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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2017

Open Access 01-12-2017 | Research article

Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model

Authors: Victor A. Alegana, Jim Wright, Carla Pezzulo, Andrew J. Tatem, Peter M. Atkinson

Published in: BMC Medical Research Methodology | Issue 1/2017

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Abstract

Background

Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).

Methods

Using nationally representative household survey data from the 2013 Demographic and Health Survey (DHS) in Namibia, individual-level responses (n = 1138) were linked to theoretical estimates of travel time to the nearest public or private health facility. Bayesian Item Response Theory (IRT) models were fitted via Markov Chain Monte Carlo (MCMC) simulation to estimate parameters related to fever treatment and estimate probability of treatment for children under five years. Different models were implemented to evaluate computational needs and the effect of including predictor variables such as rurality. The mean treatment rates were then estimated at regional level.

Results

Modelling results suggested probability of fever treatment was highest in regions with relatively high incidence of malaria historically. The minimum predicted threshold probability of seeking treatment was 0.3 (model 1: 0.340; 95% CI 0.155–0.597), suggesting that even in populations at large distances from facilities, there was still a 30% chance of an individual seeking treatment for fever. The agreement between correctly predicted probability of treatment at individual level based on a subset of data (n = 247) was high (AUC = 0.978), with a sensitivity of 96.7% and a specificity of 75.3%.

Conclusion

We have shown how individual responses in national surveys can be transformed to probabilistic measures comparable at population level. Our analysis of household survey data on fever suggested a 30% baseline threshold for fever treatment in Namibia. However, this threshold level is likely to vary by country or endemicity. Although our focus was on fever treatment, the methodology outlined can be extended to multiple health seeking behaviours captured in routine national survey data and to other infectious diseases.
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Literature
1.
Rudan I, El Arifeen S, Bhutta ZA, Black RE, Brooks A, Chan KY, Chopra M, Duke T, Marsh D, Pio A, et al. Setting Research Priorities to Reduce Global Mortality from Childhood Pneumonia by 2015. PLoS Med. 2011;8(9):e1001099.CrossRefPubMedPubMedCentral
2.
Herbert HK, Lee ACC, Chandran A, Rudan I, Baqui AH. Care Seeking for Neonatal Illness in Low- and Middle-Income Countries: A Systematic Review. PLoS Med. 2012;9(3):e1001183.CrossRefPubMedPubMedCentral
3.
Colvin CJ, Smith HJ, Swartz A, Ahs JW, de Heer J, Opiyo N, Kim JC, Marraccini T, George A. Understanding careseeking for child illness in sub-Saharan Africa: a systematic review and conceptual framework based on qualitative research of household recognition and response to child diarrhoea, pneumonia and malaria. Soc Sci Med. 2013;86:66–78.CrossRefPubMed
4.
World Health Organizastion: Integrated community-based interventions. In., vol. TDR/BL 11.10. Geneva: World Health Organization; 2009.
5.
The malE. R. A. Consultative Group on Health Systems Operational R. A Research Agenda for Malaria Eradication: Health Systems and Operational Research. PLoS Med. 2011;8(1):e1000397.CrossRef
6.
Bhutta ZA, Sommerfeld J, Lassi ZS, Salam RA, Das JK. Global burden, distribution, and interventions for infectious diseases of poverty. Infect Dis Poverty. 2014;3:21–1.
7.
World Health Organization. Guidelines for the treatment of malaria. 3rd ed. Geneva: WHO; 2015.
8.
Landier J, Parker DM, Thu AM, Carrara VI, Lwin KM, Bonnington CA, Pukrittayakamee S, Delmas G, Nosten FH. The role of early detection and treatment in malaria elimination. Malar J. 2016;15(1):1–8.CrossRef
9.
Littrell M, Gatakaa H, Evance I, Poyer S, Njogu J, Solomon T, Munroe E, Chapman S, Goodman C, Hanson K, et al. Monitoring fever treatment behaviour and equitable access to effective medicines in the context of initiatives to improve ACT access: baseline results and implications for programming in six African countries. Malar J. 2011;10(1):327.CrossRefPubMedPubMedCentral
10.
Sundararajan R, Mwanga-Amumpaire J, Adrama H, Tumuhairwe J, Mbabazi S, Mworozi K, Carroll R, Bangsberg D, Boum Ii Y, Ware NC. Sociocultural and Structural Factors Contributing to Delays in Treatment for Children with Severe Malaria: A Qualitative Study in Southwestern Uganda. AmJTrop Med Hyg. 2015;92(5):933–40.CrossRef
11.
O’Neill S, Gryseels C, Dierickx S, Mwesigwa J, Okebe J, d’Alessandro U, Grietens KP. Foul wind, spirits and witchcraft: illness conceptions and health-seeking behaviour for malaria in the Gambia. Malar J. 2015;14(1):167.CrossRefPubMedPubMedCentral
12.
Hadley M. Does increase in utilisation rates alone indicate the success of a user fee removal policy? A qualitative case study from Zambia. Health Policy. 2011;103(2–3):244–54.CrossRefPubMed
13.
Battle KE, Bisanzio D, Gibson HS, Bhatt S, Cameron E, Weiss DJ, Mappin B, Dalrymple U, Howes RE, Hay SI, et al. Treatment-seeking rates in malaria endemic countries. Malar J. 2016;15(1):1–11.CrossRef
14.
Alegana V, Wright J, Petrina U, Noor A, Snow R, Atkinson P. Spatial modelling of healthcare utilisation for treatment of fever in Namibia. Int J Health Geogr. 2012;11(1):6.CrossRefPubMedPubMedCentral
15.
Wasunna B, Okiro EA, Webster J, Todd J, Snow RW, Jones C. The Impact of a Community Awareness Strategy on Caregiver Treatment Seeking Behaviour and Use of Artemether-Lumefantrine for Febrile Children in Rural Kenya. PLoS ONE. 2015;10(7):e0130305.CrossRefPubMedPubMedCentral
16.
Aung T, Lwin MM, Sudhinaraset M, Wei C. Rural and urban disparities in health-seeking for fever in Myanmar: findings from a probability-based household survey. Malar J. 2016;15(1):386.CrossRefPubMedPubMedCentral
17.
Matovu F, Nanyiti A, Rutebemberwa E. Household health care-seeking costs: experiences from a randomized, controlled trial of community-based malaria and pneumonia treatment among under-fives in eastern Uganda. Malar J. 2014;13:222.CrossRefPubMedPubMedCentral
18.
Manongi R, Mtei F, Mtove G, Nadjm B, Muro F, Alegana V, Noor AM, Todd J, Reyburn H. Inpatient child mortality by travel time to hospital in a rural area of Tanzania. Trop Med Int Health. 2014;19(5):555–62.CrossRefPubMedPubMedCentral
19.
Hays RD, Morales LS, Reise SP. Item response theory and health outcomes measurement in the 21st century. Med Care. 2000;38(9 Suppl):Ii28–42.PubMedPubMedCentral
20.
21.
22.
Alegana VA, Atkinson PM, Pezzulo C, Sorichetta A, Weiss D, Bird T, Erbach-Schoenberg E, Tatem AJ. Fine resolution mapping of population age-structures for health and development applications. J Royal Soc Interface. 2015;12(105). doi:10.​1098/​rsif.​2015.​0073.
23.
Roll Back Malaria Monitoring and Evaluation Reference Group. A guide to Malaria Indicator Surveys (MIS). Geneva: Roll Back Malaria Monitoring and Evaluation Reference Group; 2008.
24.
25.
United Nations Children Fund (UNICEF). Monitoring the situation of women and children: Multiple Indicator Cluster Survey manual 2005. New York: UNICEF; 2006.
26.
Matteucci M. An Investigation of Parameter Recovery in MCMC Estimation for the Additive IRT Model. Commun Stat - Theory Methods. 2014;43(4):751–70.CrossRef
27.
Sheng Y, Wikle CK. Bayesian Multidimensional IRT Models With a Hierarchical Structure. Educ Psychol Meas. 2008;68(3):413–30.CrossRef
28.
Sheng Y. Review of Bayesian Item Response Modeling: Theory and Applications, by Jean-Paul Fox. Struct Equ Model Multidiscip J. 2015;22(3):484–8.CrossRef
29.
Béguin AA, Glas CAW. MCMC estimation and some model-fit analysis of multidimensional IRT models. Psychometrika. 2001;66(4):541–61.CrossRef
30.
Fox J-P, Glas CAW. Bayesian estimation of a multilevel IRT model using gibbs sampling. Psychometrika. 2001;66(2):271–88.CrossRef
31.
Fox J-P, Wyrick C. A Mixed Effects Randomized Item Response Model. J Educ Behav Stat. 2008;33(4):389–415.CrossRef
32.
Yang FM, Jones RN, Inouye SK, Tommet D, Crane PK, Rudolph JL, Ngo LH, Marcantonio ER. Selecting optimal screening items for delirium: an application of item response theory. BMC Med Res Methodol. 2013;13(1):8.CrossRefPubMedPubMedCentral
33.
Gorter R, Fox J-P, Twisk JWR. Why item response theory should be used for longitudinal questionnaire data analysis in medical research. BMC Med Res Methodol. 2015;15(1):1–12.CrossRef
34.
Dregan A, Grieve A, van Staa T, Gulliford MC. Potential application of item-response theory to interpretation of medical codes in electronic patient records. BMC Med Res Methodol. 2011;11(1):168.CrossRefPubMedPubMedCentral
35.
Sheng Y, Wikle CK. Comparing Multiunidimensional and Unidimensional Item Response Theory Models. Educ Psychol Meas. 2007;67(6):899–919.CrossRef
36.
Banerjee S, Carling PB, Gelfand AE. Hierarchical modeling and analysis for spatial data. London: Chapman & Hall/CRC; 2004.
37.
Stock R. Distance and the utilization of health facilities in rural Nigeria. Soc Sci Med. 1983;17(9):563–70.CrossRefPubMed
38.
Buor D. Analysing the primacy of distance in the utilization of health services in the Ahafo-Ano South district, Ghana. Int J Health Plann Manage. 2003;18(4):293–311.CrossRefPubMed
39.
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):188–96.CrossRefPubMedPubMedCentral
40.
Basu S, Andrews J, Kishore S, Panjabi R, Stuckler D. Comparative Performance of Private and Public Healthcare Systems in Low- and Middle-Income Countries: A Systematic Review. PLoS Med. 2012;9(6). doi:10.​1371/​journal.​pmed.​1001244.
41.
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):826–38.CrossRefPubMed
42.
43.
Leonard K, Mliga GR, Mariam DH. Bypassing health centers in Tanzania: Revealed preferences for observable and unobservable quality. New York: Columbia University Academic Commons; 2002.
44.
Akin JS, Hutchinson P. Health-care facility choice and the phenomenon of bypassing. Health Policy Plan. 1999;14(2):135–51.CrossRefPubMed
45.
Ray N, Ebener S. AccessMod 3.0: computing geographic coverage and accessibility to health care services using anisotropic movement of patients. Int J Health Geogr. 2008;7(1):63.CrossRefPubMedPubMedCentral
46.
Corsi DJ, Neuman M, Finlay JE, Subramanian S. Demographic and health surveys: a profile. Int J Epidemiol. 2012;41(6):1602–13.CrossRefPubMed
47.
Aliaga A, Ren R. Optimal sample sizes for two-stage cluster sampling in demographic and health surveys. In: DHS Working Papers No 30. Calverton: ORC Macro; 2006.
48.
Le TN, Verma VK. An analysis of sampling designs and sampling errors of the demographic and health surveys. In: DHS Analytical Reports No 3. Calverton: Macro International; 1997.
49.
McLaren ZM, Ardington C, Leibbrandt M. Distance decay and persistent health care disparities in South Africa. BMC Health Serv Res. 2014;14(1):1–9.CrossRef
50.
Bailey CT, Gatrell CA. Interactive Spatial Data Analysis. Essex, England: Longman Scientific & Technical; 1995.
51.
Cromley EK, McLafferty SL. GIS and public health. New York: Guilford Press; 2002.
52.
Febir LG, Asante KP, Afari-Asiedu S, Abokyi LN, Kwarteng A, Ogutu B, Gyapong M, Owusu-Agyei S. Seeking treatment for uncomplicated malaria: experiences from the Kintampo districts of Ghana. Malar J. 2016;15(1):1–11.CrossRef
53.
Chibwana AI, Mathanga DP, Chinkhumba J, Campbell Jr CH. Socio-cultural predictors of health-seeking behaviour for febrile under-five children in Mwanza-Neno district. Malawi Malar J. 2009;8:219.CrossRefPubMed
54.
Rasch G. On general laws and the meaning of the measurement in psychology. In: Proceedings of the 4th Berkley Symposium on Mathematical Statistics: 1961. London: University of California Press; 1961. p. 321–34.
55.
McDonald RP. Numerical methods for polynomial models in nonlinear factor analysis. Psychometrika. 1967;32(1):77–112.CrossRef
56.
Lord FM, Novick MR, Birnbaum A. Statistical Theories of Mental Test Scores. MA: Addison-Wesley; 1968.
57.
Darrell Bock R. Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika. 1972;37(1):29–51.CrossRef
58.
Andersen EB. The Numerical Solution of a Set of Conditional Estimation Equations. J R Stat Soc Ser B Methodol. 1972;34(1):42–54.
59.
Swaminathan H, Gifford JA. Bayesian estimation in the two-parameter logistic model. Psychometrika. 1985;50(3):349–64.CrossRef
60.
Swaminathan H, Gifford JA. Bayesian estimation in the three-parameter logistic model. Psychometrika. 1986;51(4):589–601.CrossRef
61.
Albert JH. Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling. J Educ Stat. 1992;17(3):251–69.CrossRef
62.
Braeken J, Tuerlinckx F. Investigating latent constructs with item response models: A MATLAB IRTm toolbox. Behav Res Methods. 2009;41(4):1127–37.CrossRefPubMed
63.
Sahu SK. Bayesian Estimation and Model Choice in Item Response Models. J Stat Comput Simul. 2002;72(3):217–32.CrossRef
64.
Fu Z-H, Tao J, Shi N-Z. Bayesian estimation in the multidimensional three-parameter logistic model. J Stat Comput Simul. 2009;79(6):819–35.CrossRef
65.
Rupp AA. Item Response Modeling With BILOG-MG and MULTILOG for Windows. Int J Test. 2003;3(4):365–84.CrossRef
66.
Skrondal A, Rabe-Hesketh S. Prediction in multilevel generalized linear models. J R Stat Soc Ser A (Stat Methodol). 2009;172(3):659–87.CrossRef
67.
Hattie J. Methodology Review: Assessing Unidimensionality of Tests and ltenls. Appl Psychol Meas. 1985;9(2):139–64.CrossRef
68.
de la Torre J, Douglas JA. Higher-order latent trait models for cognitive diagnosis. Psychometrika. 2004;69(3):333–53.CrossRef
69.
Barnard J, McCulloch R, Meng X-L. Modelling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Stat Sin. 2000;10(4):1281–311.
70.
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Ser B (Stat Methodol). 2002;64(4):583–639.CrossRef
71.
Plummer M. Penalized loss functions for Bayesian model comparison. Biostatistics. 2008;9(3):523–39.CrossRefPubMed
72.
Plummer M. rjags: Bayesian Graphical Models using MCMC. In. Plummer, M; 2016.
73.
Gelman A, Rubin DB. Inference from Iterative Simulation Using Multiple Sequences. Stat Sci. 1992;7(4):457–72.CrossRef
74.
Raftery AE, Lewis SM. Implementing MCMC. In: Gilks WR, Spiegelhalter DJ, Richardson S, editors. Markov Chain Monte Carlo in Practice. London: Chapman and Hall; 1996. p. 115–30.
75.
Kizito J, Kayendeke M, Nabirye C, Staedke SG, Chandler CI. Improving access to health care for malaria in Africa: a review of literature on what attracts patients. Malar J. 2012;11:55.CrossRefPubMedPubMedCentral
76.
Lagarde M, Palmer N. The impact of user fees on access to health services in low- and middle-income countries. Cochrane Database Syst Rev. 2011;(4):Cd009094.
77.
Chen I, Clarke SE, Gosling R, Hamainza B, Killeen G, Magill A, O?Meara W, Price RN, Riley EM: "Asymptomatic" Malaria: A Chronic and Debilitating Infection That Should Be Treated. PLoS Med 2016;13(1):e1001942.
78.
Sturrock HJW, Hsiang MS, Cohen JM, Smith DL, Greenhouse B, Bousema T, Gosling RD. Targeting Asymptomatic Malaria Infections: Active Surveillance in Control and Elimination. PLoS Med. 2013;10(6):e1001467.CrossRefPubMedPubMedCentral
79.
Ferrer BE, Webster J, Bruce J, Narh-Bana SA, Narh CT, Allotey NK, Glover R, Bart-Plange C, Sagoe-Moses I, Malm K, et al. Integrated community case management and community-based health planning and services: a cross sectional study on the effectiveness of the national implementation for the treatment of malaria, diarrhoea and pneumonia. Malar J. 2016;15(1):340.CrossRefPubMedPubMedCentral
80.
Kirigia J, Asbu E. Technical and scale efficiency of public community hospitals in Eritrea: an exploratory study. Heal Econ Rev. 2013;3(1):6.CrossRef
81.
Little R, Rubin D. Statistical Analysis with Missing Data. 2nd ed. New York: John Wiley & Sons, Incorporated; 2002.
82.
Little RJA, Rubin DB. On Jointly Estimating Parameters and Missing Data by Maximizing the Complete-Data Likelihood. Am Stat. 1983;37(3):218–20.
83.
Roll Back Malaria, Measure Evaluation, USAID, et al. Guidelines for core population-based indicators. Calverton: MEASURE Evaluation; 2009.
84.
DiLiberto DD, Staedke SG, Nankya F, Maiteki-Sebuguzi C, Taaka L, Nayiga S, Kamya MR, Haaland A, Chandler CI. Behind the scenes of the PRIME intervention: designing a complex intervention to improve malaria care at public health centres in Uganda. Glob Health Action. 2015;8:29067.CrossRef
85.
Ministry of Health and Social Services. Namibia Health Facility Census, 2009. Windhoek: Ministry of Health and Social Services and ICF Macro; 2010. p. 585.
86.
Alegana VA, Atkinson PM, Lourenço C, Ruktanonchai NW, Bosco C, Erbach-Schoenberg E, Didier B, Pindolia D, Le Menach A, Katokele S, et al. Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence. Scientific Reports. 2016;6:29628.CrossRefPubMedPubMedCentral
Metadata
Title
Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model
Authors
Victor A. Alegana
Jim Wright
Carla Pezzulo
Andrew J. Tatem
Peter M. Atkinson
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2017
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-017-0346-0

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