Epidemiology – Use of Mortality Data, Essay Example

Three articles were posted for review. Article one is entitled‘correcting for numerator/denominator bias when assessing changing inequalities in occupational class mortality, Australia 1981-2002.’ The issue highlighted in the first article relates to unadjusted biases in reporting mortality figures. When biases are not adjusted before figures are published it offers inaccuracies and misleads audiences for which the report or study was intended. In some instances thiscould be deliberate because of political reasons. However, when minorities are targeted it is a serious underreporting offence from an epidemiological perspective. Three types of biases could emerged in epidemiology and it is important that epidemiologists address them before presenting their reports. They are selection, information and confounding.Numerator/denominator biases could fall into the category of confounding biases (Ioannidis,   Tarone,  & McLaughlin 450). This is a limitation.

Article two reviewed the state infant mortality: an ecologic study to determine modifiable risks and adjusted infant mortality rates. The issue here was non-modifiable demographic variables and accuracy in identifying them. The research successfully identified infant mortality variations in relation to African American and Hispanics. Adjusted risks were perceived as smoking during pregnancy. Epidemiological data in this case is used to influence decisions regarding obstetric care within the community. Identifying non modifiable variables means that these issues must be addressed without any alteration in presentation of the variables retrieved. This is a strong element in aerticulating of this data.

Article three offers data showing comparison of ischemic stroke outcomes and patient and hospital characteristics by race/ethnicity and socioeconomic status. The issue researched was risk adjusted inpatient mortality among 8 states for patients admitted with ischemic stroke. Further data was collected drawing inferences to length of stay and mortality. The risk adjusted factor is important to this report. Risks are calculated based on elements in the population predisposing to issue being researched. This was a fully addressed and data presented appears authentic.

Works cited

Ioannidis, J. P. A.; Tarone, R.; McLaughlin, J. K. The False-positive to False-negative Ratio in Epidemiologic Studies. Epidemiology 22 (4):  2011; 450–456