Environmental and Demographic Effects on Vector Borne Disease Incidence: Welfare Role on DHF Reduction

Samsul Bakri, Adella Putri Apriliani, Evi Kurniawaty, Henky Mayaquezz


Many regions in developing countries are transitioning to an industrial economic model, accompanied by rapid population growth, which from one side is a welfare driver (WLF) and on the other side is a demographic pressure, especially a health problem such as vector-borne disease. The problem climaxes when this transition is always accompanied by environmental degradation (ENV), which begins with deforestation. Objective: [1] Determine the direct influence of: [1a] Demographic on DHF incidence, [1b] Demographic on Welfare improvement, [1c] Welfare on DHF incidence, [1d] Environment improvement on DHF incidence, [1e] Environment improvement on performance Welfare; and [2] The indirect influence of Welfare in mediating [2a] Demographic pressure and [2b] Environment improvement on DHF incidence. Research Method: Lampung Province was used as the research locus. Forest Resources Inventory Laboratory of Lampung University as a place for analysis. Postulate SEM (Structural Equation Model) was employed at a 95% confidence level. The endogenous variable was vector-borne disease (reflected by DHF incidence). The two exogenous variables were DMG (reflected by population density and the proportion of age of productive, industrial, and service workers) and ENV (reflected by maximum & minimum air temperature, forested areas, and other land uses). The mediating variable is WLF (reflected by poverty and HDI). Findings: [1] Directly, with a significant effect: [1a] DMG pressure increases DHF (P=0.000) and [1.b] WLF (P=0.000); [1.c] Environment improvement increases welfare (P=0.000) while [1d] reduces DHF; and [1.e] WLF improvement can reduce DHF (P=0.010) and [2] The role of WLF improvement [2a] can significantly reduce the incidence of DHF due to demographic pressure (P=0.007) while also [2b] amplifying environmental improvement in reducing DHF significantly (P=0.023). Novelty:To reduce DHF incidence, Welfare improvement can reverse the negative effects of Demographic pressure as well as act as an amplifier for the role of environmental improvement.


Doi: 10.28991/HEF-2024-05-01-03

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Environmental; Land Cover; Global Warming; Reforestation; Structural Equation Modelling (SEM).


Guo, F., Bonebrake, T. C., & Gibson, L. (2019). Land-Use Change Alters Host and Vector Communities and May Elevate Disease Risk. EcoHealth, 16(4), 647–658. doi:10.1007/s10393-018-1336-3.

Patz, J. A., Olson, S. H., Uejio, C. K., & Gibbs, H. K. (2008). Disease Emergence from Global Climate and Land Use Change. Medical Clinics of North America, 92(6), 1473–1491. doi:10.1016/j.mcna.2008.07.007.

Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., & Snyder, P. K. (2005). Global Consequences of Land Use. Science, 309(5734), 570–574. doi:10.1126/science.1111772.

Gottdenker, N. L., Streicker, D. G., Faust, C. L., & Carroll, C. R. (2014). Anthropogenic Land Use Change and Infectious Diseases: A Review of the Evidence. EcoHealth, 11(4), 619–632. doi:10.1007/s10393-014-0941-z.

Murni, M., Nelfita, N., Risti, R., Mustafa, H., & Maksud, M. (2020). Virtual index and vector entomological index of Dengue Hemorrhagic Fever in Central Mamuju Regency, West Sulawesi. BALABA. 16(2), 189–198. doi:10.22435/blb.v16i2.3319.

Santoso, M. I., Taviv, Y., Wempi, I. G., Mayasari, R., and Marini, M. (2018). The Relationship between Container Characteristics and the Presence of Aedes aegypti Larvae in the Extraordinary Incident of Dengue Hemorrhagic Fever: Case Study in Ogan Komering Ulu Regency. Journal of Disease Vectors. 12(1), 9–18. doi:10.22435/vektorp.v12i1.229.

Syahdan, S., Arif, A., & Megawati, M. (2022). Analysis of the Factors that Cause Dengue Hemorrhagic Fever (DHF) Using Chi-Square Automatic Interaction Detection (CHAID). International Journal of Natural Science and Engineering, 5(3), 104–113. doi:10.23887/ijnse.v5i3.41123.

Alonso, P., & Noor, A. M. (2017). The global fight against malaria is at crossroads. The Lancet, 390(10112), 2532–2534. doi:10.1016/S0140-6736(17)33080-5.

Gianchecchi, E., Cianchi, V., Torelli, A., & Montomoli, E. (2022). Yellow Fever: Origin, Epidemiology, Preventive Strategies and Future Prospects. Vaccines, 10(3). doi:10.3390/vaccines10030372.

Lippi, C. A., Mundis, S. J., Sippy, R., Flenniken, J. M., Chaudhary, A., Hecht, G., Carlson, C. J., & Ryan, S. J. (2023). Trends in mosquito species distribution modeling: insights for vector surveillance and disease control. Parasites and Vectors, 16(1). doi:10.1186/s13071-023-05912-z.

Norris, D. E. (2004). Mosquito-borne Diseases as a Consequence of Land Use Change. EcoHealth, 1(1), 19–24. doi:10.1007/s10393-004-0008-7.

Pelita, A., Suwandi, J. F., Bakri, S., & Riniarti, M. (2019). The role of terrestrial aquatic ecosystems in the incidence of malaria: Study in Lampung Province. Proceedings of the 2019 World Water Day National Seminar. Sriwijaya University Postgraduate Program, Palembang.

Bakri, S., Ramos, V., Kurniawan, B., Setiawan, A., & Dewi, B. S. (2022). The utilization of Landsat imagery for valuing forest environmental service in controlling pneumonia incidence rate under the scenario of global warming: study at Lampung Province_sumatera. Natural Volatiles & Essential Oils, 9(1), 1654–1665.

Seno, Y., Bakri, S., and Wardani, D.W.R. (2018). Utilization of Landsat satellite imagery for the valuation of the state’s forest environmental services in the dengue fever (DF) in Lampung Province. Journal of Tropical Forests, 6(3), 237–248.

Wulandari, C., Bakri, S., Riniarti, M., & Supriadi, S. (2021). Fostering the sustainability of community forestry program: Case study in lampung-sumatra. Forestry Ideas, 27(1), 210–232.

Sanudin, S., Sadono, R., & Purwanto, R. H. (2016). Progress of community forest in Lampung Province. Jurnal Manusia Dan Lingkungan, 23(2), 276–283.

BPS (2023). Lampung Province in Figure. Central Bureau of Statistics, BPS Lampung Province, Bandar Lampung, Indonesia.

Hartuty, G., Wardani, D. W., Wulandari, C., & Bakri, S. (2019). The role of mangrove forests in terrestrial aquatic ecosystems in controlling dengue hemorrhagic fever (DHF) under a global warming scenario. Proceedings of the 2019 World Water Day National Conference, Sriwijaya University Postgraduate Program, Palembang.

Sarwono, Y. (2010). Basic Understanding of Structural Equation Modeling (SEM). Jurnal Ilmiah Manajemen Bisnis Ukrida, 10(3), 173–182.

Apriliani, A. P. (2021). The role of social capital in increasing coffee bean productivity and household income in agroforestry communities: Study on Community Forests (HKm) in the Batu Tegi Protected Forest Management Unit (KPHL). Thesis, University of Lampung, Lampung, Indonesia.

Rizaldi, A., Darmawan, A., Kaskoyo, H., & Mubarok, H. (2022). Identification of Land Cover Changes as a Basis for Forest Management Strategy (Case Study of Batutegi Lampung Protected Forest Management Unit. Fahutan Proceedings, 2(02), 167–175.

Bakri, S., Ramos, V., Kurniawan, B., Dewi, B. S., Kurniawaty, E., & Kaskoyo, H. (2023). How much is the Cost to Reduce the Incidence Rate of Infectious Diseases through Reforestation? (Case Study on Pulmonary TB under Global Warming Scenario). Polish Journal of Environmental Studies, 32(2), 1519–1529. doi:10.15244/pjoes/157212.

Siregar, D. I., and Asbi, A. M. (2020). Utilization of Landsat 8 Operational Land Imager (OLI) imagery for land cover classification in Mount Merbabu National Park. Wahana Forestra: Forestry Journal, 15(2), 28-39.

Hair, J.F., Anderson, R.F., Tatham, R.L., Black, W.C. (1998). Multivariate Data Analysis. Prentice Hall. Englewood Cliffs, New Jersey, United States.

Wijanto, S. H. (2008). Structural Equation Modelling dengan LISREL 8.8: Concept and Tutorial. Graha Ilmu. Yogyakarta, Indonesia.

Hair, J. F., Hult, G. T. M., Ringle, C., Sarstedt, M., Andks, N., and Ray, S. (2021) Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer International Publishing, 1-197. doi:10.1007/978-3-030-80519-7.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.

Gandhiadi, G. K., Dharmawan, K., & Sari, K. (2015). Structural Equation Model to Study the Influence of Social Capital through Entrepreneurial Orientation Dimensions on Community Welfare in Jembrana Regency, Bali. Proceedings of the Fmipa Undiksha V National Conference, 355–363.

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2020). Handbook of Market Research. Handbook of Market Research, 1– 38. doi:10.1007/978-3-319-05542-8.

Ghozali, I. (2021). Partial Least Squares Concepts, Techniques and Applications Using SmartPLS 3.2.9 (Edition 3). Publishing Agency University of Diponegoro, Indonesia.

Tenenhaus, M., Amato, S., & Vinzi, V. E. (2000). A global Goodness – of – Fit index for A or PLS structural. Proceedings of the XLII SIS Scientific Meeting, 739-742.

Asnifatima, A. (2013). Spatial trend patterns of malaria incidence (Case study: in Selayar Islands Regency 2011-2013. Journal of Public Health, 5(1), 1–10.

Arisanti, M., & Suryaningtyas, N. H. (2021). The incidence of dengue hemorrhagic fever (DHF) in Indonesia in 2010-2019. Spirakel, 13(1), 34–41.

Novrita, B., Mutahar, R., & Purnamasari, I. (2017). The Analysis of Incidence of Dengue Hemorrhagic Fever in Public Health Center of Celikah Ogan Komering Ilir Regency Year 2016. Jurnal Ilmu Kesehatan Masyarakat, 8(1), 19–27. doi:10.26553/jikm.2017.8.1.19-27.

Ortiz, D. I., Piche-Ovares, M., Romero-Vega, L. M., Wagman, J., & Troyo, A. (2022). The impact of deforestation, urbanization, and changing land use patterns on the ecology of mosquito and tick-borne diseases in central America. Insects, 13(1), 20. doi:10.3390/insects13010020.

Athni, T. S., Shocket, M. S., Couper, L. I., Nova, N., Caldwell, I. R., Caldwell, J. M., Childress, J. N., Childs, M. L., De Leo, G. A., Kirk, D. G., MacDonald, A. J., Olivarius, K., Pickel, D. G., Roberts, S. O., Winokur, O. C., Young, H. S., Cheng, J., Grant, E. A., Kurzner, P. M., … Mordecai, E. A. (2021). The influence of vector-borne disease on human history: socio-ecological mechanisms. Ecology Letters, 24(4), 829–846. doi:10.1111/ele.13675.

Bakri, S. (2012). Intrinsic Functions of Forests and Endogenic Factors of Economic Growth as Determinants of Regional Development in Lampung Province. Doctoral Dissertation, Bogor Agriculture University, Indonesia.

Hidayat, M. A., & Noor, A. (2020). The Effect of Economic Growth on Land Conversion in Samarinda City. Inovasi, 16(2), 10.

Duarsa, A. B. S. (2008). The impact of global warming on the risk of malaria. Andalas Public Health Journal, 2(2), 181-185.

Listyarini, A. D., & Rosiyanti, E. (2021). Description of Family Behavior about Dhf Prevention in Ngemplak Village, Undaan District, Kudus Regency. Indonesian Journal of Medical and Health Sciences, 1(3), 91–99.

Hikmah, M., & Kasmini H, O. W. (2015). Factors Associated with Deaths Due to Dengue Hemorrhagic Fever. Unnes Journal of Public Health, 4(4), 189. doi:10.15294/ujph.v4i4.9693.

Hendayani, N. (2022). Relationship between environmental factors and 3M Plus habits with the incidence of Dengue Hemorrhagic Fever (DHF) in the Manonjaya Community Health Center working area. Doctoral dissertation, University of Siliwangi, Indonesia.

Bakri, S., Darusman, D., Juanda, B., & Bahruni. (2014). Regional development under resource constraints. Journal of Socio-Economics, 18(2), 161–170.

Affandi, M. (2009). The Roles of Agroindustry in the Regional Economy of Lampung Province: Analysis of Intersectoral Linkages and Agglomeration of Industries. Doctoral Dissertation, IPB University, Indonesia.

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DOI: 10.28991/HEF-2024-05-01-03


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