Prevalence and Risk Factors of Tuberculosis

Introduction 

Tuberculosis is a major global health concer , and accounts for an estimated 9 million incident cases , with an average mortality rate of about 1.5 million people in the year 2013. Information is key in curbing the spread of tuberculosis or any other ailment. It is considered by the WHO as a preventive measure , since it creates awareness and enables an individual to know how TB is transmitted, hence, enhancing the process of control ling infection s . The Centre for Disease Control and WHO have established an infection control guideline that will help both the rich and the poor settings by putting more emphasis on the critical responsibility of limiting TB transmission via appropriate health care systems.  

The aging population in C hina is an issue of concern, given that t he proportion of the elderly people in China , aged 65 years and above is 10.8 %, while those aged 60 years a n d above is 17 % (NBS China, 2016). According to Chadha et al. (2012), the risk of TB has a significantly positive correlation with age. They are implying that when the age of a person increases, it raises the risk of TB. Wang and Lui (2011) estimated that almost half of the people diagnosed with TB in China were elderly, with 39.8 % being symptomatic, while 53.2 % does not seek any medical care. Most of the aging population is found in rural areas and are the most vulnerable, with a high risk of TB.   

Rapid screening, identification, and treatment of TB are vital in reducing the incidence risk. Systematic screening, more so in the high-risk groups, has proven to be more effective in helping to curb the spread of the disease globally. However, there are risk factors that contribute to or affect the spread of TB among the elderly in the society. However, recent studies indicate that the HIV prevalence in Peru is remarkably lower , as compared to sub-Sahara n Africa. This result demonstrates that with drug-sensitive TB, XDT-TB, and MDR ca n be spread via household s that are either HIV positive or negative. This study suggests that TB can be spread regardless of HIV status.  

A program that was introduce d by the World Health Organization (WHO) in the year 1994 known as D irectly O bserved T reatment S hort-course (DOTS), has been fundamental in controlling the spread of tuberculosis globally. DOTS was introduced in hospitals and primary health care facilities between 1999 and 2014 , with the help of the WHO and the Japan I nternational C o r p o ration A gency. Cambodia conducted a prevalence survey in the early stages of DOTS decentralization. The results revealed that the prevalence was 362 cases per 100,000 persons. In the year 2015, another prevalence study was conducted to ascertain the prevalence of tuberculosis. In this period, the treatment or access to medication was at 90 percent , thanks to the DOTS program.  

Problem S tatement  

Tuberculosis (TB) remain s a major public health challenge globally, ranking among the top ten most deadly diseases in the world. The WHO (2018) reported 2.3 million new cases, including 251,000 individuals with HIV contracted TB . In total, t en million suffered from TB in 2018, where men constituted 5.7 million, women 3.2 million, and children 1.1 million. Although TB is preventable and curable, it accounted for 1.5 million deaths, with children being more than 200, 000 in number . There is little information available concerning the prevalence of TB and its risk factors in Cambodia, Asia. Hence, this study will investigate the Prevalence and risk factors of tuberculosis. The finding s will help the relevant authorit ies to come with measures to contain the spread of TB.   

Objective  

To determine the prevalence and risk factors of tuberculosis in Cambodia.  

Research questions  

What is the prevalence and risk factors of tuberculosis in Cambodia?  

Does HIV affect the prevalence of tuberculosis in Cambodia?  

Hypotheses  

Hypothesis 1 

H0: There is no significant association linking an understanding of TB and its prevalence in Cambodia.  

H1: There is a significant association linking an understanding of TB and its prevalence in Cambodia. 

Hypothesis 2 

H1: HIV does not affect the prevalence of tuberculosis. 

H0: HIV affects the prevalence of tuberculosis. 

Significance of the S tudy 

The study targets to estimate the prevalence of TB in Cambodia; hence, vital for public health planning. The findings from the study will be of great significance to the relevant authorities , as they will advise on the best measure measures to embrace to mitigate the spread of TB. The measures may include equipping the health facilities with enough medical officers and facilities to test and treat TB. Public awareness is another option that the authorit ies will capitalize on, to ensure that the general public is informed and educated on the cause, signs, symptoms and preventi ve measure s against TB. The study will be of significance to the World Health Organization , as assessing the situation in Cambodia and advis ing the authorities appropriately helps meet the global need to reduce the prevalence of TB . Further, it will aid in guiding future studies, with the intent of understanding the risk factors associated with TB.   

Methodology 

The study employed a cross-sectional study to analyse the data and establish the prevalence of TB. The cross-sectional study is an observational study that analy z ed data from a given population at a specific point in time. The study sampled three hospitals in Cambodia. A sample of 100 patients was taken , from those being diagnosed with TB aged 60 years and above. The questionnaire was designed, and a simple random sampling technique was employed , w here the individuals who ca me for diagnosis were given equal opportunit ies of being selected f or the study . T he study use s descriptive statistics to have a general description of the data. It also employed the Chi-square (χ2) test or Fisher’s exact test to measure the association and compared the groups. The logistic regression analysis was used to identify the potential risk factors. The multiple logistic regression model was used to control potential cofounders. The odds ratio w as used to measure the strength of association linking the prevalence of TB and risk factors. They examined the prevalence of TB and how it is affected by the understanding of TB and other risk factors. The cases in this study are the number of people who were diagnosed with TB, and the control is those who were not diagnosed with TB. The number of people affect ed by TB is divided into two categories, that is , the understanding (those who have the understanding and no understating of TB) and the HIV community.  

The dependent variable is the number of cases of individuals who are diagnosed with TB. This is a categorical variable , measured based on people who tested positive and negative for TB (1= negative, 2 = positive). The independent variables are HIV status (1 = negative, 2 = positive), understanding of TB (1= not understanding negative, 2 = understanding), age groups and gender (female 2 = male). These are binary variables that are measure d in levels, whether an individual has an understanding of the TB or not, and if HIV positive.  

Results 

Table 1 : Gender 

Figure 1 above shows the pie chart for the gender of the participants. It indicates that females comprised of 56.6% of the sample population, compared to males (43.4%). 

Table 2 : Understanding 

Understanding
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1	68	68.7	68.7	68.7
	2	31	31.3	31.3	100.0
	Total	99	100.0	100.0

Table 1 above shows that total number of individuals who have understanding of TB. Those with understanding to TB (exposed) denoted by ‘ 1 ’ are less compared to those who lack understating about TB, ‘ 2 ’ (Non-exposed). 

Table 3 : Prevalence for TB 

Variables in the Equation
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 0	Constant	-.305	.203	2.255	1	.133	.737

Table 2 above shows that the coefficient for TB is -0.305 and the odd ratio [Exp(B)] is 0.737. This means that the odds of testing positive are higher compared those who are negative. 

Logistic regression 

Table 4 :Logistic Regression 

Variables in the Equation
		B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I.for EXP(B)
								Lower	Upper
Step 1 a	HIV	.457	.441	1.073	1	.300	1.579	.665	3.745
	Understanding	-.081	.451	.033	1	.857	.922	.381	2.230
	Gender	.575	.420	1.871	1	.171	1.777	.780	4.048
	age	.014	.029	.229	1	.632	1.014	.957	1.074
	Constant	-2.655	2.269	1.369	1	.242	.070
a. Variable(s) entered on step 1: HIV, Understanding , Gender, age.

Table 3 above shows the logistic regression for the determinants of TB. HIV (OR = 1.579, 95% CI: 0.665-3.745). Gender (M: F) (OR = 1.777 with a 95% CI: 0.780-4.0448). The age of the participants (odd ratio = 1.044 with a 95 % confidence interval between 0.957 and 1.074. Understanding (OR = 0.922, 95% CI: 10.81-2.230). 

Discussion and Conclusion 

The odds ratio for the HIV status is 1.579, which is more than 1 , implying that TB transmission is very high among individuals with a positive HIV status. Hence, people with a positive HIV status are at a higher risk of contracting TB. The odds ratio for understanding is 0.922, which is less compared to 1. This implies that the odds for people who understand TB but have tested positive is 0.922, which is 7.8 % lower compared to those who are negative. Therefore, understanding TB reduces the number of cases. The number of TB cases will increase, given that people or individuals lack an understanding of the disease. Hence, understanding and having information on TB is crucial in reducing TB transmission. The adults are at a higher risk of contracting TB , compared to young people. Further, the male s are at a higher risk compared to the female s, of developing TB. 

Overly, t he main purpose of the study is to examine the effect of the prevalence of TB , based on the lack off understanding and HIV status. We formulated the hypothesis that established if lack of understanding and HIV community will affect the prevalence of TB. 

From the result s , we reject the null hypothesis and deduce that when a person understands TB, he or she is likely to test negative. This is because they know the prevention and cure for the disease. Further, an individual with a positive HIV status is at a very high risk for tuberculosis. The other risk factor for TB is the age , where the more elderly individual s are likely to be more prone to TB compared to the younger population. This stud y is of great significance to the public health officer s, because it enables them to come up with measures for protecting the public , including ed ucating masses on the best practices of preventing TB . They will also be sensitive to the public on the importance of hygiene and regular visit s to health facilities for diagnosis. 

There are possible errors that may arise from the study; which include random and systematic errors . The random error s arise when the sample measurement diverges from the true population value. This error s result in inaccuracy in the measures of association. It comprises of the sampling and measurement error. The sampling error arises when the sample is no t representative of the entire population. The systematic error or bias error comprises the selection and measurement bias. The selection bias happens when individuals are selected with different characteristics from those who are supposed to participate in the study. Also, measurement bias arises when the classification of the disease or exposure is inaccurate. This bias may result in a wrong conclusion and decisions. Further research should be done on individuals battling diabetes, heart attack, and arthritis. 

References 

Chadha, V. K., Kumar, P., Anjinappa, S. M., Singh, S., Narasimhaiah, S., Joshi, M. V., ... & Babu, S. (2012). Prevalence of pulmonary tuberculosis among adults in a rural sub-district of South India.  PLoS One ,  7 (8), e42625. 

National Bureau of Statistics of China. Statistical Communiqué of the People’s Republic of China on the 2016 National Economic and Social Development: National Bureau of Statistics of China. Available from: http://www.stats.gov.cn/english/PressRelease/201702/t20170228_1467503.html. Accessed 17 Oct 2017 

Wang, L., Gao, P., Zhang, M., Huang, Z., Zhang, D., Deng, Q., ... & Zhou, M. (2017). Prevalence and ethnic pattern of diabetes and prediabetes in China in 2013.  Jama ,  317 (24), 2515-2523. 

World Health Organization. (2018). Technical manual for drug susceptibility testing of medicines used in the treatment of tuberculosis. 

Yang, G., Wang, Y., Zeng, Y., Gao, G. F., Liang, X., Zhou, M., ... & Vos, T. (2013). Rapid health transition in China, 1990–2010: findings from the Global Burden of Disease Study 2010.  The lancet ,  381 (9882), 1987-2015. 

Appendix 

TB
		Value	Count	Percent
Standard Attributes	Position	1
	Label	<none>
	Type	Numeric
	Format	F1
	Measurement	Nominal
	Role	Input
Valid Values	1		57	57.6%
	2		42	42.4%

Age
		Value
Standard Attributes	Position	2
	Label	<none>
	Type	Numeric
	Format	F2
	Measurement	Scale
	Role	Input
N	Valid	99
	Missing	0
Central Tendency and Dispersion	Mean	72.00
	Standard Deviation	7.174
	Percentile 25	66.00
	Percentile 50	72.00
	Percentile 75	79.00

Gender
		Value	Count	Percent
Standard Attributes	Position	3
	Label	<none>
	Type	Numeric
	Format	F1
	Measurement	Nominal
	Role	Input
Valid Values	1		56	56.6%
	2		43	43.4%

HIV
		Value	Count	Percent
Standard Attributes	Position	4
	Label	<none>
	Type	Numeric
	Format	F1
	Measurement	Nominal
	Role	Input
Valid Values	1		66	66.7%
	2		33	33.3%

Understanding
		Value	Count	Percent
Standard Attributes	Position	5
	Label	Understanding
	Type	Numeric
	Format	F1
	Measurement	Nominal
	Role	Input
Valid Values	1		68	68.7%
	2		31	31.3%