| dc.description.abstract | Tuberculosis is an infectious disease caused by the bacteria Mycobacterium tuberculosis and is one of the deadliest diseases in the world. In the city of Medan, the prevalence of TB showed an increasing trend during the period 2020-2023. The research method used an ecological study design with a Geographic Information System (GIS) approach based on secondary data. Data analysis was conducted using spatial autocorrelation with Global Moran’s I and Local Moran’s I (LISA). The study results showed that the pattern of TB case prevalence distribution was not statistically significant (p>0.05). However, in 2023, univariate LISA analysis identified a significant local cluster in Medan Sunggal District (High-High). In 2023, there was significant bivariate spatial autocorrelation between the proportion of male residents and the prevalence of TB cases with a dispersed distribution pattern (I=-0.1931; p=0.039), and between the proportion of female residents and the prevalence of TB cases, showing a clustered distribution pattern (I=0.1931; p=0.039). The priority area for both variables are Medan Sunggal District (Low-High) and (High-High). In 2021, there was significant bivariate spatial autocorrelation between TB cure rates and prevalence of TB cases, showing a dispersed distribution pattern (I=-0.1596; p=0.040), with priority area being Medan Helvetia District (Low-Low). In 2020, there was significant bivariate spatial autocorrelation between the proportion of low-income families and prevalence of TB cases, showing a clustered distribution pattern (I=0.1612; p=0.043), with priority area being Medan Marelan District (High-High). In the same year, there was significant bivariate spatial autocorrelation between population density and prevalence of TB cases, showing a dispersed distribution pattern (I = -0.1897; p = 0.046), with priority area being Medan Marelan District (Low-High). Based on these findings, it is expected that the Medan City Health Department to prioritize TB interventions in priority areas according to the spatial characteristics of each variable. | en_US |
