Pulmonary TB is a disease caused by a bacterium called Mycobacterium tuberculosis that attacks the lungs in human beings (WHO). The major symptoms include: coughing; chest pains, shortness of breath; appetite loss; loss of weight; night sweats; and fatigue. If untreated the disease is fatal and leads to death. In 2007, alone there was 13.7 million cases infection and 9.3 million new cases globally (WHO, 2008). In 1993, WHO (World Health Organization) declared TB a global emergency. In addition, the organization predicts that the disease will kill over 35 million people over the next twenty years if strategies are not set to counter the disease spread.
The disease is transmitted through the air by inhaling the bacteria carrying the droplets. In the body the bacterial lodges itself in the lungs and starts to multiply. The disease progresses in two stages, that are, primary and secondary TB. Primary TB is where the risk of infecting others is low but in secondary TB the bacteria spreads to other body parts through the blood stream. At the secondary stage infection is very high and characterized majorly by coughing blood.
A research paper was conducted in Abia province in Nigeria and the results, analysis and discussions are outlined in the preceding sections. Eight regions in the province were researched on and factors influencing disease rates also analysed. The factors analysed are: age; occupation; and population size. From the data the prevalence and incidence rates of the disease in Abia province will be calculated
As depicted in figure 1 the number of males infected (series 1) by Pulmonary TB was higher than that reported by females (series 2) in 2005. In 2005, the number of males infected was 40 while that number was 32 in females. Over one year in 2006 there was an increase in the number of males infected by 23 while that of females remained fairly constant, only increasing by one. Furthermore, from the table 1, Umuahia North recorded the highest infection rates compared to Abia South over the two years in which the research was conducted.
Figure 1: Total number of males and females infected in 2005 and 2006
Age is also a factor to be considered in relation to infection rates and prevalence of Pulmonary TB in Abia province. The population at the age bracket of 18-50 reported high infection rates followed by population above 50, and that below 18 in decreasing order as shown in figure 2 which was derived from table 2.
Figure 2: TB infection rates amongst different age groups in Abia Province
Differences in infection rates also existed during the two main seasons—Dry and Wet seasons—over a two year period. From table 3, the total infection rated over the two seasons in 2005 and 2006 can be analysed. In 2005, the rates of infection during the dry season (Series 1) were higher than that during the wet season (series 2) by 27 people. In 2006, there was an increase in infection rates over the year (Figure 3). Dry season recorded a total of 59 people compared to 36 people during the wet season.
Figure 3: TB Infection during dry and wet seasons in 2005 and 2005
Another factor influencing the infection rates in Abia province is the populations’ occupation. From table 4 it is evident that traders are majorly infected representing 35% of infected population, farmers 32.5%, Civil servants 10%, Soldiers 12.5%, Students and Foresters 5%, and Teachers and Drivers reporting nil.
The incidence rate is a statistical measure of the possible advancement of new infections within a certain population (Silman & MacFarland, 1995). It conveys information about the risk of contracting pulmonary TB in Abia province. The formula is:
Number of new cases of TB in 2006= 95-73 = 22 new cases. Note: The numbers of new cases are 22 because the total numbers that were reported to be infected in 2005 were 73 and in 2006 the number rose by 22 to be 95. It is assumed that the new cases are twenty two over the two year period.
Population risk during 2006= population in 2006-number already diagnosed in 2005
= 1,600,605- 73
Incidence rate is 1.375 people per 100,000 people
The prevalence rate on the other hand is the proportion of people in a particular area who have a particular disease at a specified point (Silman & Mcfarland). It is a measure of the widespread of the disease in a particular area. The formula is:
The prevalence rate is 5.94 people per 100,000 people.
From our data it is evident that in Abia province, the number of males infected by Pulmonary TB increased over a year compared to the number of females infected. This scenario is probably due to the fact that women spend a lot of time in their locality compared to men who are outgoing and mingle a lot with the population. Population size of the constituent regions also plays a major role in infected rates. Less populated regions such as Bende and Abia South recorded the lowest infection rates compared with the other regions probably because of less likely hood of overcrowding which is a major way in pulmonary TB is transmitted.
Traders and farmers reported the highest number of infections, which might have been as a result of their job description involving interacting with crowds or with diverse people who might be infected in the market. Populations in teaching and driving occupations reported the least numbers because they spend most of their times alone or with children who have less incidence and prevalence rates. The age group between 18 and 50 had the highest numbers of TB rates probably because of their high activity and likely hood of working and interacting with many people. Older group of above 50 years recorded fewer infection rates because of their less active lives with children less than eighteen years recording the lowest infection rates probably due to their least inactive life amongst the other two groups.
The possible biases that might be in the research are: sampling bias; and Information bias. Sampling bias is where certain members of a population are less likely to be included in the research than others. This might be due to the large geographical size of certain regions or researchers intentionally not sampling certain populations. Information bias might have resulted when the rates of infection is obtained from certain limited hospitals near the population, ignoring exposure to certain populations. Potential sources of bias can be eliminated or minimized through accurate design considerations and detailed conduct of the research (Ziegel, 2006).
Design for an Improved Research Programme
The design to be undertaken is a cross-sectional study, which is a type of observational study. The reason is that cross-sectional studies study the correlation between diseases as they exist in a certain population at a specific time (Ziegel, 2006). The first step is the selection of an exposed position which would depend on feasibility factors such as records availability, the hypothesis being tested, and ease of following up the research. In this stage a special cohort will be selected from professionals such as doctors, nurses and well defined geographical areas so as to ease follow-up and ascertain results from the research. The cohort selected will be used in areas where the infection risks and rates are minimal.
The next stage is in designing is in selecting the comparison group which in our case is the general population. The general population is used since it is difficult to find a comparative internal comparison group. The general population will be in accordance to current population data on disease incidence, mortality and total population. The source of information that is going to be used in designing the study is a combination of primary and secondary sources. This is to take advantage on the merits of both sources and dilute the demerits. The sources include: medical and employment records; interviews; physical examinations, and lab tests.
Since it is a cohort study a follow-up approach is implemented. The follow up approach involves collecting information—full names, social security number—that will aid in locating participants as study commences. In dealing with participants who do not respond initially additional mailings are to be used.
After the study the data is to be analysed using cumulative incidence or incidence rates. Furthermore, the relationship between contact and outcome quantified should be done using absolute differences between the risks and rates of infection.
The merits of conducting a cross-sectional analysis in this case is that it is: quick; feasible; and useful in estimating the population burden (Ziegel, 2006). The major limitation of this design study is that it has a risk of temporary ambiguity where one cannot determine whether exposure of the disease preceded the outcome.
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EDITORIAL: World TB Day: Fighting a resurgent killer. 2007. McClatchy - Tribune Business News March 24, accessed March 2, 2011.
Eric R Ziegel. 2006. Epidemiology: Study Design and Data Analysis (2nd ed.). Technometrics 48, no. 2, (May 1): 316-317, accessed March 2, 2011).
WHO, 1997. Anti-Tuberculosis drug resistance in the world. The WHO/IUATLD global project on anti-tuberculosis drug resistance surveillance 1994-1997. Geneva: World Health Organization.
Silman,A. J., & Macfarland, G. J., 1995. Epidemiological Studies: A practical guide (2nd ed). New York: Cambridge university press. Pp. 13-24