Limited evidence is available to acknowledge the association between opium use and liver cancer. In a case–control study, we recruited 117 cases of primary liver cancer (PLC) and 234 age and sex-matched neighborhood controls from 2016 to 2018. We calculated odds ratios (OR) for opium use and 95% confidence intervals (95% CI), using conditional logistic regressions. Compared with non-users the adjusted OR (AOR, 95% CI) for opium use was 6.5 (95% CI, 2.87–13.44). Compared with people who had no history of use, a strong dose–response effect of opium use was observed by amount of use (AOR, 10.70; 95% CI, 3.92–28.70). Cumulative use of opium also indicated that using over 30 gr-year could increase the PLC risk dramatically (AOR, 11.0; 95% CI, 3.83–31.58). Those who used opium for more than 21 years were highly at risk of PLC (AOR, 11.66; 95% CI, 4.43–30.67). The observed associations were significant even among never tobacco smokers (including cigarette and water-pipe smoking).

Prevention Relevance:

The results of this study indicate that opium use dramatically increased the risk of liver cancer. Because opioids are increasing for medical and non-medical use globally; accordingly, severe health consequences such as liver cancer have to be investigated widely.

In 2017, 35 million people worldwide suffered from drug use disorders (1). Opiate use (opium and heroin) is a significant health challenge in many countries because of adverse health consequences related to opiates abuse. World drug report has estimated that 29.2 million opiates users ages 15–64 (1). The early onset of opiates abuses leads to long-term exposure to the drugs, which raised a hypothesis of the carcinogenicity of opiates.

The most recent monograph working group on opium consumption has achieved strong consensus that opium use is a Group 1 human carcinogen for lung, larynx, and urinary bladder cancers by sufficient evidence (2). Many studies have associated opium use and various types of cancers such as esophagus, pancreas, stomach, and pharynx by epidemiologic studies, including cohorts and cases–controls, some other cancers like liver cancer are ignored (3–14). Limited experimental studies have shown that opium use could increase opium receptors. Increased receptor activity could lead to DNA damage and liver cancer (15).

Primary liver cancer (PLC), which afflicts approximately 841,000 new patients and causes 782,000 deaths annually, was predicted to have been the sixth most commonly diagnosed cancer and the fourth leading cause of cancer-related death worldwide in 2018 (16). Approximately 80% of liver cancer occurs in less developed countries (17, 18). Iran has a low incidence of liver cancer, and the age-adjusted incidence rate (ASR) in 2016 was 0.19 per 100,000 (19). Some regions of Iran showed higher ASRs than others, almost three times the difference between regions; meanwhile, the prevalence of common risk factors like hepatitis and alcohol drinking is almost consistent in the country. In a primary look at ecologic evidence, high ASR of liver cancer has occurred in the regions with a high prevalence of opium use (20, 21).

Alcohol trading and drinking are forbidden in Iran (22, 23); hence the prevalence of alcohol drinking (10%) is less than in other countries (24). The prevalence of opium use in Iran is the highest worldwide, the southeastern of Iran in particular (up to 20%) because Iran is the main trading pathways of opium from Middle-east to Europe-Golden Crescent (25, 26). By the ecologic concepts, it has been suggested to investigate opium use as a risk factor for liver cancer comprehensively (27).

Evaluating the possibility of the association needs well-designed epidemiological studies. To our knowledge, a cohort study in Golestan, Northern Iran, has reported a positive association between opium use and the risk of liver cancer, whereas a dose–response relationship was not evaluated. Furthermore, as the main confounder, cigarette smoking did not adjust, and the interaction of opium and cigarette smoking was not measured (28). The current study is a neighborhood case–control study in a highly prevalent area of opium use, which varies between 11% and 15% in the Southeast of Iran (29, 30), designed to evaluate the association between opium use and the risk of PLC.

A population-based case–control study was designed and carried out in a highly prevalent area of opium use worldwide-Southeastern of Iran.

Ethical statement

Written informed consent was obtained from all subjects involved in the study and the study was conducted in accordance with the Declaration of Helsinki, and approved by ethical committee of Kerman University of Medical Sciences. The Ethic approval Code is IR.KMU.REC.1397.432.

Case selection

A total of 117 pathologically confirmed patients with PLC were included in the study. The source of case selection was the comprehensive pathology-based cancer registry of Kerman University of Medical Sciences (KUMS) from January 2016 to December 2018. All PLCs were assumed as potential cases for our study. We excluded any history of hepatitis as a major risk factor for PLC. The lag time between cancer diagnoses and the interview had to be less than six months. Being Iranian and having Kerman residency for at least five years previous enrollment was another inclusion criteria

Having had IRB code, contact information, and cancer type was extracted from patients' medical records. After obtaining the consent via phone calls, trained interviewers set the dates and times for in-person interviews.

Control selection

The neighbors were used as the source of controls. Two appropriate neighborhood controls were recruited. The pattern of control selection was described in detail elsewhere (8). Briefly, on the basis of the first two houses located on the right side of the case's house, two neighbors that met the inclusion criteria and consented to participate were enrolled and interviewed. The controls had to be matched by age (±5 years) and gender. The controls had not had a history of any other cancer diagnosis or hepatitis. Moreover, to exclude positive cases of hepatitis B and C, in addition to self-reporting by the participants, medical documents, pathology, and cancer registry reports were also examined. We ascertained that the controls did not suffer from a history of cancer by searching the control information on the cancer registry system. Other inclusion criteria were similar to cases except for PLC condition. In case of rejections or unavailability of control, the next neighbor was assessed for potential controls.

Data collection

A standard and validated questionnaire was used (31). A family representative helped us fill out the questionnaire in case of any problems. To reduce information bias (recall bias) of opium use, a trained interviewer assured the participants that all information gathered anonymously and data would be stored confidential.

Detailed sections of the questionnaire were illustrated elsewhere (8). Our primary exposure of interestopium use "ever opium user" defined as those who had experienced opium use ever. Other opium metrics included age of initiation, duration of use (years), route of use (smoking and ingestion), type of opium, and the daily amount consumed (gram). Opium for daily consumption was estimated using the local unit "Nokhod" equal to 0.2 grams (32). The cumulative use of opium (gram-year) was measured by multiplying the intensity of use (gram) and duration of use (year). The dietary section of the questionnaire included red meats, white meats (chicken and fish), fruits and vegetables, and oils like frying oils. All the foodstuffs were categorized into low and high by scoring the frequency of use.

One of the main concerns about the link between opium and PLC is the reverse causality: Patients may consume opium to release pain (33). Therefore, for preventing the reverse causality, 17 participants who only had opium and drug use from the past until 2 years before the diagnosis of PLC or the enrollment into the study (for the control group), were excluded.

Statistical analysis

Conditional logistic regression models calculated the odds ratio (OR) and 95% confidence interval (95% CI). Potential confounders, including cigarette smoking, alcohol drinking, marital status, and education, using change estimation method were included in the multivariate models for adjusting the model. Adjusted OR (AOR) was reported for potential confounders such as Dose–response estimated using Ptrend. Metrics of opium were classified into two groups by the median of the control group. Multiplicative interactions were assessed between cigarette smoking (ever use) and opium use (ever use and cumulative dose) via the likelihood ratio test. We did a sensitivity analysis of self-reporting opium use in both cases and controls to check misclassification bias. Moreover, by reducing the duration of opium use by 5 years and performing a sensitivity analysis, we assessed the stability of the OR between cumulative opium use and PLC. To identify confounding behavioral factors that might explain the increased odds of PLC associated with opium use, we adjusted for cigarette smoking, Marital status, and education using Equation as follows:
formula
formula

All analyses were done with STATA (version 14.0, Stata Corp.).

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to patient confidentiality but are available from the corresponding author on reasonable request.

A total of 117 patients with PLC and 234 neighborhood controls were analyzed. Most participants were men (68.09%), and subjects mostly were married (96.01%). All participants were over 55 years old. Controls and cases' mean age was 62.70 ± 13.22 and 64.29 ± 13.43 (P = 0.85). Patients were more cigarette smokers (38.46% vs. 20.94%) and opium users (18.10% vs. 9.83) than controls. About 2 (1.71%) of the cases and 3 (1.28%) of the controls had a history of alcohol drinking (Table 1).

Table 1.

Demographic and essential characteristics variables in case and control groups.

VariablesCases N (%)Controls N (%)Pa
Total 117 234  
Age 
 <65 59 (50.43) 124 (52.99) 0.65 
 ≥65 58 (49.57) 110 (47.01)  
 Mean ± SD 64.29 ± 13.43 62.70 ± 13.22 0.85 
Gender 
 Men 79 (67.52) 160 (68.38) 0.87 
 Women 38 (32.48) 74 (31.62)  
Marital status 
 Married 113 (96.58) 224 (95.73) 0.7 
 Unmarried 4 (3.42) 10 (4.27)  
Education 
 Illiterate or Elementary 104(44.44) 63 (53.85) 0.04 
 Middle or High school 90 (38.46) 45 (38.46)  
High School Diploma or above 40 (17.09) 9 (7.69)  
Opium Use 
 Never user 70 (81.90) 211 (90.17) <0.001 
 Ever user 47 (18.1) 23 (9.83)  
Cigarette Smoking 
 Never smoker 72 (61.54) 185 (79.06) <0.001 
 Ever smoker 45 (38.46) 49 (20.94)  
Alcohol drinking 
 Never user 115 (98.29) 231 (98.72) 0.75 
 Ever user 2 (1.71) 3 (1.28)  
VariablesCases N (%)Controls N (%)Pa
Total 117 234  
Age 
 <65 59 (50.43) 124 (52.99) 0.65 
 ≥65 58 (49.57) 110 (47.01)  
 Mean ± SD 64.29 ± 13.43 62.70 ± 13.22 0.85 
Gender 
 Men 79 (67.52) 160 (68.38) 0.87 
 Women 38 (32.48) 74 (31.62)  
Marital status 
 Married 113 (96.58) 224 (95.73) 0.7 
 Unmarried 4 (3.42) 10 (4.27)  
Education 
 Illiterate or Elementary 104(44.44) 63 (53.85) 0.04 
 Middle or High school 90 (38.46) 45 (38.46)  
High School Diploma or above 40 (17.09) 9 (7.69)  
Opium Use 
 Never user 70 (81.90) 211 (90.17) <0.001 
 Ever user 47 (18.1) 23 (9.83)  
Cigarette Smoking 
 Never smoker 72 (61.54) 185 (79.06) <0.001 
 Ever smoker 45 (38.46) 49 (20.94)  
Alcohol drinking 
 Never user 115 (98.29) 231 (98.72) 0.75 
 Ever user 2 (1.71) 3 (1.28)  

aP values calculated according to the McNemar's test.

Table 2 showed the association of opium use and the risk of primary liver cancer. The risk of PLC was six times higher among opium users than controls (AOR, 6.50; 95% CI, 2.87–13.44). Those who consumed more than 1 gr/d were at risk of PLC (AOR, 10.70; 95% CI, 3.99–28.72).

Duration of opium use was significantly associated with the risk of PLC. Subjects who used opium for more than 21 years were about 11 times at higher risk of PLC (AOR, 11.65; 95% CI, 4.43–30.67).

The cumulative use of opium, as we expected it, was increasing to about 11 times among higher levels of cumulative use of opium, >30 gr-year (AOR, 11.00; 95% CI, 3.83–31.58).

Early start of opium use, less than 50 years old, increased the risk of PLC to AOR, 11.31; 95% CI, 4.41–29.0.

The opium smoking method increased the odds of PLC more than 5 times (AOR, 5.94; 95% CI, 2.47–14. 25) Oral ingestion leads to an extremely high risk of PLC (AOR, 30.57; 95% CI, 3.45–270.37).

Raw opium use was prevalent in both groups. None of the controls used refined opium.

The risk of PLC increased to 4.67 (95% CI, 2. 07–10.0) in raw opium users than never opium users. Cigarette smoking in the adjusted model was not associated with the risk of PLC.

The results showed that on an additive scale, 79% of the association goes through the path of opium consumption (ever/never) and the other 21% of the association goes through the path of other risk factors. In addition, on a multiplicative scale, 90% of the relationship goes through the path of opium use (ever/never) and 10% through the path of other behavioral risk factors.

Moreover, the results showed that the consumption of opium ≤1gr/d on an additive scale causes 75% of the odds of getting PLC, and on a multiplicative scale 85% of the odds of getting PLC. Although consumption of >1gr/d on an additive scale causes 79% of the odds of getting cancer and on a multiplicative scale 92% of the odds of getting PLC.

On an additive scale, the duration of opium use of ≤21 years predicts 68% of the odds of getting PLC and 22% of the odds of getting PLCs predicted by other behavioral risk factors. Although the duration of use of opium ≤21 years on a multiplicative scale causes 77% of the odds of developing PLC, other behavioral risk factors predict only 23% of the OR. Also, on the additive scale, the duration of opium use >21 years caused 80% of the odds of getting PLC, and on the multiplicative scale, 93% of the odds of getting PLC.

Cumulative use of opium ≤30 gr-year caused 72% of the odds of getting PLC on an additive scale and 84% of the odds of getting PLC on a multiplicative scale. On an additive scale, the cumulative consumption of opium >30 gr-year caused 78% of the odds of getting PLC, and on a multiplicative scale, 91% of the odds of getting PLC.

Also, the results showed that the age of starting opium consumption ≤50 years predicts 79% of the OR on the additive scale and 92% of the chance ratio on the multiplicative scale.

In addition, the age of starting opium use <50 years predicts 45% of the OR in the additive scale and 48% of the OR in the multiplicative scale.

Also, the results showed that, on an additive scale, the smoking method of opium use predicted 73% of the odds of getting PLC, and on a multiplicative scale, it predicted 87% of the odds of getting PLC. In addition, the ingestion method of opium consumption includes 94% on additive scale and 98% on multiplicative scale of the odds of PLC.

Furthermore, on an additive scale, 69% of association goes through the path of raw opium and 31% through the path of other behavioral risk factors. Also, the raw type of opium in the multiplicative scale includes 84% of the association (Table 2).

Table 2.

The association of opium use and the risk of primary liver cancer.

     Excess Odds Explain(%)
VariablesControl (%)PLCa Case (%)Crude OR (95% CIb)Adjusted ORc (95% CI)Additive scaleMultiplicative scale
Opium use   
 Never 211 (90.17) 70 (59.83)   
 Ever 23 (9.83) 47 (40.17) 7.98 (3.96–16.06) 6.5 (2.87–13.44) 21 10 
Amount of daily opium   
 Never 211 (90.17) 70 (59.83)   
 ≤1 gr/d 12 (5.13) 13 (11.11) 4.03 (1.61–10.06) 3.27 (1.22–8.74) 25 15 
 >1 gr/d 11 (4.70) 34 (29.06 13.33 (5.34–33.24) 10.70 (3.99–28.72) 21 
 P trend   <0.0001 <0.0001   
Duration of opium use   
 Never 211 (90.17) 70 (59.83)   
 ≤21 years 13 (5.56) 8 (6.84) 2.67 (0.98–7.30) 2.13 (0.7–6.46) 32 23 
 >21 years 10 (4.27) 39 (33.33) 14.41 (5.89–35.25) 11.65 (4.43–30.67) 20 
Ptrend   <0.0001 <0.0001   
Cumulative use of opium   
 Never 211 (90.17) 70 (59.83)   
 ≤30 gr-year 12 (5.13) 16 (13.68) 4.51 (1.87–10. 87) 3.5 (1.36–9.37) 28 16 
 >30 gr-year 11 (4.70) 31 (26.50) 13.96 (5.2–37.46) 11.00 (3.83–31.58) 22 
Ptrend 14.06 (5.88–23.61) 11.31 (4.41–29.0) <0.0001 <0.0001   
Initial age of opium use   
 Never user 211 (90.17) 70 (59.83)   
 >50 years old 14 (5.98) 4 (3.42) 1.34 (0.37–4.8) 1.15 (0.3–4.33) 55 52 
 ≤50 years old 9 (3.85) 43 (36.75) 14.06 (5.88–23.61) 11.31 (4.41–29.0) 21 
Route of opium use   
 Never user 211 (90.17) 70 (59.83)   
 Smoking 22 (9.40) 36 (30.77) 7.84 (3.5–17.55) 5.94 (2.47–14.25) 27 13 
 Ingestion 1 (0.43) 11(9.4) 32.77 (3.96–270.63) 30.57 (3.45–270.37) 
Type of opium   
 Never user 211 (90.17) 70 (59.83)   
 Raw opium 23 (9.83) 37 (31.62) 6.36 (3.10–13.06) 4.67 (2.07–10.0) 31 16 
 Refined opium 0(0.0) 8(6.84) — — — — 
 Both 0(0.0) 2(1.71) — — — — 
     Excess Odds Explain(%)
VariablesControl (%)PLCa Case (%)Crude OR (95% CIb)Adjusted ORc (95% CI)Additive scaleMultiplicative scale
Opium use   
 Never 211 (90.17) 70 (59.83)   
 Ever 23 (9.83) 47 (40.17) 7.98 (3.96–16.06) 6.5 (2.87–13.44) 21 10 
Amount of daily opium   
 Never 211 (90.17) 70 (59.83)   
 ≤1 gr/d 12 (5.13) 13 (11.11) 4.03 (1.61–10.06) 3.27 (1.22–8.74) 25 15 
 >1 gr/d 11 (4.70) 34 (29.06 13.33 (5.34–33.24) 10.70 (3.99–28.72) 21 
 P trend   <0.0001 <0.0001   
Duration of opium use   
 Never 211 (90.17) 70 (59.83)   
 ≤21 years 13 (5.56) 8 (6.84) 2.67 (0.98–7.30) 2.13 (0.7–6.46) 32 23 
 >21 years 10 (4.27) 39 (33.33) 14.41 (5.89–35.25) 11.65 (4.43–30.67) 20 
Ptrend   <0.0001 <0.0001   
Cumulative use of opium   
 Never 211 (90.17) 70 (59.83)   
 ≤30 gr-year 12 (5.13) 16 (13.68) 4.51 (1.87–10. 87) 3.5 (1.36–9.37) 28 16 
 >30 gr-year 11 (4.70) 31 (26.50) 13.96 (5.2–37.46) 11.00 (3.83–31.58) 22 
Ptrend 14.06 (5.88–23.61) 11.31 (4.41–29.0) <0.0001 <0.0001   
Initial age of opium use   
 Never user 211 (90.17) 70 (59.83)   
 >50 years old 14 (5.98) 4 (3.42) 1.34 (0.37–4.8) 1.15 (0.3–4.33) 55 52 
 ≤50 years old 9 (3.85) 43 (36.75) 14.06 (5.88–23.61) 11.31 (4.41–29.0) 21 
Route of opium use   
 Never user 211 (90.17) 70 (59.83)   
 Smoking 22 (9.40) 36 (30.77) 7.84 (3.5–17.55) 5.94 (2.47–14.25) 27 13 
 Ingestion 1 (0.43) 11(9.4) 32.77 (3.96–270.63) 30.57 (3.45–270.37) 
Type of opium   
 Never user 211 (90.17) 70 (59.83)   
 Raw opium 23 (9.83) 37 (31.62) 6.36 (3.10–13.06) 4.67 (2.07–10.0) 31 16 
 Refined opium 0(0.0) 8(6.84) — — — — 
 Both 0(0.0) 2(1.71) — — — — 

aPLC, Primary Liver Cancer. Cases and controls were matched on age and gender.

b95% CI, 95% confidence interval.

cOdds ratio were adjusted for, education, marital status, cigarette smoking.

Furthermore, in the present study, the interactions between cigarette smoking (ever use) and opium consumption (ever use or cumulative dose) were not statistically significant (P = 0.3 and 0.7, respectively).

To evaluate the stability of the estimate of cumulative consumption OR, we perform a sensitivity analysis of the association between cumulative opium use and PLC, and we observed that the OR changed by a maximum of 5% (Supplementary Table S1). This indicates stability and robustness in the estimated OR. Moreover, sensitivity analysis of self-reporting of opium use was significant after reducing the number of opium users to 50% of cases and controls. Moreover performing a sensitivity analysis between the cumulative use of opium and the occurrence of PLC showed that the estimated OR changes by a maximum of 5%, and this indicates the stability and robustness of the estimate.

In this study, we found that opium use was highly associated with the risk of PLC. We found a strong increase in the risk of PLC associated with the early onset of opium use and oral ingestion of opium. Moreover, the dose–response relationship between opium and PLC was convincingly demonstrated. Our results were in line with one of the largest cohort studies in Iran, the Golestan cohort study; the incidence rate of PLC was about 2.5 times higher among opium users than non-users (AOR, 2.46; 95% CI, 1.23–4.95; ref. 28). PLC mortality increased to six times among opioid-dependent participants registered in the opioid substitution therapy (OST) program (34).

Our data would suggest that confounding behavioral factors reduce the PLC ORs approximately 21% on additive scale and from 10% on multiplicative scale of the predicted effect of opium use on PLC, meaning that other behavioral factors have limited potential for reducing opium use in PLC ORs, and it is noteworthy to consider opium use as a substantial element of PLC disease in our society if there are no residual confounding factors.

On the basis of the recent publication in nature genetic it was shown that mutational profiles were similar across all countries. In addition, association between specific mutational signature and esophageal squamous cell carcinoma were defined for some risk factors like tobacco, alcohol, and opium. In this study, opium consumption was identified as a mutational signature associated with ESCC. However, the researchers were unable to fully distinguish it from other signatures with overlapping contexts. Further in vitro studies are recommended to determine the signature effect of opium as a mutagen on cancers (35).

Aside from the biological plausibility of opium and the etiology of PLC, a brief look at the incidence rate of PLCin Iran and comparing it with the prevalence of opium use could further clarify the association. The current study was carried out in Kerman, Southeastern Iran, where the prevalence of opium use has been estimated to be about 10% in 15 to 75 years the population (29); in addition, the prevalence of risk factors such as hepatitis B and alcohol drinking was similar to other parts of Iran (36). Therefore, another risk factor like opium might be considered. ASR of PLC of Kerman was 0.7, whereas the liver ASR of Iran was less than 0.2 (19, 37). Three folds higher ASR of the high prevalence area of opium use suggests that opium could play an important role in PLC etiology.

Opium use is a stigma behavior in Iran. The major challenge of our study was to collect reliable data and avoid misclassification bias. Previous studies have shown that neighbors could be a suitable source for control selection for studies with sensitive questions like drug abuse; nonetheless, neighbors suffered from a sort of under-reporting (38, 39). We trained the interviewer to deal with the stigma situation by explaining the confidentiality of contact information, data gathering, and storage. Patients are less under-reported for drug abuse than controls because doctors have to know about drug abuse before starting treatment to reduce withdrawal syndrome. Moreover, the association was valid in sensitivity analysis of opium self-reporting, even by half positive opium reporting in both cases and controls.

OR could be an appropriate measure of the risk ratio in rare diseases, including cancer. So, the excess odds explained in the current study would be a proper estimate of excess risk explain.

We chose neighborhood controls for this study because they were a better choice than hospital controls because opium use causes a variety of known and unknown diseases (38). Furthermore, friend/family controls had the disadvantage of being overmatched for opium use, as these habits predominate in specific social networks.

This study has other limitations. Although we tried to adjust major risk factors, some were missed, like the history of hepatitis B or C, cirrhosis, family history of cancer, and exposure to Aflatoxin. Drug abusers are more likely to have criminal records and a higher prevalence of syringe sharing and hepatitis incidence (40). Broad hepatitis vaccination has been done since the 1990s; nevertheless, future studies must seriously consider the infections. However, on the basis of the systematic and met regression analysis that was done in Iran from 2000 to 2016 it was shown that the age standardized hepatitis B prevalence in Iran decreased from 3.92% in 2000 to 1.09% in 2016. In addition, the annual prevalence of hepatitis in Kerman was 3.22, and 4.44 in 2000, and this prevalence declined to 0.88 and 0.81 in 2016. Also, Iran has shown a low prevalence of hepatitis B compared with its neighboring countries. Therefore, we can conclude that the very low prevalence of hepatitis B in Kerman especially in recent years can decline the magnitude effect of hepatitis for inducing PLC (41).

Long-term alcohol drinking has been linked to an increased risk of PLC, but the Islamic culture of Iran's alcohol drinking is deficient (24). Therefore, Iran is a unique place to study other risk factors (such as opioids) of PLC (41). The use of opioids like hydrocodone, oxycodone, codeine, tramadol, and fentanylize increasing for medical and non-medical use globally (42); accordingly, severe health consequences such as PLC have to be investigated widely, and a consortium may pave the way to study this health challenge comprehensively.

In conclusion, the results of this study indicate that opium use dramatically increased the risk of PLC.

No disclosures were reported by all authors.

M. Marzban: Conceptualization, resources, data curation, software, formal analysis, funding acquisition, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. E. Mohebbi: Conceptualization, data curation, software, formal analysis, investigation, visualization, methodology, writing–original draft. A.A. Haghdoost: Investigation, methodology. M. Aryaie: Visualization, writing–original draft, writing–review and editing. M.J. Zahedi: Investigation, methodology, writing–original draft. Z. Khazaei: Data curation, investigation, visualization, methodology, writing–original draft. M. Gholizade: Conceptualization, data curation, software, formal analysis, investigation, visualization, methodology, writing–original draft. A. Naghibzadeh-Tahami: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing.

This study was partly funded by Clinical Research Development Unit, Shafa Hospital of Kerman University of Medical Sciences (KUMS) through grant number 97000899 (A. Naghibzadeh-Tahami). The funding bodies were not involved in the study design, study implementation, or writing the article not-for-profit sectors. The authors would like to express their gratitude to all study participants for their cooperation in conducting face-to-face interviews. We also thank Pouya Dehghani, who interviewed the participants. The authors also would like to thank the Physiology Research Center at Kerman University of Medical Sciences (KUMS) for financially supporting this research through grant number 97000899.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Cancer Prevention Research Online (http://cancerprevres.aacrjournals.org/).

1.
World drug report
.
Vienna: United Nations Office on Drugs and Crime, UNODC; 2019
.
E.19.XI.8. Available from
: https://wdr.unodc.org/wdr2019/.
2.
WHO
.
Carcinogenicity of opium consumption
.
Lancet Oncol
2020
;
21
:
1407
8
.
3.
Shakeri
R
,
Malekzadeh
R
,
Etemadi
A
,
Nasrollahzadeh
D
,
Aghcheli
K
,
Sotoudeh
M
, et al
.
Opium: an emerging risk factor for gastric adenocarcinoma
.
Int J Cancer
2013
;
133
:
455
61
.
4.
Naghibzadeh Tahami
A
,
Khanjani
N
,
Yazdi Feyzabadi
V
,
Varzandeh
M
,
Haghdoost
AA
.
Opium as a risk factor for upper gastrointestinal cancers: a population-based case–control study in Iran
.
Arch Iran Med
2014
;
17
:
2
6
.
5.
Bidary
MZ
,
Sahranavard
M
,
Rezayat
AA
,
Omranzadeh
A
,
Hoseiny
SH
,
Kabirian
A
, et al
.
Opium as a carcinogen: a systematic review and meta-analysis
.
EClinicalMedicine
2021
;
33
:
100768
.
6.
Rashidian
H
,
Haghdoost
AA
,
Hadji
M
,
Marzban
M
,
Gholipour
M
,
Zendehdel
K
.
Association between opium use and bladder cancer: a case–control study in a high-risk area of Iran
.
Clin Epidemiol Global Health
2021
:
100772
.
7.
Mohebbi
E
,
Hadji
M
,
Rashidian
H
,
Rezaianzadeh
A
,
Marzban
M
,
Haghdoost
AA
, et al
.
Opium use and the risk of head and neck squamous cell carcinoma
.
Int J Cancer
2021
;
148
:
1066
76
.
8.
Naghibzadeh-Tahami
A
,
Marzban
M
,
Yazdi-Feyzabadi
V
,
Dabiri
S
,
Mohseni
S
,
Rayeni
RA
, et al
.
Is opium use associated with an increased risk of lung cancer? A case–control study
.
BMC Cancer
2020
;
20
:
1
8
.
9.
Akbari
M
,
Naghibzadeh-Tahami
A
,
Khanjani
N
,
Baneshi
MR
,
Kamali
E
,
Hesampour
M
, et al
.
Opium as a risk factor for bladder cancer: a population-based case–control study in Iran
.
Arch Iran Med
2015
;
18
:
567
71
.
10.
Hadji
M
,
Rashidian
H
,
Marzban
M
,
Naghibzadeh-Tahami
A
,
Gholipour
M
,
Mohebbi
E
, et al
.
Opium use and risk of bladder cancer: a multi-centre case-referent study in Iran
.
Int J Epidemiol
2022
;
51
:
830
8
.
11.
lankarani
KB
,
Khosravizadegan
Z
,
Naghibzadeh-Tahami
A
,
Akbari
M
,
Khodadost
M
,
Honarvar
B
, et al
.
Opium use and risk of lower gastrointestinal cancers: population-based case–control study in South of Iran
.
Int J Cancer Manag
2017
;
10
:
e8227
.
12.
Naghibzadeh-Tahami
A
,
Marzban
M
,
Yazdi-Feyzabadi
V
,
Khazaei
Z
,
Zahedi
MJ
,
Moazed
V
, et al
.
Opium use as an independent risk factor for pancreatic cancer: A case-control study
.
Cancer Epidemiol
2021
;
75
:
102017
.
13.
Hadji
M
,
Rashidian
H
,
Marzban
M
,
Gholipour
M
,
Naghibzadeh-Tahami
A
,
Mohebbi
E
, et al
.
The Iranian study of opium and cancer (IROPICAN): rationale, design, and initial findings
.
Arch Iran Med
2021
;
24
:
167
76
.
14.
Rashidian
H
,
Hadji
M
,
Gholipour
M
,
Naghibzadeh-Tahami
A
,
Marzban
M
,
Mohebbi
E
, et al
.
Opium use and risk of lung cancer: a multicenter case-control study in Iran
.
Int J Cancer
2022
.
[Epub ahead of print]
.
15.
Tang
B
,
Li
Y
,
Yuan
S
,
Tomlinson
S
,
He
S
.
Upregulation of the δ opioid receptor in liver cancer promotes liver cancer progression both in vitro and in vivo
.
Int J Oncol
2013
;
43
:
1281
90
.
16.
Liu
Z
,
Suo
C
,
Mao
X
,
Jiang
Y
,
Jin
L
,
Zhang
T
, et al
.
Global incidence trends in primary liver cancer by age at diagnosis, sex, region, and etiology, 1990–2017
.
Cancer
2020
;
126
:
2267
78
.
17.
Petrick
JL
,
Braunlin
M
,
Laversanne
M
,
Valery
PC
,
Bray
F
,
McGlynn
KA
.
International trends in liver cancer incidence, overall and by histologic subtype, 1978–2007
.
Int J Cancer
2016
;
139
:
1534
45
.
18.
Wang
H
,
Naghavi
M
,
Allen
C
,
Barber
RM
,
Bhutta
ZA
,
Carter
A
, et al
.
Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of disease study 2015
.
Lancet North Am Ed
2016
;
388
:
1459
544
.
19.
Mirzaei
M
,
Ghoncheh
M
,
Pournamdar
Z
,
Soheilipour
F
,
Salehiniya
H
.
Incidence and trend of liver cancer in Iran
.
J Coll Physicians Surg Pak
2016
;
26
:
306
9
.
20.
Nikfarjam
A
,
Shokoohi
M
,
Shahesmaeili
A
,
Haghdoost
AA
,
Baneshi
MR
,
Haji-Maghsoudi
S
, et al
.
National population size estimation of illicit drug users through the network scale-up method in 2013 in Iran
.
Int J Drug Policy
2016
;
31
:
147
52
.
21.
Roshandel
G
,
Ferlay
J
,
Ghanbari-Motlagh
A
,
Partovipour
E
,
Salavati
F
,
Aryan
K
, et al
.
Cancer in Iran 2008 to 2025: recent incidence trends and short-term predictions of the future burden
.
Int J Cancer
2021
:
594
605
.
22.
Bosetti
C
,
Turati
F
,
La Vecchia
C
.
Hepatocellular carcinoma epidemiology
.
Best Pract Res Clin Gastroenterol
2014
;
28
:
753
70
.
23.
Smoke
T
,
Smoking
I
.
IARC monographs on the evaluation of carcinogenic risks to humans
.
IARC, Lyon.
2004
:
1
1452
.
24.
Peacock
A
,
Leung
J
,
Larney
S
,
Colledge
S
,
Hickman
M
,
Rehm
J
, et al
.
Global statistics on alcohol, tobacco and illicit drug use: 2017 status report
.
Addiction
2018
;
113
:
1905
26
.
25.
Amin-Esmaeili
M
,
Rahimi-Movaghar
A
,
Sharifi
V
,
Hajebi
A
,
Radgoodarzi
R
,
Mojtabai
R
, et al
.
Epidemiology of illicit drug use disorders in Iran: prevalence, correlates, comorbidity and service utilization results from the Iranian Mental Health Survey
.
Addiction
2016
;
111
:
1836
47
.
26.
Nakhaee
N
,
Divsalar
K
,
Meimandi
MS
,
Dabiri
S
.
Estimating the prevalence of opiates use by unlinked anonymous urine drug testing: a pilot study in Iran
.
Subst Use Misuse
2008
;
43
:
513
20
.
27.
Bewley-Taylor
DR
,
Nougier
M
.
Measuring the ‘world drug problem’: 2019 and beyond. Collapse of the Global Order on Drugs: From UNGASS 2016 to Review 2019
:
Emerald Publishing Limited
;
2018
;
65
83
.
28.
Sheikh
M
,
Shakeri
R
,
Poustchi
H
,
Pourshams
A
,
Etemadi
A
,
Islami
F
, et al
.
Opium use and subsequent incidence of cancer: results from the golestan cohort study
.
Lancet Glob Health
2020
;
8
:
e649
e60
.
29.
Najafipour
H
,
Masoomi
M
,
Shahesmaeili
A
,
Haghdoost
AA
,
Afshari
M
,
Nasri
HR
, et al
.
Effects of opium consumption on coronary artery disease risk factors and oral health: results of kerman coronary artery disease risk factors study a population-based survey on 5,900 subjects aged 15–75 years
.
Int J Prev Med
2015
;
6
:
42
.
30.
Ziaaddini
H
,
Ziaaddini
MR
.
The household survey of drug abuse in Kerman, Iran
.
J Appl Sci
2005
;
5
:
380
2
.
31.
Abnet
CC
,
Saadatian-Elahi
M
,
Pourshams
A
,
Boffetta
P
,
Feizzadeh
A
,
Brennan
P
, et al
.
Reliability and validity of opiate use self-report in a population at high risk for esophageal cancer in Golestan, Iran
.
Cancer Epidemiol Biomarkers Prev
2004
;
13
:
1068
70
.
32.
Mohebbi
E
,
Kamangar
F
,
Rahimi-Movaghar
A
,
Haghdoost
AA
,
Etemadi
A
,
Amirzadeh
S
, et al
.
An exploratory study of units of reporting opium usein iran: implications for epidemiologic studies
.
Arch Iran Med
2019
;
22
:
541
5
.
33.
Hidalgo
M
.
Pancreatic cancer
.
N Engl J Med
2010
;
362
:
1605
17
.
34.
Randall
D
,
Degenhardt
L
,
Vajdic
CM
,
Burns
L
,
Hall
WD
,
Law
M
, et al
.
Increasing cancer mortality among opioid-dependent persons in Australia: a new public health challenge for a disadvantaged population
.
Aust N Z J Public Health
2011
;
35
:
220
5
.
35.
Moody
S
,
Senkin
S
,
Islam
S
,
Wang
J
,
Nasrollahzadeh
D
,
Cortez Cardoso Penha
R
, et al
.
Mutational signatures in esophageal squamous cell carcinoma from eight countries with varying incidence
.
Nat Genet
2021
;
53
:
1553
63
.
36.
Mohsenizadeh
M
,
Mollaei
HR
,
Ghaziizadeh
M
.
Seroepidemiological study of hepatitis B, C, and HIV among blood donors in Kerman. Asian Pacific journal of cancer prevention
:
APJCP
2017
;
18
:
3267
.
37.
Moghaddam
SD
,
Haghdoost
AA
,
Hoseini
SH
,
Ramazani
R
,
Rezazadehkermani
M
.
Incidence of hepatocellular carcinoma in Southeast Iran
.
Hepat Mon
2010
;
10
:
270
.
38.
Shakeri
R
,
Kamangar
F
,
Nasrollahzadeh
D
,
Nouraie
M
,
Khademi
H
,
Etemadi
A
, et al
.
Is opium a real risk factor for esophageal cancer or just a methodological artifact? Hospital and neighborhood controls in case–control studies
.
PLoS ONE
2012
;
7
:
e32711
.
39.
Mohebbi
E
,
Rashidian
H
,
Tahami
AN
,
Haghdoost
AA
,
Rahimi-Movaghar
A
,
Seyyedsalehi
MS
, et al
.
Opium use reporting error in case–control studies: neighborhood controls versus hospital visitor controls
.
Med J Islam Repub Iran
2021
;
35
:
60
.
40.
Xia
X
,
Luo
J
,
Bai
J
,
Yu
R
.
Epidemiology of hepatitis C virus infection among injection drug users in China: systematic review and meta-analysis
.
Public Health
2008
;
122
:
990
1003
.
41.
Rezaei
N
,
Asadi-Lari
M
,
Sheidaei
A
,
Gohari
K
,
Parsaeian
M
,
Khademioureh
S
, et al
.
Epidemiology of Hepatitis B in Iran from 2000 to 2016: a systematic review and meta-regression analysis
.
Arch Iran Med
2020
;
23
:
189
96
.
42.
Merz
F
.
United nations office on drugs and crime: world drug report 2017. 2017
.
SIRIUS-Zeitschrift für Strategische Analysen
2018
;
2
:
85
6
.

Supplementary data