{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T04:09:08Z","timestamp":1781669348810,"version":"3.54.5"},"reference-count":97,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.<\/jats:p>","DOI":"10.3390\/ijerph20136277","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:53:16Z","timestamp":1688345596000},"page":"6277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3878-7956","authenticated-orcid":false,"given":"Zemenu Tadesse","family":"Tessema","sequence":"first","affiliation":[{"name":"School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia"},{"name":"Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6812-1659","authenticated-orcid":false,"given":"Getayeneh Antehunegn","family":"Tesema","sequence":"additional","affiliation":[{"name":"School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia"},{"name":"Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susannah","family":"Ahern","sequence":"additional","affiliation":[{"name":"School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arul","family":"Earnest","sequence":"additional","affiliation":[{"name":"School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1097\/01.ede.0000071473.69307.8a","article-title":"Place, space, and health: GIS and epidemiology","volume":"14","author":"Krieger","year":"2003","journal-title":"Epidemiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sste.2010.10.001","article-title":"Spatio-temporal epidemiology: Principles and opportunities","volume":"2","author":"Meliker","year":"2011","journal-title":"Spat. Spatio-Temporal Epidemiol."},{"key":"ref_3","first-page":"111","article-title":"Use of GIS mapping as a public health Tool\u2014From cholera to cancer","volume":"6","author":"Musa","year":"2013","journal-title":"Health Serv. Insights"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lawson, A.B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Chapman and Hall\/CRC.","DOI":"10.1201\/9781351271769"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1289\/ehp.10816","article-title":"Methodologic issues and approaches to spatial epidemiology","volume":"116","author":"Beale","year":"2008","journal-title":"Environ. Health Perspect."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101939","DOI":"10.1016\/j.canep.2021.101939","article-title":"A multi-level spatio-temporal analysis on prostate cancer outcomes","volume":"72","author":"Wah","year":"2021","journal-title":"Cancer Epidemiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lope, V., Poll\u00e1n, M., P\u00e9rez-G\u00f3mez, B., Aragon\u00e9s, N., Ramis, R., G\u00f3mez-Barroso, D., and L\u00f3pez-Abente, G. (2006). Municipal mortality due to thyroid cancer in Spain. BMC Public Health, 6.","DOI":"10.1186\/1471-2458-6-302"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF00116466","article-title":"Bayesian image restoration, with two applications in spatial statistics","volume":"43","author":"Besag","year":"1991","journal-title":"Ann. Inst. Stat. Math."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1289\/ehp.6735","article-title":"Spatial epidemiology: Current approaches and future challenges","volume":"112","author":"Elliott","year":"2004","journal-title":"Environ. Health Perspect."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1002\/ecm.1283","article-title":"Spatial autoregressive models for statistical inference from ecological data","volume":"88","author":"Peterson","year":"2018","journal-title":"Ecol. Monogr."},{"key":"ref_11","first-page":"10680","article-title":"Spatial modelling of some conditional autoregressive priors in a disease mapping model: The Bayesian approach","volume":"14","author":"Obaromi","year":"2019","journal-title":"Biomed. J. Sci. Tech. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1191\/0962280205sm388oa","article-title":"A comparison of Bayesian spatial models for disease mapping","volume":"14","author":"Best","year":"2005","journal-title":"Stat. Methods Med. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/1476-072X-6-54","article-title":"Evaluating the effect of neighbourhood weight matrices on smoothing properties of Conditional Autoregressive (CAR) models","volume":"6","author":"Earnest","year":"2007","journal-title":"Int. J. Health Geogr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.sste.2011.03.001","article-title":"A comparison of conditional autoregressive models used in Bayesian disease mapping","volume":"2","author":"Lee","year":"2011","journal-title":"Spat. Spatio-Temporal Epidemiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/s12942-020-00236-y","article-title":"Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps","volume":"19","author":"Tuson","year":"2020","journal-title":"Int. J. Health Geogr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1177\/0309133307083294","article-title":"The modifiable areal unit problem (MAUP) in physical geography","volume":"31","author":"Dark","year":"2007","journal-title":"Prog. Phys. Geogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1111\/1742-6723.13727","article-title":"Impact of the modifiable areal unit problem in assessing determinants of emergency department demand","volume":"33","author":"Kok","year":"2021","journal-title":"Emerg. Med. Australas"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hanigan, I.C., Cochrane, T., and Davey, R. (2017). Impact of scale of aggregation on associations of cardiovascular hospitalization and socio-economic disadvantage. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0188161"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s12942-017-0120-x","article-title":"Spatial smoothing in Bayesian models: A comparison of weights matrix specifications and their impact on inference","volume":"16","author":"Duncan","year":"2017","journal-title":"Int. J. Health Geogr."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Louzada, F., Nascimento, D.C.d., and Egbon, O.A. (2021). Spatial statistical models: An overview under the Bayesian approach. Axioms, 10.","DOI":"10.3390\/axioms10040307"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43586-020-00001-2","article-title":"Bayesian statistics and modelling","volume":"1","author":"Depaoli","year":"2021","journal-title":"Nat. Rev. Methods Prim."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gelman, A., Simpson, D., and Betancourt, M. (2017). The prior can often only be understood in the context of the likelihood. Entropy, 19.","DOI":"10.3390\/e19100555"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s00038-020-01384-5","article-title":"A systematic review of Bayesian spatial\u2013temporal models on cancer incidence and mortality","volume":"65","author":"Wah","year":"2020","journal-title":"Int. J. Public Health"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e33","DOI":"10.1017\/S0950268818002807","article-title":"Bayesian spatial and spatio-temporal approaches to modelling dengue fever: A systematic review","volume":"147","author":"Aswi","year":"2019","journal-title":"Epidemiol. Infect."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"301","DOI":"10.3961\/jpmph.21.160","article-title":"A systematic review of spatial and spatio-temporal analyses in public health research in Korea","volume":"54","author":"Byun","year":"2021","journal-title":"J. Prev. Med. Public Health"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105906","DOI":"10.1016\/j.ijsu.2021.105906","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"88","author":"Page","year":"2021","journal-title":"Int. J. Surg."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/pubmed\/fdg075","article-title":"Systematic review of the use and value of computer simulation modelling in population health and health care delivery","volume":"25","author":"Fone","year":"2003","journal-title":"J. Public Health"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2813","DOI":"10.1080\/21645515.2016.1205769","article-title":"Systematic review of mathematical models exploring the epidemiological impact of future TB vaccines","volume":"12","author":"Harris","year":"2016","journal-title":"Hum. Vaccines Immunother."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11408","DOI":"10.1038\/s41598-021-90997-8","article-title":"Fine scale analysis of malaria incidence in under-5: Hierarchical Bayesian spatio-temporal modelling of routinely collected malaria data between 2012\u20132018 in Cameroon","volume":"11","author":"Danwang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Donkor, E., Kelly, M., Eliason, C., Amotoh, C., Gray, D.J., Clements, A.C., and Wangdi, K. (2021). A bayesian spatio-temporal analysis of malaria in the greater Accra region of Ghana from 2015 to 2019. Int. J. Environ. Res. Public Health, 18.","DOI":"10.21203\/rs.3.rs-143819\/v2"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s12942-016-0043-y","article-title":"Geography and social distribution of malaria in Indonesian Papua: A cross-sectional study","volume":"15","author":"Hanandita","year":"2016","journal-title":"Int. J. Health Geogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11498","DOI":"10.1038\/s41598-022-15561-4","article-title":"Spatial variation and risk factors of malaria and anaemia among children aged 0 to 59 months: A cross-sectional study of 2010 and 2015 datasets","volume":"12","author":"Ibeji","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kigozi, S.P., Kigozi, R.N., Sebuguzi, C.M., Cano, J., Rutazaana, D., Opigo, J., Bousema, T., Yeka, A., Gasasira, A., and Sartorius, B. (2020). Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019. BMC Public Health, 20.","DOI":"10.1186\/s12889-020-10007-w"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lubinda, J., Bi, Y., Hamainza, B., Haque, U., and Moore, A.J. (2021). Modelling of malaria risk, rates, and trends: A spatiotemporal approach for identifying and targeting sub-national areas of high and low burden. PLoS Comput. Biol., 17.","DOI":"10.1371\/journal.pcbi.1008669"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e2021041","DOI":"10.4178\/epih.e2021041","article-title":"Modeling the relationship between malaria prevalence and insecticide-treated bed net coverage in Nigeria using a Bayesian spatial generalized linear mixed model with a Leroux prior","volume":"43","author":"Okunlola","year":"2021","journal-title":"Epidemiol. Health"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1186\/1475-2875-11-170","article-title":"Characterizing the spatial and temporal variation of malaria incidence in Bangladesh, 2007","volume":"11","author":"Reid","year":"2012","journal-title":"Malar. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10995","DOI":"10.1038\/s41598-022-14012-4","article-title":"Spatial patterns and climate drivers of malaria in three border areas of Brazil, Venezuela and Guyana, 2016\u20132018","volume":"12","author":"Wangdi","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7060","DOI":"10.1038\/s41598-020-63896-7","article-title":"A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan","volume":"10","author":"Wangdi","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"110285","DOI":"10.1016\/j.envres.2020.110285","article-title":"Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis","volume":"195","author":"Akter","year":"2021","journal-title":"Environ. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100335","DOI":"10.1016\/j.sste.2020.100335","article-title":"Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling","volume":"33","author":"Aswi","year":"2020","journal-title":"Spat. Spatio-Temporal Epidemiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1289\/ehp.1003270","article-title":"Spatial patterns and socioecological drivers of dengue fever transmission in Queensland, Australia","volume":"120","author":"Hu","year":"2012","journal-title":"Environ. Health Perspect."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1080\/22221751.2020.1775497","article-title":"Spatial and temporal patterns of dengue incidence in Bhutan: A Bayesian analysis","volume":"9","author":"Tsheten","year":"2020","journal-title":"Emerg. Microbes Infect."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13071-017-2588-4","article-title":"Spatial and temporal patterns of dengue infections in Timor-Leste, 2005\u20132013","volume":"11","author":"Wangdi","year":"2018","journal-title":"Parasites Vectors"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s12942-020-00233-1","article-title":"Evaluating the impact of a small number of areas on spatial estimation","volume":"19","author":"Aswi","year":"2020","journal-title":"Int. J. Health Geogr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ijid.2019.02.046","article-title":"Spatial clustering and socio-demographic determinants of HIV infection in Ethiopia, 2015\u20132017","volume":"82","author":"Gelaw","year":"2019","journal-title":"Int. J. Infect. Dis."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.sste.2017.01.001","article-title":"Relative risk for HIV in India\u2013An estimate using conditional auto-regressive models with Bayesian approach","volume":"20","author":"Kandhasamy","year":"2017","journal-title":"Spat. Spatio-Temporal Epidemiol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Okango, E., Mwambi, H., and Ngesa, O. (2016). Spatial modeling of HIV and HSV-2 among women in Kenya with spatially varying coefficients. BMC Public Health, 16.","DOI":"10.1186\/s12889-016-3022-0"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Okango, E., Mwambi, H., Ngesa, O., and Achia, T. (2015). Semi-parametric spatial joint modeling of HIV and HSV-2 among women in Kenya. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0135212"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Alene, K.A., Xu, Z., Bai, L., Yi, H., Tan, Y., Gray, D.J., Viney, K., and Clements, A.C. (2021). Spatiotemporal Patterns of Tuberculosis in Hunan Province, China. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18136778"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.archger.2019.05.011","article-title":"Spatial-temporal analysis of tuberculosis in the geriatric population of China: An analysis based on the Bayesian conditional autoregressive model","volume":"83","author":"Amsalu","year":"2019","journal-title":"Arch. Gerontol. Geriatr."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1590\/S0037-86822012000500013","article-title":"Spatio-temporal patterns of tuberculosis incidence in Ribeir\u00e3o Preto, State of S\u00e3o Paulo, southeast Brazil, and their relationship with social vulnerability: A Bayesian analysis","volume":"45","author":"Roza","year":"2012","journal-title":"Rev. Soc. Bras. Med. Trop."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.canep.2013.03.009","article-title":"Spatio-temporal trends in gastric cancer mortality in Spain: 1975\u20132008","volume":"37","author":"Goicoa","year":"2013","journal-title":"Cancer Epidemiol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Baker, J., White, N., Mengersen, K., Rolfe, M., and Morgan, G.G. (2017). Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183653"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.canep.2015.03.001","article-title":"Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis","volume":"39","author":"Cramb","year":"2015","journal-title":"Cancer Epidemiol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Darikwa, T.B., and Manda, S.O. (2020). Spatial Co-Clustering of Cardiovascular Diseases and Select Risk Factors among Adults in South Africa. Int. J. Envion. Res. Public Health, 17.","DOI":"10.3390\/ijerph17103583"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"219","DOI":"10.4081\/gh.2013.82","article-title":"Bayesian spatio-temporal modelling of tobacco-related cancer mortality in Switzerland","volume":"7","author":"Ess","year":"2013","journal-title":"Geospat. Health"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.idh.2019.08.002","article-title":"Spatial clusters of Clostridium difficile infection and an association with neighbourhood socio-economic disadvantage in the Australian Capital Territory, 2004\u20132014","volume":"25","author":"Lal","year":"2020","journal-title":"Infect. Dis. Health"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ogunsakin, R.E., and Ginindza, T.G. (2022). Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19158886"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"645","DOI":"10.4081\/gh.2018.645","article-title":"Bivariate spatiotemporal disease mapping of cancer of the breast and cervix uteri among Iranian women","volume":"13","author":"Raei","year":"2018","journal-title":"Geospat. Health"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/jgf2.140","article-title":"Relationships between road-distance to primary care facilities and ischemic heart disease and stroke mortality in Hokkaido, Japan: A Bayesian hierarchical approach to ecological count data","volume":"19","author":"Saijo","year":"2018","journal-title":"J. Gen. Fam. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.1007\/s00477-018-1531-3","article-title":"Bayesian spatio-temporal analysis of stomach cancer incidence in Iran, 2003\u20132010","volume":"32","author":"Sharafi","year":"2018","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s12942-020-00237-x","article-title":"Bayesian spatial modelling of early childhood development in Australian regions","volume":"19","author":"Li","year":"2020","journal-title":"Int. J. Health Geogr."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lome-Hurtado, A., Lartigue-Mendoza, J., and Trujillo, J.C. (2021). Modelling local patterns of child mortality risk: A Bayesian Spatio-temporal analysis. BMC Public Health, 21.","DOI":"10.1186\/s12889-020-10016-9"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"102521","DOI":"10.1016\/j.apgeog.2021.102521","article-title":"Patterns of low birth weight in greater Mexico City: A Bayesian spatio-temporal analysis","volume":"134","author":"Li","year":"2021","journal-title":"Appl. Geogr."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1017\/S095026881400171X","article-title":"The geographical co-distribution and socio-ecological drivers of childhood pneumonia and diarrhoea in Queensland, Australia","volume":"143","author":"Xu","year":"2015","journal-title":"Epidemiol. Infect."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2692","DOI":"10.4314\/ahs.v19i3.45","article-title":"Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: A cross-sectional study of small-scale geographical disparities","volume":"19","author":"Adeyemi","year":"2019","journal-title":"Afr. Health Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"100224","DOI":"10.1016\/j.puhip.2021.100224","article-title":"Shared geographic spatial risk of childhood undernutrition in Malawi: An application of joint spatial component model","volume":"3","author":"Ngwira","year":"2022","journal-title":"Public Health Pract."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Odhiambo, J.N., and Sartorius, B. (2020). Mapping of anaemia prevalence among pregnant women in Kenya (2016\u20132019). BMC Pregnancy Childbirth, 20.","DOI":"10.1186\/s12884-020-03380-2"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Chou, M.P., Clements, A.C.A., and Thomson, R.M. (2014). A spatial epidemiological analysis of nontuberculous mycobacterial infections in Queensland, Australia. BMC Infect. Dis., 14.","DOI":"10.1186\/1471-2334-14-279"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3934\/publichealth.2016.1.65","article-title":"Exploring the specifications of spatial adjacencies and weights in Bayesian spatial modeling with intrinsic conditional autoregressive priors in a small-area study of fall injuries","volume":"3","author":"Law","year":"2016","journal-title":"AIMS Public Health"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ntirampeba, D., Neema, I., and Kazembe, L. (2018). Modelling spatio-temporal patterns of disease for spatially misaligned data: An application on measles incidence data in Namibia from 2005\u20132014. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0201700"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Qi, X., Hu, W., Mengersen, K., and Tong, S. (2014). Socio-environmental drivers and suicide in Australia: Bayesian spatial analysis. BMC Public Health, 14.","DOI":"10.1186\/1471-2458-14-681"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1080\/02664763.2019.1572722","article-title":"Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model","volume":"46","author":"Alam","year":"2019","journal-title":"J. Appl. Stat."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1017\/S0950268813001015","article-title":"Spatial variation in the risk of hospitalization with childhood pneumonia and empyema in the North of England","volume":"142","author":"Blain","year":"2014","journal-title":"Epidemiol. Infect."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.4269\/ajtmh.20-0080","article-title":"Space\u2013Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia","volume":"103","author":"Desjardins","year":"2020","journal-title":"Am. J. Trop. Med. Hyg."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"101437","DOI":"10.1016\/j.tmaid.2019.06.008","article-title":"Spatial and temporal variation of dengue incidence in the island of Bali, Indonesia: An ecological study","volume":"32","author":"Dhewantara","year":"2019","journal-title":"Travel Med. Infect. Dis."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1080\/02664763.2014.999031","article-title":"Bayesian joint modeling of correlated counts data with application to adverse birth outcomes","volume":"42","author":"Feng","year":"2015","journal-title":"J. Appl. Stat."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1017\/S0950268816003289","article-title":"Assessing the social and environmental determinants of pertussis epidemics in Queensland, Australia: A Bayesian spatio-temporal analysis","volume":"145","author":"Huang","year":"2017","journal-title":"Epidemiol. Infect."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"823","DOI":"10.4081\/gh.2019.823","article-title":"Bayesian conditional autoregressive models to assess spatial patterns of diarrhoea risk among children under the age of 5 years in Mbour, Senegal","volume":"14","author":"Thiam","year":"2019","journal-title":"Geospat. Health"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Wangdi, K., and Clements, A.C. (2017). Spatial and temporal patterns of diarrhoea in Bhutan 2003\u20132013. BMC Infect. Dis., 17.","DOI":"10.1186\/s12879-017-2611-6"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/2049-9957-3-21","article-title":"Global burden, distribution, and interventions for infectious diseases of poverty","volume":"3","author":"Bhutta","year":"2014","journal-title":"Infect. Dis. Poverty"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1038\/s41579-021-00639-z","article-title":"Infectious disease in an era of global change","volume":"20","author":"Baker","year":"2022","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.actatropica.2018.04.023","article-title":"Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016","volume":"185","author":"Desjardins","year":"2018","journal-title":"Acta Trop."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1111\/biom.12198","article-title":"A Bayesian nonparametric model for spatially distributed multivariate binary data with application to a multidrug-resistant tuberculosis (MDR-TB) study","volume":"70","author":"Kang","year":"2014","journal-title":"Biometrics"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1111\/insr.12206","article-title":"Modelling the ecological comorbidity of acute respiratory infection, diarrhoea and stunting among children under the age of 5 years in Somalia","volume":"85","author":"Kinyoki","year":"2017","journal-title":"Int. Stat. Rev."},{"key":"ref_86","unstructured":"Price, S., Center, G., and Shipping, F. (2023). \u201cEverything Is Related to Everything Else, but Near Things Are More Related than Distant Things\u201d.~Waldo Tobler, American-Swiss Geographer (1970) First Law of Geography, Mini Museum."},{"key":"ref_87","unstructured":"Rutstein, S.O., and Rojas, G. (2006). Guide to DHS Statistics, ORC Macro."},{"key":"ref_88","first-page":"1553","article-title":"Joint spatio-temporal shared component model with an application in Iran Cancer Data","volume":"19","author":"Mahaki","year":"2018","journal-title":"Asian Pac. J. Cancer Prev. APJCP"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"O2","DOI":"10.1186\/2049-3258-73-S1-O2","article-title":"Comparing MCMC and INLA for disease mapping with Bayesian hierarchical models","volume":"73","author":"Simons","year":"2015","journal-title":"Arch. Public Health"},{"key":"ref_90","unstructured":"Cramb, S., Duncan, E., Baade, P., and Mengersen, K. (2018). Investigation of Bayesian Spatial Models, Cancer Council Queensland and Queensland University of Technology (QUT)."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Bohr, A., and Memarzadeh, K. (2020). Artificial Intelligence in Healthcare, Elsevier.","DOI":"10.1016\/B978-0-12-818438-7.00002-2"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Koo, K., Papa, N., Evans, M., Jefford, M., IJzerman, M., White, V., Evans, S.M., Ristevski, E., Emery, J., and Millar, J. (2022). Mapping disadvantage: Identifying inequities in functional outcomes for prostate cancer survivors based on geography. BMC Cancer, 22.","DOI":"10.1186\/s12885-022-09389-4"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1515\/em-2014-0001","article-title":"Model choice using the Deviance Information Criterion for latent conditional individual-level models of infectious disease spread","volume":"4","author":"Deeth","year":"2015","journal-title":"Epidemiol. Methods"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"20297","DOI":"10.1038\/s41598-022-24475-0","article-title":"Bayesian spatio-temporal modelling of child anemia in Ethiopia using conditional autoregressive model","volume":"12","author":"Tessema","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Kamary, K., and Robert, C.P. (2014). Reflecting about selecting noninformative priors. arXiv.","DOI":"10.4172\/2168-9679.1000175"},{"key":"ref_96","unstructured":"Berger, J.O., Insua, D.R., and Ruggeri, F. (2000). Robust Bayesian Analysis, Springer."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"608045","DOI":"10.3389\/fpsyg.2020.608045","article-title":"The importance of prior sensitivity analysis in Bayesian statistics: Demonstrations using an interactive Shiny App","volume":"11","author":"Depaoli","year":"2020","journal-title":"Front. Psychol."}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/20\/13\/6277\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:04:43Z","timestamp":1760126683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/20\/13\/6277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":97,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["ijerph20136277"],"URL":"https:\/\/doi.org\/10.3390\/ijerph20136277","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]}}}