ASSOCIATED FACTORS FOR TUBERCULOSIS CASES IN PASIR PUTEH, KELANTAN 2012-2015 Hafizuddin A1, Zawiyah D1, Sharina D2 1Pasir Puteh District Health Office 2Kelantan State Health Department

INTRODUCTION

Tuberculosis (TB) is one of the main public health issues in many countries including Malaysia, and remains a major source of morbidity and mortality worldwide.7 Malaysia spent $15 million for TB control programmes in 2015.12 As for 2014, the incidence and prevalence rates for TB in Malaysia are 99 per 100,000 and 131 per 100,000 population respectively. Malaysia’s TB mortality rate is 5.8 per 100,000 population (excluding TB/HIV infection).11,12

TABLE 1 : TB CASE NOTIFICATIONS FOR 2014 & 2015 TOTAL TB CASE NOTIFICATIONS 2014 2015 MALAYSIA 24711 24220 KELANTAN 1366 1233 (TB/Leprosy Unit, Kelantan State Health Department,2016)

Risk factors for prevalence of TB have been reported to include: 3,7,8 socio-economic factors (age, sex, employment and education) patient factors (diabetes status, smoking, HIV status) disease factors (history of multiple disease treatment)

(TB/Leprosy Unit, Kelantan State Health Department,2016)

GRAPH 2 : KELANTAN TB CASES FOR 2014 & 2015 (TB/Leprosy Unit, Kelantan State Health Department,2016)

Studying the associated factors for the prevalence of TB is a convenient way to evaluate the effectiveness of TB control programmes and may help us to identify patients with the highest vulnerability.7,10 This study certainly provided a façade of the current situation impacted by this old nemesis of ours, and could be useful for us to establish preventive measures to curb the future incidence of TB among our population.

OBJECTIVES

To determine the socio-demography, risk factors and categories in TB cases for Pasir Puteh district from 2012-2015. To determine the factors associated with TB cases in Pasir Puteh district from 2012-2015.

METHODOLOGY

Study design: Objective 1 (Phase 1) : Cross sectional study Objective 2 (Phase 2) : Case-control study Study location: Pasir Puteh District Health Office Reference population: Tuberculosis cases in Pasir Puteh district Source population: Tuberculosis case patients notified to PKD Pasir Puteh Sampling frame: Tuberculosis cases notified to PKD Pasir Puteh between 1st of January 2012 to 31st of December 2015

Study sample: Tuberculosis cases notified to PKD Pasir Puteh between 1st of January 2012 to 31st December 2015 who fulfilled the inclusion and no exclusion criteria Inclusion criteria: Confirmed cases of tuberculosis that is notified to PKD Pasir Puteh Exclusion criteria: Incomplete record of 30% of variables.

Sampling method: Phase 1 : Universal sampling Phase 2 : Simple random sampling for control group with ratio of 1:1 and all TB cases are selected for case group. Mode of data collection: This study involve secondary data collection from MyTB (TBIS) Registry; TB Cases Registry for case group, and TB Contacts Registry for control group. All data information will be collected using checklist performa and data collection form designed by researcher consisting of socio-demography, risk factors and outcome of cases.

STUDY VARIABLES INDEPENDENT VARIABLES Socio-demographic factors Risk factors OUTCOMES TB (Case) : Confirmed and registered cases of TB Non-TB (Control): Registered TB contacts who have no symptoms, negative Mantoux and no CXR abnormality.

FIGURE 1 : FLOW CHART OF THE STUDY

SAMPLE SIZE CALCULATION P0 : Proportion of Diabetes among Malaysian population (NHMS, 2015) P1 : Proportion of diabetes among TB patients in Kelantan (Expert opinion) Sample size needed for power of 80% and alpha 0.05 : 406 x 2 = 812 812 + (10% non response group) = 812 + 81 = 893 samples (794 samples were used for this research)

STATISTICAL ANALYSIS

(Using IBM SPSS statistics version 18) 1) Data entry, exploration and cleaning Data checking for any missing value 2) Descriptive statistics Categorical data: n (%) Numerical data: mean (sd) or median (IQR) 3) Simple logistic regression All the variables will be tested whether there is an association with tuberculosis cases. Only variables with p-value < 0.25 or clinically important is selected for variable selection.

4) Multiple logistic regression 3 rules in selecting model: statistical significant, parsimony, biological plausible. Checking interaction: two-way interaction will be checked in the model. Checking overall fitness of the model: using Hosmer-Lemeshow test, classification table and area under ROC curve

5) Data presentation, interpretation and conclusion Crude OR, adjusted OR, 95% CI, Wald statistic and p-value will be reported. p-value < 0.05 will be considered significant.

RESULTS

TABLE 2 : SOCIO-DEMOGRAPHY OF TUBERCULOSIS PATIENTS IN PASIR PUTEH 2012-2015 (n=397) Factors Frequency (%) Ethnicity Malay Others 378 (95.2) 19 (4.8) Gender Male Female 248 (62.5) 149 (37.5) Occupation Student Govt/Private Sector Self-employed Unemployed 15 (3.8) 78 (19.6) 109 (27.5) 195 (49.1)

TABLE 3 : SOCIO-DEMOGRAPHY OF TUBERCULOSIS PATIENTS IN PASIR PUTEH 2012-2015 (n=397) Factors Frequency (%) Age *47 (18) Age Group Children (5-12) Teenagers (13-18) Adults (19-60) Elderly (>60) 2 (0.5) 17 (4.3) 264 (66.5) 114 (28.7) Education Level None Primary Secondary Tertiary 71 (17.9) 90 (22.7) 228 (57.4) 8 (2.0) *Mean (SD)

TABLE 4 : RISK FACTORS AMONG TUBERCULOSIS PATIENTS IN PASIR PUTEH 2012-2015 (n=397) Factors Frequency (%) Diabetes No Yes 282 (71.0) 115 (29.0) Smoking 226 (56.9) 171 (43.1) HIV Status Negative Positive 354 (89.2) 43 (10.8) BCG Scar Absent Present 98 (24.7) 299 (75.3)

BY SIMPLE LOGISTIC REGRESSION (n=794) TABLE 5 : FACTORS ASSOCIATED WITH TUBERCULOSIS CASES IN PASIR PUTEH 2012-2015 BY SIMPLE LOGISTIC REGRESSION (n=794) Factors TB Case frequency (%) Crude OR (95% CI) Wald statistics (df) p value Non-TB n=397 TB Gender Female Male 213 (53.7) 184 (46.3) 149 (37.5) 248 (62.5) 1.00 1.97 (1.45,2.55) 20.60 (1) <0.01 Ethnicity Malay Others 387 (97.5) 10 (2.5) 378 (95.2) 19 (4.8) 1.94 (0.89,4.23) 2.80 (1) 0.094 Occupation Student Govt/Private Self-employed Unemployed 176 (44.3) 67 (16.9) 57 (14.4) 97 (24.4) 15 (3.8) 78 (19.6) 109 (27.5) 195 (49.1) 13.66 (7.34,25.39) 22.43 (12.10,41.58) 23.58 (13.19,42.15) 68.28 (1) 97.67 (1) 113.80 (1)

BY SIMPLE LOGISTIC REGRESSION (n=794) TABLE 6 : FACTORS ASSOCIATED WITH TUBERCULOSIS CASES IN PASIR PUTEH 2012-2015 BY SIMPLE LOGISTIC REGRESSION (n=794) Factors TB Case frequency (%) Crude OR (95% CI) Wald statistics (df) p value Non-TB n=397 TB Age Group Teenagers (13-18) Infants/Toddlers (<5) Children (5-12) Adults (19-60) Elderly (>60) 99 (24.9) 12 (3.0) 64 (16.1) 183 (46.1) 39 (9.8) 17 (4.3) 0 (0.0) 2 (0.5) 264 (66.5) 114 (28.7) 1.00 0.00 (0.00,0.00) 0.182 (0.04,0.81) 8.40 (4.85,14.53) 17.02 (9.06,31.96) 0.00 (1) 4.96 (1) 57.94 (1) 77.75 (1) 0.999 0.026 <0.01 Diabetes No Yes 396 (99.7) 1 (0.3) 282 (71.0) 115 (29.0) 161.48 (22.42,1163.07) 25.47 (1) Smoking 325 (81.9) 72 (121.5) 226 (56.9) 171 (43.1) 3.41 (2.47,4.72) 55.38 (1) HIV Status Negative Positive 395 (99.5) 354 (89.2) 43 (10.8) 23.99 (5.77,99.74) 19.10 (1)

BY MULTIPLE LOGISTIC REGRESSION (n=794) TABLE 7 : FACTORS ASSOCIATED WITH TUBERCULOSIS CASES IN PASIR PUTEH 2012-2015 BY MULTIPLE LOGISTIC REGRESSION (n=794) Factors Crude ORa (95% CI) Adjusted ORb Wald statisticsb (df) p valueb Gender Female Male 1.00 1.97 (1.45,2.55) 2.28 (1.61,3.23) 21.90 (1) <0.01 Age Group Teenagers (13-18) Adults (19-60) Elderly (>60) 8.40 (4.85,14.53) 17.02 (9.06,31.96) 6.01 (3.43,10.52) 10.16 (5.28,19.55) 39.45 (1) 48.22 (1) Diabetes No Yes 161.48 (22.42,1163.07) 105.32 (14.52,763.54) 21.23 (1) Smoking 3.41 (2.47,4.72) 1.49 (1.00,2.22) 3.94 (1) 0.047 HIV Status Negative Positive 23.99 (5.77,99.74) 25.78 (5.04,131.73) 15.24 (1) aSimple logistic regression bMultiple logistic regression Classification table : 78.3% Area under ROC curve : 0.85 No interaction. The model reasonably fits well.

DISCUSSION

Ethnicity has no significant association as this study is carried out in a Malay majority area. A striking feature of TB, which is apparent in prevalence surveys from most part of the world, is that TB is more a disease of men than of women as reported by Borgdorff et al.5 There is significant association between male gender and TB prevalence as reported by this study and could be explained by the higher mobility of the male group due to work requirement. McKenna et al reported that farm [AOR 3.7(95%CI 3.4,4.1)], industrial and healthcare workers [AOR 1.0 (95%CI 0.9,1.1)] have higher risk of getting TB but in this study, all occupational groups have higher odds of getting TB with significant association.9

From our study, the TB infection was comparatively higher in adult and elderly groups. Elderly (>60 year old) has 10-fold higher risk of getting TB with significant p-value, and such finding is apparent in developed countries as well (Canada, Australia, Scandinavian) as reported by Raviglione et al and Nissapatorn et al.3 The association between smoking and TB has rarely been highlighted. However, Slama et al reported that currents smokers were found to be 2.6 (95%CI 1.6,4.3) times more likely to develop TB. As for our study, smoking is a significant associated factor as well with AOR 1.49 (95%CI 1.0,2.2).6

Giri et al in their study found out that 17% of their HIV patients have TB co-infection (χ2=27.8, p<0.0001) indicating that severely depressed immunity makes them susceptible to TB infection. In our study, HIV positive patients have 25 times higher odd of getting TB infection with significant p-value as well.4 Similar to previous study (Nissapatorn et al), we found that host comorbidity (diabetes mellitus) was significantly associated with TB infection. Patients with diabetes were 100 times more likely to develop TB than those without diabetes with significant p-value (<0.01).1

CONCLUSION & RECOMMENDATIONS

In conclusion, male, elderly, diabetic, smoking and HIV positive status are the significant associated factors for getting infected with tuberculosis in Pasir Puteh district. Population groups with the above risk factors must be identified and screened for TB meticulously. Elderly people are considered as high risk group for TB, hence chest x-ray and TB screening is mandatory.

Quit Smoking Clinics must be empowered to curb smoking and later mitigating the risk of getting TB. Patients who attend Quit Smoking Clinic must be educated regarding TB disease and others adverse effects of smoking, and recommended for TB screening as well. All newly-diagnosed HIV patients are compulsory to do TB screening (inclusive of chest x-ray).

Considering the increasing burden of DM, particularly in areas with highly prevalent TB, TB control programs will need to expand efforts to focus on treatment and monitoring of patients with DM and TB disease. Annual CXR screening for all diabetic patients must be done proactively so that TB cases among diabetic patients will not be overlooked. As Kelantan has high prevalence of diabetes among population, TB health education and screening programs must be done regularly in the community to ensure that larger proportion of diabetic patients are grabbed and screened for TB.

ACKNOWLEDGEMENT

Health Inspectors, TB Unit, Pasir Puteh District Health Office Assistant Medical Officers, Pasir Puteh District Health Office Staff nurses, Chest Clinic, HRPZ II Staff nurses, Chest Clinic, HTA

REFERENCES

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