Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017
Published: 7 January 2021
Abstract Views: 2812
PDF: 878
HTML: 23
HTML: 23
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
Typhoid disease continues to be a global public health burden. Uganda is one of the African countries characterized by high incidences of typhoid disease. Over 80% of the Ugandan districts are endemic for typhoid, largely attributable to lack of reliable knowledge to support disease surveillance. Spatial-temporal studies exploring major characteristics of the disease within the local population have remained limited in Uganda. The main goal of the study was to reveal spatial-temporal trends and distribution patterns of typhoid disease in Uganda for the period 2012 to 2017. Spatial-temporal statistics revealed monthly and annual trends of the disease at both regional and national levels. Results show that outbreaks occurred during 2015 and 2017 in central and eastern regions, respectively. Spatial scan statistic using the discrete Poisson model revealed spatial clusters of the disease for each of the years from 2012 to 2017, together with populations at risk. Most of the disease clustering was in the central region, followed by western and eastern regions (P <0.01). The northern region was the safest throughout the study period. This knowledge helps surveillance teams to i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; and iv) evaluate effectiveness of the intervention methods in the study period. This exploratory research forms a foundation of using Geographical Information Systems (GIS) in other related subsequent research studies to discover hidden spatial patterns that are difficult to discover with conventional methods.
Altmetrics
Downloads
Download data is not yet available.
Citations
Adams F, 2017. On Airs, Waters, and Places, translated by Francis Adams. Available at http://classics.mit.edu/Hippocrates/airwatpl.html. Accessed on (20/01/2020)
Agboola S, Mataimaki BJ, 2017. Spatial Statistical Analysis in the Classification of Some Seasonal Diseases. Journal of Family Medicine and Healthare 3(2):36-44. doi: 10.11648/j.jfmhc.20170302.12. DOI: https://doi.org/10.11648/j.jfmhc.20170302.12
Agwu E, 2012. Distribution of Community Acquired Typhoid Fever among Febrile Patients Attending Clinics in Bushenyi, Uganda: Case Study of the Year 2005. J Medical Microbiol Diagnosis 1:101. doi: 10.4172/2161-0703.1000101. DOI: https://doi.org/10.4172/2161-0703.1000101
Ahangarcani M, Farnaghi M, Shirzadi MR, Pilesjö P, Mansourian A, 2019. Predictive risk mapping of human leptospirosis using support vector machine classification and multilayer perceptron neural network. Geospatial Health 14(1):53-61. DOI: https://doi.org/10.4081/gh.2019.711
Antillon M, Warren JL, Crawford FW, Weinberger DM, Kürüm E, Pak GD, PitzerVE, 2017. The burden of typhoid fever in low- and middle-income countries: A meta-regression approach. PLoS Neglected Tropical Diseases, 11(2), e0005376, http://doi.org/10.1371/journal.pntd.0005376. DOI: https://doi.org/10.1371/journal.pntd.0005376
Baaghideh M, Hamidian A, Roudbari AD, Mayvaneh, F, 2016. Modeling and Spatial Epidemiology of Diarrhea in Mazandaran Province. irje. 2016; 12 (1) :41-50.
Bergquist R, Rinaldi L, 2010. Health research based on geospatial tools: a timely approach in a changing environment, Journal of Helminthology (2010) 84, 1–11, doi: 10.1017/S0022149X09990484. DOI: https://doi.org/10.1017/S0022149X09990484
Colin GW, Pietro, Matthew J, Peter CA, Robert R, 2015. Analyzing hospitalization data: potential limitations of Poisson regression, Nephrology Dialysis Transplantation, Volume 30, Issue 8, Pages 1244–1249, https://doi.org/10.1093/ndt/gfv071. DOI: https://doi.org/10.1093/ndt/gfv071
Elliott P, Wartenberg D, 2004. Spatial epidemiology: current approaches and future challenges. Environmental health perspectives, 112(9), 998–1006. doi:10.1289/ehp.6735. DOI: https://doi.org/10.1289/ehp.6735
Gu H, Fan W, Liu K, Qin S, Li X, Jiang J, Chen E, Zhou Y, Jiang Q, 2017. Spatio-temporal variations of typhoid and paratyphoid fevers in Zhejiang Province, China from 2005 to 2015. Scientific reports, 7(1), 5780. https://doi.org/10.1038/s41598-017-05928-3. DOI: https://doi.org/10.1038/s41598-017-05928-3
Hoon JK, 2017. African Risk Prediction Model: Mapping the burden of Typhoid Fever in Africa, 10th International Conference on typhoid fever and other invasive salmonelloses, Kampala Uganda.
Kabwama SN, Bulage L, Nsubuga F, Pande G, Oguttu DW, Mafigiri R, Kihembo C, Kwesiga B, Masiira B, Okullo AE, Kajumbula H, Matovu J, Makumbi I, Wetaka M, Kasozi S, Kyazze S, Dahlke M, Hughes P, Sendagala JN, Musenero M, Nabukenya I, Hill VR, Mintz E, Routh J, Gómez G, Bicknese A, Zhu BP, 2015. A large and persistent outbreak of typhoid fever caused by consuming contaminated water and street-vended beverages: Kampala, Uganda, January – June 2015; BMC Public Health BMC series – open, inclusive and trusted 2017 17:23. DOI: https://doi.org/10.1186/s12889-016-4002-0
Kasoro F, Yoti Z, Bakyaita N, Gaturuku P, Katz R, Fischer JE, Peryy HN, .2013. IDSR as a Platform for Implementing IHR in African Countries, Biosecur Bioterror. 2013 Sep; 11(3): 163–169, doi: 10.1089/bsp.2013.0032. DOI: https://doi.org/10.1089/bsp.2013.0032
Kulldorff M, 1997. A spatial scan statistic. Communications in Statistics: Theory and Methods. 1997; 26:1481–1496. DOI: https://doi.org/10.1080/03610929708831995
Lee JS, Mogasale V, Mogasale V V, Lee K, 2016. Geographical distribution of typhoid risk factors in low and middle-income countries, BMC Infectious Diseases 16(1), DOI. 10.1186/s12879-016-2074-1. DOI: https://doi.org/10.1186/s12879-016-2074-1
Malone JB, Bergquist R, Martins M, Luvall JC, 2019. Use of Geospatial Surveillance and Response Systems for Vector-Borne Diseases in the Elimination Phase, Trop. Med. Infect. Dis. 2019, 4(1), 15; https://doi.org/10.3390/tropicalmed4010015. DOI: https://doi.org/10.3390/tropicalmed4010015
Marks F, et al., 2017. Incidence of invasive salmonella disease in sub-Saharan Africa: a multicentre population-based surveillance study, Volume 5, No. 3, e310–e323, March 2017.
Masiira B, 2015. Rapid Assessment of Risks to Public Health Among Refugees from Burundi at Nakivale and Oruchinga Refugee Camps: Isingiro District, May 2015, Public Health Fellowship, Field Epidemiology Track, http://www.musphcdc.ac.ug/files/pdf/.
Mirembe BB, Mazeri S, Callaby R, Nyakarahuka L, Kankya C, Muwonge A, 2019. Temporal, spatial and household dynamics of Typhoid fever in Kasese district, Uganda. PLoS ONE 14(4):e0214650.https://doi.org/10.1371/journal.pone.0214650. DOI: https://doi.org/10.1371/journal.pone.0214650
Mir X, Ochen EA, 2016. Fresh Analysis of the Humanitarian Capacity in Uganda, OXFAM, https://cng-cdn.oxfam.org/uganda.oxfam.org/s3fs-public/file_attachments/ELNHA%20REPORT.pdf
Muhindo R, Joloba EN, Nakanjako D, 2016. Health Management Information System (HMIS); Whose Data is it Anyway? Contextual Challenges. Review Pub Administration Manag 4: 190. doi:10.4172/2315-7844.1000190. DOI: https://doi.org/10.4172/2315-7844.1000190
NEMA, 2009. The Uganda Atlas of Our Changing Environment, United Nations Environment Programme, https://na.unep.net/atlas/uganda/book.php.
Nsubuga FWN, Botai OJ, Olwoch JM, Rautenbach CJD, Bevis Y, Adetunji AO, 2014. The nature of rainfall in the main drainage sub-basins of Uganda. Hydrological Sciences Journal, 59 (2), 278–299. DOI: https://doi.org/10.1080/02626667.2013.804188
Osuret J, Atuyambe LM, Mayega RW, Ssentongo J, Tumuhamye N, Mongo Bua G, Tuhebwe D, Bazeyo W, 2016. Coping Strategies for Landslide and Flood Disasters: A Qualitative Study of Mt. Elgon Region, Uganda. PLOS Currents Disasters. 2016 Jul. DOI: https://doi.org/10.1371/currents.dis.4250a225860babf3601a18e33e172d8b
Prates MO, Kulldorff M, Assunção RM, 2014. Relative risk estimates from spatial and space-time scan statistics: are they biased?. Statistics in medicine, 33(15), 2634–2644. doi:10.1002/sim.6143. DOI: https://doi.org/10.1002/sim.6143
Rajabi M, 2015. Disease susceptibility mapping using spatial modeling techniques, GIS Center, Department of Physical Geography and Ecosystem Science, Lund University.
Satscan, 2019. SaTScan software for the spatial, temporal and space-time scan statistics. Available at https://www.satscan.org/download_satscan.html. Accessed on (17/08/2020) )
Smith GD, 2002. Commentary: Behind the Broad Street pump: aetiology, epidemiology and prevention of cholera in mid-19th century Britain, International Epidemiological Association 2002, International Journal of Epidemiology 2002; 31:920–932. DOI: https://doi.org/10.1093/ije/31.5.920
Thieme EG, Jacobs C, 2012. Risk Mapping Uganda: Sector Disaster Risk Production and Emergency Aid 2012, pp. 2-3, https://www.cordaid.org/en/wp-content/uploads/sites/3/2013/08/Uganda_risk_mapping_20120130_ETG.pdf
Tiwari R, Nayak S, 2017. Drinking water, Sanitation and Water borne diseases, Economic & Weekly Journal, Vol. 52, Issue No. 23, 10 jun. 2017.
UBOS, 2012. Statistical Abstract 2012, Uganda Bureau of Statistics, http://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/2012StatisticalAbstract.pdf.
UBOS, 2016. Uganda Bureau of Statistics 2016, The National Population and Housing Census 2014 – Main Report, Kampala, Uganda. https://www.ubos.org/wp-content/uploads/publications/03_20182014_National_Census_Main_Report.pdf.
UNHCR, 2014. Oruchinga Fact Sheet 2014, UNHCR Uganda, https://data2.unhcr.org/fr/documents/download/48491.
UNMA, 2019. The Seasonal Rainfall outlook for June to August 2019 over Uganda, Uganda National Meteorological Authority, http://www.unma.go.ug/index.php/climate/seasonal-forecasts/document/4-the-seasonal-rainfall-outlook-for-june-to-august-2019-over-uganda/23.
UNMA, 2016, September to December 2016 Seasonal Rainfall outlook over Uganda, National Meteorological Authority, https://www.newvision.co.ug/new_vision/news/1435709/uganda-national-meteorological-authority.
Walters MS, Routh J, Mikoleit M, Kadivane S, Ouma C, Mubiru D, 2014. Shifts in Geographic Distribution and Antimicrobial Resistance during a Prolonged Typhoid Fever Outbreak - Bundibugyo and Kasese Districts, Uganda, 2009-2011. PLoS Negl Trop Dis 8(3): e2726. https://doi.org/10.1371/journal.pntd.0002726. DOI: https://doi.org/10.1371/journal.pntd.0002726
WHO, 2015. Water, sanitation and hygiene for accelerating and sustaining progress on neglected tropical diseases, a global strategy 2015-2020.
WHO, 2017. WHO Health Emergencies Programme in the African Region: Annual Report 2016. Geneva: World Health Organization; 2017. Licence: CC BY-NC-SA 3.0 IGO. 2016, ISBN: 978-929023365-7.
Wamono E, 2015. Uganda Situation Report on Refugees from Burundi, UNICEF Co-Situation Report, June 2015. https://www.unicef.org/appeals/files/UNICEF_Uganda_Burundi_Refugee_SitRep_17_June_2015.pdf
WPR, 2019. World Population Review, Uganda Population 2019, http://worldpopulationreview.com/countries/uganda-population/
WHO, 2015. Typhoid fever-Uganda, Emergencies, Preparedness, Response, https://www.who.int/csr/don/17-march-2015-uganda/en/
How to Cite
Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017. (2021). Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.860
Copyright (c) 2020 The Authors
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.
Most read articles by the same author(s)
- Mohammadreza Rajabi, Ali Mansourian, Petter Pilesjö, Ahad Bazmani, Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks: a case study in north-western Iran , Geospatial Health: Vol. 9 No. 1 (2014)
- Mehrdad Ahangarcani, Mahdi Farnaghi, Mohammad Reza Shirzadi, Petter Pilesjö, Ali Mansourian, Predictive risk mapping of human leptospirosis using support vector machine classification and multilayer perceptron neural network , Geospatial Health: Vol. 14 No. 1 (2019)
- Gilbert Nduwayezu, Pengxiang Zhao, Clarisse Kagoyire, Lina Eklund, Jean Pierre Bizimana, Petter Pilesjo, Ali Mansourian, Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda , Geospatial Health: Vol. 18 No. 1 (2023)
- Mohamadreza Rajabi, Ali Mansourian, Ahad Bazmani, Susceptibility mapping of visceral leishmaniasis based on fuzzy modelling and group decision-making methods , Geospatial Health: Vol. 7 No. 1 (2012)
