Analisis Spasial-Temporal dan Faktor Risiko Kejadian Tuberkulosis di Kabupaten Mandailing Natal

Abstract

Tuberculosis is an infectious disease. North Sumatra had total of 15.577 new cases in 2019, increase from previous year, especially in Mandailing Natal District, which increase in cases of 826 cases (4,9) to 997 cases (6,4%) in 2015-2019. The objective of the research is to analyze the spatial temporal distribution of TB cases in 2015-2019 and explain the risk factors. This type of research was observational analytics with case-control design for individual data analysis and an ecological design for aggregate data analysis. The sample consisted of case samples treated in the pulmonary poly and control samples treated in the eye and nerve poly, amounting to 120 respondents. Data analysis used logistic regression tests and spatial regression tests. The results showed that variables had an relationship with TB incidence consisted of age (p=0.043; OR=0.19; 95% CI=0.040–0.946), gender (p=0.001; OR=10.85; 95% CI = 4.65–25.32), nutritional status (p=0.001; OR=11.00; 95% CI = 3.87–31.31), education (p=0.020; OR=2.58; 95% CI = 1.16–5.75), employment (p=0.029; OR=2.27; 95% CI =1.09–4.73), income (p=0.028; OR=2.29; 95% CI =1.09–4.78), smoking history (p=0.001; OR=5.50; 95% CI =2.51–12.05), contact history (p=0.019; OR=2.41; 95% CI =1.16–5.02), ventilation usage (p=0.011; OR=14.75; 95% CI =1.85–117.51) and the habit of opening windows (p=0.004; OR=9.67; 95% CI =2.10-44.46). The dominant variable that affected TB events was the contact history variable (OR=3,22). The results of spatial regression analysis found that there was no relationship between population density and the height of the region with the number of TB cases (p = 0.460 and p = 0.966). There were sub-districts in the category of very vulnerable. It expected the district government will make policies related to controlling the contact history of TB patients, providing employment opportunities, increasing people’s income to overcome TB disease, particularly in the most vulnerable sub-districts.