|Year : 2012 | Volume
| Issue : 2 | Page : 91-92
Searching for wisdom lost in data
Department of Community Medicine, Dr. DY Patil Medical College, Pune, India
|Date of Web Publication||10-Nov-2012|
Editor in Chief, Department of Community Medicine, Dr. DY Patil Medical College, Pune - 411 018
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Banerjee A. Searching for wisdom lost in data. Med J DY Patil Univ 2012;5:91-2
Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?
Where is the information we have lost in data? 
Medical research started with observation and later came measurement. Hippocrates emphasized observations. In On Airs, Waters and Places, the father of medicine states: "Whosoever wishes to investigate medicine properly should proceed thus: In the first place to consider the seasons of the year, and what effects each of them produces. Then the winds, the hot and the cold, especially such as are common to all countries, and then such are peculiar to each locality. In the same manner, when one comes into a city to which he is a stranger, he should consider its situation, how it lies as to the winds and the rising of the sun; for its influence is not the same whether it lies to the north or the south, to the rising or to the setting sun. One should consider most attentively the waters which the inhabitants use, whether they be marshy and soft, or hard and running from elevated and rocky situations, and then if saltish and unfit for cooking; and the ground, whether it be naked and deficient in water, or wooded and well watered, and whether it lies in a hollow, confined situation, or is elevated and cold; and the mode in which the inhabitants live, and what are their pursuits, whether they are fond of drinking and eating to excess, and given to indolence, or are fond of exercise and labor." 
The point to note in his writings is that Hippocrates stressed "consider" and never mentioned "count". 
There are different expositions of qualitative research, but the simplest one for our purpose is, "Study methods whose results are expressed in words rather than numbers are called qualitative research."  Using this simple definition one may say that the father of medicine was a proponent of qualitative methods.
Since all extremes stagnate, the total lack of quantification in medical research held back medical progress for almost 2000 years following Hippocrates. 
Medical research got a boost with counting and measurement, which permitted explorations of statistical associations. The major influences were John Graunt  in the 17 th century, John Snow  and William Farr  in the 19 th century, and Doll and Hill  in the 20 th century. However, the writings of some of these early proponents of counting and measurement had appreciable qualitative inputs. 
The contribution of quantitative methods in understanding disease causation and evaluation of therapeutic procedures is immense and cannot be disputed. They led to the compilation of vital health statistics such as infant mortality, established modes of transmission of communicable diseases such as cholera, contributed to the identification of risk factors for non-communicable diseases such as lung cancer and coronary heart disease, and laid the foundation for a database (Cochrane Collaboration),  summarizing the effectiveness of various treatment interventions. However, it appears that medical researchers are gradually losing the power of observation, which was traditionally their forte. This total neglect of qualitative methods is more rampant with faster computing resources available nowadays.
With the advent of statistical software, progressively complex statistical techniques are being developed by medical researchers and biostatisticians. The disadvantage in this approach is that complex statistical outputs too often substitute for common sense or biological plausibility. 
Quantitative and qualitative methods have important lessons to offer to each other. A sharing of experience and methods could prevent bad data being subjected to "third degree" methods (data dredging), or good data being interpreted incorrectly.
It would be illustrative to narrate one "case study" of the limitations of quantitative data encountered some years ago. While doing a study on the transmission of malaria in an air force station, which was situated in a tribal pocket of central India, it was noted that malaria rates were many times higher among the Defence Security Corps (DSC) soldiers whose duty was to patrol the periphery of the defence establishment.  The DSC soldiers were staying in military barracks scattered in the periphery of the air force station. The rate of malaria among the airmen and their families staying well within the air force station was negligible. One explanation, which appeared plausible, was that one of the vectors of malaria identified in the station, the mosquito Anopheles fluviatilis, a notorious outdoor vector, had ample opportunity to bite the DSC soldiers on their frequent outdoor night patrols.
In spite of energetic anti-malaria efforts specifically targeted at these DSC soldiers, such as use of deltamethrin-impregnated mosquito nets, mosquito repellents applied on exposed skin during night patrols, and spraying of barracks with insecticides, the malaria rates among the DSC soldiers continued to be unusually high. No amount of data dredging and repeated analysis of malaria surveillance figures could provide the reason for the unusually high incidence of malaria among the DSC soldiers. Quantitative methods only illustrated that the DSC soldiers were having much high attack rates of malaria. It did not give a clue to "why?"
A visit was made to their barracks in the periphery of the air force station. Almost all the DSC soldiers hailed from rural areas from different parts of India. On informal discussion about the problems faced by them besides malaria, it came to light that most of the toilets in these barracks were poorly maintained and unusable due to lack of adequate water supply. Surprisingly, none of them complained much about these unserviceable toilets.
At first anyone unaware of the cultural background of the DSC soldiers will not see the link between non-functioning toilets and the high rate of malaria among these soldiers. But to someone who is aware that in large parts of rural India (the study was carried out more than a decade earlier), until recently, people were more comfortable going to the open fields to answer the call of nature, the link made sense. The soldiers were going to the fields at dawn and dusk-perfectly coinciding with the biting habits of the outdoor mosquito vector in the region, A. fluviatilis. On realizing this, the soldiers were advised to use mosquito repellents on their exposed parts while going to relieve themselves in the fields (as it did not seem feasible to change old habits soon). This simple measure drastically brought down the malaria incidence in these soldiers.
The above wisdom beyond the data was obtained by use of qualitative methods, underlining the need to go beyond quantitative methods when statistics lead to a dead end.
For a detailed exposition of qualitative methods and how they can complement quantitative methods, we have in this issue a guest editorial by an eminent social scientist who also happens to be on our international editorial advisory board.
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