Factor Influencing Willingness to Pay for Prediabetes Screening and Prevention Program: A Systematic Review

https://doi.org/10.22146/jmpf.95004

Janne Rochmanov(1), Susi Ari Kristina(2*), Nanang Munif Yasin(3)

(1) Master Student of Pharmacy Management, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta
(2) Department of Pharmaceutics, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta
(3) Department of Pharmaceutics, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author

Abstract


Background: IDF, or The International Diabetes Federation predicts that by 2045, there will be 783 million adults (12,2%) with diabetes globally, up from 540 million (10,5%) in 2021 projects that by 2045, there will be 783 million adults (12,2%) with diabetes globally, up from 540 million (10,5%) in 2021. Research has shown that lifestyle modifications, such as changes in diet and exercise, can significantly reduce the chance that prediabetes patients will get diabetes.

Objectives: The aim of this systematic review was to examine the factor influencing willingness to pay (WTP) for prediabetes screening and prevention programs.

Methods: Between October and December 2023, relevant keywords were used to search Scopus, PubMed, Sage, and Web Science for articles published between 2006 and 2023. This provided the necessary information. The Quality assessment of the studies was assessed using JBI for Cross–Sectional studies.

Results: Finally, a total of eight studies were selected for review. Over 90% of the participants were men, and most had experience as health workers. Overall, respondents accepted it well with a positive prevalence of high willingness to pay, namely more than 78.9%. The most frequently used method for measuring WTP was CVM (25%) and DCM (25%).

Conclusion: Despite the fact that some people would prefer to pay more, the results demonstrated that WTP was comparatively high when screening tests and preventative were provided for free or at a reasonable cost. It is recommended to increase people's awareness and motivation for lay people with diabetes.


Keywords


Factor Influencing; Prediabetes; Prevention Program; Screening; Willingness to Pay

Full Text:

PDF


References

  1. Diabetes DOF. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(SUPPL. 1). doi:10.2337/dc10-S062
  2. Asmelash D, Mesfin Bambo G, Sahile S, Asmelash Y. Prevalence and associated factors of prediabetes in adult East African population: A systematic review and meta-analysis. Heliyon. 2023;9(11):e21286. doi:10.1016/j.heliyon.2023.e21286
  3. Care D, Suppl SS. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(January):S14-S31. doi:10.2337/dc20-S002
  4. Webber S. International Diabetes Federation. Vol 102.; 2013. doi:10.1016/j.diabres.2013.10.013
  5. Lin X, Xu Y, Pan X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020;10(1):1-11. doi:10.1038/s41598-020-71908-9
  6. Bell K, Shaw JE, Maple-Brown L, et al. A position statement on screening and management of prediabetes in adults in primary care in Australia. Diabetes Res Clin Pract. 2020;164:108188. doi:10.1016/j.diabres.2020.108188
  7. Duan D, Kengne AP, Echouffo-Tcheugui JB. Screening for Diabetes and Prediabetes. Endocrinol Metab Clin North Am. 2021;50(3):369-385. doi:10.1016/j.ecl.2021.05.002
  8. Fujiati II, Damanik HA, Bachtiar A, Nurdin AA, Ward P. Development and validation of prediabetes risk score for predicting prediabetes among Indonesian adults in primary care: Cross-sectional diagnostic study. Interv Med Appl Sci. 2017;9(2):76-85. doi:10.1556/1646.9.2017.18
  9. Ghahramani S, Sepehrpoor M, Kazerooni AAR, et al. Estimating willingness to pay for diabetes complications. Prim Care Diabetes. 2022;16(6):829-836. doi:10.1016/j.pcd.2022.09.012
  10. Alva ML, Samuel-Hodge CD, Porterfield D, Thomas T, Leeman J. A Feasibility Study of Supply and Demand for Diabetes Prevention Programs in North Carolina. Prev Chronic Dis. 2017;14:1-11. doi:10.5888/pcd14.160604
  11. Roberts S, Barry E, Craig D, Airoldi M, Bevan G, Greenhalgh T. Preventing type 2 diabetes: Systematic review of studies of cost-effectiveness of lifestyle programmes and metformin, with and without screening, for pre-diabetes. BMJ Open. 2017;7(11). doi:10.1136/bmjopen-2017-017184
  12. Xiao B, Gu X, Jin L, et al. Willingness to pay for diabetic retinopathy screening in Qujiang District, rural Guangdong, southern China: a cross-sectional study. BMJ Open. 2023;13(4):1-8. doi:10.1136/bmjopen-2022-065792
  13. Vlachou C, Katostaras T. Willingness to pay analysis of elderly diabetics for confronting diabetic nephropathy and diabetic neuropathy. Rev Clin Pharmacol Pharmacokinet Int Ed. 2020;34(1):23-30.
  14. Jackson SL, Safo SE, Staimez LR, et al. Glucose challenge test screening for prediabetes and early diabetes. Diabet Med. 2017;34(5):716-724. doi:10.1111/dme.13270
  15. Kangethe A, Franic DM, Corso PS. Comparing the validity of the payment card and structured haggling willingness to pay methods: The case of a diabetes prevention program in rural Kenya. Soc Sci Med. 2016;169:86-96. doi:10.1016/j.socscimed.2016.09.018
  16. Rothman KJ. Journal of Epidemiology. Public Health. 2007;17(6):587-592. doi:10.1093/aje/155.5.478
  17. Johnson FR, Manjunath R, Mansfield CA, Clayton LJ, Hoerger TJ, Zhang P. High-risk individuals’ willingness to pay for diabetes risk-reduction programs. Diabetes Care. 2006;29(6):1351-1356. doi:10.2337/dc05-2221
  18. Delpech R, Bloy G, Panjo H, Falcoff H, Ringa V, Rigal L. Physicians’ preventive practices: more frequently performed for male patients and by female physicians. BMC Health Serv Res. 2020;20(1):331. doi:10.1186/s12913-020-05136-2
  19. Saleh S, Abril J. Impact of sociodemographic factors on the relationship between perceived general health and number of healthcare visits. Reg Sci Policy Pract. Published online 2022. https://doi.org/10.1101/2023.06.18.23291552
  20. Kabir S, Farrokhvar L, Russell MW, Forman A, Kamali B. Regional socioeconomic factors and length of hospital stay: a case study in Appalachia. J Public Health (Bangkok). 2022;30(6):1513-1522. doi:10.1007/s10389-020-01418-5



DOI: https://doi.org/10.22146/jmpf.95004

Article Metrics

Abstract views : 1654 | views : 1253

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 JURNAL MANAJEMEN DAN PELAYANAN FARMASI (Journal of Management and Pharmacy Practice)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

©Jurnal Manajemen dan Pelayanan Farmasi
Faculty of Pharmacy
Universitas Gadjah Mada
Creative Commons License
View My Stats