Temporal trend in the national and sub-national burden of cancers attributable to risk factors in Iran from 1990 to 2021: Findings from the global burden of disease study 2021

Seyede Maryam Mousavi; Sobhan Younesian; Saba Katebian; Ali Golestani; Shaghayegh Khanmohammadi; Sepehr Khosravi; Yasaman Etemadi; Nazila Rezaei; Sina Azadnajafabad; Bagher Larijani

doi:10.1371/journal.pone.0330993

. 2025 Aug 26;20(8):e0330993. doi: 10.1371/journal.pone.0330993

Temporal trend in the national and sub-national burden of cancers attributable to risk factors in Iran from 1990 to 2021: Findings from the global burden of disease study 2021

Seyede Maryam Mousavi ^1,^2,^#, Sobhan Younesian ^1,^2,^#, Saba Katebian ¹, Ali Golestani ¹, Shaghayegh Khanmohammadi ^1,³, Sepehr Khosravi ¹, Yasaman Etemadi ¹, Nazila Rezaei ¹, Sina Azadnajafabad ^1,^*, Bagher Larijani ^4,^*

Editor: Claudio Alberto Dávila-Cervantes,⁵

PMCID: PMC12380304 PMID: 40857242

Abstract

Background

Cancer is among leading causes of death globally and in Iran. However, studies exploring cancer risk factors trends in Iran are scarce. In this study, we provide estimations of risk-attributable cancer burden at the national and subnational levels in Iran from 1990 to 2021.

Methods

This study utilized data from the Global Burden of Disease (GBD) 2021 Study to estimate cancer-related years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs), and deaths attributable to behavioral, metabolic, and environmental/occupational risks in Iran nationally and subnationally, from 1990 to 2021. Summary exposure values (SEV) were given to assess the level of exposure. All estimations were reported along with 95% uncertainty intervals (UI).

Results

In 2021, 29.2% (95% UI: 22.9%–35.7%) of cancer deaths, equaling 16,893 (13,332–20,914) deaths and age-standardized rate of 22.66 (17.90–28.14), were attributable to risk factors in Iran. Since 1990, the number of risk-attributable cancer deaths increased by 192% (146% to 242%). Regarding attributable DALYs and deaths, the key risk factors were tobacco, dietary risks, and high body-mass index (BMI), with high BMI and high fasting plasma glucose increasing by two-fold in DALYs. Tracheal, bronchus, and lung cancer, followed by colorectal cancer and stomach cancer, had the highest risk-attributable number of DALYs and deaths in both sexes. The risk-attributable age-standardized DALY rates for ovarian cancer [207% (87%–382%)], thyroid cancer [198% (74%–294%)], and multiple myeloma [192% (98%–349%)] showed the most significant increases.

Conclusions

The all-age number of cancer deaths attributable to risk factors have increased in Iran. The age-standardized DALY rates attributable to high BMI and high FPG doubled from 1990 to 2021, indicating the emerging role of metabolic risk factors in cancer burden. These insights will guide effective cancer prevention strategies in Iran.

1. Introduction

Cancer remains a leading public health threat worldwide, causing 20 million new cases and 9.7 million deaths in 2022. Based on the Global Cancer Observatory (GCO) projections, over 35 million new cancer cases will occur in 2050 [1]. In 2019, 44% of cancer-related deaths were attributable to modifiable risk factors, with tobacco, alcohol, and high body mass index (BMI) as the leading risk factors globally [2]. In 2020, of the 5.28 million cancer-related deaths, 3.63 million deaths could have been averted worldwide through risk factor mitigation and early diagnosis [3].

Despite the higher incidence of cancer in high-income countries (HICs), cancer death rates are higher in low- and middle-income countries (LMICs). By 2030, up to three-fourths of all cancer deaths will happen in LMICs [4]. Iran, classified as an LMIC, had 137,138 incident cancer cases in 2022 [5,6], and this number is anticipated to increase by 110% until 2045 [7]. The Sustainable Development Goals (SDG), proposed by the United Nations in 2015, encompass reducing premature mortality caused by Non-Communicable Diseases (NCDs), including cancer, by one-third by 2030 as its Target 3.4 [8]. However, in many LMICs, national preventive cancer control plans do not exist. As a primary prevention strategy, reducing cancer risk factors is a major step towards this SDG target and should be undertaken at governmental, community, and individual levels [9]. Although many cancer risk factors are consistently reported across different countries, genetic, cultural, and socioeconomic variations between regions shape country-specific risk factors associated with various cancer types [10,11]. The World Health Organization (WHO) framework for national cancer control programs involves three planning steps, with the identification and prioritization of country-level risk factors as the first step [12]. Therefore, studies to estimate the relevance and significance of cancer-related risk factors in each country are mandated, as they aid in assessing progress toward SDG targets and inform planning for future policies.

Costs associated with cancer treatment are a substantial economic burden in Iran, underscoring the necessity of preventive strategies against cancer development [13]. The Iran National Cancer Control Program (IrNCCP), developed in 2013, is dedicated to cancer prevention, early detection, diagnosis, and treatment. Regarding cancer prevention, the initial goal is to determine priorities based on prevalent cancer types and the attributable risk factors at national and provincial levels [14]. A previous study indicated that in 2020, 33.8% of new cancer cases in Iran were attributable to preventable risk factors, with smoking, excess weight, and opium use identified as the leading risk factors [15]; in the same year, more than half of the years of life lost (YLLs) due to premature cancer deaths in Iran were avoidable through control measures, including elimination of the risk factors [3]. In particular, the prevalence of opium use among Iranian males is 10%, and it is projected that unless this risk factor is mitigated, one-third of the total incident cancer cases between 2020 and 2030 will be attributable to opium use [16]. Tobacco is yet another identified cancer risk factor in Iran, with 43% of cancer patients being tobacco users [17].

Currently available information on the relevant cancer risk factors in Iran is scarce and mainly comes from studies that focus on a single cancer type or risk factor [18]. To fill this gap of knowledge, we explored the burden of total and site-specific cancers attributable to risk factors and their patterns in Iran from 1990 to 2021 based on the Global Burden of Disease (GBD) 2021 study [19,20]. As the GBD 2021 study provided provincial information on cancer-related risk factors and their attributable burden in Iran, we also presented and compared subnational risk factors and their associated cancer types. By the means mentioned above, this study imparts updated information on cancer burden attributable to behavioral, metabolic, and environmental/occupational risk factors in Iran over 32 years. Furthermore, the role of sociodemographic differences in determining regional cancer risk factors was assessed by categorizing provinces based on their sociodemographic index (SDI) levels. The findings of this study will provide insights into the current status of risk-attributable cancer burden and introduce the related risk factors nationwide, guiding the forthcoming preventive strategies while evaluating the effectiveness of previously taken actions.

2. Materials and methods

2.1. Overview

The GBD study, conducted by the Institute for Health Metrics and Evaluation (IHME), provides insights into various health metrics, such as incidence, prevalence, mortality, YLLs, years lived with disability (YLDs), and disability-adjusted life years (DALYs), across 204 countries and 811 sub-national locations from 1990 to 2021. GBD 2021, containing two new causes of death and one non-fatal cause since the last iteration, is the most up-to-date version of GBD [21]. The 2021 GBD study offers annual estimates on the burden of 371 diseases and injuries [21], 288 causes of death [20], and 88 risk factors [19]. The cancer estimates in GBD 2021 differ from GBD 2019 for several reasons. First, the new data and updates from registries included a significant amount of new pediatric cancer registry data, which informed the MIRs. Second, GBD 2021 introduced new cancers, requiring a shift from “other malignant neoplasms” to these new causes. These additions necessitated the development of new models and, in some instances, new estimation approaches due to the rarity and limited data of these cancers. The GBD study and current report adhere to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) throughout its analytical process [22]. All data used in this study are publicly accessible via the GBD Compare (https://vizhub.healthdata.org/gbd-compare) and GBD Results (http://ghdx.healthdata.org/gbd-results-tool) web pages.

2.2. Definitions

The GBD study categorizes diseases and injuries hierarchically, from level 1 to level 4, and further into levels 5 and 6 for sequelae. NCDs, a level 1 cause, comprise 12 categories. Cancer, also termed neoplasms, is a level 2 cause and includes 34 level 3 cancers. Cervical, uterine, and ovarian cancers are restricted to females, while prostate and testicular cancers are limited to males. Details on age restrictions, modeling strategies, covariates for each cancer type, and International Classification of Diseases (ICD) codes have been described elsewhere [19,21].

The GBD project employs comparative risk assessment (CRA) to estimate the contribution of risk factors to disease burden. Attributable burden, the main risk assessment used by GBD, targets the change in current disease burden if past populations had adopted a counterfactual risk exposure. This quantification involves various scenarios, including the theoretical minimum risk exposure level (TMREL), representing the lowest achievable risk exposure. TMREL is used to calculate the population attributable fraction (PAF), which measures the proportional reduction in a disease’s burden within a specific year if a counterfactual exposure to a risk factor had occurred [20].

2.3. Data source

In GBD 2021, data on non-fatal causes were obtained from systematic reviews of published literature, survey data, disease registers, and hospital data. The GBD cause of death (CoD) database contains cancer mortality data from various sources like vital registration, verbal autopsy, and cancer registries. The GBD 2021 utilized mortality-to-incidence ratios (MIRs) to transform cancer incidence data to mortality estimates. MIRs were estimated using cancer registry data matched by cancer type, age, sex, year, and location. A spatiotemporal Gaussian process regression (ST-GPR) model incorporated covariates such as age, sex, and the Healthcare Access and Quality Index, with smoothing across time, age, and geography. Additionally, adjustments were made for rare cancers and outliers to ensure reliable estimates across all cancer types and demographic groups. Detailed explanation on the estimation of MIRs are provided elsewhere [19]. Before entering the CoD database, cancer registry data undergo multiple processing steps, such as mapping to GBD causes, age/sex splitting, and cause disaggregation, as detailed elsewhere [19,21]. In GBD 2021, from a total of 544 input citations from Iran, 34 citations have been used to estimate cancer CoD, and an additional 20 citations were input data for the estimation of non-fatal health outcomes of cancer. These input sources, along with their metadata, can be explored from the GBD 2021 Sources Tool (https://ghdx.healthdata.org/gbd-2021/sources). Furthermore, all data sources are accessible via the Global Health Data Exchange (GHDx) web tool (http://ghdx.healthdata.org). Also, the code used to perform all estimations are publicly available at https://github.com/ihmeuw/ihme-modeling/tree/main/gbd_2021. The data for the purpose of this research work was accessed on October 20, 2024.

2.4. Statistical analysis

The PAF for each risk-outcome was multiplied by the burden measures to calculate the risk factor attributable burden, including deaths, YLDs, YLLs, and DALYs. YLDs were determined by multiplying the prevalence of general and procedure sequelae of each cancer type by their respective disability weights before summing them. YLLs were calculated by multiplying the life expectancy of each age group by the number of deaths in that age group. DALYs were then obtained as the sum of YLDs and YLLs [19–21]. The SDI for a location was calculated as the geometric mean of income per capita, average years of education for individuals aged 15 and older, and the total fertility rate for females under 25, scaled from 0 to 1 [23,24]. In 2021, the SDI of various provinces in Iran ranged from 0.55 to 0.78. These provinces were subsequently categorized into five quintiles. The Summary Exposure Value (SEV) measures exposure to a risk factor by considering its risk level and the severity of its outcomes [20]. The 95% uncertainty interval (UI) for each metric was calculated using the 2.5th and 97.5th percentiles from the uncertainty distribution, based on 1000 draws from the posterior distribution [19–21].

To explore the relationship between SEV values and age-standardized DALY rates attributable to the leading risk factors, the annualized rate of change (ARC) was estimated from 1990 to 2021 as below:

\sqrt[n]{\frac{{v a l u e}_{2}}{{v a l u e}_{1}}} - 1

Where value₁ and value₂ are the estimated values at the beginning and end of the time interval, n equals the length of the time interval. ARC of SEV a specific risk factor was compared to the ARC of age-standardized DALY rate attributable to the same risk factor over the same time period.

To discover the non-linear relationship between SDI and percentage of cancer DALYs attributable to risk factors, Estimated Scatterplot Smoothing (LOESS) regression was performed, with SDI values of provinces throughout the study period as the independent variable and the age-standardized percentage of cancer DALYs attributed to all and the top-five risk factors as the dependent variables.

Data analysis and visualization were performed with R (version 4.4.0) using ggplot2 package Tableau Desktop (version 2019.4), and Python programming language (version 3.12.4) using pandas library.

2.5. Ethics statement

This study is a secondary analysis of publicly available, previously collected, and de-identified data from the Global Burden of Disease Study 2021. Therefore, participant consent is not applicable. This study received approval from the institutional review board of the Endocrinology and Metabolism Research Institute at Tehran University of Medical Sciences (IR.TUMS.EMRI.REC.1401.165). The findings are derived from estimates provided by the GBD 2021 study and comply with applicable guidelines and regulations. The data that support the findings of this study are available from the IHME through https://vizhub.healthdata.org/gbd-compare and http://ghdx.healthdata.org/gbd-results-tool.

3. Results

3.1. Overview

In 2021, a total number of 16,893 (95% UI: 13,332–20,914) cancer deaths attributable to risk factors, which was 29.2% (22.9–35.7) of all cancer deaths, occurred in Iran. The total number of risk-attributable cancer deaths has increased by 192% (146–242). In females, the number of risk factor-attributable cancer deaths has risen from 1,758 (1,207–2,376) in 1990–5,933 (3,863–7,802) in 2021; in males, the total number of risk factor-attributable deaths was 4,019 (3,241–5,312) in 1990 and increased to 10,960 (9,195–13,183) in 2021 (Table 1). In 2021, the cancer age-standardized death rate (ASDR) attributable to risk factors was 22.7 (17.9–28.1) for both sexes, and the change in ASDR from 1990 to 2021 was not significant in either sex. Overall, the age-standardized DALY rate declined from 1990 to 2003, but an inclining trend took over from 2004 to 2019; in 2020, there was a relatively sharp decrease in the age-standardized DALY rate (S1 Fig). A similar trend was observed for the death rates. In 2021, the total number of risk factor-attributable cancer DALYs was 447,269 (350,569–550,594) for both sexes, which equals 27.2% (21.3–33.6) of total DALYs associated with cancers. The age-standardized DALY rate was 544.42 (427.39–669.64) per 100,000 in both sexes in 2021 and had not significantly changed compared to 1990 (Table 1); moreover, the percentage of cancer DALYs attributable to risk factors had not significantly increased since 1990 (S2 Fig). Overall, risk-attributable death and DALY rates were higher in the older age groups than the younger. Similar to 1990, the highest risk-attributable cancer death rate was in the > 80-year-old age group [275.1 (204.8–352.2) per 100,000] in 2021. In 1990 and 2021, the highest DALY rate was seen in the 70–74 and 75–79 age groups, respectively (Fig 1).

Table 1. DALYs, deaths, YLDs, and YLLs of cancer attributable to all risk factors and level 1 risk factors among females, males, and both sexes in Iran in 1990 and 2021 and their percent change.

Risk factors	Measure	Age, Metric	Year						Percent Change (1990-2021)
			1990			2021			Percent Change (1990-2021)
			Both	Female	Male	Both	Female	Male	Both	Female	Male
All risk factors	DALYs	All age number	169057.47 (135238.74 to 218941.85)	55645.71 (38161.28 to 74699.43)	113411.76 (91220.05 to 149142.32)	447268.61 (350568.82 to 550593.51)	165709.97 (108823.27 to 218899.75)	281558.64 (236594.8 to 340049.01)	164.57% (124.58% to 207.05%)	197.79% (138.06% to 259.31%)	148.26% (103.2% to 194.34%)
	DALYs	Age-standardized rate (per 100,000)	584.85 (467.81 to 762.74)	387.69 (264.57 to 524.82)	766.6 (619.83 to 1010.0)	544.42 (427.39 to 669.64)	395.53 (258.69 to 523.92)	696.22 (585.81 to 842.78)	-6.91% (-21.36% to 8.15%)	2.02% (-18.42% to 23.12%)	-9.18% (-25.19% to 7.75%)
	Deaths	All age number	5778.17 (4639.16 to 7583.0)	1758.85 (1207.42 to 2375.57)	4019.31 (3240.63 to 5312.21)	16893.25 (13331.94 to 20914.49)	5933.16 (3863.45 to 7801.84)	10960.09 (9195.23 to 13182.79)	192.36% (146.15% to 241.64%)	237.33% (168.59% to 306.94%)	172.69% (125.22% to 224.12%)
	Deaths	Age-standardized rate (per 100,000)	23.34 (18.59 to 30.75)	14.62 (9.93 to 19.96)	31.81 (25.62 to 42.21)	22.66 (17.9 to 28.14)	15.76 (10.22 to 20.8)	29.66 (24.84 to 35.87)	-2.94% (-18.34% to 12.71%)	7.81% (-14.25% to 29.48%)	-6.77% (-23.14% to 10.58%)
	YLDs	All age number	2845.35 (1847.75 to 4070.69)	1311.37 (681.54 to 2036.5)	1533.98 (1069.24 to 2137.58)	13559.52 (8146.46 to 20113.56)	7782.84 (3716.82 to 12517.0)	5776.69 (4051.35 to 7933.35)	376.55% (286.23% to 462.01%)	493.49% (359.83% to 619.06%)	276.58% (214.66% to 346.92%)
	YLDs	Age-standardized rate (per 100,000)	10.05 (6.61 to 14.41)	9.16 (4.71 to 14.01)	10.9 (7.64 to 15.19)	16.3 (10.0 to 24.07)	18.04 (8.56 to 28.91)	14.62 (10.24 to 20.11)	62.08% (30.17% to 90.13%)	96.99% (53.01% to 139.18%)	34.13% (11.69% to 58.62%)
	YLLs	All age number	166212.12 (133170.12 to 215972.6)	54334.34 (37506.63 to 72761.98)	111877.78 (90113.1 to 147301.29)	433709.08 (341463.17 to 535885.13)	157927.13 (104944.03 to 207852.0)	275781.95 (232326.61 to 333612.37)	160.94% (121.64% to 203.05%)	190.66% (132.45% to 251.2%)	146.5% (101.7% to 192.79%)
	YLLs	Age-standardized rate (per 100,000)	574.8 (460.34 to 751.92)	378.53 (259.51 to 511.17)	755.69 (610.9 to 997.17)	528.13 (416.41 to 652.31)	377.49 (248.29 to 497.18)	681.59 (573.3 to 826.37)	-8.12% (-22.28% to 6.74%)	-0.27% (-20.19% to 20.22%)	-9.81% (-25.76% to 7.1%)
Behavioral risks	DALYs	All age number	142892.05 (110809.4 to 192368.52)	42275.63 (26733.56 to 60877.75)	100616.42 (79386.99 to 135717.35)	326480.52 (255929.1 to 414441.41)	98827.69 (59362.83 to 139891.98)	227652.83 (190099.36 to 280800.23)	128.48% (98.77% to 161.75%)	133.77% (92.47% to 181.73%)	126.26% (91.59% to 167.67%)
	DALYs	Age-standardized rate (per 100,000)	494.79 (385.25 to 668.34)	291.66 (181.89 to 420.96)	681.73 (538.2 to 918.24)	395.46 (310.18 to 501.52)	230.17 (139.87 to 323.29)	563.8 (469.11 to 695.23)	-20.07% (-30.47% to -8.48%)	-21.08% (-33.71% to -5.26%)	-17.3% (-29.99% to -1.79%)
	Deaths	All age number	4898.23 (3817.91 to 6661.06)	1312.31 (819.91 to 1899.37)	3585.92 (2816.76 to 4858.93)	12273.34 (9683.94 to 15558.44)	3380.83 (2108.91 to 4728.31)	8892.51 (7379.37 to 11065.44)	150.57% (118.62% to 187.51%)	157.62% (117.62% to 207.65%)	147.98% (109.5% to 194.86%)
	Deaths	Age-standardized rate (per 100,000)	19.67 (15.21 to 26.94)	10.78 (6.71 to 15.7)	28.28 (22.21 to 38.36)	16.38 (12.89 to 20.86)	8.82 (5.5 to 12.42)	24.05 (19.92 to 30.04)	-16.72% (-27.47% to -4.4%)	-18.21% (-30.19% to -3.45%)	-14.97% (-28.17% to 0.41%)
	YLDs	All age number	2382.43 (1517.96 to 3466.0)	1006.78 (473.65 to 1660.02)	1375.65 (956.97 to 1940.98)	9525.38 (5343.87 to 14616.85)	4879.91 (1814.66 to 8427.02)	4645.47 (3186.4 to 6491.01)	299.82% (226.64% to 370.61%)	384.7% (241.68% to 494.21%)	237.69% (187.75% to 297.25%)
	YLDs	Age-standardized rate (per 100,000)	8.37 (5.41 to 12.12)	6.87 (3.22 to 11.15)	9.77 (6.79 to 13.67)	11.29 (6.51 to 17.05)	10.89 (4.18 to 18.57)	11.79 (8.2 to 16.43)	34.88% (11.47% to 57.66%)	58.59% (13.57% to 93.05%)	20.77% (3.32% to 41.07%)
	YLLs	All age number	140509.62 (109245.59 to 189714.04)	41268.84 (26257.14 to 59388.66)	99240.78 (78076.1 to 133942.6)	316955.14 (250119.81 to 400182.47)	93947.79 (57475.55 to 132101.72)	223007.36 (186641.46 to 275955.62)	125.58% (96.38% to 157.94%)	127.65% (90.38% to 173.04%)	124.71% (90.42% to 166.0%)
	YLLs	Age-standardized rate (per 100,000)	486.42 (379.06 to 658.98)	284.79 (178.67 to 409.78)	671.96 (530.54 to 905.21)	384.17 (302.99 to 485.33)	219.28 (135.89 to 306.4)	552.01 (460.65 to 682.86)	-21.02% (-31.21% to -9.8%)	-23.0% (-35.15% to -7.68%)	-17.85% (-30.54% to -2.42%)
Environmental/ Occupational risks	DALYs	All age number	24208.82 (16421.48 to 33040.01)	4874.97 (2980.65 to 7311.18)	19333.86 (13106.69 to 26737.92)	63656.78 (44318.05 to 83178.45)	17379.15 (10885.28 to 23811.26)	46277.63 (33365.53 to 59918.59)	162.95% (106.28% to 243.48%)	256.5% (145.21% to 418.66%)	139.36% (79.74% to 214.18%)
	DALYs	Age-standardized rate (per 100,000)	83.17 (56.18 to 114.36)	35.34 (21.53 to 52.63)	127.18 (85.53 to 176.35)	77.1 (53.2 to 101.44)	42.37 (26.4 to 57.97)	112.18 (79.99 to 145.71)	-7.3% (-28.01% to 21.64%)	19.91% (-16.99% to 74.45%)	-11.79% (-33.76% to 16.37%)
	Deaths	All age number	834.58 (561.55 to 1152.27)	165.19 (100.18 to 246.46)	669.39 (448.36 to 934.7)	2412.34 (1641.14 to 3200.11)	675.86 (415.99 to 927.14)	1736.48 (1222.81 to 2274.85)	189.05% (123.95% to 280.44%)	309.15% (183.55% to 497.42%)	159.41% (94.04% to 242.9%)
	Deaths	Age-standardized rate (per 100,000)	3.33 (2.23 to 4.64)	1.45 (0.87 to 2.16)	5.14 (3.41 to 7.18)	3.21 (2.15 to 4.28)	1.84 (1.12 to 2.54)	4.6 (3.2 to 6.05)	-3.72% (-25.59% to 27.09%)	26.76% (-11.32% to 85.99%)	-10.5% (-32.59% to 19.1%)
	YLDs	All age number	203.36 (128.07 to 304.9)	39.87 (21.62 to 62.44)	163.5 (104.79 to 251.45)	583.02 (362.28 to 836.6)	154.21 (86.62 to 232.46)	428.82 (270.49 to 605.75)	186.69% (125.89% to 272.11%)	286.79% (166.23% to 458.06%)	162.28% (99.4% to 246.04%)
	YLDs	Age-standardized rate (per 100,000)	0.74 (0.47 to 1.13)	0.31 (0.17 to 0.5)	1.14 (0.72 to 1.75)	0.73 (0.45 to 1.05)	0.39 (0.22 to 0.59)	1.07 (0.67 to 1.53)	-1.39% (-22.47% to 29.67%)	24.94% (-13.98% to 81.08%)	-5.82% (-29.15% to 24.09%)
	YLLs	All age number	24005.46 (16287.02 to 32742.93)	4835.1 (2951.37 to 7255.22)	19170.36 (13015.86 to 26531.46)	63073.75 (43875.8 to 82488.85)	17224.94 (10792.43 to 23575.04)	45848.81 (33061.35 to 59380.95)	162.75% (106.18% to 243.34%)	256.25% (145.03% to 418.26%)	139.17% (79.55% to 214.02%)
	YLLs	Age-standardized rate (per 100,000)	82.43 (55.69 to 113.27)	35.02 (21.3 to 52.19)	126.03 (84.78 to 174.92)	76.37 (52.69 to 100.54)	41.98 (26.14 to 57.36)	111.1 (79.28 to 144.36)	-7.35% (-28.04% to 21.62%)	19.86% (-17.04% to 74.39%)	-11.85% (-33.81% to 16.28%)
Metabolic risks	DALYs	All age number	20470.22 (7477.21 to 32806.83)	11491.2 (4504.37 to 18951.31)	8979.02 (3381.1 to 14551.34)	116123.75 (37867.2 to 193645.66)	63512.63 (19837.2 to 105683.25)	52611.12 (17358.8 to 89768.72)	467.28% (332.39% to 559.86%)	452.71% (293.18% to 558.65%)	485.93% (364.91% to 607.99%)
	DALYs	Age-standardized rate (per 100,000)	71.16 (24.23 to 116.63)	83.11 (31.54 to 139.01)	60.31 (20.75 to 99.16)	144.22 (45.55 to 243.49)	157.79 (48.26 to 264.72)	130.62 (41.69 to 224.05)	102.67% (61.59% to 133.65%)	89.85% (43.16% to 125.52%)	116.57% (76.64% to 156.52%)
	Deaths	All age number	697.03 (233.08 to 1146.88)	386.83 (144.36 to 648.31)	310.2 (102.91 to 513.67)	4474.22 (1367.62 to 7628.18)	2422.59 (731.54 to 4030.2)	2051.63 (627.52 to 3558.83)	541.9% (404.44% to 637.16%)	526.27% (373.48% to 639.85%)	561.39% (434.72% to 687.69%)
	Deaths	Age-standardized rate (per 100,000)	2.9 (0.91 to 4.86)	3.3 (1.16 to 5.56)	2.51 (0.75 to 4.2)	6.09 (1.81 to 10.44)	6.59 (1.95 to 11.0)	5.6 (1.66 to 9.78)	110.27% (71.7% to 138.89%)	99.46% (56.29% to 134.84%)	122.67% (83.25% to 161.84%)
	YLDs	All age number	481.68 (159.28 to 847.89)	328.44 (102.53 to 592.1)	153.24 (56.23 to 268.68)	4680.81 (1342.7 to 8322.0)	3314.99 (855.63 to 5954.92)	1365.82 (490.47 to 2391.1)	871.77% (667.11% to 1039.38%)	909.31% (652.33% to 1145.03%)	791.29% (635.64% to 984.15%)
	YLDs	Age-standardized rate (per 100,000)	1.75 (0.54 to 3.11)	2.48 (0.73 to 4.47)	1.11 (0.38 to 1.95)	5.83 (1.66 to 10.42)	8.18 (1.97 to 14.89)	3.45 (1.2 to 6.08)	232.54% (169.08% to 287.66%)	230.43% (156.19% to 305.48%)	212.07% (158.9% to 274.88%)
	YLLs	All age number	19988.54 (7347.68 to 32059.43)	11162.76 (4392.84 to 18298.95)	8825.78 (3317.07 to 14298.02)	111442.94 (36456.1 to 186744.73)	60197.64 (18976.46 to 98904.24)	51245.3 (16886.85 to 87240.67)	457.53% (327.62% to 545.99%)	439.27% (285.09% to 540.65%)	480.63% (360.68% to 601.34%)
	YLLs	Age-standardized rate (per 100,000)	69.41 (23.75 to 113.73)	80.64 (30.74 to 134.37)	59.21 (20.33 to 97.16)	138.39 (43.88 to 233.46)	149.61 (46.22 to 247.2)	127.17 (40.58 to 217.63)	99.39% (60.18% to 129.04%)	85.53% (39.86% to 119.95%)	114.79% (75.17% to 154.39%)

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DALYs: Disability-Adjusted Life Years. YLDs: Years Lived with Disability. YLLs: Years of Life Lost.

3.2. Risk factors at the national level

At the national level, behavioral risks were the leading level 1 risk factor for cancer deaths and DALYs in 2021, followed by metabolic and environmental/occupational risks in females, males, and both sexes (Table 1). In total, eleven level 2 risk factors were identified, and the trends of their attributable cancer burden are depicted in Figure 2. Among level 2 risk factors, tobacco, dietary risks, and high BMI were the top three risk factors for cancer deaths and DALYs in both sexes, followed by high FPG and air pollution. In females, dietary risks and high BMI were the top contributors to cancer deaths and DALYs; however, high FPG and tobacco were the third leading risk factors of all-age cancer death and DALY numbers in females, respectively. In males, tobacco and dietary risks had the highest attributable all-age cancer DALY and death numbers, and the third leading risk factor was air pollution for deaths and high BMI for DALYs (S1 Table). The contribution of various risk factors to cancer DALYs was different between males and females throughout the 32-year study period (S3 Figure). In 2021, the age-standardized death and DALY rates attributable to the top five cancer risk factors for both sexes had declined since 1990, except for high BMI and high FPG, which had increased. The high BMI and high FPG-attributable age-standardized DALY rates in both sexes elevated by 101% (55–137) and 113% (78–149), respectively from 1990 to 2021. A similar pattern was observed among behavioral risks in males, such as drug use and alcohol use, according to the attributable DALYs (S1 Figure).

3.3. Risk factors at the subnational level

In 2021, the highest age-standardized DALY rates attributable to all cancer risk factors were 705.93 (539.15–929.93) in Ardebil and 678.65 (519.31–814.91) in Golestan and the lowest were 358.20 (274.55–453.20) in Hormozgan and 395.00 (299.91–540.58) in Kohgiluyeh and Boyer-Ahmad. West-Azarbayejan had the highest risk factor-attributable cancer deaths, which was 29.43 (22.59–37.79) in 2021, followed by 29.25 (22.48–38.46) in Ardebil. Similar to DALYs, the lowest attributable death rates were seen in Hormozgan and Kohgiluyeh and Boyer-Ahmad. Cancer-related burden attributable to level 1 risk factors at the subnational level are provided in S2 Table. Overall, behavioral risks were the top risk factor of cancer DALYs in all provinces from 1990 to 2021. In females, a higher proportion of risk factor-attributable cancer DALYs were attributed to metabolic risks, compared to males in 2021 in all provinces; however, the contribution of metabolic risk factors to cancer DALYs has increased in both females and males from 1990 to 2021 (S4 Figure).

The proportion cancer burden attributable to each level 2 risk factor at the subnational level in 2021 is shown in S5 Figure. In general, tobacco, followed by dietary risks and high BMI, are the leading risk factors across all provinces. In 2021, the highest age-standardized cancer DALY rates attributable to tobacco, dietary risks, and air pollution were observed in Ardebil, East Azarbayejan, and Bushehr, respectively, while Tehran had the highest rates for high BMI and high FPG (Figure 3). Among these top-five level 2 risk factors, cancer death rates attributable to high BMI and high FPG have surged the most markedly from 1990 to 2021 in both sexes in all provinces. Ilam, a high-middle SDI province, has experienced the greatest rise in both high BMI- and high FPG-attributable cancer death rates between 1990 and 2021 (Figure 4). Among the top four risk factors of cancer-related DALYs, the burden of high BMI and high FPG has increased across provinces. Still, cancer DALYs attributable to tobacco and dietary risks have declined from 1990 to 2021.

Fig 4 — BMI: Body-mass index; FPG: Fasting plasma glucose.

3.4. Cancer types

The leading cancer types for risk factor-attributable deaths in 2021 were tracheal, bronchus, and lung cancer, with an all-age death count of 5,194 (4,497–5,897) in both sexes, followed by colon and rectum cancer and stomach cancer. In the same order, these three cancer types also led in all-age DALY numbers for males. For females, the top cancer types in terms of all-age DALY numbers were colon and rectum cancer, breast cancer, and tracheal, bronchus, and lung cancer. The risk-attributable age-standardized DALY rates related to ovarian cancer [207% (87–382)], thyroid cancer [198% (74–294)], and multiple myeloma [192% (98–349)] had the greatest surges from 1990 to 2021 (S3 Table). After tracheal, bronchus, and lung cancer, the second leading cancers attributable to tobacco were breast cancer in females and larynx cancer in males, based on both age-standardized death and DALY rates. In females and males, colon and rectum cancer was responsible for the greatest proportion of age-standardized DALY rate attributable to dietary risks, followed by breast cancer in females and stomach cancer in males. The highest age-standardized DALY rate attributable to high BMI in females was related to colon and rectum cancer and breast cancer (Figure 5 and S2 Table).

3.5. SEV

Figure 6 demonstrates the variations in DALY rates attributable to the top four cancer risk factors in relation to changes in their SEV. From 1990 to 2021, the SEV of high BMI and high FPG increased across all provinces. Conversely, tobacco and dietary risks predominantly exhibited declining of SEV. Overall, the ARC of SEV was positively related to ARC of cancer DALYs for tobacco, dietary risks, high BMI, and high FPG. As evident in Figure 6, the reduction in tobacco SEV has been confined to the extremes of SDI values in low and high SDI regions. Additionally, the pattern of change in dietary risks SEV in high SDI provinces mirrors the national trend.

Fig 6 — AL: Alborz; AR: Ardebil; BS: Bushehr; CM: Chahar Mahaal and Bakhtiari; EA: East Azarbayejan; FA: Fars; GI: Gilan; GO: Golestan; HD: Hamadan; HG: Hormozgan: IL: Ilam; ES: Isfahan; KE: Kerman; BK: Kermanshah; KV: Khorasan-e-Razavi; KZ: Khuzestan; KB: Kohgiluyeh and Boyer-Ahmad; KD: Kurdistan; LO: Lorestan; MK: Markazi; MN: Mazandaran; KS: North Khorasan; QZ: Qazvin; QM: Qom; SM: Semnan; SB: Sistan and Blauchistan; KJ: South Khorasan; TE: Tehran; WA: West Azarbayejan; YA: Yazd; Zanjan: ZA.

3.6. The relationship between SDI and PAF for risk factors

Figure 7 illustrates the results of the LOESS analysis. As indicated, the age-standardized percentage of DALYs attributed to all risk factors increased with higher SDI values. Although tobacco showed an overall negative relationship between SDI and the age-standardized attributable percentage of DALYs, also known as PAF, this relationship reversed within the SDI range of about 0.5 to 0.6. Regarding dietary risks, there is a negative relationship between the PAF and SDI within the early SDI range, followed by a transition to a positive relationship in the higher SDI values. High BMI and high FPG exhibit an upward trend in the PAF with increasing SDI values. Moving to air pollution, a negative relationship between PAF and SDI becomes evident in the upper range of SDI values.

4. Discussion

The present study provided an estimation of the cancer burden attributable to risk factors in Iran from 1990 to 2021, based on the GBD 2021 study. Although age-standardized death and DALY rates attributable to risk factors declined between 1990 and 2021, both rates displayed an upward trend throughout most of the intervening years. The net reduction in risk-attributable age-standardized DALY and death rates may largely be explained by their substantial decline in 2020, potentially linked to the COVID-19 pandemic’s impact on delayed cancer screening and diagnosis [25]. During the COVID-19 pandemic, many cancer screening programs were disrupted, primarily because of lockdown measures and the requirement to reallocate healthcare resources. In addition, a reduced quality of data collection and reporting, including possible misclassification of the cause of death, might have resulted in underestimation of cancer burden in Iran throughout the pandemic [26]. Also, a higher proportion of cancer DALYs and deaths was attributable to the estimated risk factors in 2021 compared to 1990. The risk factor-attributable cancer burden was higher in males than in females within all years during the study period. Generally, risk-attributable cancer burden increased with aging, with the highest DALY rate in the 75–79 age group and the highest death rate in the > 80 age group in 2021. Iran shares its leading cancer risk factors, namely tobacco smoking, dietary risks, and obesity, with many other countries in the North Africa and Middle East (NAME) region [27].

While behavioral risks remained the leading cancer risk factor in Iran in all years from 1990 to 2021, the role of metabolic risks in cancer-related burden has become more prominent in recent years. Despite the past, metabolic risks have a greater contribution to cancer burden than environmental/occupational harms. Previous estimations have revealed that the cancer burden attributable to metabolic risk factors is surging worldwide [20]. The global age-standardized cancer DALY rates attributable to high FPG and high BMI increased by less than a fifth from 1990 to 2019 [28]; nevertheless, the age-standardized cancer DALY rate in Iran for high FPG and high BMI each increased over twofold, indicating that Iran is facing a greater surge than the global average. In addition, the proportion of cancer DALYs attributable to high BMI and FPG was higher in provinces with higher SDI values. The shift toward urbanization and, hence, lifestyle alteration within the developing nations, including Iran, has contributed to an increase in non-communicable disease burden, such as cancers [29].

Based on our findings, high BMI was the leading metabolic risk for cancer burden in females and males; however, an analysis of the Tehran Lipid and Glucose study revealed that high BMI alone does not increase cancer incidence in Iranian females and males. Instead, a notable elevation in cancer risk occurs when high BMI is accompanied by other metabolic risks, such as high FPG and high systolic blood pressure (SBP) [30]. Therefore, the rising impact of high BMI and high FPG as significant risk factors on cancer burden in Iran demands urgent attention and policy intervention. Iran enacted the Sugar-Sweetened Beverages (SSBs) taxation program in 2013, which was revised in 2021, imposing a 16% and 36% tax on domestic and imported SSBs, respectively; however, household expenditure on SSBs has not declined in Iran despite taxation [31]. This is while other countries in the Eastern Mediterranean region, such as Saudi Arabia, have successfully reduced SSB consumption through taxation [32]. Iran’s lower taxation rates, lack of consistent enforcement, and inefficient allocation of tax revenues have limited the effectiveness of its SSB taxation program compared to Saudi Arabia [33]. In order to overcome these, implementing a strongly enforced higher taxation rate and redirecting tax revenues toward subsidizing fruits and vegetables are strongly recommended policy options in Iran’s context [31].

Since 1990, there has been a reduction in age-standardized rate of cancer YLLs attributed to behavioral risks, while YLDs have increased. This observation might be derived by advances in cancer survival in Iran over time due to enhanced therapies and increased healthcare access [18]. Also, the survival rates of some leading cancers associated with behavioral risks, including breast cancer, have improved in Iran, although it is still lower than developed countries [34]. From 1990 to 2021, the percent change in the cancer burden of drug and alcohol use was markedly greater in males than females, with males exhibiting a two-fold increase in attributable DALY and death rates. This might largely be explained by the higher prevalence of opium use in males than in females in Iran. A study conducted in Tehran, the capital city of Iran, indicated that the prevalence of opium use was 20 times greater in males than females; additionally, opium and alcohol are consumed in significant association with each other [35]. Opium has been announced as carcinogenic to humans by the International Agency on Research for Cancer (IARC) since 2020 [36]. Previous investigations have attempted to delineate further the association between opium use and various cancers among the Iranian population; accordingly, opium use was found to be associated with gastric [37–39], bladder [39,40], pancreatic [41,42], lung [39,43], colorectal [44], and head and neck cancers [45]. The surge in opium use among Iranian males can be attributed to a combination of factors, including longstanding cultural tolerance of opium and socioeconomic deprivation, such as unemployment and low education [46–48]. Also, Iran’s geopolitical position along the drug trafficking routes and domestic economic instability further contribute to widespread availability and use [49].

According to Iran’s STEPwise approach to risk factors Surveillance (STEPS) 2021 study, 4.44% (4.09–4.82) and 25.88% (25.03–26.75) of females and males were tobacco smokers, respectively [50]. In the present study, the highest risk-attributable cancer death rate in 2021 was observed in West Azarbayejan, the province with the highest amount of cigarette smoking in the STEPS 2021. Our findings emphasize that although tobacco was the leading cancer risk factor at the national and provincial level in 2021, the tobacco-attributable cancer burden declined in Iran from 1990 to 2021. This decline can be attributed to the fact that Iran has adopted the WHO’s proposed measures against tobacco, including monitoring tobacco use, protecting from tobacco smoke, offering help to quit, warning about the harms, enforcing bans, and raising taxes, known as the MPOWER, and was one of the leading Eastern Mediterranean countries in terms of tobacco smoking reduction in the 2010–2023 years [51,52]. Based on the MPOWER progress report in 2023, Iran is just one measure behind achieving all measures at the best-practice level and is one of the eight countries to reach this milestone out of the 182 countries that have adopted the WHO Framework Convention on Tobacco Control (FCTC); accordingly, Iran has wholly achieved all MPOWER goals except for mass media campaigns against tobacco [53]. Therefore, Iran will benefit from emulating prosperous countries’ media policies to reduce tobacco smoking prevalence. Brazil, an outstanding model case in combating tobacco smoking, is the leading country in tobacco use prevalence reduction [54]; Brazil’s anti-tobacco media policies include banning tobacco advertisement except at enclosed points of sale and prohibiting tobacco brands’ sponsorship of public events [55].

As shown by our results, West Azarbayejan and Ardebil, two provinces in the northwest of Iran, had the highest risk-attributable cancer death rates in 2021. Likewise, a previous spatial analysis of lung, gastric, and esophageal cancer mortality related to smoking and dietary risk factors in Iran identified the northwest of the country as a high-risk area. It noted West Azarbayejan and Ardebil as the most affected provinces by risk-related cancer mortality during 2013–2015 [56]. Additionally, a secondary exploration of the Iran STEPS 2016 data spotted the northwestern part of Iran, encompassing West Azarbayejan, Ardebil, and Gilan, as the area with the highest tobacco- and diet-associated incidence of colorectal cancer [57], the second leading cancer in terms of risk factor-attributable burden in Iran in 2021. Preceded by Ardebil, Golestan, located in the northeast of Iran, was the second leading province regarding risk-attributable cancer DALYs. Results from the Golestan Cohort Study have provided exhaustive information on the major risk factors of cancer in this province throughout more than 14 years, highlighting the impact of tobacco and opium use [39,58,59] and dietary risks [59–61]. Importantly, the cancer burden is projected to continuously grow in Golestan province [62], calling for policy interventions, including risk factor prevention and screening programs, to mitigate cancer incidence in this high-risk area.

From 1990 to 2021, the age-standardized DALY rate of ovarian cancer attributable to all risk factors increased the most compared to other cancers. Accordingly, reports from the Iran National Cancer Registry suggest an increasing trend in 2009–2014 [63]. Of note, high BMI and occupational asbestos exposure were the only risk factors related to ovarian cancer in the GBD 2021 study [20]. High BMI is a major risk for ovarian cancer worldwide [64]; in 2021, 68% of the Iranian female adult population had a BMI of ≥ 25 [65]. Thyroid cancer was the second top cancer regarding the increase in risk-attributable DALY rate between 1990 and 2021, followed by multiple myeloma. In the GBD 2021 study, the sole risk factor associated with thyroid cancer and multiple myeloma was high BMI. According to the previously published GBD 2019 data, the burden of thyroid cancer attributable to high BMI has increased in all provinces since 1990 in Iran [66]. Hence, the top three cancers with the highest increase in risk-attributable DALY rate shared high BMI as their primary risk factor, which further raises concern about the increasing trend of high BMI-attributable cancer burden in the country. Effective policy interventions to reduce BMI among Iranian adults should focus on integrating primary healthcare services for population-level weight management and reducing inequalities in healthy lifestyle behaviors [67].

This study is the first to assess the temporal trends in cancer burden attributable to risk factors over a three-decade interval, utilizing the most updated GBD data. Nevertheless, this study inherits limitations of the GBD methodology. GBD estimates rely heavily on statistical modeling using national input data when available. However, high-quality and updated data, mainly from the Iran National Cancer Registry, may not be frequently incorporated into the GBD estimation methods. While subnational estimates were provided in this study, they might be biased in provinces with sparse data, affecting the accuracy and relevance of the subnational estimations. Another limitation is that the GBD 2021 utilizes universal relative risks for cancer risk factors, which may reduce the reliability of the attributable burden estimates specific to the Iranian population. In addition, the PAFs and cancer burden were estimated in the same year, disregarding the time required for a risk factor to contribute to cancer incidence [28]. The sub-classifications of risk factors and cancer types in the GBD 2021 data are not comprehensive, limiting the ability to include pathologically distinct cancer subtypes and to adequately represent etiologically significant risk factors, such as viral infections. Of note, this study captured trends until 2021, not accounting for changes in risk factor-attributable cancer burden during the post-pandemic era.

5. Conclusions

Risk-attributable cancer burden in Iran had an overall upward trend in the last 32 years. Males exhibited a higher risk-attributable cancer burden in Iran throughout all of the study period. The attributable burden was relatively higher in the elderly age groups. While the cancer burden attributable to major behavioral risk factors, including tobacco and dietary risks, has declined, the burden of metabolic risk factors such as high FPG and high BMI has increased tremendously. Among the behavioral risk factors, drug and alcohol use are emerging threats of cancer in the male population. Geographically, the northern and northwestern provinces in the country harbored the greatest burden of risk-attributable cancer; of note, targeted interventions in these regions should align with their cultural and infrastructural aspects. These results shed light on the potential targets of future policies and action plans aiming to mitigate the cancer burden in the country. Our findings provide a comprehensive understanding of the local trends and relevant risk factors, an essential step prior to policy making and strategy implementation. As cancer prevention has proven the most effective way to curtail the rising cancer burden, future strategies focused on the alleviation of cancer risk factors should be given high priority by health policymakers.

Supporting information

S1 Table. DALYs, deaths, YLDs, and YLLs of cancer attributable to level 2 risk factors by sex in Iran in 1990 and 2021 and their percent change.

DALYs: Disability-Adjusted Life Years. YLDs: Years Lived with Disability. YLLs: Years of Life Lost.

(DOCX)

pone.0330993.s001.docx^{(50.4KB, docx)}

S2 Table. DALYs, deaths, YLDs, and YLLs of neoplasms and level 3 cancers attributable risk factors among females, males, and both sexes in Iran for the years 1990, 2021, and their percent change.

GBD 2021 did not estimate any burden attributable to risk factors for the following 11 level 3 cancers: Brain and central nervous system cancer, Eye cancer, Hodgkin lymphoma, Malignant neoplasm of bone and articular cartilage, Malignant skin melanoma, Neuroblastoma and other peripheral nervous cell tumors, Non-melanoma skin cancer, Other malignant neoplasms, Other neoplasms, Soft tissue and other extraosseous sarcomas, and Testicular cancer. The risk-factor-attributable burden of cervical cancer, ovarian cancer, and uterine cancer was not estimated for males, and the risk-factor-attributable burden of prostate cancer was not estimated for females. DALYs: Disability-Adjusted Life Years. YLDs: Years Lived with Disability. YLLs: Years of Life Lost.

(DOCX)

pone.0330993.s002.docx^{(73.9KB, docx)}

S3 Table. DALYs, deaths, YLDs, and YLLs of cancer attributable to all and level 1 risk factors by sex at the subnational level in Iran in 1990 and 2021 and their percent change.

DALYs: Disability-Adjusted Life Years. YLDs: Years Lived with Disability. YLLs: Years of Life Lost.

(DOCX)

pone.0330993.s003.docx^{(404KB, docx)}

S1 Fig. Trends of age-standardized DALY rates of cancer attributable to all and level 2 risk factors by gender in Iran from 1990 to 2021.

(DOCX)

pone.0330993.s004.docx^{(252.6KB, docx)}

S2 Fig. Trends of age-standardized percentages of cancer DALYs, deaths, YLDs, and YLLs attributed to risk factors in Iran from 1990 to 2021.

(DOCX)

pone.0330993.s005.docx^{(288KB, docx)}

S3 Fig. Trends of the percentage of age-standardized cancer DALY rates attributable to each level 2 risk factor relative to cancer DALY rate attributable to all risk factors in Iran from 1990 to 2021.

(DOCX)

pone.0330993.s006.docx^{(654.9KB, docx)}

S4 Fig. The percentage of age-standardized cancer DALY rates attributable to each level 1 risk factor relative to cancer DALY rate attributable to all risk factors by gender at the national and subnational levels in Iran in 1990 and 2021.

(DOCX)

pone.0330993.s007.docx^{(391.1KB, docx)}

S5 Fig. The percentage of age-standardized cancer DALY, death, YLD, and YLL rates attributable to each level 2 risk factor relative to those attributable to all risk factors at the national and subnational levels in Iran in 2021.

(DOCX)

pone.0330993.s008.docx^{(856.8KB, docx)}

Data Availability

The data underlying the results presented in the study are publicly available from https://ghdx.healthdata.org/gbd-2021

Funding Statement

The author(s) received no specific funding for this work.

References

1.Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. doi: 10.3322/caac.21834 [DOI] [PubMed] [Google Scholar]
2.Tran KB The global burden of cancer attributable to risk factors, 2010-19: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2022;400(10352):563–91. doi: 10.1016/S0140-6736(22)01438-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Frick C, Rumgay H, Vignat J, Ginsburg O, Nolte E, Bray F, et al. Quantitative estimates of preventable and treatable deaths from 36 cancers worldwide: a population-based study. Lancet Glob Health. 2023;11(11):e1700–12. doi: 10.1016/S2214-109X(23)00406-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Shah SC, Kayamba V, Peek RMJr, Heimburger D. Cancer control in low- and middle-income countries: is it time to consider screening?. J Glob Oncol. 2019;5:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Group W. World Bank national accounts data, and OECD national accounts data files. 2022. [Google Scholar]
6.Ferlay JEM, Lam F, Laversanne M, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F. Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer 2024. [Available from: https://gco.iarc.who.int/media/globocan/factsheets/populations/364-iran-islamic-republic-of-fact-sheet.pdf [Google Scholar]
7.Ferlay JLM, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F Global Cancer Observatory. Lyon, France: International Agency for Research on Cancer. 2024. Available from: https://gco.iarc.fr/en [Google Scholar]
8.UN. Sustainable Development Goals (SDG). 2015. Available from: https://unstats.un.org/sdgs/metadata
9.Akinyemiju T, Ogunsina K, Gupta A, Liu I, Braithwaite D, Hiatt RA. A Socio-Ecological Framework for Cancer Prevention in Low and Middle-Income Countries. Front Public Health. 2022;10:884678. doi: 10.3389/fpubh.2022.884678 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Global Burden of Disease 2019 Cancer Collaboration, Kocarnik JM, Compton K, Dean FE, Fu W, Gaw BL, et al. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncol. 2022;8(3):420–44. doi: 10.1001/jamaoncol.2021.6987 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Rajappa S, Singh M, Uehara R, Schachterle SE, Setia S. Cancer incidence and mortality trends in Asia based on regions and human development index levels: an analyses from GLOBOCAN 2020. Curr Med Res Opin. 2023;39(8):1127–37. doi: 10.1080/03007995.2023.2231761 [DOI] [PubMed] [Google Scholar]
12.Cancer control: knowledge into action. WHO guide for effective programmes: module 6: policy and advocacy. Geneva: World Health Organization. Copyright © World Health Organization 2008; 2008. [PubMed] [Google Scholar]
13.Rezapour A, Nargesi S, Mezginejad F, Rashki Kemmak A, Bagherzadeh R. The Economic Burden of Cancer in Iran during 1995-2019: A Systematic Review. Iran J Public Health. 2021;50(1):35–45. doi: 10.18502/ijph.v50i1.5070 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Motlagh A, Ehsani-Chimeh E, Yamrali M, Moshiri F, Roshandel G, Partovipour E, et al. IRAN National Cancer Control Program (IrNCCP): Goals, Strategies, and Programs. Med J Islam Repub Iran. 2022;36:169. doi: 10.47176/mjiri.36.169 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Nemati S, Mohebbi E, Toorang F, Hadji M, Hosseini B, Saeedi E, et al. Population attributable proportion and number of cancer cases attributed to potentially modifiable risk factors in Iran in 2020. Int J Cancer. 2023;153(10):1758–65. doi: 10.1002/ijc.34659 [DOI] [PubMed] [Google Scholar]
16.Nemati S, Dardashti AR, Mohebbi E, Kamangar F, Malekzadeh R, Zendehdel K. Potential impact of controlling opium use prevalence on future cancer incidence in Iran. eClinicalMedicine. 2024;73. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Vakilzadeh MM, Khayami R, Daneshdoust D, Moshfeghinia R, Sharifnezhad F, Taghiabadi Z, et al. Prevalence of tobacco use among cancer patients in Iran: a systematic review and meta-analysis. BMC Public Health. 2024;24(1):1081. doi: 10.1186/s12889-024-18594-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Naghavi M. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Brauer M. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet. 2024;403(10440):2162–203. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Ferrari AJ. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. Lancet. 2016;388(10062):e19–23. doi: 10.1016/S0140-6736(16)30388-9 [DOI] [PubMed] [Google Scholar]
22.Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021) Socio-Demographic Index (SDI) 1950–2021. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME). 2024. [Google Scholar]
23.Murray CJL. Five insights from the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1135–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Burus T, Lei F, Huang B, Christian WJ, Hull PC, Ellis AR, et al. COVID-19 and Rates of Cancer Diagnosis in the US. JAMA Netw Open. 2024;7(9):e2432288. doi: 10.1001/jamanetworkopen.2024.32288 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Alkatout I, Biebl M, Momenimovahed Z, Giovannucci E, Hadavandsiri F, Salehiniya H. Has COVID-19 Affected Cancer Screening Programs? A Systematic Review. Front Oncol. 2021;11:675038. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Mansour R, Al-Ani A, Al-Hussaini M, Abdel-Razeq H, Al-Ibraheem A, Mansour AH. Modifiable risk factors for cancer in the middle East and North Africa: a scoping review. BMC Public Health. 2024;24(1):223. doi: 10.1186/s12889-024-17787-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Hu J, Dong H, Dong Y, Zhou R, Teixeira W, He X. Cancer burden attributable to risk factors, 1990–2019: A comparative risk assessment. iScience. 2024;27(4):109430. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Yarahmadi S, Etemad K, Hazaveh AM, Azhang N. Urbanization and non-communicable risk factors in the capital city of 6 big provinces of iran. Iran J Public Health. 2013;42(Supple1):113–8. [PMC free article] [PubMed] [Google Scholar]
29.Ramezankhani A, Azizi F, Hadaegh F. Sex-specific clustering of metabolic risk factors and cancer risk: a longitudinal study in Iran. Biol Sex Differ. 2020;11(1):21. doi: 10.1186/s13293-020-00296-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Ghodsi D, Haghighian-Roudsari A, Khoshfetrat M, Abdollah-PouriHosseini SF, Babapour M, Esfarjani F, et al. Why has the taxing policy on sugar sweetened beverages not reduced their purchase in Iranian households?. Front Nutr. 2023;10:1035094. doi: 10.3389/fnut.2023.1035094 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Alsukait R, Wilde P, Bleich S, Singh G, Folta S. Impact of Saudi Arabia’s sugary drink tax on prices and purchases. Current Developments in Nutrition. 2019;3:nzz034. [Google Scholar]
32.Abbass MM, Perucic AM, Ibrahim ET, Al-Jawaldeh A. Situation analysis of sugar‑sweetened beverages taxation in Eastern Mediterranean Region. Eur J Public Health. 2023;32(1):1. [DOI] [PubMed] [Google Scholar]
33.Farhood B, Geraily G, Alizadeh A. Incidence and Mortality of Various Cancers in Iran and Compare to Other Countries: A Review Article. Iran J Public Health. 2018;47(3):309–16. [PMC free article] [PubMed] [Google Scholar]
34.Abedi G, Janbabai G, Moosazadeh M, Farshidi F, Amiri M, Khosravi A. Survival Rate of Breast Cancer in Iran: A Meta-Analysis. Asian Pac J Cancer Prev. 2016;17(10):4615–21. doi: 10.22034/apjcp.2016.17.10.4615 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Masoudkabir F, Shafiee A, Heidari A, Mohammadi NSH, Tavakoli K, Jalali A, et al. Epidemiology of substance and opium use among adult residents of Tehran; a comprehensive report from Tehran cohort study (TeCS). BMC Psychiatry. 2024;24(1):132. doi: 10.1186/s12888-024-05561-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Warnakulasuriya S, Cronin-Fenton D, Jinot J, Kamangar F, Malekzadeh R, Dar NA, et al. Carcinogenicity of opium consumption. The Lancet Oncology. 2020;21(11):1407–8. [DOI] [PubMed] [Google Scholar]
37.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(2):455–61. doi: 10.1002/ijc.28018 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Naghibzadeh Tahami A, Khanjani N, Yazdi Feyzabadi V, Varzandeh M, Haghdoost A-A. Opium as a risk factor for upper gastrointestinal cancers: a population-based case-control study in Iran. Arch Iran Med. 2014;17(1):2–6. [PubMed] [Google Scholar]
39.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(5):e649–60. doi: 10.1016/S2214-109X(20)30059-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.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(3):830–8. doi: 10.1093/ije/dyac031 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Moossavi S, Mohamadnejad M, Pourshams A, Poustchi H, Islami F, Sharafkhah M, et al. Opium Use and Risk of Pancreatic Cancer: A Prospective Cohort Study. Cancer Epidemiol Biomarkers Prev. 2018;27(3):268–73. doi: 10.1158/1055-9965.EPI-17-0592 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.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. doi: 10.1016/j.canep.2021.102017 [DOI] [PubMed] [Google Scholar]
43.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. 2023;152(2):203–13. doi: 10.1002/ijc.34244 [DOI] [PubMed] [Google Scholar]
44.Naghibzadeh-Tahami A, Yazdi Feyzabadi V, Khanjani N, Ashrafi-Asgarabad A, Alizaeh H, Borhaninejad VR, et al. Can Opium Use Contribute to a Higher Risk of Colorectal Cancers? A Matched Case-control Study in Iran. Iran J Public Health. 2016;45(10):1322–31. [PMC free article] [PubMed] [Google Scholar]
45.Alizadeh H, Naghibzadeh Tahami A, Khanjani N, Yazdi-Feyzabadi V, Eslami H, Borhaninejad V, et al. Opium Use and Head and Neck Cancers: A Matched Case-Control Study in Iran. Asian Pac J Cancer Prev. 2020;21(3):783–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Zarghami M. Iranian common attitude toward opium consumption. Iran J Psychiatry Behav Sci. 2015;9(2):e2074. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Shahraki G, Sedaghat Z, Fararouei M. Family and social predictors of substance use disorder in Iran: a case-control study. Subst Abuse Treat Prev Policy. 2019;14(1):17. doi: 10.1186/s13011-019-0201-x [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Karimian M, Motevalian A, Damghanian M, Rahimi-Movaghar A, Sharifi V, Amin-Esmaeili M, et al. Explaining socioeconomic inequalities in illicit drug use disorders in Iran. Med J Islam Repub Iran. 2017;31:108. doi: 10.14196/mjiri.31.108 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Nikpour G. Drugs and Drug Policy in the Islamic Republic of Iran. 2018. [Google Scholar]
50.Abbasi-Kangevari M, Ghanbari A, Fattahi N, Malekpour MR, Masinaei M, Ahmadi N. Tobacco consumption patterns among Iranian adults: a national and sub-national update from the STEPS survey 2021. Scientific Reports. 2023;13(1):10272. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Taheri N, Fattahi P, Saeedi E, Sayyari M, Abdi S, Khaki M, et al. A decade of tobacco control efforts: Implications for tobacco smoking prevalence in Eastern Mediterranean countries. PLoS One. 2024;19(2):e0297045. doi: 10.1371/journal.pone.0297045 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Organization WH. MPOWER. 2023. https://www.who.int/initiatives/mpower
53.Organization GWH. WHO report on the global tobacco epidemic, 2023: protect people from tobacco smoke. 2023. Report No.: Licence: CC BY-NC-SA 3.0 IGO. [Google Scholar]
54.Tamil Selvan S, Yeo XX, van der Eijk Y. Which countries are ready for a tobacco endgame? A scoping review and cluster analysis. Lancet Glob Health. 2024;12(6):e1049–58. doi: 10.1016/S2214-109X(24)00085-8 [DOI] [PubMed] [Google Scholar]
55.Portes LH, Machado CV, Turci SRB, Figueiredo VC, Cavalcante TM, Silva VL da CE. Tobacco Control Policies in Brazil: a 30-year assessment. Cien Saude Colet. 2018;23(6):1837–48. doi: 10.1590/1413-81232018236.05202018 [DOI] [PubMed] [Google Scholar]
56.Ghasemi S, Dreassi E, Khosravi A, Mahaki B. Stomach, Esophageal, and Lung Cancer Mortality Risk and Their Shared Risk Factors in Iran: A County-Level Spatial Analysis. Int J Prev Med. 2024;15:54. doi: 10.4103/ijpvm.ijpvm_222_23 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Montaseri Z, Kargar H, Sharafi M, Afrashteh S. Spatial analysis of risk factors related to colorectal cancer in Iran: An ecological study. Health Sci Rep. 2024;7(10):e70120. doi: 10.1002/hsr2.70120 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Alcala K, Poustchi H, Viallon V, Islami F, Pourshams A, Sadjadi A, et al. Incident cancers attributable to using opium and smoking cigarettes in the Golestan cohort study. EClinicalMedicine. 2023;64:102229. doi: 10.1016/j.eclinm.2023.102229 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Sheikh M, Poustchi H, Pourshams A, Etemadi A, Islami F, Khoshnia M, et al. Individual and Combined Effects of Environmental Risk Factors for Esophageal Cancer Based on Results From the Golestan Cohort Study. Gastroenterology. 2019;156(5):1416–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Collatuzzo G, Etemadi A, Sotoudeh M, Nikmanesh A, Poustchi H, Khoshnia M, et al. Meat consumption and risk of esophageal and gastric cancer in the Golestan Cohort Study, Iran. Int J Cancer. 2022;151(7):1005–12. doi: 10.1002/ijc.34056 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Nojomi M, Tehrani Banihashemi A, Niksima H, Hashemian M, Mottaghi A, Malekzaddeh R. The relationship between dietary patterns, dietary quality index, and dietary inflammatory index with the risk of all types of cancer: Golestan cohort study. Med J Islam Repub Iran. 2021;35:48. doi: 10.47176/mjiri.35.48 [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Roshandel G, Ferlay J, Semnani S, Fazel A, Naeimi-Tabiei M, Ashaari M, et al. Recent cancer incidence trends and short-term predictions in Golestan, Iran 2004-2025. Cancer Epidemiol. 2020;67:101728. doi: 10.1016/j.canep.2020.101728 [DOI] [PubMed] [Google Scholar]
63.Akbari A, Azizmohammad Looha M, Moradi A, Akbari ME. Ovarian Cancer in Iran: National Based Study. Iran J Public Health. 2023;52(4):797–808. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Poorolajal J, Jenabi E, Masoumi SZ. Body mass index effects on risk of ovarian cancer: a meta- analysis. Asian Pac J Cancer Prev. 2014;15(18):7665–71. doi: 10.7314/apjcp.2014.15.18.7665 [DOI] [PubMed] [Google Scholar]
65.Djalalinia S, Yoosefi M, Shahin S, Ghasemi E, Rezaei N, Ahmadi N, et al. The levels of BMI and patterns of obesity and overweight during the COVID-19 pandemic: Experience from the Iran STEPs 2021 survey. Front Endocrinol (Lausanne). 2022;13:1043894. doi: 10.3389/fendo.2022.1043894 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Azangou-Khyavy M, Saeedi Moghaddam S, Rezaei N, Esfahani Z, Rezaei N, Azadnajafabad S, et al. National, sub-national, and risk-attributed burden of thyroid cancer in Iran from 1990 to 2019. Sci Rep. 2022;12(1):13231. doi: 10.1038/s41598-022-17115-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Toorang F, Amiri P, Djazayery A, Pouraram H, Takian A. Worse becomes the worst: obesity inequality, its determinants and policy options in Iran. Front Public Health. 2024;12:1225260. doi: 10.3389/fpubh.2024.1225260 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0330993.r001

Decision Letter 0

Claudio Alberto Dávila-Cervantes

19 May 2025

Dear Dr. Azadnajafabad,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: N/A

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The authors reanalyse data collected by the Global Burden of Disease 2021 study to provide an overview of the impact of cancers attributable to risk factors on life expectancy and quality of life in Iran. They show a sharp increase since 1990 in all preventable cancers, across all risk types.

The analysis and model used are appropriate for what the authors set out to do. However I have reservations regarding the correctness of the model that I would like to see addressed:

Major:

- Figure 5 shows quantification of DALY and death rates for ovarian cancer in males. On top of that this quantification is negative for dietary risks. As ovarian cancers do not happen in males (and would not be significantly detectable even including trans-men) this is likely an artifact of the model used which should be corrected.

- While the data used for the analysis is publicly available, the code used to perform it is not which prevents the evaluation of the correctness of the analysis. Please make the code available with the manuscript.

Minor:

- typos: COIVD

- The text of some cells in Table 1 is truncated (e.g. all DALY cells). Please extend the cells to fix it.

- I would like to see a discussion of the decrease in YLL but increase in YLD of behavioral risks seen in Table 1.

Reviewer #2: The study utilizes GBD to analyze the trends of cancer burden attributed to risk factors in Iran between 1990 and 2021.

While the study has merits, I think it would benefit from addressing these points.

1. It is not clear how the study is different than reframing the GBD data. The authors need to highlight how their study adds novel insights rather than just GBD.

I am not sure what are the original analyses that were done beyond the GBD

2. The methods section is basically re-writing the GBD methods. No information about what “authors” have done; version? Which R scripts? Date accession? This will ensure reproducibility.

3. How local MIRs were derived

4. The introduction reads like separate paragraphs that are not linked to each other. It needs to be tied around one idea and needs to flow seamlessly.

5. There are differences between ecological modeling and causation. The authors have to be very clear when saying that this risk factor cause X DALYs

6. Have the authors applied FDR in their statistical analysis for multiple comparisons?

7. In figure 3 the x axis min and max are large making it hard to see the differences and the intervals

8. Figure 4 have abbreviations that I assume they are countries names. But no reader will be familiar of all these abbreviations

9. The colors in Figure 5 are very similar to each other for example, bladder cancer and uterine cancer are both having the same shade of blue.

10. There are no mention of study limitations and GBD limitations at all in the discussion. This is very crucial.

11. How was percent change was calculated because there are no UIs in table 1?

12. Analyzing the subnational level data is very important so it would benefit from exploring the reasons behind these differences. A map with matrix plot like colors for showing the differences in risk factors and DALYs would be beneficial to the manuscript (spatial map)

13. Why the authors attribute the reduction to COVID19?

14. The discussion would benefit from comparing Iran to the other similar countries to should the similarities and the differences

15. Exploring the reasons behind this surge in opium use is important in the discussion

16. Table one seems to have been cut in the first couple of rows (the upper UI is not found.) for example all age numbers 169057.47 (135238.74 to )

17. Risk-attributable cancer burden in Iran had an overall upward trend” contradicts earlier claim that ASDR declined

18. The discussion section is overwhelming while it does not have a deep, evidence-based discussion linking findings to actionable policies.

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Reviewer #2: No

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PLoS One. 2025 Aug 26;20(8):e0330993. doi: 10.1371/journal.pone.0330993.r002

Author response to Decision Letter 1

6 Jun 2025

Dear Editorial Team and Esteemed Reviewers,

We sincerely thank the editors and reviewers for their valuable comments and insightful suggestions, which have significantly improved our manuscript both methodologically and in terms of presenting our findings. Your constructive feedback allowed us to refine critical aspects of the study, including the refinement of the Introduction, the clarification and strengthening of the Methods, improvements in the figures, and enhancement of the Discussion. We have provided a detailed response to each comment in the following sections of this letter. Additionally, we have revised the manuscript using tracked changes for your convenience. We hope these updates meet your expectations and facilitate the review process. Thank you once again for your thoughtful and meticulous review.

Reviewer #1 comments:

Comment: Figure 5 shows quantification of DALY and death rates for ovarian cancer in males. On top of that this quantification is negative for dietary risks. As ovarian cancers do not happen in males (and would not be significantly detectable even including trans-men) this is likely an artifact of the model used which should be corrected.

Response: We sincerely thank the reviewer for carefully pointing out this issue. The problem with Figure 5 was a mislabeling in the legend. We have recreated the figure to correct this error. The estimated DALY and death rates for prostate cancer attributable to dietary risks in males are negative in the dataset.

Comment: While the data used for the analysis is publicly available, the code used to perform it is not which prevents the evaluation of the correctness of the analysis. Please make the code available with the manuscript.

Response: Thanks for your comment! The code used to perform all estimations used in this manuscript are publicly available at https://github.com/ihmeuw/ihme-modeling/tree/main/gbd_2021. We have also added the link to the manuscript, emphasizing that the code is also publicly available. We have also clearly stated the version of R (version 4.4.0) and Python (version 3.12.4) used and specified the date on which the GBD data were accessed (October 20, 2024) in the manuscript. Additionally, we would like to clarify that Tableau, which was used for some data visualizations, does not require coding scripts. Other visualizations were generated using standard procedures from the ggplot2 package in R and pandas library in Python, and LOESS regression was implemented using base R function [loess (val ~ sdi)].

Comment: Typos: COIVD

Response: Thanks! We have corrected this fault in the text.

Comment: The text of some cells in Table 1 is truncated (e.g. all DALY cells). Please extend the cells to fix it.

Response: Thanks for noticing that. We have extended the cells vertically and horizontally so that the whole UI interval can now be seen.

Comment: I would like to see a discussion of the decrease in YLL but increase in YLD of behavioral risks seen in Table 1.

Response: Thanks for your suggestion. In response we have a added a discussion of this observation to the Discussion section in the revised manuscript, which reads: “Since 1990, there has been a reduction in age-standardized rate of cancer YLLs attributed to behavioral risks, while YLDs have increased. This observation might be derived by advances in cancer survival in Iran over time due to enhanced therapies and increased healthcare access. Also, the survival rates of some leading cancers associated with behavioral risks, including breast cancer, have improved in Iran, although it is still lower than developed countries.”

Reviewer #2 comments:

Comment 1: It is not clear how the study is different than reframing the GBD data. The authors need to highlight how their study adds novel insights rather than just GBD.

Response: We appreciate your thoughtful feedback. We respectfully contend that our study offers substantial value beyond just re-reporting GBD data and contributes to understanding the cancer burden attributable to risk factors in Iran at both national and subnational levels. Here, we outline the key aspects that distinguish our work and underscore its significance. First, Iran currently lacks sufficient peer-reviewed publications comprehensively reporting the burden of cancers attributable to risk factors, particularly at the subnational level. Our study addresses this gap by providing a detailed analysis of cancer types and attributable risk factors across Iran’s provinces from 1990 to 2021. This will inform localized public health policies, which are often constrained by the absence of such data in the Iranian context.

Moreover, our study enhances the utility of GBD data by developing original visualizations that elucidate temporal patterns and spatial variations in risk-attributable cancer burden over 32 years. These visualizations, tailored to highlight trends and patterns, offer an accessible and exploratory tool for policymakers, extending beyond the raw data outputs typically provided by GBD. Including such figures represents a novel contribution, enabling a deeper interpretation of the data not readily available in the GBD database.

Also, you’re right that our initial analysis did not include heavy statistical methods. We have since incorporated Locally Estimated Scatterplot Smoothing (LOESS) regression analysis to examine the relationship between SDI and the percentage of attributable cancer DALYs for top five risk factors at the subnational level. We have also employed the annualized rate of change (ARC) analysis to enhance the interpretation of data. The ARC analysis assessed the relationship between the annual change in summary exposure values (SEV) for specific risk factors and the corresponding annual change in cancer disability-adjusted life years (DALYs) attributable to those risk factors at the subnational level, with estimates derived for the period 1990 to 2021. The findings from this analysis are illustrated in Figure 6, which highlights the top four risk factors and categorizes subnational regions by SDI quintiles, providing deeper insights into the underlying drivers of these trends. Additionally, the LOESS regression modeled the non-linear association between SDI and the percentage of attributable DALYs for specific risk factors, with results presented in Figure 7. This analysis, incorporating data from all provinces over the 1990–2021 period, reveals that the proportion of cancer DALYs attributed to high body-mass index and high fasting plasma glucose increases with rising SDI values. These novel findings elucidate the relationship between SDI and the contribution of risk factors to cancer burden. Therefore, our study includes several analyses.

Finally, our study's originality lies in its contextual relevance and synthesis. By focusing on Iran, a country with a unique demographic, epidemiological, and socio-economic profile, we critically adapt global data to a national and subnational framework. This localized perspective is vital for translating global estimates into actionable insights, a step that GBD alone cannot achieve without region-specific interpretation and analysis.

We believe that these contributions, including filling a literature gap, enhancing data through visualizations, and applying statistical methods, demonstrate that our study is far more than a re-reporting of GBD data. Instead, it represents a valuable extension that supports evidence-based decision-making in Iran’s public health landscape. We hope this clarification addresses your concerns and reinforces the merit of our work.

Comment 2: The methods section is basically re-writing the GBD methods. No information about what “authors” have done; version? Which R scripts? Date accession? This will ensure reproducibility.

Response: Thanks for pointing out this issue. You are correct that the initial version of the Methods section devoted considerable space to describing the GBD 2021 methodology, with relatively limited emphasis on our own analytical procedures. In response, we have revised the section to provide a more concise summary of the GBD methodology and have placed greater focus on the analyses we conducted, including the calculation of the annualized rate of change (ARC) and the application of LOESS regression. We have also clearly stated the version of R (version 4.4.0) used and specified the date on which the GBD data were accessed (October 20, 2024) in the manuscript. Additionally, we would like to clarify that Tableau, which was used for some data visualizations, does not require coding scripts. Other visualizations were generated using standard procedures from the ggplot2 package, and LOESS regression was implemented using base R function [loess (val ~ sdi)]. The results of LOESS analysis are depicted in the Figure 7, which has been newly added to our manuscript in the revised version

Comment 3: How local MIRs were derived

Response: Thanks for your question. We’ll provide relatively detailed answer here based on the GBD 2021 methodology documentation. However, we have also added a few sentences clarifying your question in the manuscript. Accordingly, cancer incidence and mortality data from registries were matched by cancer type, age, sex, year, and location to calculate mortality-to-incidence ratios (MIRs), which were used as inputs for further modeling. For most cancers, MIRs were estimated using a three-step spatiotemporal Gaussian process regression (ST-GPR) approach, where logit-transformed MIRs were modeled with covariates including sex, categorical age groups, and the Healthcare Access and Quality (HAQ) Index in a linear mixed-effects model, followed by spatiotemporal smoothing and Gaussian process regression. The ST-GPR model employed fixed hyperparameters (lambda = 0.05 for time, omega = 0.5 for age, zeta = 0.01 for geography, amplitude = 1, and scale = 10) to smooth data across dimensions, with manual outlier removal for unrealistic data points. For rare cancers in young age groups, data were aggregated to the youngest five-year age bin with at least 50 cases from 1990–2015 SEER data, with MIRs applied to younger age groups. MIRs were capped at the 95th percentile by age group to allow values above 1, with pediatric age groups (under 20) capped at 1 for flexibility; values exceeding caps were Winsorised, and inputs were scaled for ST-GPR and rescaled post-modeling, with lower caps at the fifth percentile to constrain underestimates. This methodology ensures robust and flexible MIR estimates across diverse cancers and demographics.

We have briefly explained this in the Methods section: “MIRs were estimated using cancer registry data matched by cancer type, age, sex, year, and location. A spatiotemporal Gaussian process regression (ST-GPR) model incorporated covariates such as age, sex, and the Healthcare Access and Quality Index, with smoothing across time, age, and geography. Additionally, adjustments were made for rare cancers and outliers to ensure reliable estimates across all cancer types and demographic groups.”

Comment 4: The introduction reads like separate paragraphs that are not linked to each other. It needs to be tied around one idea and needs to flow seamlessly.

Response: Many thanks for your valuable insight! We have revised and reframed the introduction section to improve the coherence between paragraphs. Changes can be tracked in the “Revised Manuscript with Track Changes” file.

Comment 5: There are differences between ecological modeling and causation. The authors have to be very clear when saying that this risk factor cause X DALYs

Response: Thanks for pointing out this important consideration! We have explicitly avoided the use of the word “caused by” while referring to the cancer burden attributed to risk factors and have used the word “attributed to” instead. There was one instance in the manuscript that we wrote “cancer DALYs due to metabolic risks”, which we have now edited and replaced it with “cancer DALYs attributed to metabolic risks”. It is also worth mentioning that in order to be consistent with the GBD terminology, we have used the term “causes of death” to refer to the cancer types, as this is the standard GBD terminology.

Comment 6: Have the authors applied FDR in their statistical analysis for multiple comparisons?

Response: Thanks for your insightful question! Multiple comparisons are a major issue when the model involves many statistical tests and thus multiple comparisons, as this is the case in our work. In order to provide an answer, we should delve deeper into the GBD 2021 methodology. GBD 2021 relies on Bayesian hierarchical models, meta-regression (e.g., MR-BR), and ensemble modeling (e.g., DisMod-MR) rather than FDR for handling multiple comparisons. These methods focus on uncertainty intervals (UIs) from Bayesian posterior distributions or bootstrap methods to quantify variability. Therefore, FDR is not applied, but this aligns with the study’s reliance on Bayesian methods, which inherently handle multiple comparisons through hierarchical modeling and uncertainty quantification rather than p-value adjustments. For instance, in estimating risk factor-attributable burden, mediated-adjusted population attributable fractions (PAFs) were used to account for correlations between risk factors, suggesting a different approach to managing multiple testing issues than FDR.

Comment 7: In figure 3 the x axis min and max are large making it hard to see the differences and the intervals

Response: Thanks for your helpful feedback. We had used fixed scale for the Figure 3, which made it difficult for some values to be seen. We have now recreated the Figure 3 and used free scales in the x-axis for each risk factor. Now the percent changes and intervals can be more easily observed. Given that the X-axis scale is now tailored to each risk factor rather than being uniform across all graphs, it is important to interpret comparisons between risk factors with caution, as the differing scales may affect visual interpretation. Of note, because we now have added a spatial map plot, the name of the figure we are discussing in this comment has changed to Figure 4 in the revised manuscript.

Comment 8: Figure 4 have abbreviations that I assume they are countries names. But no reader will be familiar of all these abbreviations

Response: You are absolutely right in highlighting this issue. These abbreviations stand for the names of Iran’s provinces. As these names are too long, it was not possible to include the full names of the provinces in the figure. We have added the explanation for these abbreviations in the caption of the Figure 3.

Comment 9: The colors in Figure 5 are very similar to each other for example, bladder cancer and uterine cancer are both having the same shade of blue.

Response: Thanks for your valuable feedback. We have recreated the Figure 5, utilizing more distinct colors to improve clarity.

Comment 10: There are no mention of study limitations and GBD limitations at all in the discussion. This is very crucial.

Response: We truly appreciate your attention to this crucial point. We have added a comprehensive paragraph discussing the study’s limitations and GBD limitations at the end of the Discussion section in the manuscript, which reads:

“This study is the first to assess the temporal trends in cancer burden attributable to risk factors over a three-decade interval, utilizing the most updated GBD data. Nevertheless, this study inherits limitations of the GBD methodology. GBD estimates rely heavily on statistical modeling using national input data when available. However, high-quality and updated data, mostly from Iran National Cancer Registry, may not be frequently incorporated into the GBD estimation methods. While subnational estimates were provided in this study, they might be biased in provinces with sparse data, affecting the accuracy and relevance of the subnational estimations. Another limitation is that the GBD 2021 utilizes universal relative risks for cancer risk factors, which may reduce the reliability of the attributable burden estimates specific to the Iranian population. In addition, the PAFs and cancer burden were estimated in the same year, disregarding the time required for a risk factor to contribute to canc

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PLoS One. doi: 10.1371/journal.pone.0330993.r003

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7 Aug 2025

Temporal trend in the national and sub-national burden of cancers attributable to risk factors in Iran from 1990 to 2021: findings from the Global Burden of Disease Study 2021

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PLoS One. doi: 10.1371/journal.pone.0330993.r004

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This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. DALYs, deaths, YLDs, and YLLs of cancer attributable to level 2 risk factors by sex in Iran in 1990 and 2021 and their percent change.

DALYs: Disability-Adjusted Life Years. YLDs: Years Lived with Disability. YLLs: Years of Life Lost.

(DOCX)

pone.0330993.s001.docx^{(50.4KB, docx)}

S2 Table. DALYs, deaths, YLDs, and YLLs of neoplasms and level 3 cancers attributable risk factors among females, males, and both sexes in Iran for the years 1990, 2021, and their percent change.

(DOCX)

pone.0330993.s002.docx^{(73.9KB, docx)}

S3 Table. DALYs, deaths, YLDs, and YLLs of cancer attributable to all and level 1 risk factors by sex at the subnational level in Iran in 1990 and 2021 and their percent change.

DALYs: Disability-Adjusted Life Years. YLDs: Years Lived with Disability. YLLs: Years of Life Lost.

(DOCX)

pone.0330993.s003.docx^{(404KB, docx)}

S1 Fig. Trends of age-standardized DALY rates of cancer attributable to all and level 2 risk factors by gender in Iran from 1990 to 2021.

(DOCX)

pone.0330993.s004.docx^{(252.6KB, docx)}

S2 Fig. Trends of age-standardized percentages of cancer DALYs, deaths, YLDs, and YLLs attributed to risk factors in Iran from 1990 to 2021.

(DOCX)

pone.0330993.s005.docx^{(288KB, docx)}

(DOCX)

pone.0330993.s006.docx^{(654.9KB, docx)}

(DOCX)

pone.0330993.s007.docx^{(391.1KB, docx)}

(DOCX)

pone.0330993.s008.docx^{(856.8KB, docx)}

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Data Availability Statement

The data underlying the results presented in the study are publicly available from https://ghdx.healthdata.org/gbd-2021

[pone.0330993.ref001] 1.Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. doi: 10.3322/caac.21834 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref002] 2.Tran KB The global burden of cancer attributable to risk factors, 2010-19: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2022;400(10352):563–91. doi: 10.1016/S0140-6736(22)01438-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref003] 3.Frick C, Rumgay H, Vignat J, Ginsburg O, Nolte E, Bray F, et al. Quantitative estimates of preventable and treatable deaths from 36 cancers worldwide: a population-based study. Lancet Glob Health. 2023;11(11):e1700–12. doi: 10.1016/S2214-109X(23)00406-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref004] 4.Shah SC, Kayamba V, Peek RMJr, Heimburger D. Cancer control in low- and middle-income countries: is it time to consider screening?. J Glob Oncol. 2019;5:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref005] 5.Group W. World Bank national accounts data, and OECD national accounts data files. 2022. [Google Scholar]

[pone.0330993.ref006] 6.Ferlay JEM, Lam F, Laversanne M, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F. Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer 2024. [Available from: https://gco.iarc.who.int/media/globocan/factsheets/populations/364-iran-islamic-republic-of-fact-sheet.pdf [Google Scholar]

[pone.0330993.ref007] 7.Ferlay JLM, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F Global Cancer Observatory. Lyon, France: International Agency for Research on Cancer. 2024. Available from: https://gco.iarc.fr/en [Google Scholar]

[pone.0330993.ref008] 8.UN. Sustainable Development Goals (SDG). 2015. Available from: https://unstats.un.org/sdgs/metadata

[pone.0330993.ref009] 9.Akinyemiju T, Ogunsina K, Gupta A, Liu I, Braithwaite D, Hiatt RA. A Socio-Ecological Framework for Cancer Prevention in Low and Middle-Income Countries. Front Public Health. 2022;10:884678. doi: 10.3389/fpubh.2022.884678 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref010] 10.Global Burden of Disease 2019 Cancer Collaboration, Kocarnik JM, Compton K, Dean FE, Fu W, Gaw BL, et al. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncol. 2022;8(3):420–44. doi: 10.1001/jamaoncol.2021.6987 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref011] 11.Rajappa S, Singh M, Uehara R, Schachterle SE, Setia S. Cancer incidence and mortality trends in Asia based on regions and human development index levels: an analyses from GLOBOCAN 2020. Curr Med Res Opin. 2023;39(8):1127–37. doi: 10.1080/03007995.2023.2231761 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref012] 12.Cancer control: knowledge into action. WHO guide for effective programmes: module 6: policy and advocacy. Geneva: World Health Organization. Copyright © World Health Organization 2008; 2008. [PubMed] [Google Scholar]

[pone.0330993.ref013] 13.Rezapour A, Nargesi S, Mezginejad F, Rashki Kemmak A, Bagherzadeh R. The Economic Burden of Cancer in Iran during 1995-2019: A Systematic Review. Iran J Public Health. 2021;50(1):35–45. doi: 10.18502/ijph.v50i1.5070 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref014] 14.Motlagh A, Ehsani-Chimeh E, Yamrali M, Moshiri F, Roshandel G, Partovipour E, et al. IRAN National Cancer Control Program (IrNCCP): Goals, Strategies, and Programs. Med J Islam Repub Iran. 2022;36:169. doi: 10.47176/mjiri.36.169 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref015] 15.Nemati S, Mohebbi E, Toorang F, Hadji M, Hosseini B, Saeedi E, et al. Population attributable proportion and number of cancer cases attributed to potentially modifiable risk factors in Iran in 2020. Int J Cancer. 2023;153(10):1758–65. doi: 10.1002/ijc.34659 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref016] 16.Nemati S, Dardashti AR, Mohebbi E, Kamangar F, Malekzadeh R, Zendehdel K. Potential impact of controlling opium use prevalence on future cancer incidence in Iran. eClinicalMedicine. 2024;73. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref017] 17.Vakilzadeh MM, Khayami R, Daneshdoust D, Moshfeghinia R, Sharifnezhad F, Taghiabadi Z, et al. Prevalence of tobacco use among cancer patients in Iran: a systematic review and meta-analysis. BMC Public Health. 2024;24(1):1081. doi: 10.1186/s12889-024-18594-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref018] 18.Naghavi M. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref019] 19.Brauer M. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet. 2024;403(10440):2162–203. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref020] 20.Ferrari AJ. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133–61. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref021] 21.Stevens GA, Alkema L, Black RE, Boerma JT, Collins GS, Ezzati M, et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. Lancet. 2016;388(10062):e19–23. doi: 10.1016/S0140-6736(16)30388-9 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref022] 22.Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021) Socio-Demographic Index (SDI) 1950–2021. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME). 2024. [Google Scholar]

[pone.0330993.ref023] 23.Murray CJL. Five insights from the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1135–59. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref024] 24.Burus T, Lei F, Huang B, Christian WJ, Hull PC, Ellis AR, et al. COVID-19 and Rates of Cancer Diagnosis in the US. JAMA Netw Open. 2024;7(9):e2432288. doi: 10.1001/jamanetworkopen.2024.32288 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref025] 25.Alkatout I, Biebl M, Momenimovahed Z, Giovannucci E, Hadavandsiri F, Salehiniya H. Has COVID-19 Affected Cancer Screening Programs? A Systematic Review. Front Oncol. 2021;11:675038. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref026] 26.Mansour R, Al-Ani A, Al-Hussaini M, Abdel-Razeq H, Al-Ibraheem A, Mansour AH. Modifiable risk factors for cancer in the middle East and North Africa: a scoping review. BMC Public Health. 2024;24(1):223. doi: 10.1186/s12889-024-17787-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref027] 27.Hu J, Dong H, Dong Y, Zhou R, Teixeira W, He X. Cancer burden attributable to risk factors, 1990–2019: A comparative risk assessment. iScience. 2024;27(4):109430. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref028] 28.Yarahmadi S, Etemad K, Hazaveh AM, Azhang N. Urbanization and non-communicable risk factors in the capital city of 6 big provinces of iran. Iran J Public Health. 2013;42(Supple1):113–8. [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref029] 29.Ramezankhani A, Azizi F, Hadaegh F. Sex-specific clustering of metabolic risk factors and cancer risk: a longitudinal study in Iran. Biol Sex Differ. 2020;11(1):21. doi: 10.1186/s13293-020-00296-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref030] 30.Ghodsi D, Haghighian-Roudsari A, Khoshfetrat M, Abdollah-PouriHosseini SF, Babapour M, Esfarjani F, et al. Why has the taxing policy on sugar sweetened beverages not reduced their purchase in Iranian households?. Front Nutr. 2023;10:1035094. doi: 10.3389/fnut.2023.1035094 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref031] 31.Alsukait R, Wilde P, Bleich S, Singh G, Folta S. Impact of Saudi Arabia’s sugary drink tax on prices and purchases. Current Developments in Nutrition. 2019;3:nzz034. [Google Scholar]

[pone.0330993.ref032] 32.Abbass MM, Perucic AM, Ibrahim ET, Al-Jawaldeh A. Situation analysis of sugar‑sweetened beverages taxation in Eastern Mediterranean Region. Eur J Public Health. 2023;32(1):1. [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref033] 33.Farhood B, Geraily G, Alizadeh A. Incidence and Mortality of Various Cancers in Iran and Compare to Other Countries: A Review Article. Iran J Public Health. 2018;47(3):309–16. [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref034] 34.Abedi G, Janbabai G, Moosazadeh M, Farshidi F, Amiri M, Khosravi A. Survival Rate of Breast Cancer in Iran: A Meta-Analysis. Asian Pac J Cancer Prev. 2016;17(10):4615–21. doi: 10.22034/apjcp.2016.17.10.4615 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref035] 35.Masoudkabir F, Shafiee A, Heidari A, Mohammadi NSH, Tavakoli K, Jalali A, et al. Epidemiology of substance and opium use among adult residents of Tehran; a comprehensive report from Tehran cohort study (TeCS). BMC Psychiatry. 2024;24(1):132. doi: 10.1186/s12888-024-05561-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref036] 36.Warnakulasuriya S, Cronin-Fenton D, Jinot J, Kamangar F, Malekzadeh R, Dar NA, et al. Carcinogenicity of opium consumption. The Lancet Oncology. 2020;21(11):1407–8. [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref037] 37.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(2):455–61. doi: 10.1002/ijc.28018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref038] 38.Naghibzadeh Tahami A, Khanjani N, Yazdi Feyzabadi V, Varzandeh M, Haghdoost A-A. Opium as a risk factor for upper gastrointestinal cancers: a population-based case-control study in Iran. Arch Iran Med. 2014;17(1):2–6. [PubMed] [Google Scholar]

[pone.0330993.ref039] 39.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(5):e649–60. doi: 10.1016/S2214-109X(20)30059-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref040] 40.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(3):830–8. doi: 10.1093/ije/dyac031 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref041] 41.Moossavi S, Mohamadnejad M, Pourshams A, Poustchi H, Islami F, Sharafkhah M, et al. Opium Use and Risk of Pancreatic Cancer: A Prospective Cohort Study. Cancer Epidemiol Biomarkers Prev. 2018;27(3):268–73. doi: 10.1158/1055-9965.EPI-17-0592 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref042] 42.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. doi: 10.1016/j.canep.2021.102017 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref043] 43.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. 2023;152(2):203–13. doi: 10.1002/ijc.34244 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref044] 44.Naghibzadeh-Tahami A, Yazdi Feyzabadi V, Khanjani N, Ashrafi-Asgarabad A, Alizaeh H, Borhaninejad VR, et al. Can Opium Use Contribute to a Higher Risk of Colorectal Cancers? A Matched Case-control Study in Iran. Iran J Public Health. 2016;45(10):1322–31. [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref045] 45.Alizadeh H, Naghibzadeh Tahami A, Khanjani N, Yazdi-Feyzabadi V, Eslami H, Borhaninejad V, et al. Opium Use and Head and Neck Cancers: A Matched Case-Control Study in Iran. Asian Pac J Cancer Prev. 2020;21(3):783–90. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref046] 46.Zarghami M. Iranian common attitude toward opium consumption. Iran J Psychiatry Behav Sci. 2015;9(2):e2074. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref047] 47.Shahraki G, Sedaghat Z, Fararouei M. Family and social predictors of substance use disorder in Iran: a case-control study. Subst Abuse Treat Prev Policy. 2019;14(1):17. doi: 10.1186/s13011-019-0201-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref048] 48.Karimian M, Motevalian A, Damghanian M, Rahimi-Movaghar A, Sharifi V, Amin-Esmaeili M, et al. Explaining socioeconomic inequalities in illicit drug use disorders in Iran. Med J Islam Repub Iran. 2017;31:108. doi: 10.14196/mjiri.31.108 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref049] 49.Nikpour G. Drugs and Drug Policy in the Islamic Republic of Iran. 2018. [Google Scholar]

[pone.0330993.ref050] 50.Abbasi-Kangevari M, Ghanbari A, Fattahi N, Malekpour MR, Masinaei M, Ahmadi N. Tobacco consumption patterns among Iranian adults: a national and sub-national update from the STEPS survey 2021. Scientific Reports. 2023;13(1):10272. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref051] 51.Taheri N, Fattahi P, Saeedi E, Sayyari M, Abdi S, Khaki M, et al. A decade of tobacco control efforts: Implications for tobacco smoking prevalence in Eastern Mediterranean countries. PLoS One. 2024;19(2):e0297045. doi: 10.1371/journal.pone.0297045 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref052] 52.Organization WH. MPOWER. 2023. https://www.who.int/initiatives/mpower

[pone.0330993.ref053] 53.Organization GWH. WHO report on the global tobacco epidemic, 2023: protect people from tobacco smoke. 2023. Report No.: Licence: CC BY-NC-SA 3.0 IGO. [Google Scholar]

[pone.0330993.ref054] 54.Tamil Selvan S, Yeo XX, van der Eijk Y. Which countries are ready for a tobacco endgame? A scoping review and cluster analysis. Lancet Glob Health. 2024;12(6):e1049–58. doi: 10.1016/S2214-109X(24)00085-8 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref055] 55.Portes LH, Machado CV, Turci SRB, Figueiredo VC, Cavalcante TM, Silva VL da CE. Tobacco Control Policies in Brazil: a 30-year assessment. Cien Saude Colet. 2018;23(6):1837–48. doi: 10.1590/1413-81232018236.05202018 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref056] 56.Ghasemi S, Dreassi E, Khosravi A, Mahaki B. Stomach, Esophageal, and Lung Cancer Mortality Risk and Their Shared Risk Factors in Iran: A County-Level Spatial Analysis. Int J Prev Med. 2024;15:54. doi: 10.4103/ijpvm.ijpvm_222_23 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref057] 57.Montaseri Z, Kargar H, Sharafi M, Afrashteh S. Spatial analysis of risk factors related to colorectal cancer in Iran: An ecological study. Health Sci Rep. 2024;7(10):e70120. doi: 10.1002/hsr2.70120 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref058] 58.Alcala K, Poustchi H, Viallon V, Islami F, Pourshams A, Sadjadi A, et al. Incident cancers attributable to using opium and smoking cigarettes in the Golestan cohort study. EClinicalMedicine. 2023;64:102229. doi: 10.1016/j.eclinm.2023.102229 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref059] 59.Sheikh M, Poustchi H, Pourshams A, Etemadi A, Islami F, Khoshnia M, et al. Individual and Combined Effects of Environmental Risk Factors for Esophageal Cancer Based on Results From the Golestan Cohort Study. Gastroenterology. 2019;156(5):1416–27. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref060] 60.Collatuzzo G, Etemadi A, Sotoudeh M, Nikmanesh A, Poustchi H, Khoshnia M, et al. Meat consumption and risk of esophageal and gastric cancer in the Golestan Cohort Study, Iran. Int J Cancer. 2022;151(7):1005–12. doi: 10.1002/ijc.34056 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref061] 61.Nojomi M, Tehrani Banihashemi A, Niksima H, Hashemian M, Mottaghi A, Malekzaddeh R. The relationship between dietary patterns, dietary quality index, and dietary inflammatory index with the risk of all types of cancer: Golestan cohort study. Med J Islam Repub Iran. 2021;35:48. doi: 10.47176/mjiri.35.48 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref062] 62.Roshandel G, Ferlay J, Semnani S, Fazel A, Naeimi-Tabiei M, Ashaari M, et al. Recent cancer incidence trends and short-term predictions in Golestan, Iran 2004-2025. Cancer Epidemiol. 2020;67:101728. doi: 10.1016/j.canep.2020.101728 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref063] 63.Akbari A, Azizmohammad Looha M, Moradi A, Akbari ME. Ovarian Cancer in Iran: National Based Study. Iran J Public Health. 2023;52(4):797–808. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref064] 64.Poorolajal J, Jenabi E, Masoumi SZ. Body mass index effects on risk of ovarian cancer: a meta- analysis. Asian Pac J Cancer Prev. 2014;15(18):7665–71. doi: 10.7314/apjcp.2014.15.18.7665 [DOI] [PubMed] [Google Scholar]

[pone.0330993.ref065] 65.Djalalinia S, Yoosefi M, Shahin S, Ghasemi E, Rezaei N, Ahmadi N, et al. The levels of BMI and patterns of obesity and overweight during the COVID-19 pandemic: Experience from the Iran STEPs 2021 survey. Front Endocrinol (Lausanne). 2022;13:1043894. doi: 10.3389/fendo.2022.1043894 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref066] 66.Azangou-Khyavy M, Saeedi Moghaddam S, Rezaei N, Esfahani Z, Rezaei N, Azadnajafabad S, et al. National, sub-national, and risk-attributed burden of thyroid cancer in Iran from 1990 to 2019. Sci Rep. 2022;12(1):13231. doi: 10.1038/s41598-022-17115-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0330993.ref067] 67.Toorang F, Amiri P, Djazayery A, Pouraram H, Takian A. Worse becomes the worst: obesity inequality, its determinants and policy options in Iran. Front Public Health. 2024;12:1225260. doi: 10.3389/fpubh.2024.1225260 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Temporal trend in the national and sub-national burden of cancers attributable to risk factors in Iran from 1990 to 2021: Findings from the global burden of disease study 2021

Seyede Maryam Mousavi

Sobhan Younesian

Saba Katebian

Ali Golestani

Shaghayegh Khanmohammadi

Sepehr Khosravi

Yasaman Etemadi

Nazila Rezaei

Sina Azadnajafabad

Bagher Larijani

Roles

Abstract

Background

Methods

Results

Conclusions

1. Introduction

2. Materials and methods

2.1. Overview

2.2. Definitions

2.3. Data source

2.4. Statistical analysis

2.5. Ethics statement

3. Results

3.1. Overview

Table 1. DALYs, deaths, YLDs, and YLLs of cancer attributable to all risk factors and level 1 risk factors among females, males, and both sexes in Iran in 1990 and 2021 and their percent change.

Fig 1. DALY and death rates of cancer attributable to risk factors by different age groups in Iran in 1990 and 2021.

3.2. Risk factors at the national level

Fig 2. Trends of age-standardized DALY, death, YLD, and YLL rates of cancer attributable to level 2 risk factors in Iran from 1990 to 2021.

3.3. Risk factors at the subnational level

Fig 3. Age-standardized DALY rates for all cancers attributable to all and the top five risk factors at the subnational level in 2021.

Fig 4. Percent change of age-standardized death rate of cancer attributable to all and top five level 2 risk factors at the national and subnational levels in Iran from 1990 to 2021, categorized by SDI quintiles.

3.4. Cancer types

Fig 5. Age-standardized DALY and death rates of cancer attributable to all and level 2 risk factors by cancer type for females and males in Iran in 2021.

3.5. SEV

Fig 6. The ARC of age-standardized DALY rates of cancer attributable to top 4 level 2 risk factors by ARC of their age-standardized SEV rates at the national and subnational levels in Iran from 1990 to 2021, categorized by SDI quintiles.

3.6. The relationship between SDI and PAF for risk factors

Fig 7. The relationship between SDI and attributable age-standardized percentage of cancer DALYs for all and the top-five risk factors at the subnational level.

4. Discussion

5. Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Claudio Alberto Dávila-Cervantes

Roles

Author response to Decision Letter 1

Decision Letter 1

Claudio Alberto Dávila-Cervantes

Roles

Acceptance letter

Claudio Alberto Dávila-Cervantes

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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