5 Conclusions and final remarks
The overall incidence of cancer rates is growing throughout the world and it will constitute a serious challenge for scientists and health care systems in the years to come.
In Chapter 2 I showed the relevance of the phenomenon and I reported previous studies (Bray et al 2012; Beaulieu et al. (2009)) that, in some ways, opened the way for the correlation between GDP and cancer incidence.
In Chapter 3 a brief literature on the state of the art in the medical community concerning cancer etiology was presented, in order to select the right variables to put inside the theoretical model. The model was presented in chapter 4, together with a description concerning the sources (Globocan 2012, World Bank, United Nations Population) with which I have built my dataset: the general sample was composed of 107 countries.
Regressions and statistical analysis have been conducted using R software.
Then, I ran multivariate regressions between lagged three-year average GDP per capita and average standardized rates of cancer, also including health expenditure per capita in order to isolate the effect of screening from the data. The expectation, in fact, was to find a positive correlation with health expenditure per capita (a proxy to represent the state of the art in screening technologies for cancer incidence).
Also ex-ante expectations about the level of GDP per capita and incidence were positive, especially in the interpretation that sees GDP as a proxy for Anthropocene level in environment.
The results, instead, are more complex and graphs associated exhibit a manner of bell shape, a result that is quite robust (considering the overall sample) due to the two-step procedure implemented (Health expenditure was regressed on ASR(W) and then the residuals were regressed on GDP per capita, in linear, quadratic and cubic component).
GDP per capita seems to have different contributions to incidence rate according to its level: a low level is associated with a negative effect (more income, less incidence); a medium level is associated with a positive effect, and then a high level (a value that is very sensitive to the lag
adopted: for example it emerges after $23000 per capita in PPP with reference to the year 1996) newly correlates with a negative effect.
I also checked the hypothesis that reducing significantly the lag (from 15 years to 11 years, 6 years and, finally, to 1 year) might be associated with a loss of significance of one of the two variables included in the multivariate (three-year average GDP per capita in PPP and Health expenditure per capita in PPP). The results are quite coherent with this idea, especially in the lag of 1 year where the variable Health Expenditure per capita (for 2012) becomes insignificant in statistical terms. However, due to the difficulties of interpreting the loss of significance of Health expenditure compared to GDP, I created a sub-sample which differentiates between Low Income (70) and High Income (27) countries.
Low Income countries present a strong correlation between Health expenditure per capita and Average Standardized Rate of cancer: all possible specifications of the model, which includes GDP per capita (also using different time lags) report a statistical insignificance of the variable. The results are quite simple to interpret: in poor countries, the increasing burden of cancer, in terms of incidence rates, seems to be caused mainly from increased diagnostic techniques which are developing in, and have been imported from, High Income countries.
It would be interesting, on this respect, to see the result from High Income countries: however, general insignificance of the variables seems to suggest the idea that a sample of 27 does not provide solid results for inference.
At the end, using the already mentioned subsample (low income countries and high income countries), I created regressions with idiosyncratic cancers identified by medical literature (Breast, Prostate, Lung and Colorectum for High Income; Cervical, Liver, Oesophagus and Stomach for Low Income), using as before Health expenditure as control variable.
The High Income sub-sample, due to the small number of observations, presents problems of significance but is does not provide different results from the starting analysis.
In Low Income countries, however, GDP appears to be statistically insignificant: the result is not really strange, considering the low level (in relative and absolute levels) of health technologies (which include those which are diagnostic) and the general low quality of data registries. As a
result, it might be reasonable to assess the idea that in low income countries a large part of cancer incidence is due to increasing diagnostic techniques and not to GDP effects.
There is still huge space for further investigation: in particular, it would be very useful to have comparable (and reliable) data through the years in order to run a panel regression (and have a proper analysis on possible co-integration relationships between variables). Moreover, it would be quite interesting to include other variables that assess the role of tobacco and overall lifestyle. It is also possible to model the dynamic of health in more complex and accurate ways (and check how they are related to screening technologies). Lastly, it is important to remember that more specific regional (or national) studies may be done, in order to study idiosyncratic situations.
The battle with cancer is still a long one and accurate and multidisciplinary research is still very much required.
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