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Smokers versus Smoking: Is There Detection Bias for Keratinocyte Carcinomas?
COMMENTARY See related article on pg 1700
Smokers versus Smoking: Is There Detection Bias for Keratinocyte Carcinomas? Marlies Wakkee1 Dusingize et al. used a prospective observational cohort study to demonstrate a decreased risk of basal cell carcinoma and an increased risk of squamous cell carcinoma among smokers. This association disappeared after stratifying for skin screening visits, demonstrating the important role of detection bias. In the absence of randomized clinical trials, well-designed and critically analyzed observational studies can provide similarly valuable evidence. Journal of Investigative Dermatology (2017) 137, 1614e1616. doi:10.1016/j.jid.2017.05.002
Dusingize et al. (2017) report on the association between cigarette smoking and the incidence of keratinocyte carcinomas (KCs) using the prospective cohort study (n ¼ 43,794) from Queensland, Australia. Their main finding is that compared with never smokers, those who smoke have an increased risk of squamous cell carcinoma (hazard ratio 2.3; 95% confidence interval, 1.5e3.6), whereas the risk of basal cell carcinoma (BCC) in this group was reduced (hazard ratio 0.6; 95% confidence interval, 0.4e0.9). Attributable risk of smoking on KC risk
In KCs, the population attributable fraction (PAF), which estimates the attributable risk of an exposure factor on the proportion of cancers, is extremely high (>90%) for sunlight exposure (Olsen et al., 2015). In most other cancers, individual predictors are not that strong, except for the human papillomavirus infection attribution to uterine cervix carcinoma (PAF 100%) and Kaposi’s sarcoma herpes virus to Kaposi’s sarcoma (PAF 100%) (Whiteman et al., 2015). Smoking is the first other high impact causal factor 1
with a PAF between 80% and 86% for lung cancer (Parkin et al., 2011; Whiteman et al., 2015). For the overall group of cancer, excluding KCs, individual exposures do not exceed a PAF of 20%, which represents smoking, whereas the PAF of UV radiation for overall cancer varies between 3.5% in the UK and 6.2% in Australia (Parkin et al., 2011; Whiteman et al., 2015). Compared with most other cancers, the opposite occurs in KC for which sun exposure is the major risk factor; the PAF of smoking for squamous cell carcinoma is modest, and it may even be protective for BCC. Although the additional predictive value of smoking compared with sun exposure for KC risk was not reported by Dusingize et al., it will probably have much less impact than UV radiation. This makes UV exposure still the most important risk factor for skin cancer, one that needs to be targeted to reduce skin cancer incidence rates. Smokers versus smoking
The findings of this study are based on a large, prospective population-based skin cancer cohort. This study design has the advantage of prospectively collecting data on potential risk factors
Department of Dermatology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
Correspondence: Marlies Wakkee, Department of Dermatology, Erasmus MC Cancer Institute, Postbus 2040, 3000 CA Rotterdam, The Netherlands. E-mail: [email protected] ª 2017 The Author. Published by Elsevier, Inc. on behalf of the Society for Investigative Dermatology.
1614 Journal of Investigative Dermatology (2017), Volume 137
for skin cancer as well as the occurrence of KC during follow-up. This design allows survival analysis to reveal the presence of a temporal relationship between exposure and outcome. The authors, indeed, showed that smokers in this cohort had a lower risk of BCC and a higher risk of squamous cell carcinoma during follow-up. As with all epidemiological research that relies on observational information, one preferably searches for multiple arguments to strengthen support for a causal relationship. The most commonly used criteria for causation are those of Bradford-Hill, which include strength of association, doseresponse relationships, and consistency (Hill, 1965). However, for both BCC and squamous cell carcinoma risk, the investigators were not able to find dose-response relationships with increased duration or intensity of smoking, which would logically be expected for cancer-related risk factors. The authors therefore conclude rightly that there might be explanations other than a true causal effect for their findings. Further stratified analyses of the data, based on clinic attendance for skin checks, showed subsequently that smokers had fewer skin examinations than never smokers, which may have resulted in detection bias and a higher risk of detecting BCCs among never smokers. Figure 1 illustrates the effect of detection bias when the risk of KCs is examined among smokers. An analogy may be drawn to the protective effect of coffee consumption on first and multiple BCC risks as demonstrated by previous observational studies (Song et al., 2012; Verkouteren et al., 2015). As coffee consumption is higher in subgroups with lower socioeconomic status, this again represents the group that may seek less medical care, which may also explain the potential protective effect of coffee consumption on skin cancer (Hulshof et al., 2003). If observational studies provide the best available evidence
Randomized controlled trials are considered to provide one of the highest levels of evidence for quantitative questions. However, many questions cannot be answered in a randomized clinical trial design, as it is also
COMMENTARY
Clinical Implications Smokers have a higher risk of squamous cell carcinoma and a lower risk of basal cell carcinoma. The effect of smoking on keratinocyte carcinomas is unrelated to smoking intensity or duration. Smokers seek less medical care, and therefore detection bias likely affects the risk of keratinocyte carcinoma detection.
unethical to randomize for smoking in a randomized clinical trial design. Observational studies consequently provide the best available evidence for many questions with the tradeoff to potentially have a higher risk of bias. Bias represents a systemic error and is not random variation or lack of precision. Observational studies are at risk of a wide range of sources of bias, including selection bias, performance bias, detection bias, reporting bias, and confounding, which are also described briefly and then evaluated within the context of this study and summarized in Table 1. Selection bias covers a broad range of systematic differences between the groups that are to be compared, and it is generally differentiated into nonresponse bias, attrition bias, and the
healthy entrant effect. The QSkin study population seems to be a reasonable representation of the Queensland population with overall comparable characteristics between responders and nonresponders, although the participation fraction of 23% (43,794/ 193,344) was not very high (Olsen et al., 2012). The most important subtype of selection bias that may have occurred in this study is the healthy entrant effect because participants were less likely to be smokers (Olsen et al., 2012), and they were excluded if they had a previous diagnosis of skin cancer. Performance bias does not seem to have been a major issue in this study because participants and providers were not informed of the study hypothesis, the effect of smoking on KC risk.
Detection bias
+
smokers
Less skin screening among smokers
Study design
Keratinocyte
+
carcinoma
Smoking (study objective)
No dose response effect
Figure 1. An illustration of the difference between investigating the effect of smokers versus smoking on the risk of keratinocyte carcinoma.
Dusingize et al. (2017) demonstrated, by stratifying their data for the number of skin screening visits, a significant role for detection bias. Health care consumption is lower among people who smoke, and this affects the risk of skin cancer detection. An observational study design that would reduce the effect of detection bias could be a population-based design in which all participants would receive regular (e.g., yearly) periodic skin examinations. This would make a skin cancer diagnosis less dependent on patient-driven health care consumption. Until these data are available, the primary outcomes should be presented together with the additional criteria for causation and with stratified data, as the authors did, to reduce reporting bias and to facilitate a balanced interpretation of the observations. Because of randomization, known and unknown factors between two groups are thought to be diluted reasonably equally, but this is usually not possible in observational studies, which can lead to confounding. Collecting sufficient data on potential confounders is therefore indispensable. In this study, sunlight exposure is a potential confounder, as it is related to both the exposure (i.e., smoking) and the outcome (i.e., skin cancer). The authors have therefore collected data on sunlight exposure, which allowed them to adjust for this confounder in their multivariable Cox regression model. The authors have demonstrated nicely in this large population-based cohort that if bias is recognized within an observational study, it may be possible to reduce or prevent some types of bias or to adjust or stratify for these effects in the analyses. As a result, Dusingize et al. were able to avoid unnecessary speculation on potential biological explanations for the initial identified association between smoking and KCs. Carefully designed and analyzed observational research may have the potential to be as credible as randomized controlled trials. CONFLICT OF INTEREST The author states no conflict of interest.
www.jidonline.org 1615
COMMENTARY Table 1. Risk of bias evaluation for observational studies applied to the study of Dusingize et al. (Dusingize et al., 2017; Sedgwick, 2014) Source of Bias Description Selection bias Nonresponse bias Representation of a cohort for the population it was selected from
Attrition bias Loss to follow-up of participants related to exposure or outcome Healthy entrant effect Cohort healthier than the population it was selected from Performance bias Differences in provided care related to exposure Detection bias Assessment of outcome related to exposure
þ Population-based sample invited (electoral roll) þ Aim for high participation rate within the selected population by reminder cards and online or paper consent forms þ Information obtained from nonparticipants Overall participation rate 23% Questionnaire only available in English
þ
þ Minimal attrition due to passive follow-up through database linkage þ First evaluation after 1 y, only six participants formally withdrawn
þ Information obtained from nonparticipants Responders compared with nonresponders less likely to be current smokers Exclusion of participants with a history of skin cancer
þþ
þ Blinding of participants and providers to the study hypothesis and observation of care as usual and KC incidence derived from databases linkage
Assessment for KC depends on health care consumption that is less among smokers Smokers may receive more overall care once at the health care provider that can increase the risk of KC detection
þþþ
þ The authors present associations between smokers and KC risk, but also report on the absence of dose-response trends and on the reduced skin examinations among smokers
þ Collection of data on potential confounders such as sun exposure and number of skin examinations þ Testing for confounding by stratification/adjustment in a multivariable model Self-reported data on potential confounders
þ
Reporting bias Selective reporting of some outcomes and not others Confounding Effect of a third factor both related exposure and outcome
Potential Effect of Bias on Study Outcome
Assessment within Study
Abbreviation: KC, keratinocyte carcinoma.
REFERENCES Dusingize JC, Olsen CM, Pandeya NP, Subramaniam P, Thompson BS, Neale RE, et al. Cigarette smoking and the risks of basal cell carcinoma and squamous cell carcinoma. J Invest Dermatol 2017;137:1700e8. Hill AB. The environment and disease: association or causation? Proc R Soc Med 1965;58: 295e300. Hulshof KF, Brussaard JH, Kruizinga AG, Telman J, Lo¨wik MR. Socio-economic status, dietary intake and 10 y trends: the Dutch National Food Consumption Survey. Eur J Clin Nutr 2003;57:128e37.
Olsen CM, Green AC, Neale RE, Webb PM, Cicero RA, Jackman LM, et al. Cohort profile: the QSkin Sun and Health Study. Int J Epidemiol 2012;41. 929e929i. Olsen CM, Wilson LF, Green AC, Bain CJ, Fritschi L, Neale RE, et al. Cancers in Australia attributable to exposure to solar ultraviolet radiation and prevented by regular sunscreen use. Aust N Z J Public Health 2015;39:471e6. Parkin DM, Boyd L, Walker LC. 16. The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010. Br J Cancer 2011;105(Suppl 2):S77e81.
1616 Journal of Investigative Dermatology (2017), Volume 137
Sedgwick P. Bias in observational study designs: prospective cohort studies. BMJ 2014;349:g7731. Song F, Qureshi AA, Han J. Increased caffeine intake is associated with reduced risk of basal cell carcinoma of the skin. Cancer Res 2012;72:3282e9. Verkouteren JA, Smedinga H, Steyerberg EW, Hofman A, Nijsten T. Predicting the risk of a second basal cell carcinoma. J Invest Dermatol 2015;135:2649e56. Whiteman DC, Webb PM, Green AC, Neale RE, Fritschi L, Bain CJ, et al. Cancers in Australia in 2010 attributable to modifiable factors: summary and conclusions. Aust N Z J Public Health 2015;39:477e84.
Smokers versus Smoking: Is There Detection Bias for Keratinocyte Carcinomas? Marlies Wakkee1 Dusingize et al. used a prospective observational cohort study to demonstrate a decreased risk of basal cell carcinoma and an increased risk of squamous cell carcinoma among smokers. This association disappeared after stratifying for skin screening visits, demonstrating the important role of detection bias. In the absence of randomized clinical trials, well-designed and critically analyzed observational studies can provide similarly valuable evidence. Journal of Investigative Dermatology (2017) 137, 1614e1616. doi:10.1016/j.jid.2017.05.002
Dusingize et al. (2017) report on the association between cigarette smoking and the incidence of keratinocyte carcinomas (KCs) using the prospective cohort study (n ¼ 43,794) from Queensland, Australia. Their main finding is that compared with never smokers, those who smoke have an increased risk of squamous cell carcinoma (hazard ratio 2.3; 95% confidence interval, 1.5e3.6), whereas the risk of basal cell carcinoma (BCC) in this group was reduced (hazard ratio 0.6; 95% confidence interval, 0.4e0.9). Attributable risk of smoking on KC risk
In KCs, the population attributable fraction (PAF), which estimates the attributable risk of an exposure factor on the proportion of cancers, is extremely high (>90%) for sunlight exposure (Olsen et al., 2015). In most other cancers, individual predictors are not that strong, except for the human papillomavirus infection attribution to uterine cervix carcinoma (PAF 100%) and Kaposi’s sarcoma herpes virus to Kaposi’s sarcoma (PAF 100%) (Whiteman et al., 2015). Smoking is the first other high impact causal factor 1
with a PAF between 80% and 86% for lung cancer (Parkin et al., 2011; Whiteman et al., 2015). For the overall group of cancer, excluding KCs, individual exposures do not exceed a PAF of 20%, which represents smoking, whereas the PAF of UV radiation for overall cancer varies between 3.5% in the UK and 6.2% in Australia (Parkin et al., 2011; Whiteman et al., 2015). Compared with most other cancers, the opposite occurs in KC for which sun exposure is the major risk factor; the PAF of smoking for squamous cell carcinoma is modest, and it may even be protective for BCC. Although the additional predictive value of smoking compared with sun exposure for KC risk was not reported by Dusingize et al., it will probably have much less impact than UV radiation. This makes UV exposure still the most important risk factor for skin cancer, one that needs to be targeted to reduce skin cancer incidence rates. Smokers versus smoking
The findings of this study are based on a large, prospective population-based skin cancer cohort. This study design has the advantage of prospectively collecting data on potential risk factors
Department of Dermatology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
Correspondence: Marlies Wakkee, Department of Dermatology, Erasmus MC Cancer Institute, Postbus 2040, 3000 CA Rotterdam, The Netherlands. E-mail: [email protected] ª 2017 The Author. Published by Elsevier, Inc. on behalf of the Society for Investigative Dermatology.
1614 Journal of Investigative Dermatology (2017), Volume 137
for skin cancer as well as the occurrence of KC during follow-up. This design allows survival analysis to reveal the presence of a temporal relationship between exposure and outcome. The authors, indeed, showed that smokers in this cohort had a lower risk of BCC and a higher risk of squamous cell carcinoma during follow-up. As with all epidemiological research that relies on observational information, one preferably searches for multiple arguments to strengthen support for a causal relationship. The most commonly used criteria for causation are those of Bradford-Hill, which include strength of association, doseresponse relationships, and consistency (Hill, 1965). However, for both BCC and squamous cell carcinoma risk, the investigators were not able to find dose-response relationships with increased duration or intensity of smoking, which would logically be expected for cancer-related risk factors. The authors therefore conclude rightly that there might be explanations other than a true causal effect for their findings. Further stratified analyses of the data, based on clinic attendance for skin checks, showed subsequently that smokers had fewer skin examinations than never smokers, which may have resulted in detection bias and a higher risk of detecting BCCs among never smokers. Figure 1 illustrates the effect of detection bias when the risk of KCs is examined among smokers. An analogy may be drawn to the protective effect of coffee consumption on first and multiple BCC risks as demonstrated by previous observational studies (Song et al., 2012; Verkouteren et al., 2015). As coffee consumption is higher in subgroups with lower socioeconomic status, this again represents the group that may seek less medical care, which may also explain the potential protective effect of coffee consumption on skin cancer (Hulshof et al., 2003). If observational studies provide the best available evidence
Randomized controlled trials are considered to provide one of the highest levels of evidence for quantitative questions. However, many questions cannot be answered in a randomized clinical trial design, as it is also
COMMENTARY
Clinical Implications Smokers have a higher risk of squamous cell carcinoma and a lower risk of basal cell carcinoma. The effect of smoking on keratinocyte carcinomas is unrelated to smoking intensity or duration. Smokers seek less medical care, and therefore detection bias likely affects the risk of keratinocyte carcinoma detection.
unethical to randomize for smoking in a randomized clinical trial design. Observational studies consequently provide the best available evidence for many questions with the tradeoff to potentially have a higher risk of bias. Bias represents a systemic error and is not random variation or lack of precision. Observational studies are at risk of a wide range of sources of bias, including selection bias, performance bias, detection bias, reporting bias, and confounding, which are also described briefly and then evaluated within the context of this study and summarized in Table 1. Selection bias covers a broad range of systematic differences between the groups that are to be compared, and it is generally differentiated into nonresponse bias, attrition bias, and the
healthy entrant effect. The QSkin study population seems to be a reasonable representation of the Queensland population with overall comparable characteristics between responders and nonresponders, although the participation fraction of 23% (43,794/ 193,344) was not very high (Olsen et al., 2012). The most important subtype of selection bias that may have occurred in this study is the healthy entrant effect because participants were less likely to be smokers (Olsen et al., 2012), and they were excluded if they had a previous diagnosis of skin cancer. Performance bias does not seem to have been a major issue in this study because participants and providers were not informed of the study hypothesis, the effect of smoking on KC risk.
Detection bias
+
smokers
Less skin screening among smokers
Study design
Keratinocyte
+
carcinoma
Smoking (study objective)
No dose response effect
Figure 1. An illustration of the difference between investigating the effect of smokers versus smoking on the risk of keratinocyte carcinoma.
Dusingize et al. (2017) demonstrated, by stratifying their data for the number of skin screening visits, a significant role for detection bias. Health care consumption is lower among people who smoke, and this affects the risk of skin cancer detection. An observational study design that would reduce the effect of detection bias could be a population-based design in which all participants would receive regular (e.g., yearly) periodic skin examinations. This would make a skin cancer diagnosis less dependent on patient-driven health care consumption. Until these data are available, the primary outcomes should be presented together with the additional criteria for causation and with stratified data, as the authors did, to reduce reporting bias and to facilitate a balanced interpretation of the observations. Because of randomization, known and unknown factors between two groups are thought to be diluted reasonably equally, but this is usually not possible in observational studies, which can lead to confounding. Collecting sufficient data on potential confounders is therefore indispensable. In this study, sunlight exposure is a potential confounder, as it is related to both the exposure (i.e., smoking) and the outcome (i.e., skin cancer). The authors have therefore collected data on sunlight exposure, which allowed them to adjust for this confounder in their multivariable Cox regression model. The authors have demonstrated nicely in this large population-based cohort that if bias is recognized within an observational study, it may be possible to reduce or prevent some types of bias or to adjust or stratify for these effects in the analyses. As a result, Dusingize et al. were able to avoid unnecessary speculation on potential biological explanations for the initial identified association between smoking and KCs. Carefully designed and analyzed observational research may have the potential to be as credible as randomized controlled trials. CONFLICT OF INTEREST The author states no conflict of interest.
www.jidonline.org 1615
COMMENTARY Table 1. Risk of bias evaluation for observational studies applied to the study of Dusingize et al. (Dusingize et al., 2017; Sedgwick, 2014) Source of Bias Description Selection bias Nonresponse bias Representation of a cohort for the population it was selected from
Attrition bias Loss to follow-up of participants related to exposure or outcome Healthy entrant effect Cohort healthier than the population it was selected from Performance bias Differences in provided care related to exposure Detection bias Assessment of outcome related to exposure
þ Population-based sample invited (electoral roll) þ Aim for high participation rate within the selected population by reminder cards and online or paper consent forms þ Information obtained from nonparticipants Overall participation rate 23% Questionnaire only available in English
þ
þ Minimal attrition due to passive follow-up through database linkage þ First evaluation after 1 y, only six participants formally withdrawn
þ Information obtained from nonparticipants Responders compared with nonresponders less likely to be current smokers Exclusion of participants with a history of skin cancer
þþ
þ Blinding of participants and providers to the study hypothesis and observation of care as usual and KC incidence derived from databases linkage
Assessment for KC depends on health care consumption that is less among smokers Smokers may receive more overall care once at the health care provider that can increase the risk of KC detection
þþþ
þ The authors present associations between smokers and KC risk, but also report on the absence of dose-response trends and on the reduced skin examinations among smokers
þ Collection of data on potential confounders such as sun exposure and number of skin examinations þ Testing for confounding by stratification/adjustment in a multivariable model Self-reported data on potential confounders
þ
Reporting bias Selective reporting of some outcomes and not others Confounding Effect of a third factor both related exposure and outcome
Potential Effect of Bias on Study Outcome
Assessment within Study
Abbreviation: KC, keratinocyte carcinoma.
REFERENCES Dusingize JC, Olsen CM, Pandeya NP, Subramaniam P, Thompson BS, Neale RE, et al. Cigarette smoking and the risks of basal cell carcinoma and squamous cell carcinoma. J Invest Dermatol 2017;137:1700e8. Hill AB. The environment and disease: association or causation? Proc R Soc Med 1965;58: 295e300. Hulshof KF, Brussaard JH, Kruizinga AG, Telman J, Lo¨wik MR. Socio-economic status, dietary intake and 10 y trends: the Dutch National Food Consumption Survey. Eur J Clin Nutr 2003;57:128e37.
Olsen CM, Green AC, Neale RE, Webb PM, Cicero RA, Jackman LM, et al. Cohort profile: the QSkin Sun and Health Study. Int J Epidemiol 2012;41. 929e929i. Olsen CM, Wilson LF, Green AC, Bain CJ, Fritschi L, Neale RE, et al. Cancers in Australia attributable to exposure to solar ultraviolet radiation and prevented by regular sunscreen use. Aust N Z J Public Health 2015;39:471e6. Parkin DM, Boyd L, Walker LC. 16. The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010. Br J Cancer 2011;105(Suppl 2):S77e81.
1616 Journal of Investigative Dermatology (2017), Volume 137
Sedgwick P. Bias in observational study designs: prospective cohort studies. BMJ 2014;349:g7731. Song F, Qureshi AA, Han J. Increased caffeine intake is associated with reduced risk of basal cell carcinoma of the skin. Cancer Res 2012;72:3282e9. Verkouteren JA, Smedinga H, Steyerberg EW, Hofman A, Nijsten T. Predicting the risk of a second basal cell carcinoma. J Invest Dermatol 2015;135:2649e56. Whiteman DC, Webb PM, Green AC, Neale RE, Fritschi L, Bain CJ, et al. Cancers in Australia in 2010 attributable to modifiable factors: summary and conclusions. Aust N Z J Public Health 2015;39:477e84.