Chen W, FitzGerald JM, Rousseau R, Lynd LD, Tan WC, Sadatsafavi M (2013) Complementary and alternative asthma treatments and their association with asthma control: a population-based study. BMJ Open 3(9): e003360. http://dx.doi.org/10.1136/bmjopen-2013-003360
Data is shown in many different ways, apart from the usual scatter plot and histogram. For medical purposes, data needs to be displayed in a way that everyone will be able to understand it. It needs to be interesting to look at, easy to understand, and accessible to many so that he findings of the experiment can be shared with many people. Through the Ted talk, Hans Rosling interestingly shows the comparison between life expectancy and family size. His graph shows the trend from large family size and low life expectancy, to small family size and high life expectancy throughout the world. The data points on the graph changed every year, and draws your attention to the trends of different countries over time.
Another way that is typically used for disease data is infographics. In the first infographic, It shows data about heart disease in Canada. It effectively gets across how many people have heart disease, how many people die from it, and how many people are ideal for heart disease. The next infographic illustrates diabetes prevalence in the United States. It shows that 8% of the population has diabetes, and that Hispanics are much more likely to have diabetes.
The study that I chose to examine is titled, Effects of alcohol hangover on simulated highway driving performance. The study was an experimental study, meaning that the researchers imposed a treatment onto the participants. The research question of the study is, “What is the effect of an alcohol hangover on simulated driving performance the day after consuming alcohol.” The study targeted males and females aged 21-35 that are non-smokers, and have experience drinking more than 5 alcoholic drinks in one night. This is the population of the study. The sample of the study was 24 women, and 24 men, totaling 48 participants.
The researchers decided to use a matched-pair design for the study. The participants completed the 100km drive the morning after not consuming alcohol, which is the control of the study, then again the morning after consuming alcohol. Participants also answered questions to how they felt driving, their number of hours of sleep, and the amount of alcoholic drinks that they had the night before. They compared the results of each of the two drive simulations to each other. They allowed the participants to schedule their own drive dates, to ensure that they were consuming alcohol on their own terms, and not being forced to by the study. This particular study used weaving of the car, lapses of attention, and self-reported analyses to collect their data.
The conclusions that were made from this study were that driving ability was considerably compromised during an alcohol hangover. The researchers used the mean and standard deviation to determine baseline control values for the weaving of the car, and lapses of attention. It found that on average, people weaved a total of 1.9cm more, the total amount of lapses in attention increased by 2.125, and the total lapse time increased by 55.4 seconds. Drivers also self reported that their driving was significantly poorer, and that they felt less safe, less wise, less predictable, and less responsible.
Verster J, Bervoets A, Brookhuis K, et al. Effects of alcohol hangover on simulated highway driving performance. Psychopharmacology [serial online]. August 2014;231(15):2999-3008. Available from: Academic Search Premier, Ipswich, MA. Accessed September 22, 2016.
Statistics in biology have long been recorded. It is used to figure out and solve real-world issues within the biology field. It is also used to manipulate and understand data that is obtained from a certain experiment. Biostatistics are used in many different areas of biology, such as medicine, public health, and genetics. Biostatistics is typically used in combination between two fields, as in public health and medicine, and seeks to find the correlation between the two fields for the particular study. For example, finding the relationship between a certain disease and the population it affects. The biostatistician in this example would collect the data, and use statistics to find the solution.
In medicine specifically, it is used regularly for clinical trials and research. After the data from the trial is collected, finding the proper conclusions from the data and how to fit it on a graph is primarily done through statistics. Also, medicine uses statistics to illustrate the probability of someone who has a disease getting a secondary illness, or overcoming the disease.
Public health relies almost solely on the work biostatisticians to create their policies. Studies that are published contain data about a population of people, and the health risks that are associated with that population. For example, a study finds a certain food effects the vitamin intake of an individual. A biostatistician took the data from the study, analyzed it, and drew conclusions about the food for the general population. Biostatisticians also need to be able to take a large variety of data, since health issues really do vary from individual to individual, and shrink it to an appropriate target population so they can draw conclusions.
In genetics, statistics are used to predict the probability that you could get a certain disease. In genome mapping, this is used. In finding out that you have a particular gene, there is a calculation of if you could get whatever the gene codes for, or if you could pass the gene along to your offspring. Another example of this is cancer, where research and testing are prominent.
For me, biostatistics are of interest because I wish to make it into the medical field. It gets used almost everyday in the medical field. It is very important that physicians or health care providers understand how to calculate the probability that a certain population will get a disease. Also, be able to calculate probability that they will overcome the disease, or get infected with another disease at the same time. Also, if I were to have a clinical trial, I would need to understand how to analyze and fit the data.
As a Biology major, understanding how to interpret data will be hugely important during future research projects. I will not have to rely on the work of someone else to interpret my data for me, rather be able to work completely independent on projects. Knowing basic and more advanced statistical functions will benefit me not only in my biology classes, but in my other pre-requisites for medical/dental school, and also for the MCAT/DAT. This class will also give me more insight to the application of statistics toward biology than taking a regular statistics class would. For me, this is beneficial because I always like to know how or when something I learned will be used in the real world.
Biostatistics are very important in the field of biology. Without it, researchers would not be able to draw conclusions from the data that they have obtained. Public health would suffer, and people would not be able to know if they could transmit certain genes to their offspring.
Bhattacharjee, Suman. “1 Biostatistics Introduction.” YouTube. YouTube, 12 Mar. 2014. Web. 01 Sept. 2016.
Cumberland, William, Ph.D, and Abdelmonem Afifi, Ph.D. “Service Statistics in Public Health.” Public Health Reports (1896-1970) 71.6 (1956): 519-20. UCLA School of Public Health. Web.
Harrell, Frank, Ph.D. “Biostatistics.” What Is Biostatistics? Vanderbilt University Department of Biostatistics, n.d. Web. 01 Sept. 2016.