Displaying Data in Different Ways


In all three of these disease related info-graphics, a number of people or proportion of the population is represented by a symbol or set of icons. Important statistics are highlighted and presented in such a way that laypeople can easily understand them. The purpose of each of these graphics is to convey the most important information related to different diseases or public health issues. The Measles vaccination graphic aims to promote MMR vaccination. The Nebraska smoking diagram depicts how big of an issue smoking is in the state. The cardiac diagram shows how large a cause of mortality heart disease is compared to other causes of death.  Each graphic uses icons to make it easier to understand the data.

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A social epidemiological study on HIV/AIDS in a village of Henan Province, China

Henan Province in China has one of the highest prevalence rates for HIV/AIDS in the country. Previous studies have established that 90% of the roughly 25000 cases came from contaminated blood donation. This study set out to characterize the nature of the epidemiological link between HIV/AIDS and commercial blood donation. This paper also addressed the health outcomes and tried to determine the social factors that contributed to these outcomes.

The study design was observational in nature, with random cluster sampling being employed. The clusters used in this study were the identified HIV/AIDS infected villages. One village was randomly selected out of 38. The entire population of the selected village (2335) was used as the sample. The study was conducted over two years, with both qualitative and quantitative data being gathered. Focus groups and individual interviews were conducted to gather information on social aspects of the disease. Interviews and focus groups determined the perception of HIV and blood donation within the sample. Demographics were collected via a demographic data form filled out by visiting every family in the village. Demographic information collected included age, sex, education, income, blood donation history and the death of any family members in the last 10 years. Data collected by the local health station and population data from the census were used to supplement the primary data. The study was retrospective in nature, taking place 10 years post epidemic. Of the 2335 subjects, 484 (20.3%) were former blood donors and 107 (4.6%) were infected with HIV.

The study found that blood donation in the 1980s was stigmatized by the society as, while money could be earned by the donor, they would also be considered poor. The 1990s brought a new attitude with blood donation being promoted as an easy way to earn money and a mark of health. Those with HIV/AIDS were found to be less educated and have lower incomes. Blood donation was found to be correlated with education and HIV infection was correlated with the unsafe practice of plasma donation in the mid 1990s. This practice involved pooling together red cells from the same blood type, separating out the plasma, and then infusing this blood back in to donors. Increasing the risk of infection of HIV greatly among donors. National policy promoting blood donation was found to contribute to an increase in blood donation and therefore infection. (Yan et al., 2012)

Yan, J., Xiao, S., Zhou, L., Tang, Y., Xu, G., Luo, D., and Yi, Q. (2012). A social epidemiological study on HIV/AIDS in a village of Henan Province, China. AIDS Care 1–7.

Why Do We Care About Biostatistics?

Biostatistics is a diverse field that can be used to acquire useful information about animal conservation, the progress of a disease and gain meaningful insight in to and compare data sets. Animal populations can be tracked over time to determine fluctuations in the health of the species. This data can be extrapolated to posit causes for fluctuations in population. The method by which drugs are approved for medical use is heavily based in biostatistics, requiring statistically significant therapeutic data with at least a 90% confidence interval1. Perhaps one of the most relevant uses currently is the tracking of disease progress, also known as epidemiology. As a hopeful future doctor, tracking a disease is a key part of public health, and something that is of great interest to me. With the recent Ebola outbreak, and the ongoing spread of the Zika virus, statistics and the conclusions garnered from them are being thrown out by the media on a near daily basis. Understanding biostatistics allows us to better understand the data being used and make our own critical assessment of the situation.

The Centers for Disease Control and Prevention (CDC) was originally founded to track and eliminate malaria inside the continental US. Succeeding in that mission, the organization has been steadily expanded in to what it is today. The CDC tracks cases and compiles statistics for almost all diseases in America, as well as deaths due to common accidents (such as drunk driving). They monitor patterns of disease and injury and identify statistical surges in incidence so that they can advise the government and public. As resources are generally limited, it is important to know which diseases require the most attention to minimize the cost to quality of life. Addressing diseases early can also help reduce the medical costs of long-term care2.

The medical profession and medical insurance companies are able to use statistical analysis on data collected regarding treatment costs to show which diseases strain the healthcare system most, and focus their efforts on reducing the burden of these diseases in more cost-effective methods. This allows the recouped resources to be redirected to addressing other diseases. The conclusions garnered from this statistical analysis can also be used to help prioritize the allocation of research grants. Again, because of the limited resources available for medical research, knowing which diseases cause the most problems for a society is key. This is especially true for developing countries. The World Health Organization is a major force in addressing global health issues, as well as training local academia to build public health infrastructure, including epidemiologists and statisticians.

There is the threat of a diabetes epidemic looming over India. As the country continues to urbanize and modernize, the average Indian diet begins to shift toward the high-fat, high sugar form of the typical American. Biostatistics is currently being used to track what measures to reduce the risk can be the most effective, as well as characterize who is most at risk. In a country with a population that exceeds 1 billion, it is important to know which demographics are most at risk. Urban centers, where the population is high and data is more easily collected appear to be at risk, as their occupations are generally more sedentary in nature, and they have access to cheap, unhealthy foods. With such a large population, rural data is harder to come by, but initial studies indicate that certain areas of the country are at a higher risk due to food shortages, and a lack of access to healthcare3. Continued study and characterization of the overall situation necessitates the use of statistical analysis.

The demographics of disease are often a starting point for understanding how to prevent and disrupt its proliferation. Using rudimentary statistical analysis over a map of London, Dr. John Snow was able to determine the source of cholera infections and end an outbreak in 1854. Even more impressively, he did this without knowing the actual cause of cholera, with the popular theory at the time being “bad air”. Dr. Snow identified specific infectious water pumps based on the distribution of cholera deaths and infections in a given area. He then used the data he had collected to convince the local government to remove the pump handle. The outbreak subsided sharply following this. The findings of John Snow, and later William Farr, helped to substantiate the idea that diseases could be water-borne4.

It is my hope that while attending medical school I will be admitted in to an MD/ MPH combination program, allowing me to gain a master’s in public health. Biostatistics in the form of epidemiology will be an important part of such a degree, and I believe that understanding demographics and the progress of disease in a population will help me to be a better physician. Should I ever decide to be a part of conducting a clinical trial, an understanding of the statistical concepts will position me well to understand and contribute to the interpretation and presentation of the data. Biostatistics will continue to be important for both myself and for real-world applications.

1. Green, S. J. & Pauler, D. K. Statistics in clinical trials. Curr. Oncol. Rep. 6, 36–41 (2004).
2. Green, L. W. Public Health Asks of Systems Science: To Advance Our Evidence-Based Practice, Can You Help Us Get More Practice-Based Evidence? Am. J. Public Health 96, 406–409 (2006).
3. Diamond, J. Medicine: Diabetes in India. Nature 469, 478–479 (2011).
4. Fine, P. et al. John Snow’s legacy: epidemiology without borders. Lancet 381, 1302–1311 (2013).