Spatial Econometric Analysis of the Impact of Health Infrastructure on TBC Patients

Who

• Study population: Aggregated provincial-level data on Tuberculosis (TB) cases and TB incidence across Indonesia.

• Units of analysis: Indonesian provinces (panel data).

• Data sources:

  • Health infrastructure and facilities data from the Ministry of Health Republic of Indonesia

  • Control variables from the Indonesian Bureau of Statistics

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What

• Focus: Examines how health infrastructure and health facilities influence TB cases and TB incidence while accounting for spatial dependence between regions.

• Key findings:

  • TB cases in Indonesia exhibit significant positive spatial autocorrelation, with Moran’s I values ranging from 0.307 to 0.522 (significant at the 1% level), indicating clustering rather than random distribution.

  • TB incidence is spatially concentrated in western Indonesia, particularly on Java Island, with the highest burden in West Java Province.

  • Health infrastructure variables (households with better access to drinking water and sanitation) show no significant direct effect on TB incidence.

  • Health facilities variables (number of doctors, national health insurance participation) and control variables (government healthcare expenditure and population density) have positive direct effects on TB cases.

  • Indirect (spillover) effects are found only for access to drinking water and population density.

• Implication: Spatial dynamics are critical for understanding TB distribution, and policy responses should account for regional clustering and spillover effects.

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When

• Data period: 2017–2021.

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Where

• Geographic setting: Indonesia, with provincial-level spatial analysis and emphasis on Java Island.

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Why

• TB cases may be spatially correlated due to geographic proximity, population movement, and shared environmental and socioeconomic conditions.

• Ignoring spatial autocorrelation can bias estimates of determinants of TB incidence.

• The study addresses the gap in understanding how health infrastructure and facilities affect TB when spatial dependence is explicitly modeled.

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How

• Study design: Quantitative spatial panel study.

• Methods:

  • Moran’s I test to detect spatial autocorrelation in TB cases and independent variables.

  • Spatial econometrics modeling using the General Spatial Panel Model (GNS), including the Spatial Durbin Model with Fixed Effects (SDM-FE).

  • Cluster and spatial pattern mapping using percentile and natural breaks approaches.

• Analytical strategy:

  • Confirm spatial correlation with Moran’s I.

  • Estimate direct and indirect (spillover) effects of health infrastructure, health facilities, and control variables on TB incidence.

Source: Rahmawati, Y., Jamil, I.R., Hidayah, I., Kusumawardani, D. and Wibowo, W., 2026. Spatial Econometric Analysis of the Impact of Health Infrastructure on TBC Patients Study Case in Indonesia Provinces Level. International Review for Spatial Planning and Sustainable Development, 14(1), pp.96-117.