Role of Mathematics and Geography in Epidemiology
It is empirically proven that some diseases appear prevalent in certain regions than in others. For example, in mountainous areas, malaria has a relatively high prevalence than in low lands. To reach this important medical conclusion statistics and probability will be duly utilized. This paper dissects the varied mathematical and geographical application and implications in epidemiology. Any epidemiological study does show the implication of the varied rates of spread and prevalence through mathematical analysis.
There are varying causal factors of different diseases and conditions. To find insight into the prevention and spread of ailments, research is undertaken into the diseases in different areas. Geographical aspects such as climate of the area are analyzed alongside the other variables such as nutrition and heredity.
The idea of population in itself is a geographical aspect. Within this premise, it makes it quite difficult to delineate geography and epidemiology. The contextual definition of epidemiology within this paper, the many definitions not withstanding, will be the study of factors linked directly to the health and illness of a population that is in question.
Epidemiology serves as a basis and logic for medical interventions. This is done for sheer interest of the health of the public and prospective preventive medical reasons. The medical fraternity is emphasizing the adoption of evidenced based practice. This approach to medical practice approaches medicine strictly based on existing evidence. Further still, the evidence is location based and analytical in nature (Panikkar, Bindu, 2007, p 129). This approach not only makes epidemiology geographically and mathematically based but also makes the three inalienable.
The analyses of population structures have always given insight into the likelihood of disease spread and the subsequent disease control. The efficacy of any disease control measures is also based on empirical analysis. This analysis is undertaken using a statistical approach, further making the understanding of mathematics inevitable in epidemiology.
I feel strongly that the argument by Immaculada that research defeats the purpose of revolutionary science which ultimately endeavors to improving medical care to society has limited basis (Melo-Martin, Immaculada, 2007, p.220). Besides, if the data is genuinely gathered for the sake of improving the wellbeing of human welfare, then strongly this perception by Immaculada should be given a second thought.
On the contrary, I tend to concur with her assertion that despite the researches there is still discrimination in medical practice in America. Whatever the extent the assertion may be true, it bring forth numerous questions. Given the relative nature of the various researches that are undertaken, I feel the analytical aspects of the research provide varied solutions to very specific setting. This provides a practical approach to evidence based practice in medical practices.
The existence of homoplasic genes that particularly exhibit themselves as SNP with prevalence rate put at 0.88 that were only explained through analysis shows the development of resistance that is attained differently in different geographical settings. Besides, the developmental tree developed, shows unwavering consistence with the relative geographical sources.
The comparison undertaken by Harris, et al (Harris, Simmon R 2010, p.473) shows that there in single variant through the continent. This shows an expansion trend if exposed to distinctly and uniformly clustered clade. In China the isolates formed a single but diverse clade. In Europe much diversity was exhibited with their position being basal. Further, the European isolates manifested clustering that appeared geographically oriented. This intercontinental spread of MRSA in very distinctive forms is indicative of the role that geography plays in the development of epidemiological theory.
It appears disheartening that fewer philosophers are content with in-depth analysis of epidemiological data. The contentious anecdote, though not sufficient enough, looks at epidemiological mathematical and geographical approach from a very lopsided point of view leaving out the basic principles on which the foundation of this analysis are laid.
Nonetheless, the idea of health equity that was gaining ground pushed for the adoption of this vital and pivotal approach to epidemiology (Amsterdamska, 2007). The World Health organization conceptualization of health equity gave further teeth to this course. It is the three tier framework of ethical bridging that gave reenergized meaning to the social study of epidemiology. Subsequently, this has generally motivated social response to this epidemiological approach. But the interdependence between the three subjects will remain whatever the controversy.
A Dissection into Relationship with Geography and Mathematics
Cancer Prevalence
In the analysis of cancer epidemiology, there exists a set of combinable approaches. The extraction of the requisite data takes the take-off stage in the analysis. The data is summarized and delineated thereof. Given the geographical setting of the delineated studies, the study design is equally delineated. This ascribes to the principle practice of evidence based clinical practice and endeavors to address the diseases in strategic delineated spheres.
Research has shown that the spread of cancerous cells is high in areas that have high levels of uranium mining (Panikkar, Bindu, 2007, p.140). In Saskatchewan, where mining of uranium have led to high level of radon being deposited in water resources, research does shows that the prevalence of cancer is not only high but the trend has been an increasing one. Mining takes a geographical orientation and the setting of the very mineral remains equally so.
According to a manager in AECB in Canada, of every 1000 people exposed to radon gas at least one was diagnosed of lung cancer. Of those who had contracted lung cancer, 87 of the hundred who had the disease died not more than 12 months after contracting the disease. In understanding the depth of the problem at hand, the utility of mathematical analysis become inevitable.
Probability of contracting cancer within mining precincts will be calculated by dividing the number of those likely to contract the ailment (1) by the sample space (1000).
P (contracting cancer in Saskatchewan) Those likely to contract cancer
The sample space
1
1000
This gives a likelihood of 0.001that at any give time a residency of Saskatchewan would contract lung cancer.
A similar research carried out in Australia reveals that the prevalence of the cancerous cells is relatively low. The contributory factors here are pecked on the fact that country in not having high levels of uranium mining. These presents a geographical disparity between the two countries in as far as cancer incidence is concerned. From a study in 2003 it emerged that of every 19000 persons in Australia, 3 had cancer.
The deduction arising from this analysis shows that geographical setting play a substantial role in the prevalence and incidence of cancerous cells. Similarly, according to RD Morris (Morris, 1994, p 67), there have to be assumptions of homogeneity in the analysis of such data. He asserts that the inter-study represents the consequences of predetermined and indiscriminate subject response.
Prevalence of Measles
Demography is very pivotal in the analysis of disease incidence and spread. The transmissions of measles in densely populated areas in higher than in areas where the population is spatial. Demographic analysis presents geographically analyzed features. In slum areas, the prevalence and incidence of measles is quite high, standing at 4 out of 17 in Sub-Saharan Africa. While the prevalence amongst the middle class residing in areas that are decongested is 1 out of 51 children aged between 1 to7 years.
The respective probabilities are
Probability of contracting measles in Slums likely number of persons contracting
The total sample space in sums
P (contracting measles in Slums) 4
17
0.235
Probability of middle class residents contracting likely number of persons contracting
The total sample space in middle class
1
51
0.0196
The two present divergent transmission trends. This shows that the geographical setting of persons plays a role in the likelihood of disease contraction. Besides, this can only be established through subjecting certain variables to mathematical and statistical analysis.
These probabilities give quantitative support to the argument by Amsterdamska that diseases outbreak is not bound within bacteria, but rather it is dependent on aspects such as population, water accessibility, mortality, and morbidity data which is duly accessed through geographical practice. This shows the inalienability of epidemiology, mathematics, and geographical study.
There appears to be general fears that mathematics presents very many unnecessary calculations and formulae in the analysis of epidemiological issues. This assertion not withstanding, the very proponents of this fears tend to apply formulae without recording them on their worksheets. They feel very discontented when they see the formulae and discontented on the sight of them.
Influenza Debacle
The influenza debacle of post World War 1 marked a new beginning in the approach to epidemiology. This meant that all diseases and ailment should be given attention but relative to their setting. Besides, bacteriology was seen in a similar wave with emphasized of geographical setting being termed as integral in the quest to treat, prevent, and analyze the different diseases and ailments. Provided epidemiology has to remain true to its objectives, amongst which is identification of epidemiological correlations, then geographical and mathematical essence will remain inevitable (Amsterdamska p 33).
This paper agrees and contents with the assertion by Frost that epidemiology will remain broad minded and multi-pronged in it approach to diseases. Seeing epidemiology as a mass phenomenal attention to infectious diseases, Frosts ideally satisfies the fact that epidemiology presents itself as a distinct science. It (Epidemiology) Frost argues attracts sufficient coherence and character from an immense range of spheres inclusive of immunology, protozoology, statistics and demography. Within the prelude of this analysis, though feared by some epidemiologists, mathematical analysis is inevitable and foundational.
During the interwar period data was collected to help analyze morbidity (Melo-Martin, 2007, p. 219) and
mortality in public healthy facilities. The data was used to give insight into the specific hypothesis of diseases and various environmental conditions. Besides, the data was used to offer support or to dispute the worth of certain public strategy. Within this mandate, the use of mathematical statistics became a subjected of protracted controversy for the shear proof of the fact that epidemiologists were stiff scared about mathematical calculations. Ironically, the very epidemiologists who were largely opposed to the use of mathematics were largely using mathematical concepts in the analysis of medical concepts.
The influenza debacle brought a mathematical reawakening to epidemiologists. The use of quantitative data in medicine became inevitable (Theodore H., 2007, p. 72). The research that also utilized largely geographical setting was meant for discriminate analysis of disease prevalence under varying geographical settings. In doing this, the intricacy and the imposing control of unconnected factors were addressed. The advent of epidemiological research marked the beginning of inevitable use of mathematical and geographical applications. Researchers were expected to indicate the location of the study which implied location obligation.
The two pronged approach to epidemiology implied that diseases were to be looked at as collective phenomena relying on a range of widely used scientific approaches orienting the study as an empirical science. The empiricism was to be location specific and character specific. This implied that whatever the approach, data needed to be specifically collected and empirically analyzed. In deed, the interaction of epidemiologists and statistical data became unwaveringly inevitable.
Ethnicity and race are geographical aspects. Resent researches have identified the two aspects as playing roles in diseases and ailment patterns (Calyampudi Radhakrishna Rao, 2004, p.123). The study of races takes geographical orientations with varying resource availability. Besides even where resources exists in similar aspects, for example water, the mineral in the said waters vary considerably. These resources may contribute to the dental disease prevalence and sensitivities.
Flow chart showing the interaction of social factors and their epidemiological implications
Stilted, it needs to be noted that through the study of the different races we can be able to understand their respective genealogy (Jewell, Nicholas, 2003). Subsequently if research is carried out relative to the races, interventional measures can developed that are race specific and that can have a high efficiency level relative to generalized intervention measures the could not be race specific.
Peptic Ulcers
Woolfs research on the prevalence of peptic ulcers was meant to prove the unwavering essence of studying varying races in varied locations. While Woolf looks at the exposure odds in respect to the analyzed cohorts, one finds his approach grounding. Within his approach the essence of etiological studies is quite inherent (Harris, et al., 2010, p. 472). The information gathered by Woolf has the least reproach level. The logistical regression and Cox regression approach offered a reliable classical analytical approach though were subjected to restrictions and data strata that appeared ill thought.
Notable within the survey of peptic ulcers was that it appeared prevalent among a cohort that was largely low income. The data to discriminate the income strata was attained from the geographical statistic available. The income stratification is a sufficient pointer that the separation of geography and mathematics from epidemiology is far from possible.
In a study by Schneeweiss, et al. it emerges that a restricted study population would lead to increased homogeneity. This, Schneeweiss argues, will always reduce the confounding factors that would have a potential effect (Hanley, James A., 2009 p, 95). The use of restricted population rather than a randomized population yielded results closer to the trial results. The population restriction serves to attain the diversity in disease prevalence and incidence.
Segregation of disease and ailment data is worth the course towards it treatment. Claytons argument that these segregational results then ought to be matched offers a striking medical balance between two analytical values attained. The method herein described, focuses mainly on the efficiency attainable by partial selection of precise subjects. Though the approach lends itself to what Langholz terms as nested control, I feel it does offer a relatively satisfactory countermarching approach particularly where the cohort appears large enough.
Comparatively provided epidemiology is relying on non experimental comparisons confounded by unmeasured factors the result measure of interest should be evaluated. In specific situations the risk factors may not be correlated with the choice of medication (Venkatapuram, Sridhar, 2009 p. 84). The basis for emphasizing divergence in the cohorts is to determine the prescriptions. Such prescriptions have been sometimes without subtle consideration of pertinent chemical compositions. The ignoring of the possible unintended outcomes such as genotoxicity would be ultimately avoided, hence increasing the medical outcome. It need be noted further that the discrimination of population may assist in the quantifying of any maximum bias need for such unmeasured factor.
The effective utilization of the two sciences, mathematics and geography offers a basis for undertaking syndemic. The biological interaction of any two diseases may well be captured through statistical analysis of diseases patterns and ailments (Dworkin, M S). Comorbidity may only be attained within certain geographical setting. This gives epidemiologists insight into the relationships between diseases and communities. This perspective shows the interconnectedness of geography mathematics and epidemiology.
Conclusion
The foregoing discussion gives sufficient insight into the inevitable interaction between epidemiology, geography and mathematics. The deductible inference if that there exist an in-inexorable correlation between the incidence and prevalence of diseases and ailment and geographical aspects.
It is also evidenced that the various geographical aspects can be analyzed mathematical alongside the prevalence to deduce a meaning relationship can be utilized in developing causative factors and preventive measures for specific ailments (Loannidis, 2008, p. 645). This will be addressed relative to the different geographical setting.
Further the paper finds unwavering correlation between the confusing factors and key geographical aspects such as climate and mineral composition of natural resources such as water and diseases. In earnest the paper finds an inalienable correlation between geography and epidemiology. Subsequently, the paper addresses mathematics as the main interlude between the two studies picking a factor in geography and subjecting if to epidemiological discourse that dully gives medical meaning and confounding application therefore.
The foregoing discussion therefore is inclined and stands for the interdisciplinary approach to epidemiology. Ideally, all diseases will not have a similar treatment and control strategy across different geographical setting hence the need adopt a multi pronged approach to epidemiology.
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