Edited by: Dimirios Nikolopoulos, University of West Attica, Greece
Reviewed by: Pouné Saberi, United States Department of Veterans Affairs, United States
Younas Khan, East China University of Science and Technology, China
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Climate change poses a significant threat to public health, exacerbating health inequalities. Women in the Middle East and North Africa (MENA) region, identified as high-risk, are particularly affected.
This study investigates the influence of rising temperatures on cancer prevalence and mortality among women in the MENA region, filling critical knowledge gaps.
We employed Multiple Linear Regression (MLR) analysis to examine the correlation between increased ambient temperatures and the prevalence and mortality of four types of cancer (breast, cervical, ovarian, and uterine) across 17 MENA countries.
Our analysis indicates a significant correlation between prolonged exposure to high ambient temperatures and all four cancer types studied. Notably, the prevalence of breast, ovarian, and cervical cancers is markedly influenced by temperature increases.
The findings underscore the necessity of incorporating climate change adaptation strategies into national cancer control plans. Such integration is vital to mitigate the health impacts of climate change on women’s cancer prevalence and mortality in the MENA region.
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Climate change is a global dilemma (
Cancer is a leading cause of death among women; in 2020, 4.4 million women worldwide succumbed to the disease, with 25% of these deaths attributed to breast cancer (
Additionally, climate change can disrupt healthcare delivery systems, hindering timely cancer screening, diagnosis, and treatment, thereby adversely affecting cancer outcomes (
Climate change disproportionately harms women due to social factors, such as cultural norms and economic disparities (
Additionally, physiological vulnerabilities, including pregnancy-related health risks, make women more susceptible to climate-related health impacts. Rising temperatures and exposure to particulate matter (PM) from air pollution have also been linked to increased morbidity and mortality in women. Furthermore, pregnant women are more susceptible to climate change. Pregnancy-related hazards, such as preterm birth, fetal development retardation, and hypertensive disorders, are increased (
Gender disparities, sociocultural norms, and historical inequalities. In the MENA region, gender disparities, sociocultural norms, and historical inequalities significantly impact women’s cancer outcomes. Limited healthcare autonomy, economic inequalities, and restrictive laws delay diagnoses and hinder timely treatment (
The MENA region is particularly at risk due to global warming (
An eco-social perspective reveals how climate change amplifies environmental exposures contributing to cancer risk, while the health equity framework highlights its unequal burden on marginalized women with limited healthcare access. Sociological insights further underscore the role of gender norms, cultural restrictions, and historical inequalities in shaping cancer disparities in the MENA region.
Despite the growing body of research on the relation between climate change and women’s cancer, there are still many gaps in our knowledge. The current study aims at investigating the relationship between climate change and cancer in women in the MENA region by assessing the influence of rising ambient temperature on the prevalence and deaths caused by four types of women’s cancer: breast cancer, cervical cancer, ovarian cancer, and uterine cancer.
The study applies quantitative methods using Multiple Linear Regression (MLR) to analyze the correlation between rising ambient temperatures and female cancer burden using a two-level approach. First, an overall regional model (
Correlation between women cancers prevalence and deaths and temperature.
Dependent variable | Independent variable | Standard error (SE) | B coefficient | Model summary | |||
---|---|---|---|---|---|---|---|
Prevalence in females (Percentage) | Ovarian cancer | Temperature | 0.000 | 0.280 | 5.564 | 0.000 | R2 = 0.08; F = 15.962 |
GDP | 0.000 | −0.083 | −1.657 | 0.98 | |||
Cervical cancer | Temperature | 0.000 | 0.207 | 4.038 | 0.000 | R2 = 0.058; F = 11.310 |
|
GDP | 0.000 | −0.151 | −2.979 | 0.003 | |||
Breast cancer | Temperature | 0.000 | 0.173 | 3.338 | 0.001 | R2 = 0.030; F = 5.595 |
|
GDP | 0.000 | −0.032 | −0.620 | 0.536 | |||
Uterine cancer | Temperature | 0.000 | 0.220 | 4.304 | 0.000 | R2 = 0.000; F = 0.005 |
|
GDP | 0.000 | −0.099 | −1.929 | 0.054 | |||
Deaths in females (Percentage) | Ovarian cancer | Temperature | 0.000 | 0.332 | 6.755 | 0.000 | R2 = 0.123; F = 25.754 |
GDP | 0.000 | −0.159 | −3.234 | 0.001 | |||
Cervical cancer | Temperature | 0.000 | 0.171 | 3.344 | 0.001 | R2 = 0.055; F = 10.752 |
|
GDP | 0.000 | −0.184 | −3.598 | 0.000 | |||
Breast cancer | Temperature | 0.002 | 0.269 | 5.411 | 0.000 | R2 = 0.102; F = 20.974 |
|
GDP | 0.000 | −0.209 | −4.196 | 0.000 | |||
Uterine cancer | Temperature | 0.000 | 0.213 | 4.243 | 0.000 | R2 = 0.083; F = 16.610 |
|
GDP | 0.000 | −0.221 | −4.392 | 0.000 |
Second, for each cancer type prevalence and mortality, MLR analysis was done on the disaggregated data (
The correlation between BCP/BCD and TEMP for each country.
Country | Breast cancer prevalence (BCP) | Breast cancer death (BCD) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R square | F | independent variable | Beta | R square | F | independent variable | Beta | |||||||
1 | 0.873 | 65.497 | 0.000 | Temp | −0.030 | −0.366 | 0.719 | 0.966 | 271.018 | 0 | Temp | −0.012 | −0.287 | 0.777 |
GDP | 0.937 | 11.411 | 0.000 | GDP | 0.984 | 23.172 | 0 | |||||||
2 | 0.826 | 45.192 | 0.000 | Temp | 0.333 | 3.285 | 0.004 | 0.662 | 18.631 | 0 | Temp | 0.165 | 1.165 | 0.258 |
GDP | 0.743 | 7.326 | 0.000 | GDP | 0.744 | 5.266 | 0 | |||||||
3 | 0.910 | C | 0.000 | Temp | −0.059 | −0.709 | 0.487 | 0.589 | 13.591 | 0 | Temp | −0.092 | −0.518 | 0.61 |
GDP | 0.986 | 11.855 | 0.000 | GDP | 0.816 | 4.573 | 0 | |||||||
4 | 0.543 | C | 0.000 | Temp | 0.300 | 1.917 | 0.070 | 0.728 | 25.391 | 0 | Temp | 0.245 | 2.033 | 0.056 |
GDP | 0.635 | 4.062 | 0.001 | GDP | 0.786 | 6.509 | 0 | |||||||
5 | 0.626 | 15.906 | 0.000 | Temp | 0.111 | 0.717 | 0.482 | 0.663 | 18.726 | 0 | Temp | 0.13 | 0.891 | 0.384 |
GDP | 0.739 | 4.784 | 0.000 | GDP | 0.751 | 5.13 | 0 | |||||||
6 | 0.211 | 2.543 | 0.105 | Temp | 0.408 | 2.000 | 0.060 | 0.457 | 7.989 | 0.003 | Temp | 0.418 | 2.469 | 0.023 |
GDP | 0.216 | 1.060 | 0.303 | GDP | 0.535 | 3.165 | 0.005 | |||||||
7 | 0.167 | 1.910 | 0.175 | Temp | 0.282 | 1.170 | 0.257 | 0.251 | 3.18 | 0.064 | Temp | 0.18 | 0.787 | 0.441 |
GDP | −0.187 | −0.774 | 0.448 | GDP | −0.386 | −1.685 | 0.108 | |||||||
8 | 0.750 | 28.515 | 0.000 | Temp | 0.105 | 0.789 | 0.440 | 0.652 | 17.819 | 0 | Temp | −0.163 | −1.04 | 0.312 |
GDP | 0.809 | 6.092 | 0.000 | GDP | 0.877 | 5.602 | 0 | |||||||
9 | 0.285 | 3.593 | 0.049 | Temp | 0.240 | 1.199 | 0.246 | 0.11 | 1.117 | 0.349 | Temp | 0.234 | 1.047 | 0.309 |
GDP | −0.458 | −2.288 | 0.034 | GDP | −0.217 | −0.975 | 0.343 | |||||||
10 | 0.977 | 400.259 | 0.000 | Temp | −0.011 | −0.319 | 0.754 | 0.991 | 1065.496 | 0 | Temp | −0.002 | −0.068 | 0.946 |
GDP | 0.991 | 27.635 | 0.000 | GDP | 0.996 | 44.989 | 0 | |||||||
11 | 0.160 | 1.815 | 0.190 | Temp | 0.399 | 1.813 | 0.086 | 0.154 | 1.733 | 0.203 | Temp | 0.39 | 1.766 | 0.093 |
GDP | −0.005 | −0.023 | 0.982 | GDP | −0.009 | −0.039 | 0.969 | |||||||
12 | 0.637 | 14.892 | 0.000 | Temp | 0.557 | 3.554 | 0.002 | 0.647 | 15.6 | 0 | Temp | 0.554 | 3.582 | 0.002 |
GDP | 0.404 | 2.574 | 0.020 | GDP | 0.417 | 2.699 | 0.015 | |||||||
13 | 0.846 | 52.285 | 0 | Temp | 0.846 | 52.285 | 0 | Temp | 0.314 | 3.085 | 0.006 | |||
GDP | GDP | 0.731 | 7.187 | 0 | ||||||||||
14 | 0.757 | 29.543 | 0.000 | Temp | 0.240 | 1.880 | 0.076 | 0.172 | 1.979 | 0.166 | Temp | 0.464 | 1.967 | 0.064 |
GDP | −0.732 | −5.721 | 0.000 | GDP | 0.278 | 1.18 | 0.253 | |||||||
15 | 0.946 | 165.832 | 0 | Temp | 0.946 | 165.832 | 0 | Temp | 0.034 | 0.633 | 0.534 | |||
GDP | GDP | 0.966 | 17.863 | 0 | ||||||||||
16 | 0.303 | 4.129 | 0.032 | Temp | 0.443 | 2.220 | 0.039 | 0.514 | 10.044 | 0.001 | Temp | 0.348 | 2.09 | 0.05 |
GDP | −0.225 | −1.129 | 0.273 | GDP | −0.536 | −3.219 | 0.005 | |||||||
17 | 0.767 | 31.188 | 0.000 | Temp | 0.140 | 1.081 | 0.293 | 0.858 | 57.23 | 0 | Temp | 0.139 | 1.374 | 0.185 |
GDP | 0.794 | 6.111 | 0.000 | GDP | 0.846 | 8.336 | 0 |
Green highlights indicate statistically significant results (
The correlation between CCP/CCD and TEMP for each country.
Country | Cervical cancer prevalence (CCP) | Cervical cancer death (CCD) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R square | F | independent variable | Beta | R square | F | independent variable | Beta | |||||||
1 | 0.818 | 40.334 | 0.000 | Temp | −0.028 | −0.274 | 0.787 | 0.899 | 84.635 | 0.000 | Temp | −0.017 | −0.235 | 0.816 |
GDP | 0.905 | 8.980 | 0.000 | GDP | 0.950 | 12.956 | 0.000 | |||||||
2 | 0.601 | 14.281 | 0.000 | Temp | 0.382 | 2.488 | 0.022 | 0.419 | 6.847 | 0.006 | Temp | 0.210 | 1.131 | 0.272 |
GDP | 0.559 | 3.637 | 0.002 | GDP | 0.547 | 2.949 | 0.008 | |||||||
3 | 0.816 | 42.211 | 0.000 | Temp | 0.067 | 0.562 | 0.581 | 0.959 | 224.143 | 0.000 | Temp | −0.001 | −0.010 | 0.992 |
GDP | 0.864 | 7.249 | 0.000 | GDP | 0.980 | 17.474 | 0.000 | |||||||
4 | 0.354 | 5.215 | 0.016 | Temp | 0.370 | 1.990 | 0.061 | 0.654 | 17.938 | 0.000 | Temp | 0.300 | 2.204 | 0.040 |
GDP | 0.421 | 2.265 | 0.035 | GDP | 0.713 | 5.237 | 0.000 | |||||||
5 | 0.641 | 16.941 | 0.000 | Temp | 0.081 | 0.536 | 0.598 | 0.687 | 20.888 | 0.000 | Temp | 0.141 | 0.999 | 0.330 |
GDP | 0.763 | 5.043 | 0.000 | GDP | 0.760 | 5.385 | 0.000 | |||||||
6 | 0.434 | 7.291 | 0.004 | Temp | −0.138 | −0.802 | 0.432 | 0.453 | 7.865 | 0.003 | Temp | 0.449 | 2.645 | 0.016 |
GDP | −0.645 | −3.740 | 0.001 | GDP | 0.505 | 2.978 | 0.008 | |||||||
7 | 0.190 | 2.228 | 0.135 | Temp | −0.139 | −0.585 | 0.565 | 0.167 | 1.905 | 0.176 | Temp | 0.169 | 0.699 | 0.493 |
GDP | 0.349 | 1.467 | 0.159 | GDP | −0.297 | −1.231 | 0.233 | |||||||
8 | 0.679 | 20.095 | 0.000 | Temp | 0.298 | 1.982 | 0.062 | 0.777 | 33.098 | 0.000 | Temp | −0.106 | −0.849 | 0.407 |
GDP | 0.633 | 4.204 | 0.000 | GDP | −0.823 | −6.564 | 0.000 | |||||||
9 | 0.235 | 2.768 | 0.090 | Temp | 0.257 | 1.242 | 0.230 | 0.060 | 0.570 | 0.575 | Temp | 0.244 | 1.066 | 0.301 |
GDP | −0.390 | −1.887 | 0.075 | GDP | 0.006 | 0.025 | 0.980 | |||||||
10 | 0.996 | 2265.057 | 0.000 | Temp | −0.015 | −0.996 | 0.332 | 0.992 | 1121.518 | 0.000 | Temp | 0.001 | 0.024 | 0.981 |
GDP | 1.001 | 65.789 | 0.000 | GDP | 0.996 | 46.135 | 0.000 | |||||||
11 | 0.335 | 4.793 | 0.021 | Temp | 0.240 | 1.227 | 0.235 | 0.283 | 3.747 | 0.042 | Temp | 0.237 | 1.167 | 0.258 |
GDP | 0.603 | 3.078 | 0.006 | GDP | 0.551 | 2.711 | 0.014 | |||||||
12 | 0.467 | 7.452 | 0.005 | Temp | 0.505 | 2.658 | 0.017 | 0.624 | 14.093 | 0.000 | Temp | 0.605 | 3.790 | 0.001 |
GDP | 0.313 | 1.647 | 0.118 | GDP | 0.334 | 2.095 | 0.051 | |||||||
13 | 0.799 | 37.685 | 0.000 | Temp | 0.243 | 2.084 | 0.051 | 0.729 | 25.543 | 0.000 | Temp | 0.199 | 1.477 | 0.156 |
GDP | 0.754 | 6.484 | 0.000 | GDP | 0.742 | 5.498 | 0.000 | |||||||
14 | 0.820 | 43.419 | 0.000 | Temp | 0.254 | 2.309 | 0.032 | 0.430 | 7.167 | 0.005 | Temp | 0.185 | 0.943 | 0.357 |
GDP | −0.759 | −6.915 | 0.000 | GDP | 0.721 | 3.684 | 0.002 | |||||||
15 | 0.878 | 68.142 | 0.000 | Temp | 0.070 | 0.862 | 0.400 | 0.013 | 0.126 | 0.883 | Temp | −0.043 | −0.185 | 0.855 |
GDP | 0.923 | 11.353 | 0.000 | GDP | −0.099 | −0.430 | 0.672 | |||||||
16 | 0.236 | 2.941 | 0.077 | Temp | 0.472 | 2.261 | 0.036 | 0.503 | 9.605 | 0.001 | Temp | 0.344 | 2.042 | 0.055 |
GDP | −0.044 | −0.209 | 0.837 | GDP | −0.531 | −3.150 | 0.005 | |||||||
17 | 0.612 | 14.982 | 0.000 | Temp | 0.153 | 0.913 | 0.373 | 0.832 | 47.031 | 0.000 | Temp | 0.159 | 1.441 | 0.166 |
GDP | 0.692 | 4.129 | 0.001 | GDP | 0.819 | 7.432 | 0.000 |
Green highlights indicate statistically significant results (
The correlation between OCP/OCD and TEMP for each country.
Country | Ovarian cancer prevalence (OCP) | Ovarian cancer death (OCD) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R square | F | independent variable | Beta | T | R Square | F | independent variable | Beta | t | |||||
1 | 0.888 | 75.584 | 0.000 | Temp | −0.022 | −0.288 | 0.776 | 0.938 | 144.191 | 0.000 | Temp | −0.007 | −0.119 | 0.907 |
GDP | 0.945 | 12.250 | 0.000 | GDP | 0.969 | 16.893 | 0.000 | |||||||
2 | 0.799 | 37.817 | 0.000 | Temp | 0.390 | 3.583 | 0.002 | 0.775 | 32.755 | 0.000 | Temp | 0.334 | 2.893 | 0.009 |
GDP | 0.685 | 6.287 | 0.000 | GDP | 0.712 | 6.171 | 0.000 | |||||||
3 | 0.919 | 108.324 | 0.000 | Temp | −0.035 | −0.445 | 0.661 | 0.955 | 200.663 | 0.000 | Temp | −0.078 | −1.316 | 0.204 |
GDP | 0.978 | 12.389 | 0.000 | GDP | 1.019 | 17.236 | 0.000 | |||||||
4 | 0.662 | 18.596 | 0.000 | Temp | 0.267 | 1.981 | 0.062 | 0.721 | 24.516 | 0.000 | Temp | 0.249 | 2.033 | 0.056 |
GDP | 0.735 | 5.463 | 0.000 | GDP | 0.780 | 6.381 | 0.000 | |||||||
5 | 0.636 | 16.611 | 0.000 | Temp | 0.097 | 0.636 | 0.532 | 0.664 | 18.781 | 0.000 | Temp | 0.129 | 0.881 | 0.389 |
GDP | 0.752 | 4.939 | 0.000 | GDP | 0.753 | 5.143 | 0.000 | |||||||
6 | 0.273 | 3.570 | 0.048 | Temp | 0.457 | 2.334 | 0.031 | 0.406 | 6.488 | 0.007 | Temp | 0.477 | 2.697 | 0.014 |
GDP | 0.258 | 1.320 | 0.202 | GDP | 0.426 | 2.411 | 0.026 | |||||||
7 | 0.504 | 9.636 | 0.001 | Temp | 0.074 | 0.398 | 0.695 | 0.468 | 8.340 | 0.003 | Temp | 0.138 | 0.715 | 0.483 |
GDP | −0.670 | −3.592 | 0.002 | GDP | −0.604 | −3.130 | 0.006 | |||||||
8 | 0.726 | 25.215 | 0.000 | Temp | 0.154 | 1.111 | 0.281 | 0.706 | 22.789 | 0.000 | Temp | 0.054 | 0.376 | 0.711 |
GDP | 0.764 | 5.501 | 0.000 | GDP | 0.812 | 5.634 | 0.000 | |||||||
9 | 0.259 | 3.145 | 0.067 | Temp | 0.235 | 1.156 | 0.263 | 0.102 | 1.017 | 0.382 | Temp | 0.221 | 0.985 | 0.338 |
GDP | −0.432 | −2.121 | 0.048 | GDP | −0.212 | −0.945 | 0.357 | |||||||
10 | 0.974 | 356.596 | 0.000 | Temp | −0.014 | −0.376 | 0.711 | 0.973 | 341.573 | 0.000 | Temp | −0.008 | −0.198 | 0.845 |
GDP | 0.990 | 26.100 | 0.000 | GDP | 0.988 | 25.508 | 0.000 | |||||||
11 | 0.220 | 2.674 | 0.095 | Temp | 0.400 | 1.883 | 0.075 | 0.268 | 3.483 | 0.051 | Temp | −0.007 | −0.119 | 0.907 |
GDP | −0.154 | −0.724 | 0.478 | GDP | 0.969 | 16.893 | 0.000 | |||||||
12 | 0.606 | 13.058 | 0.000 | Temp | 0.542 | 3.317 | 0.004 | 0.619 | 13.821 | 0.000 | Temp | 0.334 | 2.893 | 0.009 |
GDP | 0.396 | 2.423 | 0.027 | GDP | 0.712 | 6.171 | 0.000 | |||||||
13 | 0.837 | 48.960 | 0.000 | Temp | 0.288 | 2.754 | 0.013 | 0.836 | 48.525 | 0.000 | Temp | −0.078 | −1.316 | 0.204 |
GDP | 0.745 | 7.124 | 0.000 | GDP | 1.019 | 17.236 | 0.000 | |||||||
14 | 0.757 | 29.557 | 0.000 | Temp | 0.262 | 2.047 | 0.055 | 0.154 | 1.735 | 0.203 | Temp | 0.249 | 2.033 | 0.056 |
GDP | −0.717 | −5.606 | 0.000 | GDP | 0.780 | 6.381 | 0.000 | |||||||
15 | 0.872 | 64.915 | 0.000 | Temp | 0.051 | 0.614 | 0.547 | 0.879 | 68.723 | 0.000 | Temp | 0.129 | 0.881 | 0.389 |
GDP | 0.924 | 11.132 | 0.000 | GDP | 0.753 | 5.143 | 0.000 | |||||||
16 | 0.386 | 5.971 | 0.010 | Temp | 0.421 | 2.245 | 0.037 | 0.533 | 10.832 | 0.001 | Temp | 0.477 | 2.697 | 0.014 |
GDP | −0.354 | −1.892 | 0.074 | GDP | 0.426 | 2.411 | 0.026 | |||||||
17 | 0.767 | 31.297 | 0.000 | Temp | 0.141 | 1.088 | 0.290 | 0.834 | 47.888 | 0.000 | Temp | 0.138 | 0.715 | 0.483 |
GDP | 0.794 | 6.119 | 0.000 | GDP | −0.604 | −3.130 | 0.006 |
Green highlights indicate statistically significant results (
The correlation between UCP/UCD and TEMP for each country.
Country | Ovarian cancer prevalence (UCP) | Uterine cancer death (UCD) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R square | F | independent variable | Beta | R square | F | independent variable | Beta | |||||||
1 | 0.806 | 39.489 | 0.000 | Temp | −0.043 | −0.419 | 0.680 | 0.913 | 99.592 | 0.000 | Temp | −0.014 | −0.206 | 0.839 |
GDP | 0.901 | 8.870 | 0.000 | GDP | 0.957 | 14.050 | 0.000 | |||||||
2 | 0.494 | 9.259 | 0.002 | Temp | 0.135 | 0.781 | 0.445 | 0.570 | 12.587 | 0.000 | Temp | 0.242 | 1.518 | 0.146 |
GDP | 0.646 | 3.733 | 0.001 | GDP | 0.639 | 4.008 | 0.001 | |||||||
3 | 0.959 | 223.304 | 0.000 | Temp | 0.021 | 0.377 | 0.711 | 0.909 | 95.180 | 0.000 | Temp | −0.015 | −0.183 | 0.857 |
GDP | 0.967 | 17.220 | 0.000 | GDP | 0.962 | 11.485 | 0.000 | |||||||
4 | 0.623 | 15.712 | 0.000 | Temp | 0.281 | 1.976 | 0.063 | 0.466 | 8.287 | 0.003 | Temp | 0.326 | 1.927 | 0.069 |
GDP | 0.702 | 4.946 | 0.000 | GDP | 0.559 | 3.306 | 0.004 | |||||||
5 | 0.611 | 14.903 | 0.000 | Temp | 0.110 | 0.697 | 0.494 | 0.689 | 21.083 | 0.000 | Temp | 0.113 | 0.804 | 0.432 |
GDP | 0.729 | 4.629 | 0.000 | GDP | 0.777 | 5.519 | 0.000 | |||||||
6 | 0.340 | 4.891 | 0.019 | Temp | 0.484 | 2.597 | 0.018 | 0.361 | 5.367 | 0.014 | Temp | 0.437 | 2.384 | 0.028 |
GDP | 0.329 | 1.766 | 0.094 | GDP | 0.416 | 2.268 | 0.035 | |||||||
7 | 0.051 | 0.505 | 0.611 | Temp | 0.254 | 0.986 | 0.336 | 0.120 | 1.300 | 0.296 | Temp | 0.220 | 0.888 | 0.386 |
GDP | 0.083 | 0.322 | 0.751 | GDP | −0.180 | −0.725 | 0.477 | |||||||
8 | 0.598 | 14.139 | 0.000 | Temp | 0.246 | 1.460 | 0.161 | 0.500 | 9.497 | 0.001 | Temp | −0.153 | −0.816 | 0.425 |
GDP | 0.620 | 3.682 | 0.002 | GDP | 0.772 | 4.110 | 0.001 | |||||||
9 | 0.083 | 0.813 | 0.459 | Temp | 0.214 | 0.946 | 0.357 | 0.063 | 0.601 | 0.559 | Temp | 0.239 | 1.042 | 0.311 |
GDP | −0.175 | −0.773 | 0.450 | GDP | −0.057 | −0.250 | 0.805 | |||||||
10 | 0.770 | 31.777 | 0.000 | Temp | −0.003 | −0.023 | 0.982 | 0.988 | 808.266 | 0.000 | Temp | −0.005 | −0.215 | 0.832 |
GDP | 0.878 | 7.772 | 0.000 | GDP | 0.995 | 39.218 | 0.000 | |||||||
11 | 0.301 | 4.082 | 0.034 | Temp | 0.408 | 2.029 | 0.057 | 0.132 | 1.441 | 0.261 | Temp | 0.375 | 1.674 | 0.111 |
GDP | −0.265 | −1.320 | 0.202 | GDP | 0.172 | 0.769 | 0.451 | |||||||
12 | 0.575 | 11.478 | 0.001 | Temp | 0.622 | 3.664 | 0.002 | 0.736 | 23.675 | 0.000 | Temp | 0.434 | 3.246 | 0.005 |
GDP | 0.263 | 1.552 | 0.139 | GDP | 0.599 | 4.481 | 0.000 | |||||||
13 | 0.829 | 45.986 | 0.000 | Temp | 0.357 | 3.330 | 0.004 | 0.766 | 31.013 | 0.000 | Temp | 0.226 | 1.803 | 0.087 |
GDP | 0.687 | 6.404 | 0.000 | GDP | 0.746 | 5.942 | 0.000 | |||||||
14 | 0.737 | 26.557 | 0.000 | Temp | 0.139 | 1.042 | 0.311 | 0.329 | 4.668 | 0.022 | Temp | 0.404 | 1.905 | 0.072 |
GDP | −0.785 | −5.898 | 0.000 | GDP | 0.637 | 3.001 | 0.007 | |||||||
15 | 0.732 | 26.013 | 0.000 | Temp | 0.041 | 0.338 | 0.739 | 0.871 | 64.248 | 0.000 | Temp | 0.078 | 0.938 | 0.360 |
GDP | 0.848 | 7.057 | 0.000 | GDP | 0.918 | 11.000 | 0.000 | |||||||
16 | 0.465 | 8.243 | 0.003 | Temp | 0.366 | 2.094 | 0.050 | 0.567 | 12.440 | 0.000 | Temp | 0.325 | 2.063 | 0.053 |
GDP | −0.481 | −2.751 | 0.013 | GDP | −0.595 | −3.780 | 0.001 | |||||||
17 | 0.773 | 32.419 | 0.000 | Temp | 0.129 | 1.009 | 0.326 | 0.846 | 52.238 | 0.000 | Temp | 0.148 | 1.407 | 0.176 |
GDP | 0.805 | 6.290 | 0.000 | GDP | 0.834 | 7.905 | 0.000 |
Green highlights indicate statistically significant results (
Dependent variables:
Breast cancer prevalence—percentage (BCP)
Cervical cancer prevalence—percentage (CCP)
Ovarian cancer prevalence—percentage (OCP)
Uterine cancer prevalence—percentage (UCP)
Breast cancer deaths—percentage (BCD)
Cervical cancer deaths—percentage (CCD)
Ovarian cancer deaths—percentage (OCD)
Uterine cancer deaths—percentage (UCD)
Independent variable: Temperature change with respect to a baseline climatology (TEMP).
Controlling variable: Gross Domestic Product per capita (GDP).
Data for all these variables was collected for the years 1998 till 2019.
Secondary data was used in this research. Datasets were derived from three main sources:
Data for the eight dependent variables on the prevalence and deaths caused by cancer were extracted from the Global Burden of Disease data provided by the Institute of Health Metrics and Evaluation (IHME) (
Data on temperature change was obtained from the Food and Agriculture Organization of the United Nations (FAO) – FAOSTAT Climate Change database (
GDP per capita data was derived from the World Bank Databank (
The countries included in the study were selected based on two criteria: geographic representation (i.e., part of the MENA region according to the World Bank classification) and the availability of data. Taking into account these two criteria the study ended up with 17 countries. The countries included are: 1 = Algeria, 2 = Bahrain, 3 = Egypt, 4 = Iran, 5 = Iraq, 6 = Jordan, 7 = Kuwait, 8 = Lebanon, 9 = Libya, 10 = Morocco, 11 = Oman, 12 = Qatar, 13 = Saudi Arabia, 14 = Syria, 15 = Tunisia, 16 = United Arab Emirates (UAE), and 17 = Palestine.
The study did not involve human participants, materials, or data; therefore, no institutional review board, ethics committee approval, or consent was required.
MLR models the relationship between each dependent variable and temperature, controlling for GDP. MLR was performed on all countries combined and then separately for each country. A significance cutoff of
The MLR model:
Analysis was conducted using SPSS version 26.
Multicollinearity was assessed. The Variance Inflation Factor (VIF) test was conducted, and the results showed that all independent variables had VIF values below 5 (Temperature Change: VIF = 1.015, GDP per Capita: VIF = 1.015), indicating no multicollinearity issues.
The results of this study are two parts, regional and country level. This first part covers the MLR analysis investigating the relation between the prevalence and deaths from four types of women’s cancer and the independent variable TEMP while controlling for GDP in the MENA region. The results (
For every one-unit increase in TEMP, deaths from the four cancers increased by 0.171 to 0.332 units, with ovarian cancer showing the highest correlation (0.332) and cervical cancer the lowest (0.171). Regarding prevalence, the B coefficient showed an increase between 0.173 and 0.280 units, with ovarian cancer having the highest correlation and breast cancer the lowest.
The second part examines MLR results for each of the 17 countries.
Significant positive correlations were found between BCP and TEMP in Bahrain, Qatar, and UAE, with a one-degree increase in TEMP resulting in 0.33, 0.56, and 0.44 increases in BCP, respectively. BCD also showed significant correlations in Jordan, Qatar, Saudi Arabia, and UAE, with increases of 0.42, 0.55, 0.31, and 0.35, respectively, per degree increase in TEMP (
There were significant positive correlations between CCP and TEMP in Bahrain, Qatar, and Syria, with increases of 0.38, 0.51, and 0.25, respectively, for each degree increase. CCD was significantly correlated with TEMP in Iran, Jordan, and Qatar, with increases of 0.3, 0.45, and 0.61, respectively (
OCP correlated significantly with TEMP in Bahrain, Jordan, Qatar, Saudi Arabia, and UAE, with increases of 0.39, 0.46, 0.54, 0.29, and 0.421, respectively. OCD correlated significantly with TEMP in Bahrain, Jordan, Qatar, and UAE, with changes of 0.33, 0.48, 0.33, and 0.48, respectively (
UCP was significantly correlated with TEMP in Jordan, Qatar, Saudi Arabia, and UAE, with increases of 0.48, 0.62, 0.36, and 0.37, respectively. UCD showed significant correlations with TEMP in Jordan and Qatar, increasing by 0.44 and 0.43, respectively, per degree increase (
As indicated above, MENA region is a climate change high risk area characterized by temperature average increase that is above the average for other regions. The results of this study show that climate change represented by increased temperature is significantly related to increased deaths and prevalence of cancer in women in the MENA region.
Several individual and socioeconomic factors may increase the risk of cancer in women (
Moreover, research suggests that external temperature influences the mechanical properties of cells. Even minor temperature changes can significantly alter cell characteristics, with increased temperatures enhancing optical deformability, including in breast cells (
This study concentrated on two measures for each type of women’s cancer included: mortality (deaths) and prevalence. Mortality rates reflect the incidence of the disease as well as the availability of early detection and treatment. Cancer is one of the main causes of mortality among women around the world (
Percentage of female deaths by cancer type over time.
As for the prevalence of a disease, it reflects both the exposure to risk factors and the availability of screening. Advancement in cancer screening methods over the years contributes to the increased prevalence as more cases are discovered through screening. However, this does not annulated the contribution of increased exposure to risk factors.
Percentage of female deaths by cancer type over time.
Screening and treatment are factors related to the level of medical advancement in a country as well as on the accessibility to health services. Although there are variations among the 17 countries included in this study in these aspects, these countries have been advancing in terms of medical service availability and accessibility through the years thus the provision of more screening and treatment. Increased screening means increased prevalence but it also means decreased deaths as the increased screening leads to higher probability of treatment and thus less deaths. The fact that both prevalence and deaths increased highlight the importance of the other two factors: exposure to risk factors and subsequently increased incidence. And given the results of this study that prolonged exposure to high ambient temperature is correlated to the prevalence of three women cancers in the MENA region, one can deduct that high ambient temperature can be considered as a potential risk factor.
Although the correlation results for the prevalence and deaths were significant for the region as a whole, the correlation for the disaggregated data by country showed different results. As observed in
Significant correlation values for disintegrated data by country on the prevalence of the four cancer types*.
Qatar | UAE | Bahrain | Jordan | Saudi Arabia | ||||||
---|---|---|---|---|---|---|---|---|---|---|
P | D | P | D | P | D | P | D | P | D | |
Breast cancer | 0.56 | 0.55 | 0.44 | 0.35 | 0.33 | 0.42 | 0.31 | |||
Cervical cancer | 0.51 | 0.61 | 0.38 | 0.45 | ||||||
Ovarian cancer | 0.54 | 0.33 | 0.42 | 0.48 | 0.39 | 0.33 | 0.46 | 0.48 | 0.29 | |
Uterine cancer | 0.62 | 0.43 | 0.366 | 0.48 | 0.44 | 0.36 |
P, Prevalence; D, Death. *This table summarizes only the statistically significant associations identified in
Some countries, such as Algeria and Lebanon, did not exhibit statistically significant associations between ambient temperature and cancer outcomes across the four cancer types. These non-significant results may reflect different local contexts, milder temperature variations, or other dominant health determinants not captured in the current model.
Although more research is needed to validate and generalize the findings of this study, the findings highlight the necessity of including climate change adaptation measures into national cancer control plans. This can be achieved by encouraging interdisciplinary methods to address the confluence of climate and health, promoting sustainable environmental policies to limit the implications of climate change, and including climate adaption strategies into the planning of health care infrastructure.
While this study provides significant insights, it is considered preliminary research due to several factors including: limited studies specifically focusing on the relation between increased ambient temperature and cancer or the mechanism involved. The correlation between temperature, cancer prevalence, and death does not imply causation. There can be other contributing factors including genetics, lifestyle, exposure to environmental pollutants (e.g., PFAS), and access to health care, gender disparities, long-term exposure to carcinogens such as PM2.5 and endocrine-disrupting chemicals. Additionally, differences in heat acclimatization were not considered, which may influence how populations in warmer climates respond to rising temperatures.
It is difficult to separate the precise effect of temperature from these other variables. Further research is required to investigate the underlying mechanisms and any confounding variables that can explain the observed connections between temperature, cancer, and GDP in the MENA region. Finally, the study’s scope is limited to a small number of MENA countries, which may affect the generalizability of findings. Expanding the analysis to other regions could help determine whether similar patterns exist globally.
This study highlights how climate change is no longer a distant environmental concern but a pressing threat to women’s health. The findings support the existence of a correlation between prolonged exposure to high temperature and the burden of women’s cancers in the MENA region. The relationship was evident at both the regional and country levels and was more pronounced in countries experiencing extreme heat.
The implications of these findings are important. It calls for immediate attention from policymakers and health planners. It highlights the urgency of integrating climate-related risks into health policy, with a focus on women’s health. Countries with high exposure should strengthen their cancer early detection and response systems. This includes improving awareness, screening, and access to care. Cross-sectoral collaboration between health, environment, and planning institutions is needed. Countries with lower burden should also act early, using these findings as an early warning.
The study adds to the growing recognition of the intersection between climate and health. It contributes to shaping future dialog and action on equitable, climate-resilient health systems in the region. Nevertheless, the study has limitations. Further work is needed to validate the findings using larger samples and to explore the mechanisms linking temperature and cancer.
Finally, this research’s results call for coordinated, climate-informed public health policies that protect vulnerable populations—especially women—from compounding environmental risks.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
WA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Visualization, Writing – original draft. SC: Conceptualization, Methodology, Supervision, Validation, Writing – review & editing.
The author(s) declare that financial support was received for the research and/or publication of this article. This Study was made possible in part by support from the Reproductive Health Working Group, Faculty of Health Sciences (FHS) at the American University of Beirut (AUB) as part of a grant funded by the International Development Research Centre (IDRC).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declare that no Gen AI was used in the creation of this manuscript.
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