Spatial modelling of healthcare utilisation for treatment of fever in Namibia

International Journal of Health Geographics

Peter M Atkinson²  Robert W Snow⁴  Abdisalan M Noor⁴  Uusiku Pentrina³  Jim A Wright²  Victor A Alegana¹ 

[1] Malaria Public Health & Epidemiology Group, Centre for Geographic Medicine Research - Coast, Kenya Medical Research Institute/Wellcome Trust Research Programme, P.O. Box 43640, 00100 GPO Nairobi, Kenya;Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield Southampton SO17 1BJ, UK;National Vector-Borne Disease Control Programme, Ministry of Health and Social Services, Private Bag 13198, Windhoek, Namibia;Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, CCVTM, Oxford OX3 7LJ, UK

关键词: Malaria;    Utilisation;    Spatial;    Treatment;    Fevers;    Namibia;   

Others  :  811973
DOI  :  10.1186/1476-072X-11-6

 received in 2011-12-15, accepted in 2012-02-15,  发布年份 2012

【 摘 要 】

Background

Health care utilization is affected by several factors including geographic accessibility. Empirical data on utilization of health facilities is important to understanding geographic accessibility and defining health facility catchments at a national level. Accurately defining catchment population improves the analysis of gaps in access, commodity needs and interpretation of disease incidence. Here, empirical household survey data on treatment seeking for fever were used to model the utilisation of public health facilities and define their catchment areas and populations in northern Namibia.

Method

This study uses data from the Malaria Indicator Survey (MIS) of 2009 on treatment seeking for fever among children under the age of five years to characterize facility utilisation. Probability of attendance of public health facilities for fever treatment was modelled against a theoretical surface of travel times using a three parameter logistic model. The fitted model was then applied to a population surface to predict the number of children likely to use a public health facility during an episode of fever in northern Namibia.

Results

Overall, from the MIS survey, the prevalence of fever among children was 17.6% CI [16.0-19.1] (401 of 2,283 children) while public health facility attendance for fever was 51.1%, [95%CI: 46.2-56.0]. The coefficients of the logistic model of travel time against fever treatment at public health facilities were all significant (p < 0.001). From this model, probability of facility attendance remained relatively high up to 180 minutes (3 hours) and thereafter decreased steadily. Total public health facility catchment population of children under the age five was estimated to be 162,286 in northern Namibia with an estimated fever burden of 24,830 children. Of the estimated fevers, 8,021 (32.3%) were within 30 minutes of travel time to the nearest health facility while 14,902 (60.0%) were within 1 hour.

Conclusion

This study demonstrates the potential of routine household surveys to empirically model health care utilisation for the treatment of childhood fever and define catchment populations enhancing the possibilities of accurate commodity needs assessment and calculation of disease incidence. These methods could be extended to other African countries where detailed mapping of health facilities exists.

【 授权许可】

   
2012 Alegana et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】

• [1]Bullen N, Moon G, Jones K: Defining localities for health planning: a GIS approach. Soc Sci Med 1996, 42(6):801-816.
• [2]Shortt NK, Moore A, Coombes M, Wymer C: Defining regions for locality health care planning: a multidimensional approach. Soc Sci Med 2005, 60(12):2715-2727.
• [3]Aday LA, Andersen R: A framework for the study of access to medical care. Heal Serv Res 1974, 9:208-220.
• [4]Joseph AE, Phillips DR: Accessibility and utilization: Geographical perspectives on health care deliver. London, UK: Harper & Row; 1984.
• [5]Gulliford M, Figueroa-Munoz J, Morgan M, Hughes D, Gibson B, Beech R, Hudson M: What does access to health care mean? J Health Serv Res Policy 2002, 7:186-188.
• [6]Guagliardo MF: Spatial accessibility of primary care: concepts, methods and challenges. Int J Health Geogr 2004, 3(1):3. BioMed Central Full Text
• [7]Haas JS, Phillips KA, Sonneborn D, McCulloch CE, Baker LC, Kaplan CP, Perez-Stable EJ, Liang SY: Variation in access to health care for different racial/ethnic groups by the racial/ethnic composition of an individual's county of residence. Med Care 2004, 42(7):707-714.
• [8]Martin D, Jordan H, Roderick P: Taking the bus: incorporating public transport timetable data into health care accessibility modelling. Environment and Planning A 2008, 40(10):2510-2525.
• [9]Robertson R, Burge P: The impact of patient choice of provider on equity: analysis of a patient survey. J Health Serv Res Policy 2011, 16:(suppl_1):22-28.
• [10]Whitty CJ, Chandler C, Ansah E, Leslie T, Staedke SG: Deployment of ACT antimalarials for treatment of malaria: challenges and opportunities. Malar J 2008, 7:(Suppl 1):S7.
• [11]Black RE, Morris SS, Bryce J: Where and why are 10 million children dying every year? Lancet 2003, 361(9376):2226-2234.
• [12]Chilundo B, Sundby J, Aanestad M: Analysing the quality of routine malaria data in Mozambique. Malar J 2004, 3:3. BioMed Central Full Text
• [13]Guerra CA, Gikandi PW, Tatem AJ, Noor AM, Smith DL, Hay SI, Snow RW: The limits and intensity of Plasmodium falciparum transmission: implications for malaria control and elimination worldwide. PLoS Med 2008, 5(2):e38.
• [14]Hay SI, Guerra CA, Gething PW, Patil AP, Tatem AJ, Noor AM, Kabaria CW, Manh BH, Elyazar IR, Brooker S, et al.: A world malaria map: plasmodium falciparum endemicity in 2007. PLoS Med 2009, 6(3):e1000048.
• [15]Cromley EK, McLafferty SL: GIS and public healt. New York: Guilford Press; 2002.
• [16]Agyepong IA, Kangeya-Kayonda J: Providing practical estimates of malaria burden for health planners in resource-poor countries. AmJTrop Med Hyg 2004, 71(2 Suppl):162-167.
• [17]Cibulskis RE, Bell D, Christophel EM, Hii J, Delacollette C, Bakyaita N, Aregawi MW: Estimating trends in the burden of malaria at country level. AmJTrop Med Hyg 2007, 77(6 Suppl):133-137.
• [18]The health mapper [http://gis.emro.who.int/PublicHealthMappingGIS/HealthMapper.aspx] webcite
• [19]Noor AM, Alegana VA, Gething PW, Snow RW: A spatial national health facility database for public health sector planning in Kenya in 2008. Int J Health Geogr 2009, 8:13. BioMed Central Full Text
• [20]The AfriPop project [http://www.clas.ufl.edu/users/atatem/index_files/AfriPop.htm] webcite
• [21]Haynes R, Bentham C, Lovett A, Gale S: Effects of distances to hospital and GP surgery on hospital inpatient episodes, controlling for needs and provision. Soc Sci Med 1999, 49:425-433.
• [22]Tanser F, Hosegood V, Benzler J, Solarsh G: New approaches to spatially analyse primary health care usage patterns in rural South Africa. Trop Med Int Health 2001, 6(10):826-838.
• [23]Buor D: Analysing the primacy of distance in the utilization of health services in the Ahafo-Ano South district, Ghana. Volume 18. Wiley; 2003::293-311.
• [24]Noor AM, Zurovac D, Hay SI, Ochola SA, Snow RW: Defining equity in physical access to clinical services using geographical information systems as part of malaria planning and monitoring in Kenya. Trop Med Int Health 2003, 8(10):917-926.
• [25]Gething PW, Noor AM, Zurovac D, Atkinson PM, Hay SI, Nixon MS, Snow RW: Empirical modelling of government health service use by children with fevers in Kenya. Acta Trop 2004, 91(3):227-237.
• [26]Noor AM, Amin AA, Gething PW, Atkinson PM, Hay SI, Snow RW: Modelling distances travelled to government health services in Kenya. Trop Med Int Health 2006, 11(2):188-196.
• [27]Martin D, Wrigley H, Barnett S, Roderick P: Increasing the sophistication of access measurement in a rural healthcare study. Health & Place 2002, 8:3-13.
• [28]Schuurman N, Fiedler RS, Grzybowski SCW, Grund D: Defining rational hospital catchments for non-urban areas based on travel-time. Int J Health Geogr 2006, 5:43. BioMed Central Full Text
• [29]Walsh SJ, Page PH, Gesler WM: Normative models and healthcare planning: network-based simulations within a geographic information system environment. Health Serv Res 1997, 32(2):243-260.
• [30]Jordan H, Roderick P, Martin D, Barnett S: Distance, rurality and the need for care: access to health services in South West England. Int J Heal Geogr 2004, 3(1):21. BioMed Central Full Text
• [31]Tanser F, Gijsbertsen B, Herbst K: Modelling and understanding primary health care accessibility and utilization in rural South Africa: an exploration using a geographical information system. Soc Sci Med 2006, 63(3):691-705.
• [32]Apparicio P, Abdelmajid M, Riva M, Shearmur R: Comparing alternative approaches to measuring the geographical accessibility of urban health services: distance types and aggregation-error issues. Int J Health Geogr 2008, 7:7. BioMed Central Full Text
• [33]Bailey CT, Gatrell CA: Interactive Spatial Data Analysi. Essex, England: Longman Scientific & Technical; 1995.
• [34]Talen E, Anselin L: Assessing spatial equity: an evaluation of measures of accessibility to public playgrounds. Environ Planning A 1998, 30(4):595-613.
• [35]Akin JS, Hutchinson P: Health-care facility choice and the phenomenon of bypassing. Health Policy Plan 1999, 14(2):135-151.
• [36]MoHSS: Health and Social services system review. Edited by Ministry of Health and Social Services (MoHSS) RoN. Windhoek; 2008.
• [37]MoHSS, ICF Macro: Namibia Health Facility Census (HFC) 2009. Windhoek, Namibia: Windhoek, Namibia. MoHSS and ICF Macro; 2010:585.
• [38]El Obeid S, Mendelsohn JM, Lejars M, Forster N, Brulé G: Health in Namibia: Progress and challenges. Windhoek, Namibia: Research and Information Services of Namibia (RAISON); 2001. Available at: http://www.raison.com.na/page_htm_raison/downloads.html webcite [Accessed 17 May 2011]
• [39]Zere E, Mbeeli T, Shangula K, Mandlhate C, Mutirua K, Tjivambi B, Kapenambili W: Technical efficiency of district hospitals: evidence from Namibia using data envelopment analysis. Cost Eff Resour Alloc 2006, 4:5. BioMed Central Full Text
• [40]Ministry of Health and Social Services (MoHSS): Namibia national health accounts 2001/02-2006/07. Edited by [Bethesda] MaHS. Windhoek, Namibia; 2008.
• [41]Google Earth [http://www.google.co.uk/intl/en_uk/earth/] webcite
• [42]The GeoNames geographical database [http://www.geonames.org/search.html?q=&country=NA] webcite
• [43]Briggs DJ, Gulliver J, Fecht D, Vienneau DM: Dasymetric modelling of small-area population distribution using land cover and light emissions data. Remote Sens Environ 2007, 108(4):451-466.
• [44]Bhaduri B, Bright E, Coleman P, Urban M: LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal 2007, 69(1):103-117.
• [45]Linard C, Gilbert M, Tatem A: Assessing the use of global land cover data for guiding large area population distribution modelling. GeoJournal 2011, 76(5):525-538.
• [46]Mendelsohn JM, Roberts CS: An environmental atlas and profile of Capriv. Windhoek: Ministry of Environment and Tourism, Windhoek; 1997.
• [47]Mendelsohn JM, El Obeid S: A preliminary profile of Kavango region. Windhoek.: Namibia Nature Foundation 2001.
• [48]Mendelsohn JM, El Obeid S, Roberts CS: A profile of north-central Namibia. Windhoek: Gamsberg MacMillan; 2000.
• [49]Water Resource Programme: Analysis of Present and Future Water demand in Namibia: Namibia Water Resources Management Review. Windhoek: Windhoek Consulting Engineers; 2007.
• [50]Land Cover Classification System (LCCS): Classification Concepts and User Manual [http://www.fao.org/docrep/003/x0596e/x0596e00.htm] webcite
• [51]Namibia 5-Year Age/Sex Population Estimates 2000-2010: Second Administrative Level Divisions [http://www.hivspatialdata.net/xslview.aspx?docid=690&country=Namibia] webcite
• [52]RBM-MERG: Roll Back Malaria-Monitoring and Evaluation Resource Group: Core Household Questionnaire. Calverton, Maryland; 2005.
• [53]Demographic and Health Surveys [http://www.measuredhs.com] webcite
• [54]Spatial Data Download Namibia [http://www.diva-gis.org/gData] webcite
• [55]Digital atlas of Namibia [http:/ / www.uni-koeln.de/ sfb389/ e/ e1/ download/ atlas_namibia/ e1_download_physical_geography_e.ht m] webcite
• [56]GlobCover 2009 (Global Land Cover Map) [http://ionia1.esrin.esa.int/] webcite
• [57]Ray N, Ebener S: AccessMod 3.0: computing geographic coverage and accessibility to health care services using anisotropic movement of patients. Int J Heal Geogr 2008, 7(1):63. BioMed Central Full Text
• [58]The Government of the Republic of Namibia: The Road Traffic and Transport Regulations, 2001. In Government Notice No No161 of 2002, published in Government Gazette No2815 of 26 September 2002. Namibia: Ministry of Transport; 2002:323-324.
• [59]Tobler W: Three presentations on geographical analysis and modeling: National Center for Geographic Information and Analysis. In Technical report 93-1. Santa Barbara, CA93106-4060: University of California, Santa Barbara; 1993.
• [60]Pinheiro J, Bates D: Mixed Effects Models in S and S-Plus. Springer; 2002.
• [61]Gething PW, Kirui VC, Alegana VA, Okiro EA, Noor AM, Snow RW: Estimating the number of paediatric fevers associated with malaria infection presenting to Africa's public health sector in 2007. PLoS Med 2010, 7(7):e1000301.
• [62]Ensor T, Cooper S: Overcoming barriers to health service access: influencing the demand side. Health Policy and Planning 2002, 19(2):69-79.
• [63]Bi-Logistic growth [http://phe.rockefeller.edu/Bi-Logistic/] webcite
• [64]Gershenfeld NA: The Nature of Mathematical Modelin. Cambridge, UK: Cambridge University Press; 1999.
• [65]Leonard K, Mliga GR, Mariam DH: Bypassing health centers in Tanzania: Revealed preferences for observable and unobservable qualit. Columbia University, Department of Economics; 2002.
• [66]Gething P, Atkinson P, Noor A, Gikandi P, Hay S, Nixon M: A local space-time kriging approach applied to a national outpatient malaria dataset. Comput Geosci 2007, 33(10):1337-1350.
• [67]Moisi JC, Nokes DJ, Gatakaa H, Williams TN, Bauni E, Levine OS, Scott JA: Sensitivity of hospital-based surveillance for severe disease: a geographic information system analysis of access to care in Kilifi district, Kenya. Bull World Health Organ 2011, 89(2):102-111.
• [68]Al-Taiar A, Jaffar S, Assabri A, Al-Habori M, Azazy A, Al-Gabri A, Al-Ganadi M, Attal B, Whitty CJ: Who develops severe malaria? Impact of access to healthcare, socio-economic and environmental factors on children in Yemen: a case-control study. Trop Med Int Health 2008, 13(6):762-770.
• [69]O'Meara WP, Noor A, Gatakaa H, Tsofa B, McKenzie FE, Marsh K: The impact of primary health care on malaria morbidity - defining access by disease burden. Trop Med Int Health 2009, 14(1):29-35.
• [70]Okwaraji Y, Cousens S, Berhane Y, Mulholland K, Edmond K: Effect of geographical access to health facilities on child mortality in rural Ethiopia: a community based cross sectional study. PLoS ONE 2011. Submitted for review
• [71]Kindermans J-M, Vandenbergh D, Vreeke E, Olliaro P, D'Altilia J-P: Estimating antimalarial drugs consumption in Africa before the switch to artemisinin-based combination therapies (ACTs). Malar J 2007, 6(1):91. BioMed Central Full Text
• [72]Okiro E, Alegana V, Noor A, Mutheu J, Juma E, Snow R: Malaria paediatric hospitalization between 1999 and 2008 across Kenya. BMC Med 2009, 7(1):75. BioMed Central Full Text
• [73]Gething PW, Noor AM, Gikandi PW, Hay SI, Nixon MS, Snow RW, Atkinson PM: Developing geostatistical space-time models to predict outpatient treatment burdens from incomplete national data. Geogr Anal 2008, 40(2):167-188.
• [74]Snow RW, Eckert E, Teklehaimanot A: Estimating the needs for artesunate-based combination therapy for malaria case-management in Africa. Trends Parasitol 2003, 19(8):363-369.
• [75]Okiro E, Snow R: The relationship between reported fever and Plasmodium falciparum infection in African children. Malar J 2010, 9(1):99. BioMed Central Full Text
• [76]Okiro EA, Alegana VA, Noor AM, Snow RW: Changing malaria intervention coverage, transmission and hospitalization in Kenya. Malar J 2010, 9:285. BioMed Central Full Text
• [77]Elmardi KA, Noor AM, Githinji S, Abdelgadir TM, Malik EM, Snow RW: Self-reported fever, treatment actions and malaria infection prevalence in the northern states of Sudan. Malar J 2011, 10:128. BioMed Central Full Text