According to Aschengrau and Seage (2008), measures of disease frequency are the “building blocks” employed by epidemiologists to evaluate effects of a disease on humans (2008, p.59).
When measures of disease frequency are compared, they consolidate building blocks in a constructive manner and permits one to define the correlation between characteristics and a disorder. It is possible to compare disease frequencies between diverse populations.
For instance, an epidemiologist may aspire to compare disease frequencies between inhabitants of the United States and Haiti in terms of demographic attributes such as gender, age, race and socio-economic aspects.
Other demographic attributes that can be compared are personal habits (i.e. cigarette smoking or alcohol consumption) and environmental aspects (i.e. water and air pollution).
What’s more, an epidemiologist may opt to compare prevalence rates of coronary heart ailment between inhabitants of the US and Haiti or amongst smokers and nonsmokers, whites and blacks, men and women, and in areas prevalent with low and high pollution levels (Aschengrau & Seage, 2008, p.60).
There are several reasons to explain why the crude birth rate in Haiti is lower than in the United States.
First of all, the United States is more populated than Haiti. Secondly, the population proportion of elderly persons in the US is higher than in Haiti. Therefore, the higher proportion of elderly persons in the US explains why the country has a higher crude mortality rate than Haiti.
In addition, the per capita income in the US is significantly higher than in Haiti. Consequently, many US citizens spent their earnings on promiscuous lifestyle activities such as smoking, alcohol drinking and drug abuse which increase disease frequency (Xu et al., 2009, p.5).
Improvements in diagnostic criteria can significantly reduce the prevalence of an existing health condition.
The critical rationale for diagnostic criteria is that it facilitates the establishment of the threshold for diagnosis of an ailment in those circumstances where the symptoms of the disease manifest themselves.
However, there are several diagnoses where the symptom criteria demand the existence of impairment with respect to level of functioning that are plainly deemed as clinically significant. Numerous DSM categories exist for which the symptom criteria infer substantial suffering.
One might assume that in such incidences, the clinical significance criterion is irrelevant since the distress condition has been fulfilled.
Nonetheless, this is not the case since the requirement for diagnostic criterion (that the ailment be clinically considerable) may raise the threshold for diagnosis thereby disregarding some false positive (Spitzer & Wakefield, 1999, p. 1860).
There is no doubt that a shorter duration of a health condition reduces the prevalence of a health condition. For instance, the disability-adjusted life year (DALY) is used to compare the significance of health interventions among diverse health outcomes among various ages.
Thus, when DALY increases (as a result of health intervention), the prevalence of a health condition reduces.
Similarly, a decrease in the incidence of a disease reduces the prevalence of a health condition since diagnostic and preventive measures are already in place to deter the recurrence of the health condition (Musgrove & Fox-Rushby, 2008, p.2).
Finally, the loss of healthy people from the population for other reasons neither increases nor decreases the prevalent of a health condition since their deaths are not caused by the existing health condition.
References
Aschengrau, A., & Seage, GR. (2008). Essentials of Epidemiology in Public Health. Sudbury, MA: Jones and Bartlett Publishers.
Musgrove, P & Fox-Rushby, J. (2008). Cost-Effectiveness Analysis for Priority Settings. New York: Oxford University Press.
Spritzer, R. & Wakefield, J. (1999). DSM-IV Diagnostic Criterion for Clinical Significance: Does it Help Solve the False Positive Problem? Am J Psychiatry, 156, 1856-1864.
Xu, J., Kochanek, KD. & Tejada-Vera, B. (2009). Preliminary Data for 2007. National Vital Statistics Reports, 58(1), 1-51.