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Strengths of Ecologic Data in Pharmacoepidemiology
Ecologic data is easily accessible for usage in pharmacoepidemiology studies. For example, Olfson and his team were able to access Census data for analysis in trying to establish the relationship between the usage of anti-depressants and suicide among the youth. Easy access to data means that the study can be carried out with ease. Also, ecologic data is more specific since it can be accessed on a region to region basis. For example, the Olfson was able to take the data from specific regions and determine the suicide rates among the youth from these regions. This is advantageous since the effects of drugs on the population of a certain region will be easily studied and this can be used to determine the role played by the unique characteristics of the people of this region in the observable reactions to drugs. For example, the observation of data from low income regions disclosed a high number of suicides at one point as it is stated by Olfson; a point he argues, may be explained by the fact that more cases of depression may mean more suicides and more depressants being used in trying to combat the depression and the consequent suicide. This kind of observation is possible due to region-specific data; which in this case is data from low income areas.
Limitations of Ecologic Data in Pharmacoepidemiology
Ecologic data may not be the best in pharmacoepidemilogy due to the fact that the results obtained may not reflect the actual epidemiological condition of the area. It is not every individual in an area who seeks help or medication for the health condition that is under investigation. This means that the ecologic data that is available may not reflect the real condition on the ground. In fact, if other factors such as cultural beliefs about a certain condition exist, it may make it harder for people to seek medical attention leading to extremely larger numbers of people from a given area not reporting their cases. The resultant scenario is that the data available will represent a different case that does not reflect the actual condition on the ground. For example, not all depressed youth from low income areas may make use of anti depressants due to such factors as absence of income to fund the anti depressants or the possibility of being ridiculed by peers over the depression. Therefore, the reported cases may be quite small in number.
The effect of this is that an ecological segment that may be assumed to be having a lower number of the population suffering from a certain condition may actually be having a higher number; meaning that the resources dedicated to the combat of the problem in question in the concerned area may be small when they should be big. In this case, ecologic data will not have served pharmacoepidemiology in a proper way.
The other weakness in ecologic data is the possibility of error in recording. This is not limited to ecologic data only but to nearly all form of data. Therefore, ecologic data is equally prone to errors and this can lead to wrong pharmaepidemiological assumptions, and this may not be in the best interest of the population under observation or study.
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