The determinants of life expectancy: an analysis of the OECD health data.1. Introduction
This study is concerned with understanding the determinants of life expectancy Life Expectancy
1. The age until which a person is expected to live.
2. The remaining number of years an individual is expected to live, based on IRS issued life expectancy tables. in developed countries. The level (and variability) of life expectancy has important implications for individual and aggregate human behavior; it affects fertility behavior, economic growth, human capital investment, intergenerational in·ter·gen·er·a·tion·al
Being or occurring between generations: "These social-insurance programs are intergenerational and all transfers, and incentives for pension benefit claims (Zhang, Zhang, and Lee 2001: Coile et al. 2002). From the social planners In welfare economics, a social planner is a decision-maker who attempts to achieve the best result for all parties involved. In neo-classical welfare economics, this means the maximization of a social welfare function. perspective, it has implications for public finance. For example, Gradstein and Kaganovich (2004) conclude that increasing longevity results in increasing public funding Public funding is money given from tax revenue or other governmental sources to an individual, organization, or entity. See also
Describes facts outside the control of the firm. Converse of endogenous. for the purpose of policy analysis, it has been argued that life expectancy (or more broadly "'health") is predetermined pre·de·ter·mine
v. pre·de·ter·mined, pre·de·ter·min·ing, pre·de·ter·mines
1. To determine, decide, or establish in advance: by behavioral and policy variables in what can be loosely described as a production function for health. Estimating this function is the goal of this study.
Auster, Leveson, and Sarachek (1969) were the first economists to study a population production function for health: a regression of state-level mortality rates on medical care and environmental variables. Today their research motivations and questions remain compelling. Indeed, given the size and rapid growth of health care-related industries and recent public interest in containing medical and insurance costs, it could be argued that understanding the socioeconomic determinants of societal health is more important today than ever. Moreover, issues concerning the government's role in sponsoring basic medical and pharmaceutical research: in regulating drug, alcohol, and tobacco consumption; and in promoting healthy lifestyles are all particularly newsworthy news·wor·thy
adj. news·wor·thi·er, news·wor·thi·est
Of sufficient interest or importance to the public to warrant reporting in the media.
news . The research questions related to public health are obvious. If societal health can be measured as life expectancy or mortality rates, what are the various socioeconomic factors that increase or decrease it? Can the marginal effects of these factors be disentangled? If so, which of these factors produces the largest health benefits (or costs) to society? These questions are as important now as when first posed by Auster, Leveson, and Sarachek in 1969.
Since Auster, Leveson, and Sarachek, several economic studies have attempted to answer these questions using data from the United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area. or multiple countries. (1) Many of these have used aggregate data from the member countries of the OECD OECD: see Organization for Economic Cooperation and Development. to explain cross-country mortality rates or life expectancies. (2) While the empirical results are mixed, the general consensus is that population life expectancy (or mortality) is a function of environmental measures (e.g., wealth, education, safety regulation, infrastructure), lifestyle measures (e.g., tobacco or alcohol consumption), and health care consumption measures (e.g., medical or pharmaceutical expenditures). However, the appropriate econometric e·con·o·met·rics
n. (used with a sing. verb)
Application of mathematical and statistical techniques to economics in the study of problems, the analysis of data, and the development and testing of theories and models. methodology lot disentangling these effects and its meaning for the relative importance (statistical or economic) of the estimated effects is more contentious.
These methodological issues are most vividly illustrated in the few studies that have focused on pharmaceutical expenditures as a separate input to life expectancy. These include Peltzman (1987), Babazono and Hillman Hillman was a famous British automobile marque, manufactured by the Rootes Group. It was based in Ryton-on-Dunsmore, near Coventry, England, from 1907 to 1976. Before 1907 the company had built bicycles. (1994), Lichtenberg (1996, 1998), Frech and Miller (1999), and Miller and Frech (2000). For example, Peltzman (1987) examined the effects of wealth and prescription drug prescription drug Prescription medication Pharmacology An FDA-approved drug which must, by federal law or regulation, be dispensed only pursuant to a prescription–eg, finished dose form and active ingredients subject to the provisos of the Federal Food, Drug, laws on infectious disease Infectious disease
A pathological condition spread among biological species. Infectious diseases, although varied in their effects, are always associated with viruses, bacteria, fungi, protozoa, multicellular parasites and aberrant proteins known as prions. mortality and on poisoning mortality across middle-income countries in a generalized least squares (GLS GLS - Guy Lewis Steele, Jr. ) framework. He found that wealth variables significantly decreased both disease and poisoning mortality rates, while prescription drug laws had a significant and positive effect on poisoning mortality only. The implication of the latter result was that mandatory prescription drug enforcement may lead to more frequent accidental poisonings (or deaths due to the overconsumption of pharmaceuticals, as interpreted by Peltzman). Peltzman also considered a GLS regression of life expectancy at birth on wealth and government health expenditures and found only wealth to be a significant determinant determinant, a polynomial expression that is inherent in the entries of a square matrix. The size n of the square matrix, as determined from the number of entries in any row or column, is called the order of the determinant. . His life expectancy variable was an average for the entire population (including males and females) of each country and was only for a single age stratum stratum /stra·tum/ (strat´um) (stra´tum) pl. stra´ta [L.] a layer or lamina.
stratum basa´le (at birth) in each country. He also ignored lifestyle variables in his regressions.
To fully appreciate the vast differences in methodological approaches in pharmaceutical studies, compare Peltzman's analysis to that of Frech and Miller (1999), a subset of whose findings were also published in Miller and Frech (2000). Frech and Miller partitioned OECD data into age strata (life expectancy at birth, age 40 years, and age 60 years) and estimated separate life expectancy regressions for each stratum (pooling data for males and females). The determinants in each stratum regression were wealth, some lifestyle variables (alcohol, tobacco, and animal fat consumption), and pharmaceutical and nonpharmaceutical medical expenditures. Using country-level OECD data, they found that pharmaceutical expenditures had a significant and positive effect on life expectancy and that this increased with age. They also found that tobacco consumption (as measured using the concatenated percentages of males and females who smoked in each country) was not a significant determinant of life expectancy at any age. Frech and Miller's study is among the better investigations that have sought to estimate a population health production function since properly constructed measures of health care and pharmaceutical expenditures were used, and the effects of environmental and lifestyle factors were also taken into account. (3)
This study considers a life expectancy production function similar to that of Frech and Miller but with some methodological innovations that have meaningful effects on the magnitude of our results. First, our data distinguish life expectancies by age (40, 60, and 65 years) and by gender. However, specification tests indicate that pooling the life expectancy data across gender and age strata cannot be rejected. Therefore, we pool the data across the distribution of ages to produce a larger effective sample size and include interactions with age and gender variables to disentangle marginal effects for different age--gender strata. Second, this study uses a nonparametric jackknife jack·knife
1. A large clasp knife.
2. Sports A dive in the pike position, in which the diver straightens out to enter the water hands first.
v. technique to quantify the sampling variability of coefficient estimates. None of the previously mentioned studies employed resampling methods to derive variance estimates. Third, we include a measure of fruit and vegetable consumption in our regression. Fourth, we also use a different measure of tobacco consumption that yields different inferences compared to those of Frech and Miller. Finally, and most important, we find that failure to adjust for the effect of a country's age distribution may create an omitted-variable bias Omitted-variable bias (OVB) is the bias that appears in estimates of parameters in a regression analysis when the assumed specification is incorrect, in that it omits an independent variable that should be in the model. in the coefficients for other determinants of life expectancy. (4) This bias emphasizes the importance of age distribution variability in macroeconomic mac·ro·ec·o·nom·ics
n. (used with a sing. verb)
The study of the overall aspects and workings of a national economy, such as income, output, and the interrelationship among diverse economic sectors. and public economic studies that incorporate aggregate life expectancy or mortality as a determinant of economic behavior.
We discuss these methodological issues and their effects on our results in the remainder of the paper. Accordingly, the next section of this paper discusses our methodology. In a following section, we discuss the results, with an emphasis of the effects of lifestyle factors and pharmaceutical consumption on life expectancy. In particular, we calculate and discuss the changes in lifestyle and pharmaceutical expenditures necessary to promote an additional year of life in different age--gender strata. Couched in these terms, our results suggest policy tactics lot improving societal longevity and behavioral strategies for extending individual life expectancy. We close with a discussion of the robustness of our results, a brief discussion of some of the limitations of our work, and our conclusions.
Data are taken from the OECD Health Data 20(10 database, which contains aggregate data on the health care systems of 29 of the 30 OECD countries. The database includes over 1200 indicators spanning the period 1960 to 1999, with official data up to 1998 and selected estimates for 1999. In particular, the data set includes various measures of health status (morbidity and mortality Morbidity and Mortality can refer to:
relating to relate prep → bezüglich +gen, mit Bezug auf +acc population demographics The attributes of people in a particular geographic area. Used for marketing purposes, population, ethnic origins, religion, spoken language, income and age range are examples of demographic data. , nonmedical determinants of health (alcohol and tobacco consumption), and economic references (GDP GDP (guanosine diphosphate): see guanine. and monetary conversion rates). (5) Variable definitions and descriptive statistics descriptive statistics
see statistics. are presented in Table 1, and a complete explanation of the data is contained in the Data Appendix.
Since we are using data on OECD countries, inferences drawn from this study are valid only for more developed countries. We recognize the importance of ascertaining the determinants of population life expectancy in developing countries. However, it has been shown in many studies that public health services health services Managed care The benefits covered under a health contract , such as clean water supply and sanitation sanitation: see plumbing; sanitary science. services, provide the biggest benefits to societal health in these countries. According to according to
1. As stated or indicated by; on the authority of: according to historians.
2. In keeping with: according to instructions.
3. Miller and Frech (2000, p. 34), these "services are a matter of civil engineering rather than healthcare." Since our study focuses on the health care determinants of life expectancy, we select a sample containing developed countries only. In addition, data regarding drug consumption in developing nations are limited, precluding a detailed analysis of the effect of drug consumption in these countries.
We hypothesize hy·poth·e·size
v. hy·poth·e·sized, hy·poth·e·siz·ing, hy·poth·e·siz·es
To assert as a hypothesis.
To form a hypothesis. that health care and lifestyle factors will have cumulative effects on life expectancy. That is, the consumption of factors over time by an individual will have either positive or negative effects on that individual's longevity. While it is conceivable that the consumption of certain factors (e.g., alcohol, tobacco) by a mother would influence the life expectancy of her offspring, this represents a different model from the one we are interested in estimating. Thus, we choose not to include life expectancy at birth as a dependent variable in our model. (6) Under the presumption that health care and lifestyle factors would have cumulative effects, we choose to lag the explanatory variables by roughly 15 years. The literature suggests that a lag of 20 years or more would be appropriate for alcohol and tobacco consumption (Corrao et al. 1993, Savolainen et al. 1993; Wise 1997; Khuder 2001). However, there is little empirical evidence regarding the appropriate lag length for indicators of health care consumption. Missing data preclude us from lagging Lagging
Strategy used by a firm to stall payments, normally in response to exchange rate projections. expenditure variables by more than 12 years or lifestyle variables by more than 17 years. A full model of this type would typically require several lags for each independent variable. Because of data and sample size limitations, we include only one lag per variable.
It is widely held that studies of the productivity of health care may suffer from endogeneity bias. The allocation of medical resources to health should promote increased life expectancy, but as longevity increases, so do outlays Outlays
Payments on obligations in the form of cash, checks, the issuance of bonds or notes, or the maturing of interest coupons. on medical care. A relatively old population is likely to consume more health care than a relatively young population because of a greater prevalence of health problems. This quandary has been called the Sisyphus syndrome Sisyphus 'syndrome' Psychiatry A mindset typical of a stress-driven type 'A' person, who obtains no gratification from accomplishing the difficult goals he or she places upon himself or herself. See 'Anal-retentive.', 'Toxic core', Type A personality. by Zweifet and Ferrari (1992) and others.
In our model, the Sisyphus syndrome is not an issue of reverse causality causality, in philosophy, the relationship between cause and effect. A distinction is often made between a cause that produces something new (e.g., a moth from a caterpillar) and one that produces a change in an existing substance (e.g. as much as an omitted-variable problem. Life expectancy in a given year cannot cause the consumption of medical care in a preceding year. However, both life expectancy and health care consumption may be influenced by the age structure of a population measured concurrently or in previous years. To account for this effect and to ensure the consistency of our regression estimates, we include in our model the percentage of the population 65 years of age or older in 1985 (the same year in which pharmaceutical and other health care consumption are measured).
We use a log-linear functional form in modeling the data. There are several reasons for this. First, it allows us to interpret our parameter estimates as elasticities. Second, it allows for diminishing marginal returns to the independent variables. In a log-linear model log-linear model
a statistical model which models frequency counts in contingency tables by using an analysis of variance approach. , the elasticity is held constant, while the absolute value of the marginal effect for each explanatory variable is forced to fall at higher and higher values of the variable. The continuous independent variables are centered to yield a more plausible interpretation of the marginal effects. A dummy variable This article is not about "dummy variables" as that term is usually understood in mathematics. See free variables and bound variables.
In regression analysis, a dummy variable for Spain is added to the model to control for the imputation IMPUTATION. The judgment by which we declare that an agent is the cause of his free action, or of the result of it, whether good or ill. Wolff, Sec. 3. of missing tobacco consumption data for this country.
Initially, ordinary least squares (OLS OLS Ordinary Least Squares
OLS Online Library System
OLS Ottawa Linux Symposium
OLS Operation Lifeline Sudan
OLS Operational Linescan System
OLS Online Service
OLS Organizational Leadership and Supervision
OLS On Line Support
OLS Online System ) is used to estimate separate models for the six strata j = 1, ... ,6, defined by age and gender. The age--gender strata are ages 40, 60, and 65 for both males and females. Country i = 1, ... , 19 is then the unit of observation in the following life expectancy regression:
(1) In [LE97.sub.ii] = [[beta].sub.0j] + [[beta].sub.1j] ln [GDP85.sub.i] + [[beta].sub.2j] ln [PHARM PHARM Pharmacy 85.sub.i] + [[beta].sub.3j] ln [HEAlTH85.sub.i] + [[beta].sub.4j] ln [AGEDIST85.sub.i] + [[beta].sub.5j] ln [ALCOHOL80.sub.i] + [[beta].sub.6j] ln [SMOKE80.sub.i] + [[beta].sub.7j] ln [BUTTER80.sub.i] + [[beta].sub.8j] ln [VEG80.sub.i] + [[beta].sub.9j] ln [SPAIN.sub.i] + [[epsilon].sub.ij].
All continuous variables are measured in logarithms; variable definitions are given in Table 1. Tests on the OLS residuals indicate that the life expectancy data for the six age--gender strata can be pooled into a single regression. (See the Technical Appendix for test details.) This is in contrast to the approach adopted by Miller and Frech (2000), who estimated a separate regression for each age stratum but pooled data across genders.
Based on the preceding results, we pool life expectancy data for the six age-gender strata, adding dummy variables for age and gender to the model to control for differences in the intercept term. (7) We use residual maximum likelihood to estimate a mixed model treating country as a random effect. (8) Given our small sample size and concerns regarding possible heteroscedasticity, inferences are based on jackknife estimates of the standard errors (MacKinnon and White 1985). However, for comparative purposes, empirical standard errors are also calculated (but not presented) based on the sandwich estimator (Huber 1967; White 1980). An important empirical finding is that the jackknife standard errors are always greater than or equal to the empirical standard errors, implying that the statistical significance of estimates in previous studies may have been overstated o·ver·state
tr.v. o·ver·stat·ed, o·ver·stat·ing, o·ver·states
To state in exaggerated terms. See Synonyms at exaggerate.
o . Equation 2 is the final model specification, where the 13s are fixed effects, the [u.sub.i] are random country effects from a N(0, [[sigma].sup.2.sub.u]) distribution, and the [[epsilon].sub.ij] are independently identically distributed errors (at the level of age--gender stratum within country) from a N(0, [[sigma].sup.2.sub.[epsilon]) distribution and are independent of the [u.sub.i]:
(2) In [LE97.sub.ij] = [[beta].sub.0] + [[beta].sub.1] ln [GDP85.sub.i] + [[beta].sub.2] ln [PHARM85.sub.i] + [[beta].sub.3] ln [HEAlTH85.sub.i] + [[beta].sub.4] ln [AGEDIST85.sub.i] + [[beta].sub.5] In [ALCOHOL80.sub.i] + [[beta].sub.6] ln [SMOKE80.sub.6i] + [[beta].sub.7] In [BUTTER80.sub.i] + [[beta].sub.8] ln [VEG80.sub.i] + [[beta].sub.9][SPAIN.sub.i] + [[beta].sub.10][MALE.sub.ij] + [[beta].sub.11][AGE60.sub.ij] + [[beta].sub.12][AGE65.sub.ij] + [[beta].sub.13][MALE.sub.ij] x [AGE60.sub.ij] + [[beta].sub.14][MALE.sub.ij] x [AGE65.sub.ij] + [[beta].sub.15][AGE60.sub.ij] x ln [GDP85.sub.i] + [[beta].sub.16][AGE65.sub.ij] x ln [GDP85.sub.i] + [[beta].sub.17][TAGE TAGE Texas Alliance for Geographic Education 60.sub.ij] x ln [PHARM85.sub.i] + [[beta].sub.18][AGE65.sub.ij] x ln [PHARM85.sub.i] + [[beta].sub.19][AGE60.sub.ij] x ln [SMOKE80.sub.i] + [[beta].sub.20][AGE65.sub.ij] x ln [SMOKE80.sub.j] + [[beta].sub.21][MALE.sub.ij] x ln [ALCOHOL80.sub.i] + [[beta].sub.22][MALE.sub.ij] x In [VEG80.sub.i] + [[beta].sub.23][AGE60.sub.ij] x ln [VEG80.sub.i] + [[beta].sub.24][AGE65.sub.ij] x ln [VEG80.sub.i] + [u.sub.i] + [[epsilon].sub.ij]
A number of tests are performed to assess the model's goodness of fit Goodness of fit means how well a statistical model fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e. . Also, sensitivity analyses are performed for the lag structure and monetary conversion rates that are used. See the Technical Appendix for details. All statistical analyses are performed using SAS (1) (SAS Institute Inc., Cary, NC, www.sas.com) A software company that specializes in data warehousing and decision support software based on the SAS System. Founded in 1976, SAS is one of the world's largest privately held software companies. See SAS System. Release 8.02 (SAS Institute SAS Institute Inc., headquartered in Cary, North Carolina, USA, has been a major producer of software since it was founded in 1976 by Anthony Barr, James Goodnight, John Sall and Jane Helwig. , Inc., Cary, North Carolina Cary is the second largest municipality in Wake County, North Carolina and the third largest municipality in The Triangle (North Carolina) behind Raleigh and Durham. It is the seventh largest municipality in North Carolina. ) and Stata/SE 8.0 (Stata Corporation, College Station, Texas College Station is a city in Brazos County, Texas, situated in Central Texas. It is located in the heart of the Brazos Valley. The city is located within the most populated region of Texas, near to three of the 10 largest cities in the United States - Houston, Dallas, and San ).
3. Results and Discussion
Table 2 presents our results for the estimation of Equation 2. To highlight the importance of a country's age distribution in estimation of the production function, we present two regressions: one including AGEDIST85 and one excluding it. To the right of the coefficient estimates are the corresponding jackknife standard errors, which we find to be more conservative than the empirical robust standard errors (clustered or unclustered). First, we find that the age distribution of a population in 1985 is a significant determinant of life expectancy with an elasticity of -0.073. That is, if the percentage of the population over 65 in an average OECD country were doubled, average life expectancy for the population of males and females in the three age strata would decline 7.3%. If the percentage of the population over 65 increased 1%, average life expectancy would decline approximately 54 days.(9)
The only independent variable that is appreciably ap·pre·cia·ble
Possible to estimate, measure, or perceive: appreciable changes in temperature. See Synonyms at perceptible. affected by the inclusion or exclusion of the age distribution variable is pharmaceutical expenditures in 1985 (PHARM85). When AGEDIST85 is excluded, its magnitude is 0.009 and is insignificant. When AGEDIST85 is included, the estimate increases to 0.027 and is significant at the 10q level based on conservative jackknife standard errors. This means that when pharmaceutical expenditures are doubled, the life expectancy at 40 years increases 2.7% (or 411 days for females, 360 days for males). (10) It also means that the conditional correlation between pharmaceutical expenditures and the age distribution in 1985 may be such that excluding the age distribution variable biases the pharmaceutical coefficient downward. Older populations use more drugs, and this must be taken into account. Again, we do not consider this a simultaneity issue because life expectancy in 1997 cannot cause drug consumption in 1985. It is an omitted-variable problem that should be recognized in subsequent health care research. As such, all discussions that follow are for the model including the age distribution measure.
Because we pool the data and include age interactions in our model, the effect of pharmaceuticals on life expectancy at ages 60 and 65 cannot be directly inferred from Table 2. Using the estimated "AGE60 x PHARM85" and "AGE65 X PHARM85" interactions of 0.019 and 0.021, respectively, our regression yields an elasticity of 0.046 for the effect for pharmaceuticals on life expectancy at age 60 and an elasticity of 0.048 for the effect for pharmaceuticals on life expectancy at age 65. With standard errors of 0.016, both elasticities are significant at the 95% level. While the elasticity of pharmaceutical consumption increases with age (e.g., 0.027 at age 40 and 0.046 at age 60), the actual predicted effect (in terms of life expectancy gained per unit increase in pharmaceutical consumption) is decreasing in age. (11)
A reasonable policy question is, What amount of pharmaceutical expenditure is required to increase average life expectancy by one year in each of the age-gender strata? Table 3 provides some insights. The left (right) half of the table depicts the results for males (females); the columns represent the age strata (40, 60, and 65 years). For example, the table indicates that for a 60-year-old male, an average increase in pharmaceutical expenditures of about $194 per capita [Latin, By the heads or polls.] A term used in the Descent and Distribution of the estate of one who dies without a will. It means to share and share alike according to the number of individuals. (a 113% increase over the sample average of $171) would increase average life expectancy by one year. The corresponding number for 60-year-old females is somewhat less, that is, about $159 (93%) per capita. Doubling annual pharmaceutical expenditures from the sample average of $171 per capita adds about one year of life expectancy for males at age 40 and a little less than one year of life expectancy for females at age 65. It is also clear from Table 3 that the marginal benefit of pharmaceutical spending is decreasing in age. For example, for males age 40, 60, and 65 years to gain an additional year of life expectancy requires pharmaceutical spending increases of 101.33%, 113.40%, and 134.76%, respectively. These results are averages across all OECD countries in the sample; however, we could use the percentages in Table 3 to impute impute v. 1) to attach to a person responsibility (and therefore financial liability) for acts or injuries to another, because of a particular relationship, such as mother to child, guardian to ward, employer to employee, or business associates. country-specific results. For example, in the United States annual per capita spending on pharmaceuticals was $155 in 1985; therefore, to add an additional year of life expectancy for 60year-old males would only require an increase of about $155 x 1.134 = $176 per capita per annum Per annum
Yearly. . (12)
Lifestyle factors, such as the consumption of alcohol, tobacco, butter, and fruits and vegetables, also have important effects on life expectancy after controlling for the effects of wealth and health care consumption. Contrary to the finding of Miller and Frech (2000), tobacco consumption (SMOKE80) has a statistically significant negative effect on life expectancy. Results in Table 2 indicate that doubling tobacco consumption per capita is associated with an approximate 6.7% reduction in population life expectancy at age 40 (1020 days for females, 894 days for males). Using the estimated "AGE60 x SMOKE80" and "AGE65 x SMOKE80" interactions of -0.036 and -0.045, respectively, the regression yields an elasticity of-0.103 for the effect for tobacco on life expectancy at age 60 and an elasticity of -0.112 for the effect for tobacco on life expectancy at age 65. With standard errors of 0.033 and 0.035, respectively, both elasticities are significant at the 5% level. Table 3 couches our tobacco results in terms of the reduction needed to increase life expectancy by one year. For example, average females at age 40 years would add an additional year of life expectancy if they decrease tobacco consumption by about 976 grams per year (a 36% reduction from an OECD average of 2727 grams per capita per year in 1980). If a cigarette contains about 1.5 grams of tobacco, this is equivalent to a per capita decrease of about 651 cigarettes per year, or just under two cigarettes per day for this group. It is not entirely clear from Table 3 whether an increase in pharmaceutical expenditures or a decrease in tobacco consumption would be more effective in improving longevity. The units presented are not comparable, and they do not reflect the true cost of creating and implementing public policy. However, the results provide insight into the relative magnitudes of changes in drug consumption and healthy lifestyle required to promote longevity in developed countries.
Per capita fruit and vegetable consumption (VEG80) has a statistically significant positive effect on life expectancy, with a coefficient (standard error) of 0.081 (0.023) for females at age 40 (Table 2). After taking into consideration interactions with the age and gender variables, the following marginal effects (with standard errors in parentheses See parenthesis.
parentheses - See left parenthesis, right parenthesis. ) result: 0.110 (0.025) for females at age 60, 0.123 (0.026) for females at age 65, 0.111 (0.029) for males at age 40, 0.140 (0.030) for males at age 60, and 0.153 (0.031) for males at age 65. All the results are significant at the 5% level. Table 3 shows that average females at 40 years would add an additional year of life expectancy by increasing fruit and vegetable consumption by about 55 kilograms per year (a 30% increase) from an OECD average of 187 kilograms per capita per year in 1980. This is equivalent to increasing fruit and vegetable consumption by about one-third pound per day.
Our findings with respect to fruit and vegetable consumption likely reflect differences in intake among groups. In many developed countries, fruit and vegetable consumption appears to increase with increasing age among adults (Krebs-Smith et al. 1995a, b; Dong and Erens 1997; Krebs-Smith et al. 1997). Further, although women tend to report eating fruits and vegetables with greater frequency than men, actual intake tends to be higher for the latter when more objective measures (e.g., average number of grams consumed dally) are used (Krebs-Smith et al. 1995a, b). Since we measure intake using the number of kilograms consumed annually, it is not surprising that the effect of fruit and vegetable consumption on life expectancy is greater for males than females.
In Table 2, the parameter estimate for butter consumption per capita (BUTFER80) is 0.022 and is statistically significant at the 5% level. This implies that doubling butter consumption increases average life expectancy by 2.2% across age and gender strata. Interactions of butter with age and gender variables are insignificant. There are several possible explanations for the apparent effect of butter consumption on life expectancy. First, it is possible that the positive effect is the result of vitamin fortification fortification, system of defense structures for protection from enemy attacks. Fortification developed along two general lines: permanent sites built in peacetime, and emplacements and obstacles hastily constructed in the field in time of war. . In developed countries, where milk products are fortified fortified (fôrt´fīd),
adj containing additives more potent than the principal ingredient. with vitamins A and D, it is conceivable that butter would have a positive effect on a population's health. Second, one might hypothesize that the positive effect of butter consumption is due to the use of butter as a spread for vegetables. Although we investigated this hypothesis by testing for an interaction between butter consumption and vegetable consumption, we found no evidence to support it. Third, the positive effect of butter consumption could be due to omitted-variable bias. Since we explicitly control for the effect of wealth, it seems unlikely that our measure of fat intake is simply capturing an omitted income effect (i.e., that people in wealthier countries consume fattier diets).
Our findings with respect to butter consumption are consistent with those of Wolfe and Gabay (1987), who studied the relationship between negative changes in lifestyle and health status in a sample of OECD countries. Although they found that negative changes in lifestyle were associated with declines in health status, butter consumption was negatively related to the former, suggesting a positive association with health status. Others have reported a nonlinear A system in which the output is not a uniform relationship to the input.
nonlinear - (Scientific computation) A property of a system whose output is not proportional to its input. relationship between fat consumption and measures of population health. Gage and O'Connor (1994) reported that increases in the dietary contribution of fats relative to proteins were associated with increased life expectancy. However, the effect was moderated by diet quality such that in the presence of a high-quality diet, the effect of a high fat-to-protein ratio on life expectancy was reversed. Similarly, Frech and Miller (1999) reported that low levels of animal fat (not butter) consumption had a strong positive effect on life expectancy, while higher levels were associated with reduced life expectancy. We chose not to model a nonlinear association between butter intake and life expectancy since there was little empirical evidence supporting a nonlinear relationship (see the Data Appendix for details).
Though alcohol consumption (ALCOHOL80) does not have a statistically significant effect on female life expectancy, its effect on the life expectancy of males is both significant and negative. This finding most likely reflects a difference in alcohol intake between males and females and is consistent with the findings of Cochrane, St. Leger
The St. Leger (pronounced saint ledger or sellinger , and Moore (1978) and Frech and Miller (2000). Although moderate drinking (i.e., no more than one drink a day for most women and no more than two drinks a day for most men) has been associated with psychological (Baum-Baicker 1985) and cardiovascular (Moore and Pearson 1986; Stampfer et al. 1988; Boffetta and Garfinkel 1990; Razay et al. 1992) benefits, it also increases risks for hemmorhagic stroke (Camargo 1989), adverse medication reactions (Shinn and Shrewsbury 1988; Gilman et al. 1990), and certain types of cancer (Willett et al. 1987; Klatsky et al. 1988). Further, various researchers have suggested that moderate drinking is not cardioprotective, arguing that higher mortality among abstainers results from including among them people who have stopped drinking because of ill health. At the ecological level, it is likely that the small health benefits provided by moderate drinking are outweighed by the risks associated with alcohol consumption. (13)
In a sample of more developed countries, we find that drug consumption, as measured by per capita pharmaceutical expenditures, has a positive effect on population life expectancy at various ages. The predicted number of days or years of expected life per unit increase in pharmaceutical consumption appears to decline with increasing age. Our research also suggests that the correlation between pharmaceutical consumption and a country's age distribution creates an omitted-variable bias in the elasticity of pharmaceutical consumption when the age distribution is ignored. This is a classic case of the omitted-variable problem, which fortunately seems to affect only the elasticity of pharmaceutical consumption and not those of the other determinants of life expectancy. The omission creates a downward bias (at least empirically), suggesting the marginal effect of drug consumption on health will be understated if age distribution is ignored. In this case, the nature of the correlation is clear: an older society Consumes more drugs in the short run, and drugs may change the age profile of society in the long run. However, correlations between the age distribution of a country and other macroeconomic or aggregate variables may be more subtle and, hence, more easily overlooked in empirical analyses. Insofar in·so·far
To such an extent.
Adv. 1. insofar - to the degree or extent that; "insofar as it can be ascertained, the horse lung is comparable to that of man"; "so far as it is reasonably practical he should practice as the age distribution of a country affects voting, politics, and ultimately policy, it is important to acknowledge the potential for correlations between the age of a society and any aggregate measure that may be influenced by policy.
Data are taken from the OECD Health Data 2000 database. Except where noted, the data are identical to those in Miller and Frech (2000) with the difference that our data are more current (taken from a more recent version of the OECD Health Data database). Because of missing data, we restrict our analysis to 19 of the 30 OECD countries. We exclude Switzerland from our sample because of the limited availability When customers of the PSTN make telephone calls, they commonly make use of a telecommunications network called a switched-circuit network. In a switched-circuit network, devices known as switches are used to connect the caller to the callee. of pharmaceutical and health-specific purchasing power parity Purchasing power parity
The notion that the ratio between domestic and foreign price levels should equal the equilibrium exchange rate between domestic and foreign currencies. (PPP (Point-to-Point Protocol) The most popular method for transporting IP packets over a serial link between the user and the ISP. Developed in 1994 by the IETF and superseding the SLIP protocol, PPP establishes the session between the user's computer and the ISP using ) exchange rates as well as tobacco and alcohol consumption data. We also exclude Turkey from our sample since it is relatively underdeveloped un·der·de·vel·oped
Not adequately or normally developed; immature. when compared with the other member countries of the OECD. Variable definitions and descriptive statistics are presented in Table 1. All continuous variables are measured in logarithms.
Life Expectancies (LE97)
The dependent variables include life expectancies for males and females at ages 40, 60, and 65. These are measured in number of years of life expectancy for each age-gender stratum in 1997. Life expectancy data are missing for Ireland in 1997, and we substitute 1995 data in each age-gender grouping for this country in our model. We include life expectancy at age 65, while Miller and Frech (2000) did not.
We measure wealth or income using per capita GDP in 1985. Following Miller and Frech (2000), it is converted into U.S. dollars by dividing by the appropriate 1985 PPP conversion factor provided in the OECD Health Data database.
Pharmaceutical Consumption (PHARM85)
Pharmaceutical consumption is measured in 1985 per capita expenditures for each country. It is computed as total per capita expenditures on pharmaceuticals and other medical nondurables minus per capita expenditures on medical nondurables (in cases where data for the latter are available). Our measure of pharmaceutical consumption includes expenditures for outpatient prescription and over-the-counter medications as well as pharmacists' remuneration. In addition to conventional GDP-based conversion factors, the OECD Health Data database includes PPP conversion factors for pharmaceutical expenditures. Following Miller and Frech (2000), we use the 1985 PPP conversion factor for pharmaceutical expenditures to convert expenditures to U.S. dollars. Miller and Frech (2000) argue that pharmaceutical expenditures converted to U.S. dollars using GDP PPP exchange rates underestimate actual pharmaceutical expenditures outside the United States. The drug-specific PPP exchange rates yield results that are consistent with those obtained using more accurate conversion factors developed by Szuba (1986) and others. Unfortunately, the exchange rates are available only for a limited number of years (i.e., 1980, 1985, 1990, 1993, and 1996). Therefore, this influenced the lag we use for pharmaceutical expenditures.
Nonpharmaceutical Health Care Consumption (HEALTH85)
Our measure of health care expenditures in 1985 is computed by subtracting PHARM85 from total per capita expenditures on health care. Total health care expenditures in 1985 are missing for Greece and are estimated by summing total current expenditures on health and total investments in medical facilities. The OECD Health Data database also includes specific PPP conversion factors for health care expenditures. Thus, following Miller and Frech (2000), these exchange rates are used to convert HEALTH85 to U.S. dollars.
Age Distribution (AGEDIST85)
This is measured as the percentage of the population 65 years of age or older In 1985 (the same year in which pharmaceutical and other health care consumption are measured). We also experimented with the percentage of the population 65 years of age or older in 1980, but this had little effect on our results. Indeed, the unconditional correlation between the age distribution in 1980 and 1985 is 0.94 and is significant at the 5% level. Frech and Miller (1999) did not include this variable in their primary analysis, though they claimed to have evaluated its influence in sensitivity analyses.
Alcohol Consumption (ALCOHOL80)
Alcohol consumption is measured in liters consumed per capita by persons aged 15 or older in 1980. We substitute 1983 data for Greece since data on alcohol consumption in 1980 are missing for this country.
Smoking Behavior (SMOKE80)
Smoking behavior is measured as grams of tobacco consumed per capita by persons aged 15 or older in 1980. Data on tobacco consumption in 1980 are missing for Germany, Ireland, and Italy. For these countries, 1979 data are used instead. Data on tobacco consumption are unavailable for Spain in any year. For this country, we substitute the mean value for tobacco consumption in 1980 for the other countries included in our sample. As noted, a dummy variable is included in the regression analyses to account for this imputation.
Fat Consumption (BUTTER80)
Miller and Frech (2000) had reported animal fat to be an important predictor of life expectancy. The measure of fat consumption they used is no longer collected by the OECD and is not available in the Health Data 2000 database. Therefore, we use butter consumption in kilograms per capita in 1980 as an alternate measure of animal fat intake. This includes quantities of butter used in food preparations or mixed with other fats to obtain particular types of margarine margarine, manufactured substitute for butter. It consists of a blend of vegetable oils or meat fats (or a combination of both) mixed with milk and salt. It was developed in the late 1860s by the French chemist Hippolyte Mège-Mouries in a contest sponsored by or cooking fats. Certain studies (Gage and O'Connor 1994; Frech and Miller 1999; Miller and Frech 2000) have suggested that the relationship between fat intake and life expectancy is parabolic par·a·bol·ic also par·a·bol·i·cal
1. Of or similar to a parable.
2. Of or having the form of a parabola or paraboloid. (i.e., low levels of fat consumption yield increased life expectancy, whereas higher levels of consumption yield reduced life expectancy). We investigated several methods of accounting for nonlinearity in the association between butter intake and life expectancy (e.g., including quadratic quadratic, mathematical expression of the second degree in one or more unknowns (see polynomial). The general quadratic in one unknown has the form ax2+bx+c, where a, b, and c are constants and x is the variable. terms, categorization using dummy variables). However, we found no strong evidence supporting a curvilinear curvilinear
a line appearing as a curve; nonlinear.
see curvilinear regression. relationship.
Fruit and Vegetable Consumption (VEG80)
As a measure of positive dietary intake, we include fruit and vegetable consumption in kilograms per capita in 1980. Miller and Frech (2000) did not include such a measure in their analysis.
For the regressions specified in Equation 1, we performed a hypothesis test to determine whether the intercept varied among the six age-gender strata followed by a test for homogeneity Homogeneity
The degree to which items are similar. of regression or parallelism An overlapping of processing, input/output (I/O) or both.
1. parallelism - parallel processing.
2. (parallel) parallelism - The maximum number of independent subtasks in a given task at a given point in its execution. E.g. . These tests are often ascribed to Chow (1960); however, they were described earlier in a number of other sources (e.g., Kendall 1948; Kempthorne 1952; Rao 1952). To maintain an overall two-tailed alpha level of 0.05, the first test was performed with an alpha of 0.025, while the second was performed with an alpha of 0.05. As would be expected, there was a significant difference among the six age-gender strata in the intercept term ([F.sub.5,99] = 7711; p < 0.0001). However, the other parameters did not appear to vary significantly among the strata ([F.sub.45,54] = 1.29; p < 0.19).
Conventional tests for poolability assume spherical spher·i·cal
Having the shape of or approximating a sphere; globular. disturbances (Baltagi 2001). In the presence of nonspberical disturbances, these tests are not robust. For example, when estimating an error components model, they may exhibit a high frequency of type I error when the variance components are large. According to Baltagi (2001), conventional tests for poolability should be used only after the disturbances have been transformed so that they are spherical. Baltagi describes a method for transforming the disturbances that follows from the work of Roy (1957) and Zelhier (1962). Using the methods described by Baltagi (2001), we performed the Roy-Zeliner analogs of the intercept and parallelism tests. These allowed for a one-way error components model in which country was treated as a random effect. The data were transformed using consistent estimates of the covariance Covariance
A measure of the degree to which returns on two risky assets move in tandem. A positive covariance means that asset returns move together. A negative covariance means returns vary inversely. matrices for the restricted and unrestricted models; thus, the test statistics followed an approximate F-distribution. The results were similar to those described in the preceding paragraph. While there was a significant difference among the six age-gender strata in the intercept term ([F.sub.5,99] = 2165; p < 0.0001), the parameter vectors (excluding the intercept) did not vary significantly among the strata ([F.sub.45,54 = 1.18; p < 0.28).
Several goodness-of-lit tests were performed for the model specified in Equation 2. The D'Agostino-Pearson test (D'Agostino and Pearson 1973; D'Agostino et al. 1990) was used to confirm the normality normality, in chemistry: see concentration. of the residuals. The combined residuals were normally distributed ([[chi square chi square (kī),
n a nonparametric statistic used with discrete data in the form of frequency count (nominal data) or percentages or proportions that can be reduced to frequencies. ].sub.2] = 0.17, p = 0.92), as were the predicted random effects Random effects can refer to:
n theoretical assumption that a given therapy will have results not statistically different from another treatment.
n ([[chi square].sub.3] = 1.58, p = 0.66), suggesting that the model's functional form was correctly specified. We added second- through fourth-order polynomials of the fitted values to the model and found them to be jointly insignificant. Finally, the Breusch-Pagan Lagrange multiplier multiplier
In economics, a numerical coefficient showing the effect of a change in one economic variable on another. One macroeconomic multiplier, the autonomous expenditures multiplier, relates the impact of a change in total national investment on the nation's total test (Breusch and Pagan 1980) and the Hausman test The Hausman test is a test in econometrics named after Jerry Hausman. The test evaluates the significance of an estimators versus an alternative estimator.
If the linear model (Hansman 1978) were performed to evaluate the efficiency and consistency, respectively, of the mixed-effects model. The Breusch-Pagan test In statistics, the Breusch-Pagan test is used to test for heteroskedasticity in a linear regression model. It tests whether the estimated variance of the residuals from a regression are dependent on the values of the independent variables. rejected OLS in favor of a mixed-effects specification ([[chi square].sub.1] = 54.47, p < 0.0001), while the Hausman test failed to reject the null hypothesis that the individual effects were uncorrelated with the other regressors. Thus, the mixed-effects model appeared to be favored over pooled OLS.
Though not entirely arbitrary, we recognize that some researchers may not agree with the lag structure used in this research. We also recognize that some may criticize our decision to exclude Switzerland or the monetary conversion rates we used. Because of these concerns, we elected to perform sensitivity analyses around several of the assumptions made in our model.
First, we evaluated the impact of excluding Spain on the base model estimates. Excluding Spain from the sample had no appreciable ap·pre·cia·ble
Possible to estimate, measure, or perceive: appreciable changes in temperature. See Synonyms at perceptible. effect on any of our findings. Second, we evaluated the impact of using GDP PPP or market exchange rates instead of the OECD PPP exchange rates on the base model estimates. While doing so, we also evaluated the impact of including Switzerland on our results. (The primary reason for Switzerland's exclusion was the lack of OECD PPP exchange rates in 1985.) When using the GDP PPP exchange rates, the estimate for the main effect of GDP was larger than that in our base model, while the estimates for pharmaceutical and nondrug health care consumption were somewhat attenuated Attenuated
Alive but weakened; an attenuated microorganism can no longer produce disease.
Mentioned in: Tuberculin Skin Test
having undergone a process of attenuation. . However, the significance of the parameter estimates was not greatly changed. Third, we evaluated the effects of different lag structures on our results. The measure of tobacco consumption we used in our base model was not available for all countries (e.g., Germany, Italy, the United States) after 1980. Thus, when performing sensitivity analyses around the lag structure of our model, tobacco consumption was measured in expenditures (U.S. dollars) per capita. Three scenarios were considered: (i) 1985 economic/age distribution data (GDP85, PHARM85, HEALTH85, AGEDIST85) and 1980 lifestyle data (ALCOHOL80, SMOKE80, BUTTERR80, VEG80) to provide a comparison with our base model; (ii) 1985 economic, age distribution, and lifestyle data; and (iii) 1990 economic, age distribution, and lifestyle data. In each of the three scenarios, Spain was included in the sample, Switzerland was excluded, and economic data were converted into U.S. dollars using OECD PPP exchange rates. The results were generally consistent with those presented in Table 2.
Table 1. Variable Definitions and Descriptive Statistics (a) Variable Definition Continuous variables [LE97.sub.40M] Years of life expectancy for males at age 40, 1997 [LE97.sub.60M] Years of life expectancy for males at age 60, 1997 [LE97.sub.65M] Years of life expectancy for males at age 65, 1997 [LE97.sub.40F] Years of life expectancy for females at age 40, 1997 [LE97.sub.60F] Years of life expectancy for females at age 60, 1997 [LE97.sub.65F] Years of life expectancy for females at age 65, 1997 GDP85 Gross domestic product per capita, 1985 U.S. dollars PHARM85 Pharmaceutical expenditures per capita, 1985 U.S. dollars HEALTH85 Health expenditures (not including pharmaceuticals) per capita, 1985 U.S. dollars AGEDIST85 Percentage of population 65 years of age and older, 1985 SMOKE80 Grams of tobacco consumed annually per capita by persons age 15 or older, 1980 ALCOHOL80 Liters of ethyl alcohol consumed annually per capita by persons age 15 or older, 1980 BUTTER80 Kilograms of butter consumed annually per capita, 1980 VEG80 Kilograms of fruits and vegetables consumed annually per capita, 1980 Discrete variables MALE Dummy variable taking on value of 1 if dependent variable was life expectancy for males and 0 otherwise AGE60 Dummy variable taking on value of 1 if dependent variable was life expectancy at age 60 and 0 otherwise AGE65 Dummy variable taking on value of 1 if dependent variable was life expectancy at age 65 and 0 otherwise SPAIN Dummy variable taking on value of 1 if country was Spain and 0 otherwise Standard Variable Mean Deviation Minimum Maximum Continuous variables [LE97.sub.40M] 36.55 1.00 34.70 38.10 [LE97.sub.60M] 19.17 0.82 17.40 20.10 [LE97.sub.65M] 15.46 0.76 13.70 16.30 [LE97.sub.40F] 41.71 1.11 39.50 43.50 [LE97.sub.60F] 23.38 0.98 21.50 25.20 [LE97.sub.65F] 19.19 0.91 17.40 20.80 GDP85 11,719.11 2751.13 6105.00 16,976.00 PHARM85 171.26 72.20 73.21 400.34 HEALTH85 1,077.71 461.27 260.48 1938.25 AGEDIST85 13.02 2.08 10.10 17.19 SMOKE80 2,727.33 530.18 1492.00 3588.00 ALCOHOL80 11.99 3.82 5.30 20.60 BUTTER80 6.01 4.05 0.50 13.90 VEG80 187.01 66.20 70.90 362.20 Discrete variables MALE AGE60 AGE65 SPAIN (a) Descriptive statistics apply to the sample of 19 countries. Independent variables included in the sensitivity analysis of model lag structure were measured in 1980, 1985, or 1990. Table 2. Regression Parameter Estimates: Life Expectancy Regression Including Age Distribution Variable Coefficient Standard Error (d) CONSTANT 3.726 (a) (0.004) MALE -0.132 (ac) (0.004) AGE60 -0.579 (ac) (0.003) AGE65 -0.777 (ac) (0.003) MALE x AGE60 -0.067 (ac) (0.003) MALE x AGE65 -0.085 (ac) (0.005) ln GDP85 -0.033 (0.058) AGE60 x ln GDP85 0.031 (a) (0.009) AGE65 x ln GDP85 0.056 (a) (0.013) ln PHARM85 0.027 (b) (0.014) AGE60 x ln PHARM85 0.019 (b) (0.009) AGE65 x ln PHARM85 0.021 (b) (0.011) ln HEALTH85 0.036 (0.030) ln AGEDIST85 -0.073 (a) (0.032) ln SMOKE80 -0.067 (a) (0.026) AGE60 x ln SMOKE80 -0.036 (a) (0.015) AGE65 x ln SMOKE80 -0.045 (a) (0.020) ln ALCOHOL80 -0.019 (0.019) MALE x ln ALCOHOL80 -0.034 (b) (0.018) ln BUTTER80 0.022 (a) (0.010) ln VEG80 0.081 (a) (0.023) MALE x ln VEG80 0.030 (b) (0.017) AGE60 x ln VEG80 0.028 (a) (0.010) AGE65 x ln VEG80 0.041 (a) (0.013) Excluding Age Distribution Variable Coefficient Standard Error CONSTANT 3.726 (a) (0.004) MALE -0.132 (ac) (0.004) AGE60 -0.579 (ac) (0.003) AGE65 -0.777 (ac) (0.003) MALE x AGE60 -0.067 (ac) (0.003) MALE x AGE65 -0.085 (ac) (0.005) ln GDP85 -0.008 (0.047) AGE60 x ln GDP85 0.031 (a) (0.009) AGE65 x ln GDP85 0.056 (a) (0.013) ln PHARM85 0.009 (0.019) AGE60 x ln PHARM85 0.019 (b) (0.009) AGE65 x ln PHARM85 0.021 (b) (0.011) ln HEALTH85 0.023 (0.029) ln AGEDIST85 -- -- ln SMOKE80 -0.075 (a) (0.018) AGE60 x ln SMOKE80 -0.036 (a) (0.015) AGE65 x ln SMOKE80 -0.045 (a) (0.020) ln ALCOHOL80 0.004 (0.019) MALE x In ALCOHOL80 -0.034 (b) (0.018) ln BUTTER80 0.019 (a) (0.007) ln VEG80 0.081 a (0.035) MALE x ln VEG80 0.030 (b) (0.017) AGE60 x ln VEG80 0.028 (a) (0.010) AGE65 x ln VEG80 0.041 (a) (0.013) (a) Significantly different from 0, p < 0.05, two tailed. (b) Significantly different from 0, p < 0.10, two tailed. (c) To be interpreted as an elasticity, this must be converted using the formula: E = [e.sup.[beta]] - 1 (Kennedy 1998). (d) Significance tests were performed using jackknife standard errors, reported in parentheses. Table 3. Additional Activity Required to Increase the Life Expectancy by One Year Males Males Males Activity (units) Age 40 Age 60 Age 65 1985 pharmaceutical 101.33% 113.40% 134.76% expenditures (173.54) (194.21) (230.79) (U.S. $/capita) 1980 tobacco -40.84% -50.65% -57.75% consumption (1113.84) (1381.39) (1575.03) (g/capita) 1980 fruit/vegetable 24.65% 37.53% 42.55% consumption (46.10) (70.18) (79.57) (kg/capita) 1980 butter 124.36% 237.11% 294.01% consumption (7.47) (14.25) (17.67) (kg/capita) 1980 alcohol -51.62% -98.42% -122.04% consumption (-6.19) (-11.80) (-14.63) (liters/capita) Females Females Females Activity (units) Age 40 Age 60 Age 65 1985 pharmaceutical 88.80% 92.98% 108.56% expenditures (152.08) (159.24) (185.92) (U.S. $/capita) 1980 tobacco -35.78% -41.53% -46.53% consumption (975.84) (1132.66) (1269.03) (g/capita) 1980 fruit/vegetable 29.60% 39.24% 42.71% consumption (55.36) (73.38) (79.87) (kg/capita) 1980 butter 108.98% 194.42% 236.87% consumption (6.55) (11.68) (14.24) (kg/capita) 1980 alcohol -126.18% -225.11% -274.27% consumption (-15.13) (-26.99) (-32.88) (liters/capita)
(1) All the studies discussed and compared herein analyzed health data at some aggregated macroeconomic level (such as state- or country-wide). While there are clinical and epidemiological studies An Epidemiological study is a statistical study on human populations, which attempts to link human health effects to a specified cause. that have examined health outcomes al the individual level, these rarely yield macroeconomic policy implications, which are a major focus of the current study.
(2) See Cochrane, St. Leger, and Moore (1978), Leu Leu leucine.
leucine. (1986), Wolfe (1986), Wolfe and Gabay (1987), Zweifel and Ferrari (1992), Babazono and Hillman (1994), Frech and Miller (1999), and Miller and Frech (2000).
(3) We have described only the methodological differences between Peltzman (1987) and Miller and Frech (2000). The Babazono and Hillman (1994) study was "'seriously flawed flaw 1
1. An imperfection, often concealed, that impairs soundness: a flaw in the crystal that caused it to shatter. See Synonyms at blemish.
2. ," according to Miller and Frech (2000), so we will mention only that the study found pharmaceutical expenditures to be an insignificant determinant of infant mortality (hardware) infant mortality - It is common lore among hackers (and in the electronics industry at large) that the chances of sudden hardware failure drop off exponentially with a machine's time since first use (that is, until the relatively distant time at which enough mechanical in a sample of OECD countries. The other studies mentioned in the paragraph (Lichtenberg 1996, 1998) used disease as the unit if observation and are not directly comparable.
(4) Frech and Miller (1999, p. 54) refer to this as the "'endogeneity of spending" effect, which is related to what has been called the Sisyphus effect" (see Zweifel and Ferrari 1992). That is, if both pharmaceutical spending and life expectancy are functions of age distribution, then omitting the age distribution from a regression of life expectancy on pharmaceutical expenditures causes an omitted-variable bias. They argue that their regressions do not suffer from this issue.
(5) These are the same data used by Miller and Frech (2000), except our data are more current.
(6) While it was not formally tested, it could be argued that the set of factors affecting life expectancy at birth are different for those affecting life expectancy in adulthood. Excluding life expectancy at birth from this study may have been the determining factor in allowing us to pool data over the adult ages 40, 60, and 65 years.
(7) For the pooled regression, we used a sequential modeling The sequential model (also known as the KNF model) is a theory that describes co-operativity of proteins subunits. Overview
This model suggests that the subunits of multimeric proteins have two conformational states. The binding of the ligand causes conformational change. procedure to determine which interactions between the continuous covariates and indicator variables for age gender strata should be included in the final model.
(8) Residual maximum likelihood produces unbiased estimates of the conditional variance In statistics, conditional variance is a special form of the variance. If we have a conditional distribution Y|X the conditional variance is defined as
where components by correcting the usual maximum likelihood estimator for the degrees-of-freedom loss associated with estimating the conditional mean. The usual maximum likelihood variance estimates are biased in small samples. See Patterson and Thompson (1971) for the theory and Brown and Prescott (1999) for applications to mixed models.
(9) Frech and Miller (1999) found a similar measure of the population age distribution to be insignificant. We suspect that the difference in significance may be attributed to the decreased sampling variability associated with the larger sample size afforded from pooling the data across age--gender strata.
(10) For example, the estimated number of dabs of life expectancy gained for females at age 40 was 0.027 x 365 x 41.71, where 41.71 was the average female life expectancy at that age. Similar calculations were performed for other age categories and for males.
(11) At age 40, the remaining number of years of expected life is large. Therefore, a small increase in the elasticity of pharmaceutical consumption would be expected to yield a large increase in the predicted gain in life expectancy. However, at more advanced ages the remaining life expectancy is sufficiently small sufficiently small - suitably small such that even a large increase in the elasticity would yield a small increase in the predicted gain. This result applies only to the sample-wide estimated parameters. The result ignores differences across countries in the percentage of drugs administered to each age-group and their relative effectiveness in each age-group of increasing life expectancy. This source of variability is unavailable in the OECD data and could not be incorporated into the analysis.
(12) This calculation ignores three things. First, there are slight differences in life expectancies across counties that should technically be taken into account. Second (and more important), the average sample-wide parameter estimates do not accurately capture differences in drug product mix within a country. Since different drugs produce different longevity effects, the calculation will underestimate life expectancy gains in a country that uses a higher (than average) percentage of drugs that enhance life expectancy. It will similarly overestimate o·ver·es·ti·mate
tr.v. o·ver·es·ti·mat·ed, o·ver·es·ti·mat·ing, o·ver·es·ti·mates
1. To estimate too highly.
2. To esteem too greatly. in a country that uses a higher percentage of drugs that do not enhance life expectancy. For example, antidepressants Antidepressants
Medications prescribed to relieve major depression. Classes of antidepressants include selective serotonin reuptake inhibitors (fluoxetine/Prozac, sertraline/Zoloft), tricyclics (amitriptyline/ Elavil), MAOIs (phenelzine/Nardil), and heterocyclics probably do not enhance life expectancy directly, so a country that consumes large relative quantities of antidepressants will tend to have overestimated life expectancy gains from increasing drug consumption. Finally, the calculation ignores price differentials across countries for individual drugs (although this is partially mitigated by indexing aggregate drug expenditures to the U.S. dollar). See, for example, Danzon and Furukawa (2003) and Danzon and Ketcham (2003).
(13) We have excluded a discussion of the effects of wealth and nonpharmaceutical health care expenditures. Our estimates for the marginal effect of wealth (GDP85) and nonpharmaceutical health care expenditures (HEALTH85) were insignificant. The effect of wealth was highly positively correlated with and swamped "Swamped" is the seventeenth episode of The Batman's second season. It originally aired in North America on June 11, 2005. Plot Synopsis
Killer Croc, a half-man, half reptile plans to submerge all of Gotham in water in order to facilitate his plundering of the city. by the effect of butter consumption (BUTTER80). This suggests that richer countries consume more butter--a reasonable result. The insignificant effect of nonpharmaceutical health care expenditures is consistent with the findings of Miller and Frech (2000).
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n a degenerative disease of the liver in which hepatic tissue is replaced with connective tissue, commonly a result of chronic alcoholism. See jaundice. : A case-control study case-control study,
n an investigation employing an epidemiologic approach in which previously existing incidents of a medical condition are used in lieu of gathering new information from a randomized population. . Journal of Clinical Epidemiology 46:601-7.
Cremer, Helmuth, Jean-Marie Lozachmeur, and Pierre Pestieau Pierre Pestieau (born 9 October, 1943) is a Belgian economist and since 1975 professor of economics at the Université de Liège at the Research Center on Public and Population Economics. . 2004. Social security, retirement age and optimal income taxation. Journal of Public Economics 88:2259-81.
D'Agostino, Ralph B., Albert Belanger, and Ralph B. D'Agostino, Jr. 1990. A suggestion for using powerful and informative tests of normality. American Statistician 44:316-21.
D'Agostino, Ralph B., and Egon S Egon can refer to:
Danzon, Patricia M., and Michael F. Furukawa. 2003. Prices and availability of pharmaceuticals: Evidence from nine countries. Health Affairs, Web Exclusive W3, pp. W3 521-36.
Danzon, Patricia M., and Jonathan D. Ketcham. 2003. Reference pricing of pharmaceuticals for Medicare: Evidence from Germany, the Netherlands and New Zealand New Zealand (zē`lənd), island country (2005 est. pop. 4,035,000), 104,454 sq mi (270,534 sq km), in the S Pacific Ocean, over 1,000 mi (1,600 km) SE of Australia. The capital is Wellington; the largest city and leading port is Auckland. . NBER NBER National Bureau of Economic Research (Cambridge, MA)
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Gage, Timothy B., and Kathleen O'Connor Kathleen O'Connor (born 30 July, 1934) is a retired Irish National School teacher from County Kerry, and a former Clann na Poblachta TD.
She was elected to Dáil Éireann as the Clann na Poblachta TD for the Kerry North constituency in the by-election on 29 February, 1956, . 1994. Nutrition and the variation in level and age patterns of mortality. Human Biology Human biology is an interdisciplinary academic field of biology, biological anthropology, and medicine which focuses on humans; it is closely related to primate biology, and a number of other fields. 66:77-103.
Gilman Alfred G., Theodore W. Rail, Alan S. Nies, and Palmer Taylor. 1990. Goodman and Gilman, the pharmacological Pharmacological
Referring to therapy that relies on drugs.
Mentioned in: Pain Management
pertaining to pharmacology. basis of therapeutics therapeutics
Treatment and care to combat disease or alleviate pain or injury. Its tools include drugs, surgery, radiation therapy, mechanical devices, diet, and psychiatry. . New York: Pergamon Press.
Gradstein, Mark, and Michael Kaganovich. 2004. Aging population and education finance. Journal of Public Economics 88:2469-85.
Greene, William H. 2000. Econometric analysis. 2nd edition. Upper Saddle River Saddle River may refer to:
In 1913, law professor Dr. .
Hausman, Jerry. 1978. Specification tests in econometrics. Econometrica 46:1251-71.
Huber, Peter J. 1967. The behavior of maximum likelihood estimates under non-standard conditions. In Proceedings of the Fifth Annual Berkeley Symposium on Mathematical Statistics Mathematical statistics uses probability theory and other branches of mathematics to study statistics from a purely mathematical standpoint.
Mathematical statistics is the subject of mathematics that deals with gaining information from data. and Probability, volume 1, edited by Lucien M. LeCam and Jerzy Neyman ''This article or section is being rewritten at
Jerzy Neyman (April 16, 1894 – August 5, 1981), born Jerzy Spława-Neyman, was a Polish-American mathematician. . Berkeley: University of California Press "UC Press" redirects here, but this is also an abbreviation for University of Chicago Press
University of California Press, also known as UC Press, is a publishing house associated with the University of California that engages in academic publishing. , pp. 221-33.
Kempthorne, Oscar. 1952. The design and analysis of experiments. New York: John Wiley & Sons.
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n. pl. his·tol·o·gies
1. The anatomical study of the microscopic structure of animal and plant tissues.
2. The microscopic structure of tissue. types of lung cancer lung cancer, cancer that originates in the tissues of the lungs. Lung cancer is the leading cause of cancer death in the United States in both men and women. Like other cancers, lung cancer occurs after repeated insults to the genetic material of the cell. : A meta-analysis. Lung Cancer 31:139-48.
Klatsky, Arthur L., M. A. Armstrong, Gary D. Friedman, and Robert A. Hiatt. 1988. The relations of alcoholic beverage alcoholic beverage
Any fermented liquor, such as wine, beer, or distilled liquor, that contains ethyl alcohol, or ethanol, as an intoxicating agent. When an alcoholic beverage is ingested, the alcohol is rapidly absorbed in the stomach and intestines because it does not use to colon and rectal cancer Rectal Cancer Definition
The rectum is the portion of the large bowel that lies in the pelvis, terminating at the anus. Cancer of the rectum is the disease characterized by the development of malignant cells in the lining or epithelium of the rectum. . American Journal of Epidemiology 128:1007-15.
Krebs-Smith, Susan M., Linda E. Cleveland, Rachel Ballard-Barbash, et al. 1997. Characterizing food intake pattems of American adults. American Journal of Clinical Nutrition Clinical nutrition
The use of diet and nutritional supplements as a way to enhance health prevent disease.
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Krebs-Smith, Susan M., Jerianne Heimendinger, Blossom H. Patterson, et al. 1995b. Psychosocial psychosocial /psy·cho·so·cial/ (si?ko-so´shul) pertaining to or involving both psychic and social aspects.
Involving aspects of both social and psychological behavior. factors associated with fruit and vegetable consumption. American Journal of Health Promotion 10:98-104.
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MacKinnon, James G., and Halbert White. 1985. Some heteroskedasticity-consistent covariance matrix In statistics and probability theory, the covariance matrix is a matrix of covariances between elements of a vector. It is the natural generalization to higher dimensions of the concept of the variance of a scalar-valued random variable. estimators with improved finite sample properties. Journal of Econometrics 29:305-25.
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Patterson, H. D., and Robin Thompson Robin Thompson (b.1931-d.2003) was an rugby union international for Ireland and a former British and Irish Lions captain.
Robin Thompson won two Ulster Schools Senior Cup medals with RBAI (Royal Belfast Academical Institution) and played for Ulster Schools. . 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58:545-54.
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n a statistical approach used to evaluate multiple variables.
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intensity, intensity level, strength - the amount of energy transmitted (as by acoustic or electromagnetic radiation); "he adjusted the intensity of the sound"; "they measured the of effects of ethanol. Alcoholism alcoholism, disease characterized by impaired control over the consumption of alcoholic beverages. Alcoholism is a serious problem worldwide; in the United States the wide availability of alcoholic beverages makes alcohol the most accessible drug, and alcoholism is : Clinical Experimental Research 17:1112-7.
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Zellner, Arnold. 1962. An efficient method of estimating seemingly unrelated regression In econometrics, seemingly unrelated regression (SUR), model developed in Zellner (1962), is a technique for analyzing a system of multiple equations with cross-equation parameter restrictions and correlated error terms. and tests for aggregation bias. Journal of the American Statistical Association Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association (JASA) has long been considered the premier journal of statistical science. 57:348-68.
Zhang, Junsen, Jie Zhang, and Ronald Lee Ronald Lee is a Canadian Romani writer, linguist and activist.
He began to work with the Canadian Roma as an activist in 1965, through the Kris Romani (Romani internal judicial assembly) trying to get a better understanding between Roma and non-Roma, to combat . 2001. Mortality decline and long run economic growth. Journal of Public Economics 80:485-507.
Zweifel, Peter, and Mateo Ferrari. 1992. Is there a Sisyphus syndrome in healthcare? In Health economics worldwide: Developments in health economics and public policy series 1, edited by Peter Zweifel and H. E. Frech, IlL Amsterdam: Kluwer, pp. 311-30.
James W. Shaw, * William C. Horrace, ([dagger]) and Ronald J. Vogel ([double dagger double dagger
A reference mark () used in printing and writing. Also called diesis.
Noun 1. ])
* Tobacco Control Research Branch, Behavioral Research Program. National Cancer Institute, Bethesda, MD 20892, USA: E-mail firstname.lastname@example.org.
([dagger]) Department of Economics, Syracuse University Syracuse University, main campus at Syracuse, N.Y.; coeducational; chartered 1870, opened 1871. Syracuse is noted for its research programs in government and industry; facilities include the Center for Science and Technology, the Newhouse Communications Center, and , 426 Eggers Eggers may refer to:
([double dagger]) Center for Health Outcomes and PhammcoEconomic Research, College of Pharmacy A college of pharmacy generally refers to a tertiary educational institution (or part of such an institution) which is involved in the education of future pharmacists and pharmaconomists. . University of Arizona (body, education) University of Arizona - The University was founded in 1885 as a Land Grant institution with a three-fold mission of teaching, research and public service. , Tucson, AZ 85721, USA: E-mail email@example.com.
We thank Julie Hotchkiss and several anonymous referees for helpful comments and discussions. The support of the Merck Company Foundation and the Office of the Vice Chancellor vice chancellor
n. Abbr. VC
1. A deputy or an assistant chancellor in a university.
2. A deputy to or a substitute for a head of state or an official bearing the title chancellor.
3. of Syracuse University is gratefully acknowledged.
Received December 2003; accepted October 2004.