The association between Breast Cancer and Female Employment among countries with high and low CO2 emission.
Breast Cancer (BC) is the commonest cause of cancer death in women worldwide. Incidence rates for BC vary greatly throughout the world (Henderson et al., 1996). The highest incidence rates are found in industrialized countries, including the US, Northern Europe, and Canada, while lower rates are found in Asia and Africa. While there is some evidence that exposure to light-at-night may suppress the hormone melatonin and increase the risk of BC (Megdal SP et al., 2005), other studies indicate that sleeping longer reduces the risk for BC (Kakizaki M et al., 2008 and Verkasalo PK et al.,2005). In addition there is growing evidence that there is a connection between environmental factors and BC. The purpuse of this paper is to examine:
. 1. Is the BC incidence associated with female employment rate?
2. Is the association between BC incidence and female employment rate similar for countries with low and high CO2 emission?
Sample Data for each country is extracted from Gapminder (www.gapminder.org). The Gapminder databases aggregate country data from a variety of other reliable sources, such as the World Bank, the World Health Organization, the International Labor Organization, and other international agencies.
Gapminder is a non-profit venture – a modern “museum” on the Internet – promoting sustainable global development and achievement of the United Nations Millennium Development Goals.
Three quantitative variables are used in this paper:
Quantitative Explainatory Variable: Female Employment Rate: Percentage of female population, age above 15, that has been employed during 2007.
Quantitative Response Variable: Breast Cancer per 100th: Number of new cases of Breast Cancer in 100.000 female residents (2002). (This will now be denoted as Breast Cancer Incidence)
Moderating Variable: CO2 emissions: Total amount of CO2 emission in metric tons since 1751. This variable will be grouped as “High” or “Low”, where the cut-off will be the median of the CO2 emissions.
In Univariate analysis, descriptive analysis will be performed. The mean, Standard Deviation (SD), range and total number of observations for each variable will be provided. In the Bivariate analysis Pearson Correlation will be used to examine the linear association between the explanatory and the response variable. Line of best Fit and Pearson correlation coefficient will appear on the Scatter plots. Research question 2 will be answered with the same means.
All calculations are done using the software SAS Enterprise Guide 4.3 OnDemand for Acedemics. P<0.05 is considered significant.
The mean Female Employment Rate was 47.55% (SD=14.63; range: 11.30 - 83.30; N=178).
The mean Breast Cancer Incidence was 37.40 (SD=22.70; range: 3.9 - 101.1; N=173).
The mean CO2 emission was 4.76 * 10^9 tons (SD=2.50 * 10 ^10; range:1.1*10^5 – 3.34*10^11 ; N=212). The median CO2 emission is 1.69032 * 10^8; this value will be the cut-off value when defining countries with high and low CO2 emission, respectively. Thus countries with CO2 emission greater then the cut-off value are countries with high CO2 emission, whereas countries with CO2 emission less then or equal to cut-off value are defined as countries with low CO2 emission.
Figure 1: Scatterplot showing the linear relationship between Breast Cancer Incidence and Female Eployment Rate.
The Pearson Correlation Test shows a negative association, ie. the Incidence of BC diminishes with increased Female Employment Rate (Figure 1). The Correlation Coeffecient r is -0,075 (p = 0.3323) indicating a weak linear relationship. As the p-value is > 0.05, the results might be caused by chance. The squared value of r (-0.075 ^2 = 0.005625 = 0,56%) implies that Female Employment Rate can explain only 0.56% of the variability in BC Incidence.
Figure 2a: Scatterplot showing the linear relationship
between Breast Cancer Incidence and Female Eployment Rate among countries with High CO2 emission.
A slightly weak positive and non-significant linear relationship between BC Incidence and Female Employment Rate is found among countries with high CO2 emission (r = 0.16965, p = 0.0883) (Figure 2a). The Female Employment rate can only explain 2.88% of the variability in BC Incidence.
Figure 2b: Scatterplot showing the linear relationship
between Breast Cancer Incidence and Female Eployment Rate among countries with Low CO2 emission.
A statistical significant association is found between BC Incidence and Female Employment Rate among countries with low CO2 emission. The relationship is slightly weak and negative (r = 0.25631, p = 0.0443). The p value indicates that there is 4.43% chance that these results are caused by chance. The Female Employment rate can only explain 6.57% (0.25631^2 = 0.0657 ) of the variability in BC Incidence.
What might the results mean
The association between BC Incidence and Female Employment Rate was not statistical significant. In countries with low level of CO2 emission, a slightly weak negative, but statistical significant correlation, was found between the two variables. A similar statistical correlation was not found for countries with high CO2 emission.
This research is based on Gapminder, which has data from different countries in the world.
The paper is based on the data from different years making it less consistent.
A significant amount of data is missing for different variables in the Gapminder dataset.
The research has not been controlled for possible confounders.
The data comes from observational studies and hence the result doesn’t imply causality.
RECOMMENDED FUTURE RESEARCH
Further studies are needed before anything can be concluded about the association; it is also important that confounders have been controlled in the studies. It is also important that more in depth information is provided about the art of employment, ie. shift work, regular day work, timings etc..
Henderson BE, Pike MC, Bernstein L, Ross RK. 1996. Breast Cancer, chapter 47 in Cancer Epidemiology and Prevention. 2nd ed. Schottenfeld D and Fraumeni JF Jr.,eds. Oxford University Press. pp: 1022-1035.
Kakizaki M, Kuriyami S, Sone T, et al. Sleep duration and breast cancer: the Ohsaki Cohort study. Br J Cancer 2008; 99:1502-5.
Megdal SP, Kroenke CH, Laden F, et al. Nightwork and breast cancer risk: a systematic review and meta-analysis. Eur J Cancer 2005; 41:2023-32.
Verkasalo PK, Liliberg K, Stevens RG, et al. Sleep duration and breast cancer: a prospective cohort study. Cancer Res 2005; 65:9595-600.
