Survival in Academy AwardWinning Actors and Actresses

Social status is a consistent, powerful, and widespread determinant of death rates. The association between high status and low mortality has appeared throughout the world, has persisted for more than a century, and extends to diverse illnesses (1-4). Uncovering the mechanisms by which external factors (such as income and level of education) influence biological processes (such as the endocrine and immune systems) represents a major challenge for health scientists and a core issue for public policy. Research is difficult because many determinants of social status are closely interrelated, such as education with income. Animal models are unrealistic, aside from some primate studies, and randomized trials are impractical, aside from a few lottery winners. Movie stars are an interesting group for the study of social status and health outcomes. First, performers can earn an enormous income without a substantial amount of education. Second, celebrity publicity is often boosted by more sustained promotion than is the attention given to politicians, singers, athletes, and other luminaries. Third, the lifestyles of movie stars can be notorious for extremes of competition, leisure, and excess. Fourth, they are highly visible public figures whose birthdays and deaths are regularly reported. Finally, big breaks to stardom are often haphazard and heavily dependent on chance. Indeed, some pundits suggest that being nominated for an Academy Award is due to talent whereas winning one is due to luck. We wondered whether the Academy Awards might shed light on how social status affects all-cause mortality. We chose this event because it generates substantial attention; for example, in 1996, almost as many Americans watched the Academy Awards as voted in the presidential election (5, 6). Moreover, the event is televised to more than 100 countries and has an estimated viewing audience of more than 1 billion people, making its broadcast one of the most widely shared current human experiences. Our theory was that winners would gain an important increase in their status but no increase in their formal education. The primary hypothesis was that the survival of winners would differ from that of less recognized performers. Methods Academy of Motion Picture Arts and Sciences Membership in the Academy of Motion Picture Arts and Sciences is limited by invitation from the Board of Governors to those with movie distinctions, currently totals about 6000 persons, and has 13 branches (for example, an actors' branch that includes about 1000 persons). The annual awards selection process is complex and is described in detail elsewhere (www.oscars.org). In brief, each December the Academy compiles a list of films that are eligible for an award; each cast member in these films is eligible to be nominated for an acting award. In January, the list is sent to all Academy members and those in the actors' branch are invited to nominate five individuals in each of four acting categories. In February, the nominations are tabulated, the top five nominations in each category are identified, and all Academy members vote for one person in each category. The Academy Award goes to the person with the most votes. Selection of Performers We identified every person nominated for an Academy Award for acting. To do so, we obtained a full listing of all actors and actresses, along with the film in which they performed, from the Academy. The selection interval spanned from the inception of the Academy Awards to the present (72 years). For each performer, we also identified another cast member who performed in the same film as the nominee, was the same sex, and was born in the same era. This ensured that both were alive, working, prevailing in casting calls, winning good movie roles, and eligible for a nomination. In cases where several matches were possible, we picked a same-sex cast member by formally checking dates and choosing the one whose birth date was closest to that of the nominated performer. Example of Matching Process For clarity, we provide an arbitrary example of this matching process to illustrate the underlying method. Kate Winslet was nominated for the leading actress award in 1997 for her performance as the character Rose DeWitt Bukater in the movie Titanic. Five other women were cast members in that film, including Suzy Amis, who performed as the character Lizzy Calvert. Kate Winslet was born in 1975, and Suzy Amis was born in 1961; these two people had a 14-year difference in age. The other four women, Kathy Bates (born in 1948), Frances Fisher (born in 1952), Jenette Goldstein (born in 1960), and Gloria Stuart (born in 1910), all had an age difference greater than 14 years compared to Kate Winslet. Hence, Suzy Amis was selected as the match for Kate Winslet in this film. Overall Matching Process We repeated this matching process for all years and all four categories. No performer was excluded from analysis, and no performer was dropped because of missing data. Matches were not possible in some cases; for example, in 1951, Katharine Hepburn was nominated in a movie in which no other woman appeared. Otherwise, the matching process was uncomplicated and complete. In the matching process, we did not attempt to balance ethnicity, past experience, or future accomplishment of the performers. As a consequence, the person who performed opposite the nominee could previously have achieved or subsequently achieve greater recognition. Such potential misclassification might cause analyses to underestimate the differences attributable to winning an Academy Award. Classification of Success Many performers were eligible for inclusion on more than one occasion; for example, Katharine Hepburn won four Academy Awards during her career. We counted each person only once by categorizing performers according to their highest achievement. The three groups were termed winners (those who were nominated for and won at least one Academy Award), nominees (those who were nominated but never won an Academy Award), and controls (those who were never nominated and never won). For example, Jack Nicholson was classified as a winner because he had three wins, Richard Burton was a nominee because he was nominated seven times but never won, and Lorne Greene was a control because he was never nominated. Statistical tests based on counting performances rather than performers gave more extreme results and are not shown. Determination of Death Rates We collected data on each person's date of birth and death from the Internet through two databases: the All Movie Guide (www.allmovie.com) and the Internet Movie Database (www.imdb.com). Each source covers more than 100 000 movies, is updated continually, and undergoes extensive public scrutiny. Data were checked by consulting written publications, and conflicts were resolved by accepting information from printed sources over that found on the Internet (7-10). No birth dates were missing. Causes of death were sought by using the same methods and by inquiry to the National Film Information Service. In addition, we checked Internet sources that listed people who have sometimes been mistakenly rumored dead. People who were not reported dead were presumed to be alive. Determination of Personal Characteristics Additional data were retrieved by using methods similar to those described above, with the following exceptions. Determination of whether the person was born in the United States and whether the person had changed his or her name from the given name was made by using the All Movie Guide. Missing data were assumed to indicate the United States as the country of origin and no change in name. Ethnicity was determined by searching Internet sources and by viewing selected films. Although performers try to avoid being typecast, we classified each performer's main film genre according to that listed first by the All Movie Guide. Similarly, although the ratings given in film reviews are debatable, the All Movie Guide five-star ratings were considered to indicate high quality. Setting Time-Zero Research on the natural history of any condition requires identifying people at an early and uniform point in their course. Unstable definitions of time-zero might otherwise lead to distorted prognoses, an error called lead-time bias (11, 12). The baseline analysis in this study set time-zero as the performer's day of birth to conform to the accepted measure of longevity (13). Other analyses were conducted to test robustness. In the first of these, time-zero was set as the day on which each performer's first film was released. In the second, time-zero was set as the day of each performer's 65th birthday; therefore, all performers who died before 65 years of age were excluded. In the third analysis, time-zero was set as each performer's 50th birthday; all performers who died before 50 years of age were therefore excluded. Reverse Causality Survival analysis also requires avoiding artifacts related to survivor treatment-selection bias: That is, persons who are destined to live longer have more opportunity to gain special treatments, thereby potentially creating an illusory link between special treatments and longer survival (14-16). One way to mitigate this bias is to use time-dependent covariates in a proportional hazards model, although doing so can produce a different bias in the opposite direction (17, 18). We analyzed survival both with and without a time-dependent step function for victory. In addition, we analyzed survival after adjusting for total films and total nominations in a person's career to see whether winning an Academy Award was distinct from other exposures that can accumulate over time. Unmeasured Confounding We used three strategies to test whether the survival associated with winning an Academy Award might be due to hidden confounding. First, we conducted analyses both with and without adjustments for baseline characteristics, on the rational