DOI: https://doi.org/10.14710/jkli.76257

Analisis Spasial Faktor Sosial, Pelayanan Kesehatan, dan Lingkungan terhadap Kasus COVID-19 di Jawa Tengah

1Jurusan Kesehatan Lingkungan, Poltekkes Kemenkes Semarang, Kabupaten Banyumas, Jawa Tengah 53151, Indonesia

2Jurusan Promosi Kesehatan, Poltekkes Kemenkes Bandung, Indonesia

Open Access Copyright 2025 Jurnal Kesehatan Lingkungan Indonesia under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Abstract

Latar belakang: Tahun 2022, tingkat positif di Jawa Tengah 40,9% melampaui ambang batas WHO (< 5%). COVID-19 menunjukkan pola yang kompleks oleh berbagai variabel seperti, sosial pelayanan kesehatan, dan lingkungan. Penelitian ini bertujuan untuk menganalisis keterkaitan antara faktor sosial, pelayanan kesehatan, dan lingkungan terhadap distribusi spasial tingkat kerentanan kasus COVID-19 di Provinsi Jawa Tengah.

Metode: Penelitian kuantitatif dengan desain ekologi eksploratori. Unit analisis 35 kabupaten/kota di Jawa Tengah. Data agregat tahun 2022 dengan variabel dependen jumlah kumulatif kasus COVID-19. Variabel independen faktor sosial (jumlah penduduk, jumlah penduduk miskin, tingkat pengangguran, tingkat pendidikan penduduk usia >15 tahun, indeks pembangunan manusia (IPM), jumlah turis domestik, Jumlah turis mancanegara), faktor pelayanan kesehatan (jumlah tenaga kesehatan, jumlah tenaga sanitasi lingkungan, akses terhadap sanitasi layak, dan akses terhadap air minum layak). faktor lingkungan (curah hujan rata-rata, kelembapan udara, suhu rata-rata, serta kecepatan angin luas wilayah, serta elevasi rata-rata wilayah). Data diperoleh dari instansi nasional (BPS dan Dinkes Provinsi Jawa Tengah) dan internasional (NASA). Dianalisis menggunakan pemodelan regresi Ordinary Least Squares dengan teknik stepwise backward elimination serta validasi uji asumsi klasik dan autokorelasi spasial. Hasil Pemodelan visualisasikan dengan bentuk peta distribusi tingkat kerentanan berbasis kuartil.

Hasil: Variabel yang berasosiasi dengan kasus COVID-19 adalah jumlah penduduk (B = 0,0164), jumlah penduduk miskin (B = -0,0951), jumlah wisatawan domestik (B = 0,0047), jumlah tenaga kesehatan (B = 3,3453), dan suhu rata-rata (B = -2638,61) dengan kekuatan prediktif model (R² = 0,9266), Distribusi spasial menunjukan wilayah dengan tingkat kerentanan sangat tinggi seperti Kota dan Kabupaten Semarang, Kota Surakarta, Kabupaten Magelang, Kabupaten Klaten, Kabupaten Banyumas dan Kabupaten Banjarnegara.

Simpulan: Faktor sosial (jumlah penduduk, jumlah penduduk miskin, wisatawan domestik), Faktor Pelayanan Kesehatan (jumlah tenaga kesehatan), dan Lingkungan (suhu rata-rata) merupakan determinan signifikan dengan kasus COVID-19 Jawa Tengah. Distribusi spasial menunjukan 6 wilayah di jawa tengah memiliki tingkat kerentanan Sangat tinggi.

 

ABSTRACT

Title: Spatial Analysis of Social, Health Service, and Environmental Factors Associated with COVID-19 Cases in Central Java

Background: In 2022, the positivity rate in Central Java reached 40.9%, surpassing the WHO threshold (<5%). COVID-19 displays a complex pattern driven by various variables, including social conditions, healthcare services, and environmental factors. This study aims to analyze the association of social conditions, healthcare services, and environmental factors with the spatial distribution of COVID-19 vulnerability in Central Java Province.

Method: This quantitative study employed an exploratory ecological design. The analytical units comprised the 35 regencies and cities of Central Java. The study used aggregated 2022 data and set the cumulative number of COVID-19 cases as the dependent variable. Independent variables included social factors (total population, number of people in poverty, unemployment rate, education level of the population aged over 15 years, Human Development Index (HDI), number of domestic tourists, and number of international tourists); healthcare service factors (number of healthcare workers, number of environmental sanitation personnel, access to adequate sanitation, and access to safe drinking water); and environmental factors (mean rainfall, humidity, average temperature, wind speed, territorial area, and mean elevation). The study obtained data from national agencies (Statistics Indonesia (BPS) and Provincial Health Office of Central Java ) and international sources (NASA). The study analyzed the data using Ordinary Least Squares (OLS) regression with backward stepwise elimination and validated the classical OLS assumptions and spatial autocorrelation. The study visualized the modeling results as quartile-based maps showing the spatial distribution of vulnerability.

Result: Variables associated with COVID-19 cases were total population (B = 0.0164), number of people living in poverty (B = -0.0951), number of domestic tourists (B = 0.0047), number of healthcare workers (B = 3.3453), and mean temperature (B = -2638.61). The model exhibited strong predictive power (R² = 0.9266). Spatial distribution showed areas with very high vulnerability, including Semarang City and Semarang Regency, Surakarta City, Magelang Regency, Klaten Regency, Banyumas Regency, and Banjarnegara Regency.

Conclusion: Social factors (total population, number of people living in poverty, and number of domestic tourists), the healthcare service factor (number of healthcare workers), and the environmental factor (mean temperature) were significant determinants of COVID-19 cases in Central Java. Spatial analysis identified six areas in Central Java with very high vulnerability.

Note: This article has supplementary file(s).

Fulltext View|Download |  CTA
Copyright Transfer Agreement
Subject
Type CTA
  Download (190KB)    Indexing metadata
 ES
Etichal Statement
Subject
Type ES
  Download (342KB)    Indexing metadata
 Turnitin
Turnitin
Subject
Type Turnitin
  Download (2MB)    Indexing metadata
 EC
Etichal Clearance
Subject
Type EC
  Download (347KB)    Indexing metadata
Keywords: Lingkungan; Corona; Pandemi; studi ekologi; kerentanan
Funding: Poltekkes Kemenkes Semarang

Article Metrics:

Article Info
Section: Research Articles
Language : ID
Recent articles
Hubungan Penerapan Personal hygiene dan Kondisi Lingkungan dengan Potensi Penularan Skabies Analisis Cemaran Mikroba E. Coli dan Total Koliform Pada Depot Air Minum Isi Ulang: Studi Kasus di Pulo Gadung, Jakarta Timur Analisis Hubungan Kadar Merkuri (Hg) dan Derajat Keasaman Saliva Terhadap Indeks DMF-T Pada Penambang Emas Skala Kecil di Desa Ramang Pulang Pisau Polusi Udara dan Dermatitis Atopik: Suatu Tinjauan Literatur More recent articles
Most cited articles
Hubungan Praktek Personal Hygiene Ibu dan Kondisi Sanitasi Lingkungan Rumah dengan Kejadian Diare pada Balita di Puskesmas Kampung Dalam Kecamatan Pontianak Timur Faktor-Faktor Lingkungan dan Perilaku Yang Berhubungan dengan Kejadian Filariasis di Kabupaten Bangka Barat Hubungan Kadar Debu Terhirup (Respirable) Dengan Kapasitas Vital Paksa Paru Pada Pekerja Mebel Kayu di Kota Jayapura Faktor Risiko Lingkungandan Kebiasaan Penduduk Berhubungan Dengan Kejadian Filariasis di Distrik Windesi Kabupaten Kepulauan Yapen Provinsi Papua Analisis Risiko Kesehatan dan Kadar Timbal Dalam Darah: (Studi Pada Masyarakat yang Mengkonsumsi Tiram Bakau (Crassostrea gigas) di Sungai Tapak Kecamatan Tugu Kota Semarang) More cited articles
  1. World Health Organization. Coronavirus Disease (COVID-19) Pandemic. World Health Organization. 2020. Accessed January 21, 2025. https://www.who.int/emergencies/diseases/novel-coronavirus-2019
  2. Chu DT, Vu Ngoc SM, Vu Thi H, et al. COVID-19 in Southeast Asia: current status and perspectives. Bioengineered. 2022;13(2):3797-3809. https://doi.org/10.1080/21655979.2022.2031417
  3. Dicky Kurniawan, Mega Putri Mahadewi. Lonjakan Kasus Covid-19 di Asia. Apa Antisipasi Pemerintah? TEMPO. June 11, 2025. Accessed June 22, 2025. https://www.tempo.co/politik/-lonjakan-kasus-covid-19-di-asia-apa-antisipasi-pemerintah--1674263
  4. Dinas Kesehatan Provinsi Jawa Tengah. Profil Kesehatan Jawa Tengah Tahun 2022 .; 2023. Accessed June 22, 2025. https://dinkesjatengprov.go.id/v2018/dokumen/Buku_Profil_Kesehatan_2022/mobile/
  5. World Health Organitation. Social Determinants of Health. World Health Organitation. 2024. Accessed June 22, 2025. https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1
  6. Rendana M, Idris WMR, Abdul Rahim S. Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves. J Infect Public Health. 2021;14(10):1340-1348. https://doi.org/10.1016/j.jiph.2021.07.010
  7. Björk J, Modig K, Kahn F, Ahlbom A. Revival of ecological studies during the COVID-19 pandemic. Eur J Epidemiol. 2021;36(12):1225-1229. https://doi.org/10.1007/s10654-021-00830-9
  8. McClymont H, Hu W. Weather Variability and COVID-19 Transmission: A Review of Recent Research. Int J Environ Res Public Health. 2021;18(2):396. https://doi.org/10.3390/ijerph18020396
  9. Bashir MF, Ma B, Bilal, et al. Correlation between climate indicators and COVID-19 pandemic in New York, USA. Science of The Total Environment. 2020;728:138835. https://doi.org/10.1016/j.scitotenv.2020.138835
  10. Ganasegeran K, Jamil MFA, Ch’ng ASH, Looi I, Peariasamy KM. Influence of Population Density for COVID-19 Spread in Malaysia: An Ecological Study. Int J Environ Res Public Health. 2021;18(18):9866. https://doi.org/10.3390/ijerph18189866
  11. Martins AS, Salles MJ, Carvajal E, et al. Privatizing sanitation concessions and the incidence of COVID-19 in slums in Rio de Janeiro. Saúde em Debate. 2021;45(spe2):82-91. https://doi.org/10.1590/0103-11042021e206i
  12. Bhattacharjee A, Mitra S, Choudhary V, Das S, Patel PP. COVID-19, “risks” and critical reflections on WASH services in Kolkata’s slums. Regional Science Policy & Practice. 2024;16(7):100051. https://doi.org/10.1016/j.rspp.2024.100051
  13. Shermin N, Rahaman SN. Assessment of sanitation service gap in urban slums for tackling COVID-19. Journal of Urban Management. 2021;10(3):230-241. https://doi.org/10.1016/j.jum.2021.06.003
  14. Moroh JE, Innocent DC, Chukwuocha UM, et al. Seasonal Variation and Geographical Distribution of COVID-19 across Nigeria (March 2020–July 2021). Vaccines (Basel). 2023;11(2):298. https://doi.org/10.3390/vaccines11020298
  15. Carhuapoma-Yance M, Apolaya-Segura M, Valladares-Garrido MJ, Failoc-Rojas VE, Díaz-Vélez C. Indice desarrollo humano y la tasa de letalidad por Covid-19: Estudio ecológico en América. Revista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo. 2021;14(3):362-366. https://doi.org/10.35434/rcmhnaaa.2021.143.1258
  16. Chuengsaman P, Boongird S, Dandecha P, et al. Fatality rate, risk factors, and functional decline in peritoneal dialysis patients with coronavirus disease 2019: A nationwide cohort study. Front Med (Lausanne). 2022;9. https://doi.org/10.3389/fmed.2022.1051448
  17. Shi R, Conrad SA. Correlation and regression analysis. Annals of Allergy, Asthma & Immunology. 2009;103(4):S35-S41. https://doi.org/10.1016/S1081-1206(10)60820-4
  18. Garcia-Morata M, Gonzalez-Rubio J, Segura T, Najera A. Spatial analysis of COVID-19 hospitalised cases in an entire city: The risk of studying only lattice data. Science of The Total Environment. 2022;806:150521. https://doi.org/10.1016/j.scitotenv.2021.150521
  19. Cuadros DF, Branscum AJ, Mukandavire Z, Miller FD, MacKinnon N. Dynamics of the COVID-19 epidemic in urban and rural areas in the United States. Ann Epidemiol. 2021;59:16-20. https://doi.org/10.1016/j.annepidem.2021.04.007
  20. Krueger T, Besenecker UC. Design-Based Research in Relation to Science-Based Research. In: Design Research Foundations. Springer, Cham; 2019:137-151. https://doi.org/10.1007/978-3-030-18557-2_7
  21. National Aeronautics and Space Administration. NASA Power DAV. National Aeronautics and Space Administration. 2025. Accessed July 21, 2025. https://power.larc.nasa.gov/data-access-viewer/
  22. Badan Pusat Statistik Provinsi Jawa Tengah. Beranda Badan Pusat Statistik Provinsi Jawa Tengah. Badan Pusat Statistik Jawa Tengah. 2023. Accessed June 22, 2023. https://jateng.bps.go.id/id
  23. Sokan-Adeaga AA, Sokan-Adeaga AM, Sokan-Adeaga ED. The Environment and COVID-19 Transmission: A Perspective. Journal of Health & Biological Sciences. 2020;8(1):1-6. https://doi.org/10.12662/2317-3076jhbs.v8i1.3361.p1-6.2020
  24. Hyde K. Residential Water Quality and the Spread of COVID-19 in the United States. SSRN Electronic Journal. Published online April 9, 2020. https://doi.org/10.2139/ssrn.3572341
  25. Zakianis, Adzania FH, Fauzia S, Aryati GP, Mahkota R. Sociodemographic and environmental health risk factor of COVID-19 in Jakarta, Indonesia: An ecological study. One Health. 2021;13:100303. https://doi.org/10.1016/j.onehlt.2021.100303
  26. Zeng DD, Yan P, Li S. Spatial Regression-Based Environmental Analysis in Infectious Disease Informatics. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 5354 LNBI. Springer, Berlin, Heidelberg; 2008:175-181. https://doi.org/10.1007/978-3-540-89746-0_18
  27. Aboalyem MS, Ismail MT. Mapping the Pandemic: A Review of GIS-based Spatial Modeling of COVID-19. Published online July 5, 2023. https://doi.org/10.21203/rs.3.rs-3094871/v1
  28. Mishra C, Mohanty L, Rath S, Patnaik R, Pradhan R. Application of Backward Elimination in Multiple Linear Regression Model for Prediction of Stock Index. In: Smart Innovation, Systems and Technologies. Vol 153. Springer, Singapore; 2021:543-551. https://doi.org/10.1007/978-981-15-6202-0_56
  29. Gupta A, Qu X. Consistent Specification Testing Under Spatial Dependence. Econ Theory. 2024;40(2):278-319. https://doi.org/10.1017/S0266466622000445
  30. Darmofal D. Spatial Lag and Spatial Error Models. In: Spatial Analysis for the Social Sciences. Cambridge University Press; 2015:96-118. https://doi.org/10.1017/CBO9781139051293.007
  31. Wang Y, Viseu Cardoso R, Forgaci C. Urban Pandemic Vulnerability and COVID-19: A New Framework to Assess the Impacts of Global Pandemics in the Metropolitan Region of Amsterdam. Sustainability. 2022;14(7):4284. https://doi.org/10.3390/su14074284
  32. Pasricha N. Research ethics and integrity. Journal of Dental Specialities. 2023;11(2):69-70. https://doi.org/10.18231/j.jds.2023.013
  33. Li, PhD H. Linear Regression. In: Numerical Methods Using Java. Apress; 2022:915-978. https://doi.org/10.1007/978-1-4842-6797-4_14
  34. Jesri N, Saghafipour A, Koopaei A, et al. Mapping and Spatial Pattern Analysis of COVID -19 in Central Iran Using the Local Indicators of Spatial Association (LISA). Published online July 22, 2021. https://doi.org/10.21203/rs.3.rs-732635/v1
  35. Yang M, Ma J, Jia P, Pu Y, Chen G. The use of spatial autocorrelation to analyze changes in spatial distribution patterns of population density in Jiangsu province, China. In: 2011 19th International Conference on Geoinformatics. IEEE; 2011:1-6. https://doi.org/10.1109/GeoInformatics.2011.5980909
  36. Wheeler DC, Páez A. Geographically Weighted Regression. In: Handbook of Applied Spatial Analysis. Springer Berlin Heidelberg; 2010:461-486. https://doi.org/10.1007/978-3-642-03647-7_22
  37. Kementerian Kesehatan Republik Indonesia. Penambahan Tenaga Kesehatan untuk Perkuat Penanganan COVID-19. Kementerian Kesehatan Republik Indonesia. August 2020. Accessed April 8, 2025. https://www.kemkes.go.id/article/view/20083100002/penambahan-tenaga-kesehatan-untuk-perkuat-penanganan-covid-19.html
  38. Henley P. COVID-19 and One Health: shifting the paradigm in how we think about health. JBI Evid Synth. 2020;18(6):1154-1155. https://doi.org/10.11124/JBIES-20-00161
  39. Khan JR, Awan N, Islam MdM, Muurlink O. Healthcare Capacity, Health Expenditure, and Civil Society as Predictors of COVID-19 Case Fatalities: A Global Analysis. Front Public Health. 2020;8. https://doi.org/10.3389/fpubh.2020.00347
  40. Heath C, Sommerfield A, von Ungern‐Sternberg BS. Resilience strategies to manage psychological distress among healthcare workers during the COVID‐19 pandemic: a narrative review. Anaesthesia. 2020;75(10):1364-1371. https://doi.org/10.1111/anae.15180
  41. Kurniawati UF, Nurlaela S, Susetyo C, Firmansyah F. Spatial analysis of health facility service coverage in handling of COVID-19 patients in the area Surabaya City Settlement. IOP Conf Ser Earth Environ Sci. 2022;1015(1):012019. https://doi.org/10.1088/1755-1315/1015/1/012019
  42. Kanble Tanushri, Bahadure S. Correlating Urban Population Density and Sustainability Using the Corona Index Method. Journal of Settlements and Spatial Planning. 2021;12(1):25-33. https://doi.org/10.24193/JSSP.2021.1.03
  43. Mirahmadizadeh A, Ghelichi-Ghojogh M, Vali M, et al. Correlation between human development index and its components with COVID-19 indices: a global level ecologic study. BMC Public Health. 2022;22(1):1549. https://doi.org/10.1186/s12889-022-13698-5
  44. Sharif N, Sarkar MK, Ahmed SN, et al. Environmental correlation and epidemiologic analysis of COVID-19 pandemic in ten regions in five continents. Heliyon. 2021;7(3):e06576. https://doi.org/10.1016/j.heliyon.2021.e06576

Last update:

No citation recorded.

Last update: 2025-09-04 14:37:24

No citation recorded.