Top

BMC Public Health

Published in:

Open Access 01-12-2021 | Stroke | Research article

Analysing and quantifying the effect of predictors of stroke direct costs in South Africa using quantile regression

Authors: Lyness Matizirofa, Delson Chikobvu

Published in: BMC Public Health | Issue 1/2021

Abstract

Background

In South Africa (SA), stroke is the second highest cause of mortality and disability. Apart from being the main killer and cause of disability, stroke is an expensive disease to live with. Stroke costs include death and medical costs. Little is known about the stroke burden, particularly the stroke direct costs in SA. Identification of stroke costs predictors using appropriate statistical methods can help formulate appropriate health programs and policies aimed at reducing the stroke burden. Analysis of stroke costs have in the main, concentrated on mean regression, yet modelling with quantile regression (QR) is more appropriate than using mean regression. This is because the QR provides flexibility to analyse the stroke costs predictors corresponding to quantiles of interest. This study aims to estimate stroke direct costs, identify and quantify its predictors through QR analysis.

Methods

Hospital-based data from 35,730 stroke cases were retrieved from selected private and public hospitals between January 2014 and December 2018. The model used, QR provides richer information about the predictors on costs. The prevalence-based approach was used to estimate the total stroke costs. Thus, stroke direct costs were estimated by taking into account the costs of all stroke patients admitted during the study period. QR analysis was used to assess the effect of each predictor on stroke costs distribution. Quantiles of stroke direct costs, with a focus on predictors, were modelled and the impact of predictors determined. QR plots of slopes were developed to visually examine the impact of the predictors across selected quantiles.

Results

Of the 35,730 stroke cases, 22,183 were diabetic. The estimated total direct costs over five years were R7.3 trillion, with R2.6 billion from inpatient care. The economic stroke burden was found to increase in people with hypertension, heart problems, and diabetes. The age group 55–75 years had a bigger effect on costs distribution at the lower than upper quantiles.

Conclusions

The identified predictors can be used to raise awareness on modifiable predictors and promote campaigns for healthy dietary choices. Modelling costs predictors using multivariate QR models could be beneficial for addressing the stroke burden in SA.

Owolabi MO, Sarfo F, Akinyemi R, Gebregziabher M, Akpa O, Akpalu A, et al. Dominant modifiable risk factors for stroke in Ghana and Nigeria (SIREN): a case-control study. Lancet Glob Health. 2018;6(4):e436–46. https://doi.org/10.1016/S2214-109X(18)30002-0.CrossRefPubMedPubMedCentral

Joo H, George F. A literature review of indirect costs associated with stroke. J Stroke Cerebrovasc Dis. 2014;7:1753–63.CrossRef

Jongen VW, Lalla-Edward ST, Vos AG, Godijk NG, Tempelman H, Grobbee DE, et al. Hypertension in a rural community in South Africa: what they know, what they think they know and what they recommend. BMC Public Health. 2019;19(1):341. https://doi.org/10.1186/s12889-019-6642-3.CrossRefPubMedPubMedCentral

Sajjad A, Chowdhury R, Felix JF, Ikram MA, Mendis S, Tiemeier H, et al. A systematic evaluation of stroke surveillance studies in low- and middle-income countries. Neurology. 2013;80(7):677–84. https://doi.org/10.1212/WNL.0b013e318281cc6e.CrossRefPubMedPubMedCentral

Adeloye D. An estimate of the incidence and prevalence of stroke in Africa: a systematic review and Meta-analysis. PLoS One. 2014;9(6):e100724. https://doi.org/10.1371/journal.pone.0100724.CrossRefPubMedPubMedCentral

Lemogoum D, Degaute J-P, Bovet P. Stroke prevention, treatment, and rehabilitation in sub-Saharan Africa. Am J Prev Med. 2005;29(5):95–101. https://doi.org/10.1016/j.amepre.2005.07.025.CrossRefPubMed

Maredza M, Chola L. Economic burden of stroke in a rural south African setting. eNeurologicalSci. 2016;3:26–32. https://doi.org/10.1016/j.ensci.2016.01.001.CrossRefPubMedPubMedCentral

Maredza M, Bertram MY, Tollman SM. Disease burden of stroke in rural South Africa: an estimate of incidence, mortality and disability adjusted life years. BMC Neurol. 2015;15(1):54. https://doi.org/10.1186/s12883-015-0311-7.CrossRefPubMedPubMedCentral

Maredza M, Bertram MY, Gómez-Olivé XF, Tollman SM. Burden of stroke attributable to selected lifestyle risk factors in rural South Africa. BMC Public Health. 2016;16(1):143. https://doi.org/10.1186/s12889-016-2805-7.CrossRefPubMedPubMedCentral

10.

Mudzi W, Stewart A, Musenge E. Case fatality of patients with stroke over a 12-month period post stroke. S Afr Med J. 2012;102(9):765–7. https://doi.org/10.7196/SAMJ.5742.CrossRefPubMed

11.

Amitrano D, da Silva IRF, Liberato BB, Batistella V, Oliveira J, Nascimento OJM. Simple prediction model for unfavorable outcome in ischemic stroke after intravenous thrombolytic therapy. Arq Neuropsiquiatr. 2016;74(12):986–9. https://doi.org/10.1590/0004-282x20160152.CrossRefPubMed

12.

Choudhury MJH, Chowdhury MTI, Nayeem A, Jahan WA. Modifiable and non-modifiable risk factors of stroke: a review update. J Natl Inst Neurosci Bangladesh. 2015;1(1):22–6. https://doi.org/10.3329/jninb.v1i1.22944.CrossRef

13.

Aheto JMK. Simultaneous quantile regression and determinants of under-five severe chronic malnutrition in Ghana. BMC Public Health. 2020;20(1):644. https://doi.org/10.1186/s12889-020-08782-7.CrossRefPubMedPubMedCentral

14.

Koenker R, Hallock KF. Quantile Regression. J Econ Perspect. 2001;15(4):143–56. https://doi.org/10.1257/jep.15.4.143.CrossRef

15.

Stats SA. STATISTICAL RELEASE P0141.Consumer Price Index. December 2020. 2020. http://www.statssa.gov.za/. Accessed 16 Feb 2021.

16.

Evers SMAA, Struijs JN, Ament AJHA, van Genugten MLL, Jager J, Hans C, et al. International Comparison of Stroke Cost Studies Stroke. 2004;35(5):1209–15. https://doi.org/10.1161/01.STR.0000125860.48180.48.CrossRefPubMed

17.

Stats SA. Statistics South Africa, statistical release, quarterly labour force survey. Pretoria: Statistics South Africa; 2018. www.statssa.gov.za. Accessed 5 Nov 2019.

18.

National Department of Health. Uniform Patient Fee Schedule For Paying Patients Attending Public Hospitals. 2009.

19.

Koenker R. Quantile Regression. Cambridge: Cambridge University Press. 2005. https://doi.org/10.1017/CBO9780511754098.

20.

Diringer MN, Edwards DF, Mattson DT, Akins PT, Sheedy CW, Hsu CY, et al. Predictors of acute hospital costs for treatment of ischemic stroke in an academic center. Stroke. 1999;30(4):724–8. https://doi.org/10.1161/01.STR.30.4.724.CrossRefPubMed

21.

Patel A, Berdunov V, Quayyum Z, King D, Knapp M, Wittenberg R. Estimated societal costs of stroke in the UK based on a discrete event simulation. Age Ageing. 2020;49(2):270–6. https://doi.org/10.1093/ageing/afz162.CrossRefPubMed

22.

van Excel J, Koopmanschap MA, van Wijingaarden JD, Reimer WJ. Costs of stroke and stroke services: determinants of patient costs and a comparison of costs of regular care and care organised in stroke services. Cost Effective Resour Alloc. 2003;26:1–11.

23.

Cha Y-J. The economic burden of stroke based on South Korea’s National Health Insurance Claims Database. Int J Health Policy Manag. 2018;7(10):904–9. https://doi.org/10.15171/ijhpm.2018.42.CrossRefPubMedPubMedCentral

24.

Wang G, Zhang Z, Ayala C, Dunet DO, Fang J, George MG. Costs of hospitalization for stroke patients aged 18-64 years in the United States. J Stroke Cerebrovasc Dis. 2014;23(5):861–8. https://doi.org/10.1016/j.jstrokecerebrovasdis.2013.07.017.CrossRefPubMed

25.

Abdo RR, Abboud HM, Salameh PG, Jomaa NA, Rizk RG, Hosseini HH. Direct medical cost of hospitalization for acute stroke in Lebanon: a prospective incidence-based multicenter cost-of-illness study. INQUIRY. 2018;55:004695801879297. https://doi.org/10.1177/0046958018792975.CrossRef

26.

Meretoja A, Kaste M, Roine RO, Juntunen M, Linna M, Hillbom M, et al. Direct costs of patients with stroke can be continuously monitored on a National Level: performance, effectiveness, and costs of treatment episodes in stroke (PERFECT stroke) database in Finland. Stroke. 2011;42(7):2007–12. https://doi.org/10.1161/STROKEAHA.110.612119.CrossRefPubMed

27.

Jennum P, Iversen HK, Ibsen R, Kjellberg J. Cost of stroke: a controlled national study evaluating societal effects on patients and their partners. BMC Health Serv Res. 2015;15(1):466. https://doi.org/10.1186/s12913-015-1100-0.CrossRefPubMedPubMedCentral

28.

Saka O, McGuire A, Wolfe C. Cost of stroke in the United Kingdom. Age Ageing. 2008;38(1):27–32. https://doi.org/10.1093/ageing/afn281.CrossRef

29.

Liu X, Kong D, Lian H, Zhao X, Zhao Y, Xu Q, et al. Distribution and predictors of hospital charges for haemorrhagic stroke patients in Beijing, China, march 2012 to February 2015: a retrospective study. BMJ Open. 2018;8(3):e017693. https://doi.org/10.1136/bmjopen-2017-017693.CrossRefPubMedPubMedCentral

Title: Analysing and quantifying the effect of predictors of stroke direct costs in South Africa using quantile regression
Authors: Lyness Matizirofa
Delson Chikobvu
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Stroke
Hypertension
Hypertension
Published in: BMC Public Health / Issue 1/2021
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-021-11592-0

At a glance: The STEP trials

Springer Medicine

Analysing and quantifying the effect of predictors of stroke direct costs in South Africa using quantile regression

Abstract

Background

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Citizen science as a tool for arboviral vector surveillance in a resourced-constrained setting: results of a pilot study in Honiara, Solomon Islands, 2019

Cultural adaptation, translation and validation of the Spanish version of Past-day Adults’ Sedentary Time

Association between electronic cigarette use and tobacco cigarette smoking initiation in adolescents: a systematic review and meta-analysis

Psychosocial beliefs related to intention to use HIV testing and counselling services among suspected tuberculosis patients in Kassala state, Sudan

Global trends and predictors of face mask usage during the COVID-19 pandemic

Can a multi-level intervention approach, combining behavioural disciplines, novel technology and incentives increase physical activity at population-level?