https://doi.org/10.4081/gh.2025.1329
Spatial association of socioeconomic and health service factors with antibiotic self-medication in Thailand
Submitted: 25 July 2024
Accepted: 11 December 2024
Published: 27 January 2025
Accepted: 11 December 2024
Abstract Views: 137
PDF: 20
Supplementary Materials: 5
Supplementary Materials: 5
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Antibiotic Self-Medication (ASM) is a major contributing factor to Antimicrobial Resistance (AMR) that can lead to both mortality and long-term hospitalizations. High provincial ASM proportions associated with mortality due to AMR have been observed in Thailand but there is a lack of studies on geographic factors contributing to ASM. The present study aimed to quantify the distribution of ASM in Thailand and its correlated factors. Socioeconomic and health services factors were included in the spatial analysis. Moran’s I was performed to identify global autocorrelation with the significance level set at p=0.05 and spatial regression were applied to identify the factors associated with ASM, the proportion of which is predominant in the north-eastern, central and eastern regions with Phitsanulok Province reporting the highest proportion of Thailand’s 77 provinces. Autocorrelation between Night-Time Light (NTL) and the proportion of ASM was observed to be statistically significant at p=0.030. The Spatial Lag Model (SLM) and the Spatial Error Model (SEM) were used with the latter providing both the lowest R2 and Akaike Information Criterion (AIC). It was demonstrated that the proportion of alcohol consumption significantly increased the proportion of ASM. The annual number of outpatient department visits and the average NTL decreased the proportion of ASM by 1.5% and 0.4%, respectively. Average monthly household expenditures also decreased the ASM proportion. Policies to control alcohol consumption while promoting healthcare visits are essential strategies to mitigate the burden of AMR in Thailand.
Akaike H, 1981. Likelihood of a model and information criteria. J Econom 16:3-14
DOI: https://doi.org/10.1016/0304-4076(81)90071-3
Anselin L,Griffith DA, 1988. Do spatial effects really matter in regression analysis? Pap Reg Sci 65:11-34.
DOI: https://doi.org/10.1111/j.1435-5597.1988.tb01155.x
Ayana H, Sileshi T, Bule MH, Chaka EE, 2021. Non-prescription antibiotics useand associated factors among drug retail outlets in Ambo, Ethiopia: A Cross-Sectional Study. Patient Prefer Adher 15:2739–47.
DOI: https://doi.org/10.2147/PPA.S337364
Ayukekbong JA, Ntemgwa M, Atabe AN, 2017. The threat of antimicrobial resistance in developing countries: causes and control strategies. Antimicrob Resist Infect Control 6:47.
DOI: https://doi.org/10.1186/s13756-017-0208-x
Bbosa GS, Wong G, Kyegombe DB, Ogwal-Okeng J, 2014. Effects of intervention measures on irrational antibiotics/antibacterial drug use in developing countries: a systematic review. Health 6:171–87.
DOI: https://doi.org/10.4236/health.2014.62027
Belkina TV, Khojiev DS, Tillyashaykhov MN, Tigay ZN, Kudenov MU, Tebbens JD, Vlcek J, 2014. Delay in the diagnosis and treatment of pulmonary tuberculosis in Uzbekistan: a cross-sectional study. BMC Infect Dis 14:1–8.
DOI: https://doi.org/10.1186/s12879-014-0624-y
Bolton J, Cox B, Clara I, Sareen J, 2006. Use of alcohol and drugs to self-medicate anxiety disorders in a nationally representative sample. J Nerv Ment Dis 194:818–25.
DOI: https://doi.org/10.1097/01.nmd.0000244481.63148.98
Bolton JM, Robinson J, Sareen J, 2009. Self-medication of mood disorders with alcohol and drugs in the National Epidemiologic Survey on Alcohol and Related Conditions. J Affect Disord 115:367–375.
DOI: https://doi.org/10.1016/j.jad.2008.10.003
Chicco D, Warrens MJ, Jurman G, 2021. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci 7:e623.
DOI: https://doi.org/10.7717/peerj-cs.623
Department of Business Development. Annual income of grocery stores. Available from: https://datawarehouse.dbd.go.th/area/overview/47114
Dixon MA, Chartier KG, 2016. Alcohol use patterns among urban and rural residents: demographic and social influences. Alcohol Res Curr Rev 38:69.
Earth Observation Group. Night-time light, 2021. Available from: https://eogdata.mines.edu/nighttime_light/monthly/v10/2021/202103/vcmslcfg/
Food and Drug Administration. Number of drug stores. Available from: https://drug.fda.moph.go.th/media.php?id=502053500088360960&name=Licensee-20210531.pdf
Ford JD, Trestman RL, Tennen H, Allen S, 2005. Relationship of anxiety, depression and alcohol use disorders to persistent high utilization and potentially problematic under-utilization of primary medical care. Soc Sci Med 61:1618–25.
DOI: https://doi.org/10.1016/j.socscimed.2005.03.017
Guo J, Hernandez I, Dickson S, Tang S, Essien UR, Mair C, Berenbrok LA, 2022. Income disparities in driving distance to health care infrastructure in the United States: a geographic information systems analysis. BMC Res Notes 15:225.
DOI: https://doi.org/10.1186/s13104-022-06117-w
Hamada S, Takahashi H, Sakata N, Jeon B, Mori T, Iijima K, Yoshie S, Ishizaki T, Tamiya N, 2019. Household income relationship with health services utilization and healthcare expenditures in people aged 75 years or older in Japan: A population-based study. J Epidemiol 29:377–383.
DOI: https://doi.org/10.2188/jea.JE20180055
Hoffman DA, 2020. Increasing access to care: telehealth during COVID-19. J Law Biosci 7:lsaa043.
DOI: https://doi.org/10.1093/jlb/lsaa043
Imai S, Inoue N, Nagai H, 2022. Economic and clinical burden from carbapenem-resistant bacterial infections: a retrospective study. BMC Infect Dis 22:581.
DOI: https://doi.org/10.1186/s12879-022-07548-3
Inthisorn P, Puttanapong N, 2022. Associations Between Mobility Indices and the COVID-19 Pandemic in Thailand. Nakhara J Environ Des Plan 21:215–215.
DOI: https://doi.org/10.54028/NJ202221215
Kassie AD, Bifftu BB, Mekonnen HS, 2018. Self-medication practice and associated factors among adult household members in Meket district, Northeast Ethiopia, 2017. BMC Pharmacol Toxicol 19:15.
DOI: https://doi.org/10.1186/s40360-018-0205-6
Laohasiriwong W, Puttanapong N, Luenam A, 2018. Spatial heterogeneity of chronic respiratory diseases in Thailand. F1000Res 6:1–16.
DOI: https://doi.org/10.12688/f1000research.12128.2
Lazar M, Davenport L, 2018. Barriers to health care access for low-income families: a review of literature. J Community Health Nurs 35:28–37.
DOI: https://doi.org/10.1080/07370016.2018.1404832
Li C, Sun J, 2022. Smoking, drinking, and inactivity and their impact on health care-seeking behavior in China. BMC Health Serv Res 22:52.
DOI: https://doi.org/10.1186/s12913-022-07462-z
Mate I, Come CE, Gonçalves MP, Cliff J, Gudo ES, 2019. Knowledge, attitudes, and practices regarding antibiotic use in Maputo, Mozambique. PLoS One 14:1–15.
DOI: https://doi.org/10.1371/journal.pone.0221452
Mboya EA, Sanga LA, Ngocho JS, 2018. Irrational antibiotic use in Moshi, Tanzania: a cross-sectional study. Pan Afr Med J 31:1–10.
DOI: https://doi.org/10.11604/pamj.2018.31.165.15991
Najmi A, Karimi F, Kunhikatta V, Varma M, Nair S, 2019. Resistance Trend, Antibiotic Utilization and Mortality in Patients with E. coli Bacteraemia. Maced J Med Sci 7:1119-23.
DOI: https://doi.org/10.3889/oamjms.2019.223
Nepal G, Bhatta S, 2018. Self-medication with antibiotics in WHO Southeast Asian Region: a systematic review. Cureus 10:24-28.
DOI: https://doi.org/10.7759/cureus.2428
Ocan M, Obuku EA, Bwanga F, Akena D, Richard S, Ogwal-Okeng J, Obua C, 2015. Household antimicrobial self-medication: a systematic review and meta-analysis of the burden, risk factors and outcomes in developing countries. BMC Public Health 15:1-11.
DOI: https://doi.org/10.1186/s12889-015-2109-3
Oleski J, Mota N, Cox BJ, Sareen J, 2010. Perceived need for care, help seeking, and perceived barriers to care for alcohol use disorders in a national sample. Psychiatr Serv 61:1223-31.
DOI: https://doi.org/10.1176/ps.2010.61.12.1223
Pfeiffer D, Robinson T, Stevenson M, et al., 2008. Spatial Analysis in Epidemiology. Oxford Univ Press.
DOI: https://doi.org/10.1093/acprof:oso/9780198509882.001.0001
Pryor JT, Cowley LO, Simonds SE, 2022. Physiological effects of air pollution. Front Public Health 10: 882569.
DOI: https://doi.org/10.3389/fpubh.2022.882569
Rabin AS, Kuchukhidze G, Sanikidze E, Kempker RR, Blumberg HM, 2013. Delays in tuberculosis diagnosis due to self-medication. Int J Tuberc Lung Dis 17:214-20.
DOI: https://doi.org/10.5588/ijtld.12.0395
Rather IA, Kim BC, Bajpai VK, Park YH, 2017. Self-medication and antibiotic resistance: Crisis, current challenges, and prevention. Saudi J Biol Sci 24:808-12.
DOI: https://doi.org/10.1016/j.sjbs.2017.01.004
Sachdev C, Anjankar A, Agrawal J, 2022. Self-medication with antibiotics: An element increasing resistance. Cureus 14:e30844.
DOI: https://doi.org/10.7759/cureus.30844
Shao Q, Tao R, Luca MM, 2022. Urbanization effects on health care expenditure in China. Front Public Health 10:850872.
DOI: https://doi.org/10.3389/fpubh.2022.850872
Sirijoti K, Hongsranagon P, Havanond P, Pannoi W, 2014. Assesment of Knowledge Attitude and Practice Regarding Antibiotic Use in Trang Province, Thailand. J Health Res 28:299–307.
Starrels JL, Barg FK, Metlay JP, 2009. Inappropriate antibiotic use in injection drug users. J Gen Intern Med 24:263–9.
DOI: https://doi.org/10.1007/s11606-008-0859-7
Sutton P, Roberts D, Elvidge C, Meij H, 1997. Nighttime satellite imagery and population density. Photogramm Eng Remote Sens 63:1303–11.
Suwanlee S, 2021. Spatial Statistic for Geography. Chulalongkorn University [in Thai].
Ternhag A, Grünewald M, Nauclér P, Wisell KT, 2014. Antibiotic consumption and sociodemographics. Scand J Infect Dis 46:888–96.
DOI: https://doi.org/10.3109/00365548.2014.954264
Thai National Statistical Office. 2021 Health and Welfare Survey. Available from: https://www.nso.go.th/nsoweb/nso/statistics_and_indicators?impt_branch=300
Thailand Health Data Center. Number of public health staffs. Available from: https://hdcservice.moph.go.th/hdc/reports/page.php?cat_id=c1
1dad88f80061c70cd1ae96b500d017
Thailand Health Data Center. Number of OPD visits. Available from: https://hdcservice.moph.go.th/hdc/reports/report.php?&cat_id=9d8c311d6336373d40437c4423508cad&id=4b35d96e225bf34a16774b13705250f4
Thailand Health Data Center. The prevalence rates of pneumonia and acute pharyngitis. Available from:https://hdcservice.moph.go.th/hdc/reports/report.php?source=pformated/format1.php&cat_id=9d8c311d6336373d40437c4423508cad&id=76e909d7af59d6ddc3fe11ebe25dad4d
Thailand Ministry of Public Health. 2016. Service Plan: Rational Drug Use. Thailand Ministry of Public Health (in Thai). Available from: https://dmsic.moph.go.th/dmsic/admin/files/userfiles/files/Manual_Service%20Plan%20RDU_Sept2016.pdf
Torres NF, Chibi B, Middleton LE, Solomon VP, Mashamba-Thompson TP, 2019. Evidence of factors influencing self-medication with antibiotics in low and middle-income countries: a systematic scoping review. Public Health 168, 92-101.
DOI: https://doi.org/10.1016/j.puhe.2018.11.018
Touat M, Brun-Buisson C, Opatowski M, Salomon J, Guillemot D, Tuppin P, de Lagasnerie G, Watier L, 2021. Costs of post-discharge care after resistant infections. J Infect 82:339–47.
DOI: https://doi.org/10.1016/j.jinf.2021.02.001
Turnbull SL, Dack C, Lei J, Aksu I, Grant S, Lasseter G, Silarova B, Ainsworth B, 2024. Digital health tool use pre- and during COVID-19. BMJ Open 14:e080055.
DOI: https://doi.org/10.1136/bmjopen-2023-080055
Wijitkunakorn P, Tanaree A, 2021. Provincial Alcohol Report 2021 (1st ed.). Sahamit Pattana Printing [in Thai].
Xu P, Jin P, Yang Y, Wang Q, 2016. Urbanization patterns using nighttime light data. Math Probl Eng 2016;9850890.
DOI: https://doi.org/10.1155/2016/9850890
Yeika EV, Ingelbeen B, Kemah BL, Wirsiy FS, Fomengia JN, van der Sande MAB, 2021. Comparative assessment of the prevalence, practices and factors associated with self‐medication with antibiotics in Africa. Trop Med Int Health 26:862–81.
DOI: https://doi.org/10.1111/tmi.13600
How to Cite
Darasawang, W., Laohasiriwong, W., Sornlorm , K., Sungsitthisawad, W., & Kumar Mahato, R. (2025). Spatial association of socioeconomic and health service factors with antibiotic self-medication in Thailand. Geospatial Health, 20(1). https://doi.org/10.4081/gh.2025.1329
Copyright (c) 2025 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Jihoon Jung, Yoonjung Ahn, Joseph Bommarito, Disparities in COVID-19 health outcomes among different sub-immigrant groups in the US - a study based on the spatial Durbin model , Geospatial Health: Vol. 17 No. s1 (2022): Special issue on COVID-19
- Seong-Yong Park, Jin-Mi Kwak, Eun-Won Seo, Kwang-Soo Lee, Spatial analysis of the regional variation of hypertensive disease mortality and its socio-economic correlates in South Korea , Geospatial Health: Vol. 11 No. 2 (2016)
- Ernest Akyereko, Frank B. Osei, Kofi M. Nyarko, Alfred Stein, Flexible scan statistic with a restricted likelihood ratio for optimized COVID-19 surveillance , Geospatial Health: Vol. 19 No. 2 (2024)
- Ei Sandar U, Wongsa Laohasiriwong, Kittipong Sornlorm, Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand , Geospatial Health: Vol. 18 No. 1 (2023)
- Amornrat Luenam, Nattapong Puttanapong, Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015 , Geospatial Health: Vol. 15 No. 2 (2020)
- Dara D. Mendez, Jessica Duell, Sarah Reiser, Deborah Martin, Robert Gradeck, Anthony Fabio, A methodology for combining multiple commercial data sources to improve measurement of the food and alcohol environment: applications of geographical information systems , Geospatial Health: Vol. 9 No. 1 (2014)
- Dan Li, Dawei Gao, Masaaki Yamada, Chuangbin Chen, Liuchun Xiang, Haisong Nie, Healthcare-seeking behavior and spatial variation of internal migrants with chronic diseases: a nationwide empirical study in China , Geospatial Health: Vol. 19 No. 1 (2024)
- Patrick Spets, Karin Ebert, Patrik Dinnétz, Spatial analysis of antimicrobial resistance in the environment. A systematic review , Geospatial Health: Vol. 18 No. 1 (2023)
- Prakash Madhav Nimbalkar, Nitin Kumar Tripathi, Space-time epidemiology and effect of meteorological parameters on influenza-like illness in Phitsanulok, a northern province in Thailand , Geospatial Health: Vol. 11 No. 3 (2016)
- Jaruwan Wongbutdee, Jutharat Jittimanee, Wacharapong Saengnill, Spatiotemporal distribution and geostatistically interpolated mapping of the melioidosis risk in an endemic zone in Thailand , Geospatial Health: Vol. 18 No. 2 (2023)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
