midpoint age is n
[W.sub.ns] = average employment ratio of a given sex in the age group where the midpoint age is n
[P.sup.n.sub.as] = the approximate probability that an individual of age a and sex s survives to age n
g = annual rate of growth of labor productivity
i = discount rate
[alpha] = inflation rate
The potential productivity years of life lost (PPYLL) were estimated according to the total premature cancer deaths and by cause-specific cancer. The first component of the formula is the sum of the estimated value of earning for persons in the labor force ([Y.sub.ns] [W.sub.ns] [P.sub.nas]) that takes into account the annual average earning, labor employment ratio and the probability of survival for each age group and sex. That estimate was adjusted for changes in labor productivity (g) and discounted (i) to convert the lifetime earning into a present value. Changes in labor productivity adjustment (g) serve to consider the fact that changes in productivity, which is a function of the availability of capital and technology, lead to real earnings growth (e.g., through new technological developments). The discounted rate adjustment is used to express the value of the future costs in present value. Finally, to express the productivity loss in constant prices we deflated average earnings using the average of the last five years of deflator of Gross Product of PR (36). This procedure is necessary to adjust for the effect of inflation (a). Inflation is an increase in the general level of prices of goods and services in an economy over a period usually as measured by the Consumer Price Index (CPI). Nevertheless, in PR the use and the validity of this indicator as a measure of inflation has been questioned (37). Therefore, we decided to use the deflator of the Gross Product of PR. Gross Product deflator is a measure of the price of all the goods and services included in the Gross National Product (GNP).
Important assumptions and parameters were used for this model. First, we assumed that no earnings are earned between the ages of 0 to 15, as the legal age to be hired for employment in PR is 16 years. Also, the age of legal retirement in PR, 65 years of age, was considered as the age limit to stay in the labor market. Nevertheless, even though 92% of people 65 years of age and older opt for retirement, the remaining 8% represent less than 0.5% of the workforce in the labor market in PR (38). Additionally, earning capacity included both wage earnings and employer provided fringe benefits (35). To include total earnings, we imputed the recommended 22.4% of earning compensation attributed to fringe benefits (20-21). These benefits include vacation pay, health insurance, and retirement benefits.
We used the annual rate of productivity growth at 1.8%, as estimated in PR in a previous study (7). In the basic model, we applied a discount rate of 3% to employment earning to reflect the present value of future productivity. This rate is the most commonly used in this type of study. In fact, CDC currently recommends that a 3% social discount rate should be used in analyses that require adjusting future costs and benefits of public health interventions, programs, and policies (31, 39). The discount rate is a financial measure that is used to determine the present value of future payments. The lower the discount rate, the higher the present value of future income. A discount rate of 0% indicates no distinction between present and future costs and benefits. Sensitivity analysis is recommended anytime there is uncertainty (30, 39). Following previously published studies (10, 14, 21-22, 40), we compared with the base scenario how the results changed when we applied different discount rates. In the sensitivity analysis, the discounted rate varied from 0% to 10%.
Mortality data were obtained for the most recent year of data publication (2004) provided by the PR Department of Health, through the Auxiliary Secretariat for Planning and Division Analysis (41). Cancer deaths were defined as all deaths of persons aged 0-65 years, for which the primary cause of death was cancer. SEER cause of death recode was used to classify the cancer deaths by means of the SEER*Stat 6.5.2 software (42). To calculate the life expectancy tables for PR for the year 2004, we used mortality data from Vital Statistics and population estimated data from the PR Planning Board. Life expectancy was calculated by sex; these estimations were calculated with the use of EpiDat 3.1 software (43). As in other studies, in the absence of sufficient data for further modeling, persons dying of cancer were assumed to otherwise have comparable life expectancies of general population (23, 43). Also, the employment ratio and the average earning by sex for the year 2005 were estimated using the Public Use Microdata Sample (PUMS) of Census Bureau's PR Community Survey (PRCS) (35). This survey collects information about population and housing characteristics for the nation, states, cities, counties, metropolitan areas, and communities on a continuous basis. The collection for the PRCS began in January 2005, with an annual sample size of approximately 36,000 addresses. For that reason we decided to use the 2005 file, instead of that for 2004.
Employment and earnings estimated, by sex and age groups, for the population of PR are presented in Table 1. In all age groups, earnings and employment ratios were higher for males than for females. For both sexes, employment ratios and earnings were smallest in the youngest age groups. These earnings and employment ratios increased substantially in later ages and dropped again before the usual age of retirement at 65 years of age.
The number of pre-retirement deaths from all causes of death and attributed to cancer, the PPYLL and the estimates of the PVLE for the year 2004 are shown in Table 2. In total, 8,953 persons died before the age of 65 in PR in 2004, of which 1,515 persons died due to cancer. Premature cancer deaths represent nearly 31% of the total cancer deaths. These cancer deaths accounted for loss of 17,475 PPYLL due to premature cancer mortality. Breast cancer had the largest relative contribution in terms of premature death and PPYLL, followed by colorectal cancer and lung and bronchus. The estimated PVLE from all malignant cancer in 2004 was approximately $64.2 million (in constant value), assuming a discount rate of 3%. This corresponds to 13.8% of total productivity cost in the labor market ($464 million) in PR (Table 2). Lung cancer premature deaths accounted for 11.8% ($7.6 million) of the total PVLE. The other most costly cancers were colorectal cancer ($7.5 million) and breast cancer ($6.6 million), which accounted for 11.7% and 10.3%, respectively, of the total PVLE loss. These three types of cancer represented more than a third (33.6%) of the total PVLE costs. By contrast, prostate cancer (the type of cancer that causes more deaths in males) accounted for only 2.6% of the total cost. When we analyzed the losses related to hematopoietic cancers and myeloma, and consider them as a total, these losses nearly reached the total costs of breast cancer.
More than 30% of the labor productivity loss was caused by the types of cancer directly related to tobacco use (lung and bronchus, oral cavity and pharynx, esophagus, pancreas, stomach and larynx). As well, the most costly cancers per death were testis cancer ($71,347.93), followed by kidney and renal pelvis cancer ($69,110.16), mesothelioma ($67,438.08), myeloma ($50,929.26) and oral cavity and pharynx ($59,194.52). Although there were few cancer deaths from these types of cancer, as compared to other cancer types, the largest proportion of deaths occurred in younger age groups.
Figure 1 shows the PVLE, by sex, for the major cancer types. The productivity loss due to all types of cancer combined was two times higher for men than for women ($21.6 vs. 42.7 million). Moreover, colorectal cancer, the second type of cancer that causes more PVLE for both men and women, in fact produces more than twice the PVLE in men as compared to women. We also found that the types of cancer that cause more PVLE differ by sex (Figure 1). For males, the most expensive cancers in terms of lost productivity are lung and bronchus ($6.2 million), colorectal ($5.3 million), liver and intrahepatic bile duct ($4.7 million) and oral cavity and pharynx ($3.4 million). For females, breast cancer is the most costly cancer ($6.4 million); almost three times more expensive than the second one (colorectal cancer, $2.2 million). The next most costly cancers for women were lung and bronchus ($1.4 million), followed by ovarian ($1.2 million) and non-Hodgkin lymphoma ($1.0 million).
Given that the estimated PVLE is sensitive to the discount rate chosen, we conducted a sensitivity analysis (using discount rates varying from 0% to 10%), in order to provide a range of possible lifetime productivity losses. Figure 2 illustrates the results of this analysis that produced productivity losses for premature mortality ranging from $28.9 million (using a discount rate of 10%) to $101.9 million (using a discount rate of 0%).
This study describes, for the first time, the economic impact of cancer in PR. Specifically, it describes the extent of the potential losses due to premature cancer death for the Island's economy. The total productivity losses in the labor market due to cancer in PR in 2004 were approximately $64 million (at a 3% discount rate and in constant value). These estimates represent nearly 14% of the total productivity loss in the labor market ($464 million) for 2004 in PR. Therefore, although cancer is a disease that usually occurs late in the life cycle, the losses of productivity caused by premature cancer death are a great burden in PR. This could be explained, in part, by a change in the cancer incidence pattern among persons aged <65 years. For the period 1987-2004, cancer incidence trends showed a significant increase (APC= 2.7%, p<0.05) in people <65 years of age, while, trends in people aged 65 years of age and older remained stable (APC = -0.1%, p>0.05) (44). Although overall cancer mortality trends have decreased in average 1.0% annually from 1987-2004 (similar in persons aged <65 and >=65 years), cancer remains the leading cause of premature death in PR, representing nearly 17% of total deaths in 2004 (45).
Although it is important to notice that cost studies generated with different methods are not directly comparable, we can recognize that the cancer sites that generate most productivity loss in PR (lung and bronchus, colorectal, and breast cancer) also represent the greater productivity cost in the US, representing 27.4%, 9.0% and 7.6%, respectively (21). These types of cancer are either highly preventable or can be diagnosed early (46-47). Furthermore, it is evident that a large proportion of the productivity loss causing cancers are those related to tobacco use. This risk factor is associated with increased risk for at least 15 cancer types including lung and bronchus, oral cavity and pharynx, and esophagus (46-47). In PR, it has been estimated that the attributable risk of oral cavity and pharynx due to alcohol and tobacco use is around 76% (95% CI: 65-87%) for men and 52% (95% CI:28-75%) for women (48). Also, the prevalence of cigarette use among adults in 2004 was 12.6%, although it showed a decrease over the last decade from 14.5% in 1996 to 11.7% in 2008 (49). Thus, even though the prevalence of current cigarette smoking is not as high as in the US (20.9%) (50), we should continue to promote public policies focused on reducing the use of tobacco in PR, if we expect to decrease the productivity loss in a significant way.
Significant costs differences were also observed by sex. The types of cancer linked to tobacco consumption had a higher cost for men as compared to women. These findings are consistent with the differences in the prevalence of tobacco use in men and women in PR. In 2004, the prevalence of current smoking in males was 17.4%, compared with 8.4% in females (49). In addition, PVLE for liver and intrahepatic bile duct cancer was higher in males compared with females. These results may be associated with a higher prevalence of alcohol consumption, hepatitis B (HBV), hepatitis C (HCV), all of them risk factors for hepatic cirrhosis, a well-known pre-malignant condition for developing liver cancer, and more common among males. In 2004, the prevalence of men having more than two drinks per day was 4.6% compared with 2.0% in females (49). Also, the prevalence for HCV in men was 4-fold as compared to women (4.0% vs. 1.0%) among the PR population (51). Moreover, the prevalence of HBV was twice as frequent in males, (4.3%) as compared to females (2.5%) (51).
We also observed that oral cavity and pharynx cancer had a very high cost per death, although this is not a typical cancer among persons aged <65 years. The median age at diagnosis is 64 years and the median age for death is 68 years (52), this type of cancer also affects adversely more males in the working age. This is of particular relevance as oral cancer is still among the top leading cancer types in men in PR (52). It is important to note that the median age of death from oral cavity and pharynx cancer is less than the median age at diagnosis for lung and bronchus cancer (70 years) (53). Given that oral cavity and pharynx cancer share an important risk factor with lung cancer, tobacco use, we can hypothesize that deaths due to the former in some way deplete the pool of people susceptible to developing and dying from lung cancer (tobacco users) years later. If we could control for oral and pharynx cancer death, smokers would still be at risk of developing and dying from lung cancer.
Another important finding from our study is that although mortality from stomach and from esophagus cancers have decreased since the 1950's in PR (54-55), both remain highly costly diseases, partly because of the poor survival typical of these types of cancer. Meanwhile, leukemia, myeloma, non-Hodgkin lymphoma, and brain and central nervous system tumors have a substantial burden in both sexes. This impact could be attributed to the greater mortality of many of these types of cancer in children, producing a higher PVLE. Although childhood cancer accounts for about 1% of all cancers in PR, leukemia, brain tumors and lymphoma account for the vast majority of childhood cancer related deaths.
It is important to note that breast cancer, the most common cancer among females in PR, is more costly than prostate cancer, the most common cancer among men. This study demonstrated that although men have higher wages, employment, and mortality from cancer than women, breast cancer ranks as the third type of cancer causing more loss of productivity in PR. These results are due, in part, to the higher proportion of younger females dying of breast cancer, while prostate cancer affects primarily older men. While the median age at diagnosis for breast cancer is 59 years, and the median age at death is 63 years, for prostate cancer the median age at diagnosis is 10 years later (69 years at diagnosis) and the median age at death is 82, well beyond retirement age (56-57). Although we observed that Puerto Rican females younger than 65 years of age showed a significant reduction in breast cancer mortality rates (58), potentially due to the progress made in reducing breast cancer mortality, it remains a deadly disease among working age females and a costly one for the Puerto Rican society.
Implications for health policy
In economic terms, cancer affects the most important productive resource, the human capital. While the productivity loss due to cancer death represents a very high cost for society, someone may be tempted to consider as a benefit the payment of pensions that will never be paid due to premature deaths. This notion, however, does not consider that public health interventions do not have as final objective the saving of monetary expenses or the budgetary control (25). The primary target of any intervention in public health must be the prolongation of survival and improvement of the quality of life of cancer survivors. The considerations in which premature mortality have a saving component could jeopardize the achievement of this objective (25).
Interventions in cancer must be implemented through a comprehensive public policy that includes attention, not only to the medical aspect, but also to social and economic issues, including scientific research and development. According to the PR Comprehensive Cancer Control Plan 2008-2012 (59) it is necessary to have a comprehensive approach to reach the goal of cancer control and prevention in PR. In order to achieve these objectives, it is necessary to create accurate and reliable estimates of cancer-related cost and others empirical studies to improve how to allocate limited economic resources for cancer control. These types of studies represent an important analytic tool for the design and implementation of public health policy.
Investments in programs that decrease lung, colorectal, breast, and liver cancer mortality are likely to generate the major decline in productivity loss in PR. As a fundamental part to maximize the social well-being, it is necessary to place emphasis in cancer prevention. The leading cancer sites that generate most productivity losses are highly preventable or can be diagnosed early. One of the most important objectives for cancer control programs, from an economic perspective of cost in terms of labor productivity, is the investment in programs that reduce the types of cancer directly related to the use of tobacco in our society.
Limitations and Recommendations
Various limitations of this study should be acknowledged. This kind of study can demonstrate which type of cancer may require increased allocation of prevention or treatment resources, but is limited in determining how resources are to be allocated, as it does not measure benefits. In addition, studies can vary by perspective, sources of data, inclusion of indirect costs, and the period of costs, which can limit the comparability of findings with the present study (19). In addition, the estimates do not include the value of care giving, household work, and earning from informal economy, contributions that could be more important for females given their traditional roles in our society. Also, it is important to point out that productivity loss due to premature mortality is only one component of a framework for estimating the economic cost of cancer in PR. The estimations presented in this study do not represent the total of productivity loss in PR's labor-market due to cancer. In addition, an important aspect that was not considered in this study was the labor productivity loss associated to disability. Although this study focused in mortality, disability represents a significant problem that has great impact in the labor market. The improvements in early detection and advances in treatment of cancer have increased the survival rate for all cancers in general, raising the prevalence of people diagnosed with cancer (60). People diagnosed with cancer have a high probability of suffering a loss of productive capacity, consequently, affecting the productivity in the labor market. One in six cancer survivor workers in the US report they were unable to work and an additional 7% indicated that they were limited in the amount and type of work they could perform (61-62). Therefore, future studies in PR should focus on obtaining reliable data to estimate the total productivity cost, including costs caused by disability, as the exclusion of disability from these estimates can result in an underestimation of the total productivity loss due to cancer.
Furthermore, if direct costs (medical expenses resulting from cancer) were added to the COI estimates, the economic impact of cancer will be substantially higher. According to a study performed in PR, 20.4% of the Gross National Product (GNP) in PR corresponds to health expenditures (63). This is twice as much as in Europe and 25% more than in the US. Therefore, the direct cost of cancer can be extremely costly and represents a great burden for PR (59, 63). These limitations suggest that the estimates of productive cost of cancer in PR could be even greater than those estimated in the current analysis. But, as mentioned earlier, we considered only productivity loss in the labor market because we focused our analysis on those components for which we had valid data to report reliable estimates. The impact of premature mortality due to cancer in the economy of PR evidenced in this study confirms the need for funding to increase research capacity in this area. It is essential to estimate the other components of COI in order to provide more accurate information of the burden of cancer. Consequently, informed decisions can be taken to allocate resources more efficiently for cancer control.
Our study shows a broader picture that includes the economic dimension of cancer as a health problem in our society. The leading cancer sites that generate most productivity losses are highly preventable or can be diagnosed early. We have identified that the mayor labor productivity loss was caused by the types of cancer directly related to tobacco use. Our results also show that despite the widespread availability of breast and colorectal cancer screening and the efforts to reduce the use of tobacco and other risk factors for developing cancer, it is evident that a substantial health and economic impact associated with these types of cancer remains. Future research including those that consider the other components of COI should be developed and considered within the framework for health and cancer control policies.
This work was supported, in part, by the National Program of Cancer Registries (NPCR) of the CDC, Grant #1U58DP000782-03 and NCI Grant #U54CA96297.
(1.) World Health Organization. Cancer. Facts sheet No 297 2009. Available from: URL: http://www.who.int/mediacentre/iactsheets/ fs297/en/print.html.
(2.) Puerto Rico Cancer Central Registry. Years of Potential Life Lost (YPLL) in Puerto Rico. 2008.
(3.) Rice DP. Estimating the cost of illness. Am J Public Health Nations Health 1967;57:424-440.
(4.) Rice DP, Hodgson TA. The value of human life revisited. Am J Public Health 1982;72:536-538.
(5.) Rice DP. Cost-off-illness studies: fact or fiction? Lancet 1994;344: 1519-1520.
(6.) Rice DP. Cost of illness studies: What is good about them? Inj Prev 2000;6:177-179.
(7.) Alameda J, Lara J. El costo economico de los accidentes de transito en Puerto Rico. Rio Piedras, PR: Unidad de Investigaciones Economicas, Departamento de Economia, UPR, 2008.
(8.) Brown ML, Lipscomb J, Snyder C. The burden of illness of cancer: economic cost and quality of life. Annu Rev Public Health 2001;22: 91-113.
(9.) Hartunian NS, Smart CN, Thompson MS. The incidence and economic costs of cancer, motor vehicle injuries, coronary heart disease, and stroke: a comparative analysis. Am J Public Health 1980;70:1249-1260.
(10.) Health Canada. Economic burden of illness in Canada, 1998. Ottawa: Health Canada; 2002.
(11.) Lidgren M, Wilking N, Jonsson B. Cost of breast cancer in Sweden in 2002. Eur J Health Econ 2007;8:5-15.
(12.) Max W, Rice DP, MacKenzie EJ. The lifetime cost of injury. Inquiry 1990;27:332-343.
(13.) Max W, Rice DP, Sung HY, Michel M, Breuer W, Zhang X. The economic burden of prostate cancer, California, 1998. Cancer 2002;94:2906-2913.
(14.) Max W, Rice DP, Sung HY, Michel M, Breuer W, Zhang X. The economic burden of gynecologic cancers in California, 1998. Gynecol Oncol 2003;88:96-103.
(15.) Max W, Sung HY, Stark B. The economic burden of breast cancer in California. Breast Cancer Res Treat 2009;116:201-207.
(16.) Scitovsky AA, Rice DP. Estimates of the direct and indirect costs of acquired immunodeficiency syndrome in the United States, 1985, 1986, and 1991. J Med Pract Manage 1988;3:234-241.
(17.) Tan A, Freeman DH, Freeman JL, Zhang DD, et al. BU. The Cost of Cancer in Texas, 2007. Texas Cancer Registry, Texas Department of State Health Services; 2009 Mar. Report No.: Publication No. 10-13121.
(18.) Kirschstein R. Disease-Specific Estimates of Direct and Indirect Costs of Illness and NIH Support: Fiscal Year 2000 Update. Washington: National Institutes of Health; 2000.
(19.) McCandless RR, Rivera-Jahnke L, Warner DC, Widoff S, Johnsrud M. The Economic Impact of Cancer: Direct and Indirect Costs Cost-Effectiveness of Cancer Prevention Hospital Inpatient Costs of Cancer. Texas Department of State Health Services; 2001. Report No.: TDH Publication No. 44-11140.
(20.) Oliva J. Loss of labour productivity caused by premature mortality in Spain in 2005. Rev Esp Salud Publica 2009;83:123-135.
(21.) Bradley CJ, Yabroff KR, Dahman B, Feuer EJ, et al. Productivity costs of cancer mortality in the United States: 2000-2020. J Natl Cancer Inst 2008;100:1763-1770.
(22.) Ekwueme DU, Chesson HW, Zhang KB, Balamurugan A. Years of potential life lost and productivity costs because of cancer mortality and for specific cancer sites where human papillomavirus may be a risk factor for carcinogenesis-United States, 2003. Cancer 2008;113:2936-2945.
(23.) Insinga RP. Annual productivity costs due to cervical cancer mortality in the United States. Womens Health Issues 2006;16:236-242.
(24.) Oliva J, Lobo F, Lopez-Bastida J, Zozaya N, et al. Indirect costs of cervical and breast cancers in Spain. Eur J Health Econ 2005;6:309-313.
(25.) Oliva J, Lobo F, Lopez-Bastida J, Zozaya N, Romay R. Perdidas de productividad laboral ocasionadas por los tumores en Espana. Documento de trabajo de la Universidad Carlos III de Madrid 2005;Working Paper 05-04.
(26.) Lipscomb J. Estimating the cost of cancer care in the United States: a work very much in progress. J Natl Cancer Inst 2008;100:607-610.
(27.) Alameda J, Gozalez A. Analisis del Impacto Economico del SIDA en Puerto Rico. In: Cunningham I, Ramos-Bellido C, Ortiz-Colon R, eds. El SIDA en Puerto Rico: Acercamientos Multidisciplinarios. Puerto Rico: Universidad de Puerto Rico, 1992:107-122.
(28.) Rubio-Stipec M, Stipec B, Canino G. The costs of schizophrenia in Puerto Rico. J Ment Health Adm 1994;21:136-144.
(29.) Choi BK, Pak AW. A method for comparing and combining cost-of-illness studies: An example from cardiovascular disease. Chronic Dis Can 2002;23:47-57.
(30.) Segel JE. Cost of Illness Studies-A primer. RTI-UNC Center of Excellence in Health Promotion Economics 2006.
(31.) Haddix A, Corso P, Gorsky RD. Cost. In: Haddix A, Teutsh S, Corso P, eds. Prevention Effectiveness: A Guide to Decision Analysis and Economics Evaluation, 2nd ed. London: Oxford University Press, 2003:53-76.
(32.) US Environmental Protection Agency. Office of Prevention. Pesticides aTS. The Cost of Illness Handbook. Washington, DC: 2004.
(33.) Goeree R, O'Brien BJ, Blackhouse G, Agro K, Goering P. The valuation of productivity costs due to premature mortality: A comparison of the human-capital and friction-cost methods for schizophrenia. Can J Psychiatry 1999;44:455-463.
(34.) Grosse SD, Krueger KV, Mvundura M. Economic productivity by age and sex: 2007 estimates for the United States. Med Care 2009;47:S94-103.
(35.) US Census Bureau. 2005 Puerto Rico Community Survey 1-Year Estimates. Public Use Microdata Sample (PUMS). 2006.
(36.) Puerto Rico Planning Board PoEaSPSoEA. Statistical Appendix of the Economic Report for the Governor and Legislative Assembly. 2009
(37.) Rodriguez I, Marazzi-Santiago M, Disdier O. Informe a la Junta de Directores del Instituto de Estadisticas de Puerto Rico sobre la Resolucion Numero 2008-001. San Juan, Puerto Rico: Instituto de Estadisticas de Puerto Rico; 2009.
(38.) Puerto Rico Department of Labor and Human Resources BoLS. Employment and unemployment in Puerto Rico Average Calendar Year 2005-2006.
(39.) Corso P, Haddix A. Time Effects. In: Haddix A, Teutsh S, Corso P, eds. Prevention Effectiveness: A Guide to Decision Analysis and Economics Evaluation, 2nd ed. London: Oxford University Press, 2003:92-102.
(40.) Glied S. Estimating the indirect cost of illness: an assessment of the forgone earnings approach. Am J Public Health 1996;86:1723-1728.
(41.) Puerto Rico Department of Health. 2004 Puerto Rico Mortality File. Division of Statistical Analysis, Auxiliary Secretariat for Planning and Development, 2006.
(42.) SEER*Stat software [computer program]. Version 6.5.2 2009.
(43.) EPIDAT [computer program]. Version 3.1 2006.
(44.) Puerto Rico Central Cancer Registry. Puerto Rico Cancer Incidence File. University of Puerto Rico Comprehensive Cancer Center: Central Cancer Registry, 2010.
(45.) Puerto Rico Department of Health. 1987-2004 Puerto Rico Mortality Files. Division of Statistical Analysis, Auxiliary Secretariat for Planning and Development, 2006.
(46.) American Cancer Society. Cancer Facts & Figures 2008. Atlanta: American Cancer Society; 2008.
(47.) Cokkinides V, Bandi P, Siegel R, Ward EM, Thun MJ. Cancer Prevention & Early Detection Facts & Figures 2008. Atlanta, GA: American Cancer Society; 2007.
(48.) Hayes RB, Bravo-Otero E, Kleinman DV, et al. Tobacco and alcohol use and oral cancer in Puerto Rico. Cancer Causes Control 1999;10:27-33.
(49.) Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2008.
(50.) Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S.: Department of Health and Human Services, Centers for Disease Control and Prevention, 2004.
(51.) Perez CM, Marrero E, Melendez M, et al. Seroepidemiology of viral hepatitis, HIV and herpes simplex type 2 in the household population aged 21-64 years in Puerto Rico. BMC Infect Dis 2010;10:76.
(52.) Figueroa N, De La Torre T, Ortiz K, Perez J, Torres M. Cancer of the Oral Cavity and Pharynx Stat Fact Sheet. Puerto Rico Central Cancer Registry, San Juan, PR 2008 Available from: URL: http://www.salud.gov.pr/RCancer/ Reports/Pages/default.aspx
(53.) Figueroa N, De La Torre T, Ortiz K, Perez J, Torres M. Cancer of the Lung and Bronchus Stat Fact Sheet. Puerto Rico Central Cancer Registry, San Juan, PR 2008 Available from: URL: http://www.salud.gov.pr/RCancer/ Reports/Pages/default.aspx
(54.) Departamento de Salud de Puerto Rico. Cancer en Puerto Rico, 1991. Registro Central de Cancer de Puerto Rico; 1993.
(55.) Ortiz AP, Soto-Salgado M, Calo WA, et al. Incidence and mortality rates of selected infection-related cancers in Puerto Rico and the United States. Infect Agent Cancer 2010 May 14;5:10.
(56.) Figueroa N, De La Torre T, Ortiz K, Perez J, Torres M. Cancer of the Breast. Puerto Rico Central Cancer Registry, San Juan, PR 2008. Available from: URL: http://www.salud.gov.pr/RCancer/ Reports/Pages/default.aspx
(57.) Figueroa N, De La Torre T, Ortiz K, Perez J, Torres M. Cancer of the Prostate. Puerto Rico Central Cancer Registry, San Juan, PR 2008 Available from: URL: http://www.salud.gov.pr/ RCancer/Reports/Pages/default.aspx
(58.) Ortiz-Ortiz KJ, Figueroa-Valles NR, Perez-Irizarry J, Torres-Cintron M, De La Torre-Feliciano T. Patrones de incidencia y Mortalidad de Cancer de Mama en Puerto Rico. Revista Puertorriquena de Medicina y Salud Publica 2009;15:19-27.
(59.) Figueroa-Valles NR. Puerto Rico Comprehensive Cancer Control Plan 2008-2012. 2010.
(60.) Lakdawalla DN, Sun EC, Jena AB, Reyes CM, Goldman DP, Philipson TJ. An economic evaluation of the war on cancer. J Health Econ 2010;29: 333-346.
(61.) Feuerstein M, Harrington CB. Recommendations for the U.S. National Occupational Research Agenda: Research on cancer survivorship and musculoskeletal disorders and work disability. J Occup Rehabil 2006;16:1-5.
(62.) Hewitt M, Rowland JH, Yancik R. Cancer survivors in the United States: Age, health, and disability. J Gerontol A Biol Sci Med Sci 2003;58:82-91.
(63.) A.T. Kearney Management Consultants. Building a Long-term Vision. 2003.
Karen J. Ortiz-Ortiz, MA, MPH *; Javier Perez-Irizarry, MPH *; Heriberto Marin-Centeno, PhD ([dagger]); Ana P. Ortiz, MPH, PhD ([double dagger]); Natalia Torres-Berrios, BS, MS(c) ([double dagger]); Mariela Torres-Cintron, MS *; Taina de la Torre-Feliciano, MS *; Jose Laborde-Rivera, PhD 9[section]); William A. Calo, MPH ([paragraph]; Nayda R. Figueroa-Valles, MD, MPH * ([dagger]) ([dagger])
* Puerto Rico Central Cancer Registry, Cancer Control and Population Sciences Program, University of Puerto Rico Comprehensive Cancer Center, San Juan, PR; ([dagger]) Department of Health Services Administration, Graduate School of Public Health, Medical Sciences Campus, University of Puerto Rico, San Juan, PR; ([section]) Department of Economics, School of Social Sciences, Rio Piedras Campus, University of Puerto Rico, San Juan, PR; ([double dagger]) Department of Biostatistics and Epidemiology, Graduate School of Public Health, Medical Sciences Campus, University of Puerto Rico, San Juan, PR; ([dagger]) ([dagger]) Cancer Control and Population Sciences Program, University of Puerto Rico Comprehensive Cancer Center, San Juan, PR; ([paragraph]) UPR-MDACC Partnership for Excellence in Cancer Research Program, Medical Sciences Campus, University of Puerto Rico, San Juan, PR
Address correspondence to: Nayda Figueroa-Valles, MD, MPH, CTR, University of Puerto Rico Comprehensive Cancer Center, PMB 711, 89 De Diego Ave. Suite 105, San Juan, PR 00927-6346. Tel: (787) 772-8300 x-1110 * Fax (787) 522-3283 * Email: email@example.com
Table 1. Estimated Employment and Earning Data in Puerto Rico in 2005 Sex and Age Average Fringe Total Employment Group Earnings ($) Benefits ($) Earning ($) Ratio Male 16-19 3,905 875 4,780 0.14 20-24 9,300 2,083 11,383 0.52 25-29 16,852 3,775 20,627 0.69 30-34 23,920 5,358 29,278 0.76 35-39 25,471 5,706 31,176 0.73 40-44 27,709 6,207 33,916 0.70 45-49 27,428 6,144 33,572 0.63 50-54 29,657 6,643 36,301 0.60 55-59 27,057 6,061 33,118 0.49 60-64 24,711 5,535 30,246 0.32 Female 16-19 3,680 824 4,504 0.08 20-24 8,685 1,945 10,630 0.36 25-29 15,453 3,462 18,915 0.52 30-34 20,333 4,555 24,887 0.57 35-39 20,863 4,673 25,537 0.56 40-44 22,274 4,989 27,263 0.51 45-49 25,109 5,624 30,734 0.48 50-54 24,018 5,380 29,398 0.40 55-59 22,244 4,983 27,227 0.26 60-64 18,335 4,107 22,442 0.15 Source of data: U.S. Census Bureau, 2005 Puerto Rico Community Survey PUMS Table 2. Present Value of Lifetime Earnings by site-specific cancer among people less than 65 years old in Puerto Rico, 2004 Cancer Site Deaths PPYLL PVLE ($) All Causes of Death 8,953 161,410 463,703,434 All Malignant Cancer 1,515 17,474 64,178,973 Lung and Bronchus 178 1,515 7,562,024 Colon and Rectum 180 1,840 7,511,262 Breast 209 2,572 6,630,413 Liver and Intrahepatic Bile Duct 121 1,094 5,460,698 Leukemia 70 1,465 3,651,523 Non-Hodgkin Lymphoma 76 1,121 3,642,117 Oral Cavity and Pharynx 61 612 3,610,865 Stomach 69 727 3,106,856 Kidney and Renal Pelvis 27 434 1,865,974 Esophagus 37 287 1,801,173 Pancreas 47 372 1,701,248 Prostate 41 262 1,678,758 Myeloma 31 232 1,578,807 Brain and Other Nervous System 31 480 1,529,117 Larynx 22 200 1,247,411 Ovary 42 545 1,226,229 Soft Tissue including Heart 17 375 897,124 Cervix Uteri 26 385 870,616 Corpus and Uterus, NOS 30 355 857,071 Urinary Bladder 16 130 579,330 Bones and Joints 11 152 379,815 Testis 4 110 285,391 Melanoma of the Skin 5 57 226,176 Vulva 4 50 139,405 Mesothelioma 2 30 134,876 Penis 2 15 103,044 Vagina 2 20 58,181 Thyroid 2 15 57,204 All other sites 152 2016 5,786,252 Cancer Site Percentage PVLE/ of total Death ($) cancer cost All Causes of Death -- 51,793 All Malignant Cancer 100.00% 42,362 Lung and Bronchus 11.78% 42,483 Colon and Rectum 11.70% 41,729 Breast 10.33% 31,724 Liver and Intrahepatic Bile Duct 8.51% 45,129 Leukemia 5.69% 52,164 Non-Hodgkin Lymphoma 5.67% 47,922 Oral Cavity and Pharynx 5.63% 59,194 Stomach 4.84% 45,026 Kidney and Renal Pelvis 2.91% 69,110 Esophagus 2.81% 48,680 Pancreas 2.65% 36,196 Prostate 2.62% 40,945 Myeloma 2.46% 50,929 Brain and Other Nervous System 2.38% 49,326 Larynx 1.94% 56,700 Ovary 1.91% 29,195 Soft Tissue including Heart 1.40% 52,772 Cervix Uteri 1.36% 33,485 Corpus and Uterus, NOS 1.34% 28,569 Urinary Bladder 0.90% 36,208 Bones and Joints 0.59% 34,528 Testis 0.44% 71,347 Melanoma of the Skin 0.35% 45,235 Vulva 0.22% 34,851 Mesothelioma 0.21% 67,438 Penis 0.16% 51,522 Vagina 0.09% 29,090 Thyroid 0.09% 28,602 All other sites 9.02% 38,067 PPYLL = Potentially productive years of life lost PVLE = Present Value of Lost Earning Figure 1. Top 15 Cancer Sites of Present Value of Lifetime Earnings Loss due to Premature Mortality by Sex in Puerto Rico, 2004 Females Brain and Other Nervous System $364,169 Soft Tissue including Heart $369,137 Pancreas $557,999 Liver and Intrahepatic Bile Duct $766,676 Stomach $803,293 Corpus and Uterus, NOS $857,071 Myeloma $860,850 Cervix Uteri $870,616 Leukemia $962,489 Non-Hodgkin Lymphoma $1,036,006 Ovary $1,226,230 Kidney and Renal Pelvis $1,251,670 Lung and Bronchus $1,363,116 Colon and Rectum $2,204,297 Breast Ma $6,419,795 Males Soft Tissue including Heart $527,988 Pancreas $1,143,249 Larynx $1,158,376 Brain and Other Nervous System $1,164,948 Prostate $1,678,759 Esophagus $1,722,136 Myeloma $2,017,817 Stomach $2,303,563 Non-Hodgkin Lymphoma $2,606,111 Leukemia $2,689,035 Oral Cavity and Pharynx $3,429,393 Kidney and Renal Pelvis $4,471,645 Liver and Intrahepatic Bile Duct $5,085,359 Colon and Rectum $5,306,966 Lung and Bronchus $6,198,908 Figure 2. Sensitivity Analysis: Value of Lost Production due to Premature Mortality varying Discount Rates, Puerto Rico, 2004 0% $101.88 1% $86.36 2% $74.05 3% $64.15 4% $56.09 5% $49.43 6% $43.89 7% $39.22 8% $35.25 9% $31.85 10% $28.92 Note: Table made from bar graph.
|Gale Copyright:||Copyright 2010 Gale, Cengage Learning. All rights reserved.|