ABSTRACT
Background: In 1985, the center for disease control coined the name: “Acquired Immune Deficiency Syndrome (AIDS)” to refer a deadly illness. The World Health Organization (WHO) estimated that about 33.4 million people were suffering with AIDS and two million people (including 330,000 children) died in 2009 alone in many parts of the world. A scary fact is that the public worry about situations which might spread AIDS according to reported survey result in Meulders et al. (2001). This article develops and illustrates an appropriate statistical methodology to understand the meanings of the data. Methods: While the binomial model is a suitable underlying model for their responses, the data mean and dispersion violates the model’s required functional balance between them. This violation is called over-under dispersion. This article creates an innovative approach to assess whether the functional imbalance is too strong to reject the binomial model for the data. In a case of rejecting the model, what is a correct way of warning the public about the spreads of AIDS in a specified situation? This question is answered. Results: In the survey data about how AIDS/HIV might spread according to fifty respondents in thirteen nations, the functional balance exists only in three cases: “needle”, “blood” and “sex” justifying using the usual binomial model (1). In all other seven cases: “glass”, “eating”, “object”, “toilet”, “hands”, “kissing”, and “care” of an AIDS or HIV patient, there is a significant imbalance between the dispersion and its functional equivalence in terms of the mean suggesting that the new binomial called imbalanced binomial distribution (6) of this article should be used. The statistical power of this methodology is indeed excellent and hence the practitioners should make use of it. Conclusion: The new model called imbalanced binomial distribution (6) of this article is versatile enough to be useful in other research topics in the disciplines such as medicine, drug assessment, clinical trial outcomes, business, marketing, finance, economics, engineering and public health.
ABSTRACT
Background: In times of an outbreak of a contagious deadly epidemic1-4 such as severe acute respiratory syndrome (SARS), the patients are quarantined and rushed to an emergency department of a hospital for treatment. Paradoxically, the nurses who treat them to become healthy get infected in spite of the nurses’ precautionary defensive alertness. This is so unfortunate because the nurses might not have been in close contact with the virus otherwise in their life. The nurses’ sufficient immunity level is a key factor to avert hospital site infection. Is it possible to quantify informatics about the nurses’ immunity from the virus? Methods: The above question is answered with a development of an appropriate new model and methodology. This new frequency trend is named Bumped-up Binomial Distribution (BBD). Several useful properties of the BBD are derived, applied, and explained using SARS data5 in the literature. Though SARS data are considered in the illustration, the contents of the article are versatile enough to analyze and interpret data from other contagious diseases. Results: With the help of BBD (3) and the Toronto data in Table 1, we have identified the informatics about the attending nurses’ sufficient immunity level. There were 32 nurses providing 16 patient care services. Though the nurses were precautionary to avoid infection, not all of them were immune to infection in those activities. Using the new methodology of this article, their sufficient immunity level is estimated to be only 0.25 in a scale of zero to one with a p-value of 0.001. It suggests that the nurses’ sufficient immunity level is statistically significant. The power of accepting the true alternative hypothesis of 0.50 immunity level, if it occurs, is calculated to be 0.948 in a scale of zero to one. It suggests that the methodology is powerful. Conclusions: The estimate of nurse’s sufficient immunity level is a helpful factor for the hospital administrators in the time of making work schedules and assignments of the nurses to highly contagious patients who come in to the emergency or regular wings of the hospital for treatment. When the approach and methodology of this article are applied, it would reduce if not a total elimination of the hospital site infections among the nurses and physicians.
ABSTRACT
Background: Smoking is generally known to be carcinogenic and health hazardous. What is not clear is whether the smoking impacts on the woman’s reproductive process. There have been medical debates on whether a woman in the child bearing age may delay her pregnancy due to smoking. A definitive conclusion on this issue has not been reached perhaps due to a lack of appropriate data evidence. The missing link to answer the question might be exercising a suitable model to extract the pertinent data information on the number of missed menstrual cycles by smoking women versus non-smoking women. This article develops and demonstrates a statistical methodology to answer the question. Methods: To construct such a needed methodology, a new statistical distribution is introduced as an underlying model for the data on the number of missed menstrual cycles by women who smoke. This new distribution is named Tweaked Geometric Distribution (TGD). Several useful properties of the TGD are derived and explained using a historical data in the literature. Results: In the data of 100 smokers and 486 non-smokers, on the average, smoking women missed 3.22 menstrual cycles and non-smoking women missed only 1.96 menstrual cycles before becoming pregnant. The smoking women exhibited more variation than the non-smoking women and it suggests that the non-smoking women are more homogeneous while the smoking women are more heterogeneous. Furthermore, the impairment level to pregnancy due to smoking among the 486 women is estimated to be 5% in a possible scale of zero to one. The 5% impairment level appears like a small amount, but its impact can be felt once it is cast in terms of fecundity. What is fecundity? The terminology fecundity refers the chance for a woman to become pregnant. The fecundity is 0.24 for smoking woman while it is 0.34 for non-smoking woman. The fecundity of a non-smoking woman is more than twice the fecundity of a smoking woman. Conclusion: The smoking is really disadvantageous to any one in general and particularly to a woman who wants to become pregnant.