TY - JOUR
AU - Altekruse, Sean F.
AB - Abstract Background The accuracy of cancer survival statistics relies on the quality of death linkages and follow-up information collected by population-based cancer registries. Methodological issues on survival data by race-ethnicity in the United States, in particular for Hispanics and Asians, have not been well studied and may undermine our understanding of survival disparities. Methods Based on Surveillance, Epidemiology, and End Results (SEER)-18 data, we analyzed existing biases in survival statistics when comparing the four largest racial-ethnic groups in the United States, whites, blacks, Hispanics and Asians. We compared the “reported alive” method for calculation of survival, which is appropriate when date of last alive contact is available for all cases, with the “presumed alive” method used when dates of last contact are unavailable. Cox regression was applied to calculate the likelihood of incomplete follow-up (those with less than 5 years of vital status information) according to racial-ethnic group and stage of diagnosis. Finally, potentially missed deaths were estimated based on the numbers of cases with incomplete follow-up for highly fatal cancers. Results The presumed alive method overestimated survival compared with the reported alive method by as much as 0.9–6.2 percentage points depending on the cancer site among Hispanics and by 0.4–2.7 percentage points among Asians. In SEER data, Hispanics and Asians are more likely to have incomplete follow-up than whites or blacks. The assumption of random censoring across race-ethnicity is not met, as among non-white cases, those who have a worse prognosis are more likely to have incomplete follow-up than those with a better prognosis (P < .05). Moreover, death ascertainment is not equal across racial-ethnic groups. Overall, 3% of cancer deaths were missed among Hispanics and Asians compared with less than 0.5% among blacks and whites. Conclusions Cancer survival studies involving Hispanics and Asians should be interpreted with caution because the current available data overtly inflates survival in these populations. Censoring is clearly nonrandom across race-ethnicity meaning that findings of Hispanic and Asian survival advantages may be biased. Problematic death linkages among Hispanics and Asians contribute to missing deaths and overestimated survival. More complete follow-up with at least 5 years of information on vital status as well as improved death linkages will decisively increase the validity of survival estimates for these growing populations. The accurate estimation of cancer survival across racial and ethnic groups is an important aspect of cancer surveillance. It relies on completeness of follow-up to identify cancer deaths. In cancer surveillance, there are two different methods of follow-up. Active follow-up is defined as any type of follow-up that involves contact with the patient, his/her next-of-kin or his/her physician to ascertain vital status. Passive follow-up involves obtaining follow-up data through linkages without contact with the patient or his/her physician. Cancer registration in the United States is administered by one of two programs that use different types of follow-up. The first, the National Cancer Institute Surveillance, Epidemiology and End Results (SEER), covers 28% of the US population and employs resource intensive activities to capture the last date of contact for each cancer case. Cases are matched to national and statewide databases such as the National Death Index, Social Security, Medicare and Medicaid, hospital discharge, as well as records of contact with physicians, pathology labs, and hospital registries. SEER registries also perform data linkages with voter registration and driver license data, and some use information obtained from research studies to supplement registry data. SEER standards require that their registries report vital status and date of last contact that is current within 22 months of the date of their annual data submission for a minimum of 95% of all registered cancer patients, living and deceased (1). The follow-up information can be from either linkages or direct contact with patients, so SEER registries perform a combination of active and passive follow-up. In spite of this, in this paper we refer to the follow-up carried out by SEER as “active” follow-up because a date of last contact is available for all cases. Because of the availability of this last date of contact, the traditional method of survival analysis with SEER data is the “reported or documented alive” method, which relies on the reported date of death and the date of alive last contact if there is no date of death available. This method censors patients at the date of last alive contact. Researchers who perform survival analyses often use SEER data because of its high quality. Moreover, this was the only source of population level survival data available until recently. The second US cancer surveillance program is the National Program for Cancer Registries (NPCR). The NPCR was established by the Centers for Disease Control and Prevention and covers the balance of the US population. NPCR registries conduct passive follow-up by matching cancer cases with in-state death lists and the National Death Index. However, NPCR registries do not systematically collect a date of last contact for living cases. As a result, NPCR survival statistics are calculated using what is known as the “presumed alive” method. This method assumes that if a person is not found to be dead, then he/she is alive on the most recent date covered by the National Death Index. Presumed alive survival statistics appear less commonly in the literature (2–5), partly because NPCR survival data have only recently become available. Both Centers for Disease Control and Prevention and the North American Association of Central Cancer Registries now recognize passive follow-up as a suitable approach for some types of population-based survival calculations (6). In the United States, Hispanics, and Asians and Pacific Islanders (APIs) are the fastest growing racial and ethnic groups, comprising 22% of the US population (7). The effects of active versus passive follow-up and their corresponding methods of estimating cancer survival have not been thoroughly examined among these groups. We hypothesized that active and passive follow-up procedures are not equally effective across the four major US racial-ethnic groups: whites, blacks, Hispanics, and APIs. In this study, five-year survival proportions were obtained and compared for these groups using the two described methods, reported alive and the presumed alive, that is, by considering the date of last contact (active follow-up conducted by SEER) and by assuming the patient was alive if not reported dead within 5 years of diagnosis (passive follow-up conducted in NPCR registries), respectively. In addition, the completeness of SEER “active” follow-up was compared across racial-ethnic groups (whites, blacks, Hispanics, and APIs) by stage at diagnosis and by racial-ethnic subgroups (eg, Mexican, Chinese, and so on) to assess potential biases across racial-ethnic populations. Methods Study Data De-identified data from 18 SEER registries were used for the analyses (November 2011 submission) (8). Active follow-up extended to December 31, 2009. Non-Hispanic white, non-Hispanic black, non-Hispanic API, and Hispanic subjects were categorized into mutually exclusive groups as whites, blacks, APIs, and Hispanics, respectively. Hispanics were identified via the North American Association of Central Cancer Registries Hispanic Identification Algorithm v2.2.1, regardless of race (9). Non-Hispanic cases were classified according to primary race. American Indian/Alaska Natives were not included in the analyses because of small numbers. The SEER site classification and the SEER historic stage variables were used to categorize cancer site and stage (10,11). Because prostate cancer stage combined localized and regional cases, the final covariate was defined in three categories—local/regional, distant, and unknown stage. Cases identified solely by death certificates were excluded from the analyses. This study was approved by the University of Nevada Las Vegas Institutional Review Board. Statistical Analyses Several analyses were carried out to complete the study objectives. In the first analysis, cases diagnosed from 2000–2008 were used to calculate and compare five-year observed survival using both the reported alive and the presumed alive methods by racial-ethnic group. To calculate survival time based on the reported alive method, the SEER dates of last contact for cases with no reported death were used. To simulate a scenario of 100% passive follow-up for the presumed alive method, the date of last contact for non-dead cases was set to December 31, 2009. As a practical example illustrating the difference between the reported alive and the presumed alive methods, if a non-dead patient was diagnosed in 2003 and had a last date of alive contact at 4 years after diagnosis, he or she would be censored as alive at 4 years with the reported alive method, but would have contributed more than 5 years with the presumed alive method. Observed survival for the first primary cancer was age-adjusted using the international cancer survival standards (12). Survival was calculated for nine cancer sites including four leading sites (prostate, female breast, lung and bronchus, and colorectal) and five sites with less favorable prognoses. Cancer sites with less favorable prognoses were the esophagus, gallbladder, liver and intrahepatic bile duct, pancreas, and stomach. These diagnoses as well as lung and bronchus cancer all have an age-adjusted five-year observed survival rate of less than 25% (13). In the second analysis, we wanted to assess the completeness of vital status information in SEER registries, or in other words, whether SEER “active” follow-up was able to gather dates of last contact equally across the four racial-ethnic groups. We set a cutoff of 5 years (60 months) after diagnosis because 5 years is the conventional period for survival statistics. All the deceased regardless of their date of death and those alive with more than 5 years of follow-up were considered to have complete follow-up. If a non-dead patient did not have at least 5 years of information as alive, then he/she was considered to have incomplete follow-up or to be “lost to follow-up.” We included cases of leading cancer sites (prostate, female breast, lung and bronchus, and colon and rectum) diagnosed between 2000 and 2003. The years of diagnosis, 2000–2003, allowed cases to potentially contribute 5 years of follow-up. The distributions of these “lost to follow-up” cases were examined by racial-ethnic group. To study the influence of race-ethnicity and stage of diagnosis on incomplete follow-up, we used Cox regression to model the time to being lost to follow-up (14,15). In these analyses, being lost to follow-up was the event of interest and deaths before 60 months (5 years) were censored at the time of death. Deaths occurring after 60 months of follow-up and cases with a date of last alive contact more than 60 months after diagnosis were censored at 60 months. The Cox regression models were adjusted for age, stage at diagnosis, diagnosis year, SEER registry, cancer site, and gender and were run first for all racial-ethnic groups combined, and then separately by racial-ethnic group. Hazard ratios values greater than 1.0 indicate a greater likelihood of being lost to follow-up (having incomplete follow-up) in comparison to the reference group. In our final analysis, we wanted to ascertain whether SEER “active” follow-up was able to gather information on deaths equally across the four racial-ethnic groups and, within Hispanics and APIs, across the different subgroups they comprise. For this, the percent of deaths missed in the death linkage process was estimated by race-ethnic group based on the proportions of those lost to follow-up (according to the 60-months cutoff) of highly fatal diagnoses. We defined highly fatal diagnoses as distant stage cancers of four sites for which survival after 5 years was less than 3%: liver and intrahepatic bile duct, gallbladder, pancreatic, and lung and bronchus (13). This subset of cases provided enough observations in every racial-ethnic group for meaningful analysis. Cases with highly fatal cancer diagnoses between 2000 and 2003 classified as alive with last contact before 60 months were classified as “missed deaths,” on the assumption that cases diagnosed with these cancers and lost to active follow-up before 60 months were likely to be missed deaths regardless of race-ethnicity. We estimated the proportion of missed deaths based on the quotient of missed deaths over the sum of missed and recorded deaths before 60-months postdiagnosis. In addition to the major racial-ethnic groups (whites, blacks, Hispanics, and APIs), Mexican, Puerto Rican, Cuban, South and Central American, and other Hispanic subgroups were examined as were Chinese, Japanese, Hawaiian, Filipino, Korean, Vietnamese, South Asians, and other APIs. The South Asian subgroup included Indian, Pakistani, and Asian Indian categories. Results Between 2000 and 2008, there were 1471789 cases diagnosed with leading cancer sites (female breast, colorectal, lung and bronchus, and prostate) and 181515 cases diagnosed with less favorable prognosis cancers (esophagus, stomach, liver and intrahepatic bile duct, gallbladder, and pancreas). Survival proportions for all cancer sites and across all racial/ethnic groups were consistently higher using the presumed alive method rather than the reported alive method (Table 1). Survival changed less for whites and for blacks than for other groups. Among Hispanics, the net differences in percent survival between the two methods varied from 0.7 percentage points for prostate cancer to 6.2 for gallbladder cancer (P < .05). Among APIs, the differences ranged from 0.4 percentage points for prostate cancer to 2.7 for gallbladder cancer. For lung and bronchus, liver and intrahepatic bile duct, and pancreatic cancers among both Hispanics and APIs, and for gallbladder and stomach cancers among Hispanics only, differences in survival were statistically significant between the two methods. Table 1. Five-year survival by cancer site based on passive and active follow-up, SEER-18, 2000–2008*,† Diagnoses  Site  Race/Ethnicity  Active follow-up (reported alive method)  Passive follow-up (presumed alive method)  Survival difference (in percent points)  Common cancers  Colorectal  Whites  56.0 (55.7–56.2)  56.2 (56.0–56.5)  0.2  Blacks  46.2 (45.6–46.8)  46.7 (46.1–47.3)  0.5  Hispanics  53.6 (52.9–54.4)  54.8 (54.1–55.5)  1.2  APIs  59.8 (59.0–60.5)  60.9 (60.1–61.6)  1.1  Female breast  Whites  78.5 (78.3–78.6)  78.7 (78.5–78.8)  0.2  Blacks  65.8 (65.1–66.4)  66.1 (65.5–66.7)  0.3  Hispanics  75.9 (75.2–76.6)  76.8 (76.1–77.4)  0.9  APIs  81.9 (81.2–82.6)  82.4 (81.7–83.8)  0.5  Lung and bronchus  Whites  14.7 (14.5–14.9)  14.9 (14.7–15.1)  0.2  Blacks  10.6 (10.2–10.9)  10.9 (10.5–11.2)  0.3  Hispanics  14.2 (13.6–14.8)  16.1 (15.5–16.8)  1.9‡  APIs  16.0 (15.3–16.6)  18.1 (17.4–18.7)  2.1‡  Prostate  Whites  82.4 (82.3–82.6)  82.5 (82.4–82.7)  0.2  Blacks  75.2 (74.8–75.6)  75.5 (75.2–75.9)  0.3  Hispanics  81.7 (81.2–82.2)  82.4 (82.0–82.9)  0.7  APIs  84.9 (84.4–85.5)  85.3 (84.8–85.8)  0.4  Less favorable prognoses  Esophagus  Whites  15.4 (14.8–16.0)  15.6 (15.0–16.2)  0.2  Blacks  8.8 (7.7–10.0)  9.1 (8.0–10.3)  0.3  Hispanics  12.7 (10.9–14.5)  14.8 (13.0–16.7)  2.1  APIs  13.9 (11.6–16.5)  16.2 (13.8–18.8)  2.3  Gall bladder  Whites  15.2 (13.8–16.7)  15.8 (14.4–17.3)  0.6  Blacks  10.7 (8.3–13.4)  11.6 (9.2–14.4)  0.9  Hispanics  15.2 (13.0–17.4)  21.4 (19.2–23.7)  6.2‡  APIs  16.8 (13.6–20.4)  19.5 (16.2–23.0)  2.7  Liver and intrahepatic bile duct  Whites  11.4 (10.9–11.9)  11.7 (11.2–12.2)  0.3  Blacks  7.1 (6.2–8.0)  8.0 (7.0–9.0)  0.9  Hispanics  9.6 (8.8–10.5)  11.6 (10.7–12.5)  2.0‡  APIs  16.5 (15.5–17.5)  18.4 (17.5–19.5)  1.9‡  Pancreas  Whites  5.7 (5.5–6.0)  6.0 (5.7–6.3)  0.3  Blacks  4.7 (4.2–5.3)  5.3 (4.7–5.9)  0.6  Hispanics  5.9 (5.2–6.6)  8.6 (7.8–9.4)  2.7‡  APIs  6.7 (5.8–7.7)  8.9 (8.0–10.0)  2.2‡  Stomach  Whites  21.6 (21.1–22.2)  22.1 (21.5–22.7)  0.5  Blacks  21.2 (20.1–22.3)  21.7 (20.6–22.8)  0.5  Hispanics  21.5 (20.4–22.6)  24.9 (23.8–26.0)  3.4‡  APIs  31.2 (29.9–32.5)  33.3 (32.0–34.5)  2.1  Diagnoses  Site  Race/Ethnicity  Active follow-up (reported alive method)  Passive follow-up (presumed alive method)  Survival difference (in percent points)  Common cancers  Colorectal  Whites  56.0 (55.7–56.2)  56.2 (56.0–56.5)  0.2  Blacks  46.2 (45.6–46.8)  46.7 (46.1–47.3)  0.5  Hispanics  53.6 (52.9–54.4)  54.8 (54.1–55.5)  1.2  APIs  59.8 (59.0–60.5)  60.9 (60.1–61.6)  1.1  Female breast  Whites  78.5 (78.3–78.6)  78.7 (78.5–78.8)  0.2  Blacks  65.8 (65.1–66.4)  66.1 (65.5–66.7)  0.3  Hispanics  75.9 (75.2–76.6)  76.8 (76.1–77.4)  0.9  APIs  81.9 (81.2–82.6)  82.4 (81.7–83.8)  0.5  Lung and bronchus  Whites  14.7 (14.5–14.9)  14.9 (14.7–15.1)  0.2  Blacks  10.6 (10.2–10.9)  10.9 (10.5–11.2)  0.3  Hispanics  14.2 (13.6–14.8)  16.1 (15.5–16.8)  1.9‡  APIs  16.0 (15.3–16.6)  18.1 (17.4–18.7)  2.1‡  Prostate  Whites  82.4 (82.3–82.6)  82.5 (82.4–82.7)  0.2  Blacks  75.2 (74.8–75.6)  75.5 (75.2–75.9)  0.3  Hispanics  81.7 (81.2–82.2)  82.4 (82.0–82.9)  0.7  APIs  84.9 (84.4–85.5)  85.3 (84.8–85.8)  0.4  Less favorable prognoses  Esophagus  Whites  15.4 (14.8–16.0)  15.6 (15.0–16.2)  0.2  Blacks  8.8 (7.7–10.0)  9.1 (8.0–10.3)  0.3  Hispanics  12.7 (10.9–14.5)  14.8 (13.0–16.7)  2.1  APIs  13.9 (11.6–16.5)  16.2 (13.8–18.8)  2.3  Gall bladder  Whites  15.2 (13.8–16.7)  15.8 (14.4–17.3)  0.6  Blacks  10.7 (8.3–13.4)  11.6 (9.2–14.4)  0.9  Hispanics  15.2 (13.0–17.4)  21.4 (19.2–23.7)  6.2‡  APIs  16.8 (13.6–20.4)  19.5 (16.2–23.0)  2.7  Liver and intrahepatic bile duct  Whites  11.4 (10.9–11.9)  11.7 (11.2–12.2)  0.3  Blacks  7.1 (6.2–8.0)  8.0 (7.0–9.0)  0.9  Hispanics  9.6 (8.8–10.5)  11.6 (10.7–12.5)  2.0‡  APIs  16.5 (15.5–17.5)  18.4 (17.5–19.5)  1.9‡  Pancreas  Whites  5.7 (5.5–6.0)  6.0 (5.7–6.3)  0.3  Blacks  4.7 (4.2–5.3)  5.3 (4.7–5.9)  0.6  Hispanics  5.9 (5.2–6.6)  8.6 (7.8–9.4)  2.7‡  APIs  6.7 (5.8–7.7)  8.9 (8.0–10.0)  2.2‡  Stomach  Whites  21.6 (21.1–22.2)  22.1 (21.5–22.7)  0.5  Blacks  21.2 (20.1–22.3)  21.7 (20.6–22.8)  0.5  Hispanics  21.5 (20.4–22.6)  24.9 (23.8–26.0)  3.4‡  APIs  31.2 (29.9–32.5)  33.3 (32.0–34.5)  2.1  * All racial groups (whites, blacks, and Asian and Pacific Islanders [APIs]) exclude Hispanics. SEER = Surveillance, Epidemiology, and End Results. † Observed, age-adjusted survival. ‡ P < .05. View Large For leading cancers, the percent of last alive contact before 60 months (“lost to follow-up”) within 5 years of diagnosis (Table 2) was higher among APIs (3.4%) and Hispanic (3.7%) than among white (0.7%) and black (1.1%) cases. The number of cases “lost to follow-up” per 10000 person-months was higher among Hispanics (8.4) and APIs (7.7) than blacks (2.7) and whites (1.7). Median time to lost follow-up was shorter among Hispanics and APIs (21 and 25 months) than among whites and blacks (33 and 29 months, respectively). Higher rates of lost to follow-up were seen among Hispanics and APIs compared with blacks and whites with less favorable diagnoses and highly fatal diagnoses. In the less favorable diagnosis cohort, median time to lost follow-up ranged from 4 months among APIs to 11 months among whites. For the highly fatal diagnosis cohort, median time to lost follow-up ranged from 3.5 months among blacks to 6 months among whites. Table 2. Percent of cases lost to follow-up within 5 years of diagnoses (date of last contact less than 5 years) and median time to loss by race and ethnicity, SEER-18, 2000–2003* Survival cohort  Race and ethnicity  No. of cases  Cases with date of last contact less than 5 years  %  No. with date of last contact less than 5 years/10000 person-months  Median time to lost follow-up (months)  Common cancer diagnoses†  Overall  777641  8451  1.1  2.6  29.0  Whites  601635  4327  0.7  1.7  33.0  Blacks  85363  910  1.1  2.7  29.0  APIs  38419  1291  3.4  7.7  25.0  Hispanics  52224  1923  3.7  8.4  21.0  Less favorable diagnoses‡ (excludes lung and bronchus)  Overall  90475  935  1.0  7.2  7.0  Whites  60457  208  0.3  2.5  11.0  Blacks  11148  76  0.7  5.1  9.0  APIs  10135  381  3.8  24.6  4.0  Hispanics  8735  270  3.1  18.1  10.5  Highly fatal diagnoses§  Overall  115388  493  0.4  5.4  4.0  Whites  89338  111  0.1  1.6  6.0  Blacks  13068  44  0.3  4.4  3.5  APIs  6283  156  2.5  26.6  5.5  Hispanics  6699  182  2.7  34.3  4.0  Survival cohort  Race and ethnicity  No. of cases  Cases with date of last contact less than 5 years  %  No. with date of last contact less than 5 years/10000 person-months  Median time to lost follow-up (months)  Common cancer diagnoses†  Overall  777641  8451  1.1  2.6  29.0  Whites  601635  4327  0.7  1.7  33.0  Blacks  85363  910  1.1  2.7  29.0  APIs  38419  1291  3.4  7.7  25.0  Hispanics  52224  1923  3.7  8.4  21.0  Less favorable diagnoses‡ (excludes lung and bronchus)  Overall  90475  935  1.0  7.2  7.0  Whites  60457  208  0.3  2.5  11.0  Blacks  11148  76  0.7  5.1  9.0  APIs  10135  381  3.8  24.6  4.0  Hispanics  8735  270  3.1  18.1  10.5  Highly fatal diagnoses§  Overall  115388  493  0.4  5.4  4.0  Whites  89338  111  0.1  1.6  6.0  Blacks  13068  44  0.3  4.4  3.5  APIs  6283  156  2.5  26.6  5.5  Hispanics  6699  182  2.7  34.3  4.0  * All racial groups (whites, blacks, and Asians and Pacific Islanders [APIs]) exclude Hispanics. SEER = Surveillance, Epidemiology, and End Results. † Lung and bronchus, colorectal, female breast, and prostate. ‡ Esophagus, gall bladder, liver and intrahepatic bile duct, pancreas, and stomach cancer. § Distant stage liver and intrahepatic bile duct, gall bladder, pancreas, and lung and bronchus cancers. View Large Predictors of “lost to follow-up” were studied among 777641 cases diagnosed between 2000 and 2003 with leading cancers: female breast, colorectal, lung and bronchus, and prostate (Table 3). Blacks, Hispanics, and APIs had significantly higher likelihood of “lost to follow-up” than the reference category, whites (Table 3). After adjustment for covariates, blacks were 43% more likely than whites to have less than 5 years of follow-up and Hispanics and APIs were 3.7 and 4.1 times more likely to be lost to follow-up, respectively. Overall, those with distant stage diagnoses were 39% more likely to be lost to follow-up than those diagnosed at localized/regional stage (Table 3). The impact of stage of diagnosis differed by racial-ethnic group. For whites, diagnosis at distant stage did not affect risk of lost follow-up compared with diagnosis at localized/regional stage. Among minorities this was not the case. Hispanics diagnosed at distant stage were 69% more likely to be lost to follow-up compared with Hispanics with localized/regional stage cancers. APIs with distant stage cancers were 77% more likely to be lost to follow-up compared with APIs with localized/regional cancers. Blacks diagnosed with distant stage cancer were 36% more likely to be lost to follow-up compared with blacks with localized/regional stage cancers. Table 3. Risk of lost to follow-up, five years postdiagnosis, by stage, race, and ethnicity, SEER-18, 2000–2003* Analysis  Group  Stage at diagnosis  N  Hazard ratio*  Total      777641    Full model  Overall  Localized/regional  595926  1.0  Distant  144180  1.39 (1.28–1.51)  Unstaged/unknown  37535    Whites    601635  1.0  Blacks    85363  1.43 (1.32–1.54)  Hispanics    52224  3.71 (3.49–3.93)  APIs    38419  4.11 (3.83–4.41)  Stratified by race  Whites  Localized/regional  462414  1.0  Distant  109965  0.99 (0.86–1.14)  Unstaged/unknown  29256    Blacks  Localized/regional  63023  1.0  Distant  17954  1.36 (1.06–1.75)  Unstaged/unknown  4386    Hispanics  Localized/regional  40815  1.0  Distant  8878  1.69 (1.45–1.96)  Unstaged/unknown  2531    APIs  Localized/regional  29674  1.0  Distant  7383  1.77 (1.48–2.11)  Unstaged/unknown  1362    Analysis  Group  Stage at diagnosis  N  Hazard ratio*  Total      777641    Full model  Overall  Localized/regional  595926  1.0  Distant  144180  1.39 (1.28–1.51)  Unstaged/unknown  37535    Whites    601635  1.0  Blacks    85363  1.43 (1.32–1.54)  Hispanics    52224  3.71 (3.49–3.93)  APIs    38419  4.11 (3.83–4.41)  Stratified by race  Whites  Localized/regional  462414  1.0  Distant  109965  0.99 (0.86–1.14)  Unstaged/unknown  29256    Blacks  Localized/regional  63023  1.0  Distant  17954  1.36 (1.06–1.75)  Unstaged/unknown  4386    Hispanics  Localized/regional  40815  1.0  Distant  8878  1.69 (1.45–1.96)  Unstaged/unknown  2531    APIs  Localized/regional  29674  1.0  Distant  7383  1.77 (1.48–2.11)  Unstaged/unknown  1362    * Adjusted for age, year of diagnosis, Surveillance, Epidemiology, and End Results (SEER) registry, gender, and cancer site. APIs = Asians and Pacific Islanders. View Large Finally, analyses of 115388 cases with highly fatal cancer diagnoses are presented in Table 4. This cohort was comprised of distant stage liver and intrahepatic bile duct, distant stage gallbladder, distant stage pancreas, and distant stage lung and bronchus cancer cases diagnosed between 2000 and 2003. The proportions of likely missed deaths differed by racial-ethnic group. Whites, blacks, and some Hispanic subgroups (Puerto Ricans), as well as some API subgroups (Japanese and Hawaiian) had low proportions of missed deaths, less than 1%. The percent of likely missed deaths was 2.8% among all Hispanics combined and 2.6% among all APIs combined. Within Hispanic and Asian groups, the percent of likely missed deaths varied substantially by subgroup. High percentages of likely missed deaths were observed among South and Central Americans (9.4%), South Asians (6.1%), Mexicans (4.7%), Filipinos (4.2%), and Chinese (2.8%). Table 4. Proportion of potentially missed deaths 5 years after diagnosis, SEER-18, 2000–2003, highly fatal diagnoses, by race, ethnicity, and country of origin* Race/ethnicity  Subgroup  Potential missed deaths, %  95% Confidence interval, % missed deaths  White    0.1  0.11%–0.15%  Black    0.3  0.26%–0.46%  All Hispanics combined    2.8  2.41%–3.21%    Mexican  4.7  3.83%–5.81%    Puerto Rican  0.3  0.05%–1.49%    Cuban  1.3  0.43%–3.67%    Central and South American  9.4  7.24%–12.12%    All other Hispanics  1.2  0.87%–1.57%  All APIs combined    2.6  2.18%–2.98%    Chinese  2.8  2.06%–3.79%    Japanese  0.3  0.10%–0.89%    Filipino  4.2  3.25%–5.30%    Hawaiian  0.3  0.05%–1.51%    Korean  1.3  0.59%–2.77%    Vietnamese  2.2  1.28%–3.71%    South Asian  6.4  3.78%–10.64%    All other APIs  3.2  2.07%–4.87%  Race/ethnicity  Subgroup  Potential missed deaths, %  95% Confidence interval, % missed deaths  White    0.1  0.11%–0.15%  Black    0.3  0.26%–0.46%  All Hispanics combined    2.8  2.41%–3.21%    Mexican  4.7  3.83%–5.81%    Puerto Rican  0.3  0.05%–1.49%    Cuban  1.3  0.43%–3.67%    Central and South American  9.4  7.24%–12.12%    All other Hispanics  1.2  0.87%–1.57%  All APIs combined    2.6  2.18%–2.98%    Chinese  2.8  2.06%–3.79%    Japanese  0.3  0.10%–0.89%    Filipino  4.2  3.25%–5.30%    Hawaiian  0.3  0.05%–1.51%    Korean  1.3  0.59%–2.77%    Vietnamese  2.2  1.28%–3.71%    South Asian  6.4  3.78%–10.64%    All other APIs  3.2  2.07%–4.87%  * API = Asians and Pacific Islanders; CI = confidence interval; SEER = Surviellence, Epidemiology, and End Results. View Large Discussion We compared survival estimates across racial-ethnic groups obtained from two possible methods; the reported alive method, which uses date of last contact, and the presumed alive method, which does not. For all cancer sites and racial-ethnic groups, the presumed alive method yielded higher survival estimates. The excess was marginal for most cancer sites and for two large, mostly US-born populations: blacks and whites (16). However, for Hispanics and APIs, the observed differences between the two methods were pronounced and in the less favorable diagnosis cohort (cancers of the lung, pancreas, and so on), survival estimates obtained from the two methods differed significantly. Overall, this analysis showed that survival proportions for Hispanics and APIs using the presumed alive method (and respective 100% passive follow-up) should be interpreted with caution, as they may be inflated especially for cancers with less favorable prognoses. If passive follow-up and the presumed alive method result in overestimated survival for Hispanics and APIs, our next question was if the SEER “active” follow-up and the respective reported alive method allow for accurate survival comparisons across racial-ethnic groups. Previous studies have revealed potential biases in survival analyses related to losing cases to follow-up (17–19). When we used SEER data (“active” follow-up), we found that Hispanics and APIs were more likely to be “lost to follow-up” (have <5 years of contact after diagnosis) compared with whites and blacks after adjusting for demographic and case attributes. Moreover, “lost to follow-up” was associated with stage at diagnosis for some groups, but not for others after stratification by racial-ethnic group. Among whites, distant versus localized/regional stage did not influence the risk of being lost to follow-up. However, among Hispanics, APIs, and blacks, advanced stage cases were more likely to be lost to follow-up. Random censoring is a basic assumption of survival analysis (20), but these results show that censoring of SEER data is clearly not random across race-ethnicity. Because minorities were more likely to be lost to follow-up when diagnosed with late stage disease, censoring for non-whites was clearly associated with the outcome of interest, survival. In other words, non-whites diagnosed at late stage disease were more likely to die and were also more likely to be lost to follow up. This association introduces biases that produce an overestimation of survival among minority groups compared with whites. This suggests that the efficacy in assessing vital status differs across racial-ethnic group. Complete death ascertainment is essential for unbiased survival comparisons. However, our analysis showed that a higher proportion of Hispanic and API cases especially if diagnosed with late stage disease were lost to follow-up compared with whites, which suggests a problem with missed deaths in cancer surveillance. There are many factors that can lead to missed deaths during linkage to death records, each of which can bias survival estimates (3,21,22). Finding a death match for cancer cases relies on variables such as date of birth, surname, and critically—a valid Social Security number (SSN) (23). Populations with large proportions of foreign-born subjects, such as APIs and Hispanics (and increasingly blacks), can be problematic for death linkage matches. This is primarily due to inaccurate or missing SSNs (3) or surname transcription errors. Currently, 13% of the US population is foreign born (16). The proportions of foreign-born Hispanics and APIs are much higher, 38% and 66%, respectively (24). In older age groups, which are most affected by cancer, these proportions are even higher. It is estimated that more than 10 million people use false SSNs in the United States (25), and the prevalence of invalid or absent SSNs is high among Hispanics and APIs (26). In regards to surnames, problems in identifying Hispanics and Asians include the order that names are presented, which differ from the Western convention. Often, paternal surnames occur before maternal surnames among Hispanics, and surnames precede given names for APIs (27,28). The variability in Chinese, Korean, and Vietnamese surnames is also small compared with Western last names (29–31). This creates difficulties in death linkages because surname is an important linkage variable. In the event of equivocal name match, true death matches are more dependent on SSN. Other problems with missed deaths include out-migration of ill Hispanics, who when faced with serious illness may return to and spend the remainder of their lives in their place of birth. This is referred to as the “salmon bias” (32). Death linkages for cases that are deceased abroad are not performed and that impacts the ability of registries to ascertain accurate vital status. In our final analysis, we compared proportions of missed deaths, estimated from cases with less than 60 months of active contact that had highly fatal cancer diagnoses. These selected diagnoses have grim five-year survival, around 3% (13), and this subset of diagnoses effectively eliminates the possibility of real survival differences associated with a “healthy immigrant” effect (33). The healthy immigrant effect is a socio-demographic phenomenon whereby immigrants are on average healthier than the native-born due to higher education, self-selection, and/or culturally based healthier behaviors. However, if cases of highly fatal cancers were lost to follow-up in SEER (and thus not detected during a death index linkage), most would have died within 1 year of last alive contact. Thus, it is reasonable to classify them as missed deaths, regardless of race-ethnicity. Our findings stress the central aspect of a successful death linkage, which is the availability of a valid SSN. The groups with the lowest proportions of missed deaths were most likely to be either US nationals or US-born. These cases included not only whites and blacks but also specific Hispanic (Puerto Ricans and Cubans) and API subgroups (Japanese and Hawaiian). Puerto Ricans and Hawaiians are US citizens with valid SSNs available to them. The Cuban Adjustment Act of 1966 allows Cuban natives or nationals to become permanent residents after 1 year in the United States, making them a unique foreign-born population with access to legal status, a smooth naturalization process, and therefore a valid SSN (34). That is the likely reason for the relatively low proportion of missed deaths in this population. Japanese Americans are more likely to have a valid SSN—they are mostly US-born and Japanese immigrants do not follow the pattern of economic opportunity sought by other Asian and Hispanic immigrants. This can be attributed to a strong economy and high standard of living in Japan, especially since the second half of the 20th century (35). However, for other foreign-born groups, for example, Mexican, Filipino, Central and South American, legal immigration into the United States is more difficult and the inevitable proportion of undocumented immigrants is higher, as is the potential for having invalid or absent SSNs (26). Accordingly, the proportion of missed deaths is elevated for these population subgroups. Overall, our results strongly suggest an overestimation of survival for population groups in which the prevalence of valid SSN is lower. Moreover, causes for incomplete death linkages (ie, inaccurate SSNs, mistyped name, inaccurate date of birth) are artificial in nature and independent of cancer stage or site. Thus, the proportion of missed deaths as a function of all deaths is likely to extend beyond highly fatal cancers to other stages and cancer sites. These differences in missed deaths will inflate survival estimates for Hispanics and APIs, not only for highly fatal cancers but also for all cancer stages and sites even though for cancer sites and stages with small numbers of deaths the effect on survival statistics may be negligible. Although studies of cancer survival restricted to US-born Hispanics and US-born APIs are theoretically possible, with complete data on mortality and limited linkage-related bias, in practice they are not a valid possibility. This is because data regarding birthplace in most cancer registry datasets is often collected from death certificates, which is a biased data source in relation to survival. The proportion of cases with unknown birthplace is especially high for cases that survive. Accordingly, the use of birthplace in cancer survival analysis is fraught with bias (36). This study has several limitations. First, we included cases with no survival time. They represent cancer cases without or with a date of last alive contact during the month of diagnosis, that is, with less than 1 month of survival time, and no date of death. SEER considers these individuals as potentially lost to follow-up or impossible to link, and as such, they are excluded in SEER default survival calculations. These cases cannot be identified in datasets that do not collect date of last contact (passive follow-up), so they are necessarily included in presumed alive survival and assumed to be alive with the maximum follow-up time. Thus, to simulate exactly the presumed alive assumption we included these cases in all analyses. However, in most situations, the reported alive survival statistics obtained by including or excluding cases with no survival times are often similar because of the very low proportion of cases with zero survival time in SEER, with the exception of cancers of very poor prognosis. The inclusion or exclusion of these cases did not change the results of the lost to follow-up multivariate analyses in this study. Second, our comparisons between the reported alive and the presumed alive methods are likely to underestimate actual differences between the two types of follow-up. This is because SEER data were used in this study to simulate passive follow-up. The SEER follow-up system uses more sources of information to track cases and is likely to detect a higher number of deaths compared with the passive type of follow-up of non-SEER registries. Third, speculation that the proportion of deaths in distant stages extends to other stages discounts the salmon bias effect, which would be stronger for more advanced stages of cancer. However, other studies suggest that the actual effect of the salmon bias at the population basis is negligible (37). In conclusion, our study found that passive follow-up and the presumed alive method inflates survival estimates for all racial-ethnic groups, but especially for Hispanics and APIs. Although active follow-up and SEER produce more accurate survival statistics for Hispanics and APIs, a bias of missed deaths in the death linkage persists and systematically inflates survival estimates for these two racial-ethnic groups. Across numerous racial-ethnic subgroups including Mexicans, South and Central Americans, Filipinos and South Asians, at least 4% of deaths are likely to be missed. These findings indicate that survival comparisons involving APIs and Hispanics can easily be biased. Although our findings cannot exclude a possible survival advantage for the foreign born or dismiss the potential for a healthy immigrant effect, researchers may want to consider residual differentials in death detection between racial-ethnic groups. Overestimation of survival and the absence of unbiased information on United States versus other place of birth can contribute to erroneous inferences regarding a presumed cancer survival advantage among Asians and Hispanics. Health advantages observed in Hispanics in comparison with non-Hispanic whites are commonly explained as part of the Hispanic Paradox, a term originally coined to characterize the lower Hispanic mortality despite their lower socio-economic status (32). However, as this study shows, the validity of such a paradox in cancer survival deserves further scrutiny. The problem of missed deaths makes accurate survival analysis for immigrant populations especially challenging. More complete follow-up of vital status in these populations is needed. Funding National Institute of General Medical Sciences (8 P20 GM103440-11 to PSP). References 1. Requirements of CoC/governing agencies. Surviellance, Epidemiology, and End Results Web site http://training.seer.cancer.gov/followup/intro/requirements.html. Accessed December 20, 2012. Google Scholar 2. Coleman MP Quaresma M Berrino F et al.   . Cancer survival in five continents: a worldwide population-based study (CONCORD). Lancet Oncol . 2008; 9( 8): 730– 756. 3. Pinheiro PS Williams M Miller EA Easterday S Moonie S Trapido EJ . Cancer survival among Latinos and the Hispanic Paradox. Cancer Cause Control . 2011; 22( 4): 553– 561. 4. Brookfield KF Cheung MC Lucci J Fleming LE Koniaris LG . Disparities in survival among women with invasive cervical cancer: a problem of access to care. Cancer . 2009; 115( 1): 166– 178. 5. Wassira LN Pinheiro PS Symanowski J Hansen A . Racial-ethnic colorectal cancer survival disparities in the mountain west region: the case of Blacks compared to Whites. Ethn Dis . 2013; 23( 1): 103– 109. 6. Weir HK Johnson CJ Mariotto AB et al.   Evaluation of North American Association of Central Cancer Registries’ (NAACCR) data for use in population-based cancer survival studies. J Natl Cancer Inst Monogr . 2014; 49: 198– 209. 7. Humes KR Jones HA Ramirez RR . Overview of Race and Hispanic Origin: 2010 . Census Briefs Publication C2010BR-02 http://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf. Published March 2011. Accessed December 4, 2012. 8. SEER*Stat Database: Incidence — SEER 18 Regs Research Data + Hurricane Katrina Impacted Louisiana Cases, Nov 2011 Sub (1973-2009 varying). Bethesda, MD: National Cancer Institute; 2012. http://seer.cancer.gov/data/seerstat/nov2011. Accessed December 12, 2012. Google Scholar 9. NAACCR Ethnicity Work Group. NAACCR Guideline for Enhancing Hispanic/Latino Identification: Revised NAACCR Hispanic/Latino Identification Algorithm [NHIA v2. 2.1.] . Springfield, IL: North American Association of Central Cancer Registries; 2009. Google Scholar 10. Fritz A Percy C Jack A et al.   International Classification of Diseases for Oncology . 3rd ed. Geneva, Switzerland: World Health Organization; 2000. 11. Young JL Jr Roffers SD Ries LAG Fritz AG Hurlbut AA (eds). SEER Summary Staging Manual – 2000; Codes and Coding Instructions . Bethesda, MD: National Cancer Institute; 2001. NIH publication 01-4969. 12. Corazziari I Quinn M Capocaccia R . Standard cancer patient population for age standardising survival ratios. Eur J Cancer . 2004; 40( 15): 2307– 2316. 13. Howlader N, Noone, AM, Krapcho M, et al., eds. SEER Cancer Statistics Review, 1975-2009 (Vintage 2009 Populations). Bethesda, MD: National Cancer Institute; 2012. http://seer.cancer.gov/csr/1975_2009_pops09. Published April 2012. Accessed December 20, 2012. Google Scholar 14. Schemper M Smith TL . A note on quantifying follow-up in studies of failure time. Control Clin Trials . 1996; 17( 4): 343– 346. 15. Maskarinec G Pagano I Faanunu A Hopping B Hernandez B . The completeness of vital status information for cancer cases varies by ethnicity in the Hawaii tumor registry. J Registry Manage . 2007; 34( 4): 140– 147. 16. Pew Research Center. Statistical Portrait of the Foreign-Born Population in the United States, 2011 . Washington, DC: Pew Research Center; 2013. Google Scholar 17. Mathew A . Removing Bias in Cancer Survival Estimates by Active Follow-up and Information on Determinants of Loss to Follow-up [dissertation] . Tampere, Finland: Acta Universitatis Tamperensis; 1996. 18. Ganesh B Swaminathan R Mathew A Sankaranarayanam R Hakama M . Loss adjusted hospital and population-based survival of cancer patients. In: Sankaranarayanan R Swaminathan R Lucas E , eds. Cancer Survival in Africa, Asia, the Caribbean and Central America (SurvCan) . Vol. 162. Lyon, France: International Agency for Research on Cancer; 2011: 15– 21. 19. Murray DW Britton AR Bulstrode CJ . Loss to follow-up matters. J Bone Joint Surg Br . 1997; 79( 2): 254– 257. 20. Bland JM Altman DG . Survival probabilities (the Kaplan-Meier method). BMJ . 1998; 317( 7172): 1572. 21. Johnson CJ Weir HK Yin D Niu X . The impact of patient follow-up on population-based survival rates. J Registry Manag . 2010; 37( 3): 86– 103. 22. Lariscy JT . Differential record linkage by Hispanic ethnicity and age in linked mortality studies: implications for the epidemiologic paradox. J Aging Health . 2011; 23( 8): 1263– 1284. 23. National Death Index User’s Guide (unpublished draft). Centers for Disease Control and Prevention Web site. http://www.cdc.gov/nchs/ndi.htm. Published August 2012. Accessed January 21, 2013. Google Scholar 24. American Community Survey. Selected Characteristics of the Native and the Foreign-Born Populations 2005–2009 Table S0501. http://factfinder2.census.gov/faces/help/jsf/pages/metadata.xhtml?lang=en&type=table&id=table.en.ACS_09_5YR_S0501#. Accessed December 22, 2012. Google Scholar 25. Federal Trade Commission. Security in numbers: Social Security numbers and identity theft: a Federal Trade Commission report providing recommendations on Social Security number use in the private sector. http://www.ftc.gov/os/2008/12/P075414ssnreport.pdf. Published December 2008. Accessed January 9, 2013. Google Scholar 26. Hoefer M Rytina N Baker B . Estimates of the unauthorized immigrant population residing in the United States: January 2011. https://www.dhs.gov/sites/default/files/publications/ois_ill_pe_2011.pdf Published March 2011. Accessed January 27, 2014. 27. de Platt L . Hispanic Surnames and Family History . Baltimore, MD: Genealogical Publishing Co; 1996. 28. Terry E . How Asia Got Rich: Japan, China and the Asian Miracle . Armonk, NY: M.E. Sharpe; 2002. 29. Yuan YD Jin F Zhang C Saitou N . The study of the distribution of Chinese surnames and the diversity of genetic population structure in the Song dynasty [in Chinese]. Yi Chuan Xue Bao . 1999; 26( 3): 187– 197. 30. Kim BJ Park SM . Distribution of Korean family names. Phys Stat Mech Appl . 2005; 347: 683– 694. 31. Taylor VM Nguyen TT Hoai Do H Li L Yasui Y . Lessons learned from the application of a Vietnamese surname list for survey research. J Immigr Minor Health . 2011; 13( 2): 345– 351. 32. Abraído-Lanza AF Dohrenwend BP Ng-Mak DS Turner JB . The Latino mortality paradox: a test of the “salmon bias” and healthy migrant hypotheses. Am J Public Health . 1999; 89( 10): 1543– 1548. 33. Shai D Rosenwaike I . Mortality among Hispanics in metropolitan Chicago: an examination based on vital statistics data. J Chronic Dis . 1987; 40( 5): 445– 451. 34. The Cuban Adjustment Act, 8 USC §1255 (1966). Google Scholar 35. Min PG . Asian Americans: Contemporary Trends and Issues . Thousand Oaks, CA: Sage Publications; 1995. 36. Pinheiro PS . The influence of Hispanic ethnicity on nonsmall cell lung cancer histology and patient survival: an analysis of the Survival, epidemiology, and end results database. Cancer . 2013; 119( 6): 1285– 1286. 37. Turra CM Elo IT . The impact of salmon bias on the Hispanic mortality advantage: new evidence from social security data. Popul Res Policy Rev . 2008; 27( 5): 515– 530. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
TI - The Impact of Follow-up Type and Missed Deaths on Population-Based Cancer Survival Studies for Hispanics and Asians
JF - JNCI Monographs
DO - 10.1093/jncimonographs/lgu016
DA - 2014-11-19
UR - https://www.deepdyve.com/lp/oxford-university-press/the-impact-of-follow-up-type-and-missed-deaths-on-population-based-kH2dMVCLbh
SP - 210
EP - 217
VL - 2014
IS - 49
DP - DeepDyve
ER -