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What predicts breast cancer rates? Testing hypotheses of the demographic and nutrition transitions

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Abstract

Modern demographic and nutritional transitions have been implicated in global epidemiological transitions intensifying over the last 60 years. These transitions include steadily declining fertility rates, improving nutritional indicators, and increasing incidence rates of chronic diseases such as breast cancer. This research draws on the well established pathways linking individual reproductive and nutritional profiles to breast cancer risk, in order to test the links among demographic, nutritional, and epidemiological transitions on a global scale. We propose two hypotheses that test the reproductive and nutritional pathways that are suggested to increase breast cancer risk at the population level. We use total fertility rate (TFR) to test the reproductive behaviour hypothesis, and we use average height and the percentage of the population that is overweight for the nutritional hypothesis; these indicators are compared to breast cancer incidence rates for 2008. Accounting for national wealth and expenditures on healthcare, we found that both hypotheses were significantly associated with breast cancer incidence, although TFR appears to have a more consistent association with incidence. Drawing on our regression model, we explain trends in breast cancer incidence in selected countries, as well as making predictions about shifting breast cancer incidence rates over the next several decades. These data suggest that greater attention should be paid to the unintended health consequences of transitions that are largely considered to bring improvements in quality of life. Our findings suggest that greater investments in screening and treatment are particularly needed in regions undergoing transitions in fertility rates, particularly those areas experiencing super-low fertility.

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Notes

  1. See Appendix for list of countries included in this analysis.

  2. These data include Central Asia, Europe, and North America. See Appendix for list of countries included in analysis.

  3. Untransformed TFR data produce a strong, significant correlation with breast cancer incidence (r = −0.754, p < 0.001). Common transformations (log, square, square root, inverse) of TFR produced similar or smaller Pearson correlations with breast cancer incidence.

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Authors and Affiliations

  1. Department of Anthropology, Emory University, 1557 Dickey Drive, Atlanta, GA, 30322, USA

    Bonnie Kaiser & Kathryn Bouskill

  2. Department of Epidemiology, Emory University, 1518 Clifton Road, Atlanta, GA, 30322, USA

    Bonnie Kaiser & Kathryn Bouskill

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  1. Bonnie Kaiser
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Correspondence to Bonnie Kaiser.

Appendix

Appendix

Countries included in selected analyses (total = 175 countries with any data available).

Age at first birth (2000–2002) and TFR (1980, 1990)

N = 46 countries.

Armenia, Austria, Azerbaijan, Belarus, Belgium, Bulgaria, Canada, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Israel, Italy, Kazakhstan, Kyrgyzstan, Ireland, Latvia, Lithuania, Luxembourg, Macedonia, Malta, Moldova, Netherlands, Norway, Poland, Portugal, Romania, Russian Federation, Serbia and Montenegro, Slovenia, Spain, Sweden, Switzerland, Tajikistan, Turkmenistan, Ukraine, United Kingdom, United States, Uzbekistan.

% of GDP spent on health and % of women who have had mammogram

N = 73 countries.

Australia, Austria, Bangladesh, Belgium, Bosnia Herzegovena, Brazil, Burkina Faso, Canada, Chad, China, Comoros, Congo, Cote d’Ivoire, Croatia, Czech Republic, Denmark, Dominican Republic, Ecuador, Estonia, Ethiopia, Finland, France, Georgia, Germany, Ghana, Greece, Guatemala, Hungary, Iceland, India, Ireland, Israel, Italy, Japan, Kazakhstan, Kenya, Laos, Latvia, Luxembourg, Malawi, Malaysia, Mali, Mauritania, Mauritius, Mexico, Morocco, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Norway, Pakistan, Paraguay, Philippines, Portugal, Russia, Senegal, Slovakia, South Africa, Spain, Sri Lanka, Swaziland, Sweden, Switzerland, Tunisia, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Vietnam, Zambia, Zimbabwe.

Final model: TFR, overweight, height, GDP on health, GNI, and breast cancer incidence

N = 98 countries

Argentina, Armenia, Australia, Bahrain, Bangladesh, Belgium, Benin, Bolivia, Brazil, Burkina Faso, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo Brazzaville, Cote d’Ivoire, Czech Republic, Denmark, Dominican Republic, Egypt, Eritrea, Ethiopia, Fiji, Finland, France, Gabon, Germany, Ghana, Greece, Guatemala, Guinea, Haiti, Honduras, Hungary, India, Indonesia Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Malaysia, Mali, Malta, Mauritania, Mexico, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Papua New Guinea, Peru, Philippines, Poland, Rwanda, Senegal, Singapore, Solomon Islands, South Africa, South Korea, Spain, Swaziland, Sweden, Switzerland, Thailand, Togo, Turkey, Uganda, United Kingdom, United States, Uzbekistan, Yemen, Zambia, Zimbabwe.

Table 5 Countries in the lower 30 % or upper 30 % for both overweight and TFR and corresponding breast cancer rates
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Kaiser, B., Bouskill, K. What predicts breast cancer rates? Testing hypotheses of the demographic and nutrition transitions. J Pop Research 30, 67–85 (2013). https://doi.org/10.1007/s12546-012-9090-9

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