Out of pocket expenditure on hypertension and diabetes mellitus among people residing in the rural field practice area of a medical college: a community based cross-sectional study

Authors

  • Megha Sharma Department of Mathematics and statistics, Banasthali Vidyapith, Rajasthan, India
  • Shalini Chandra Department of Mathematics and statistics, Banasthali Vidyapith, Rajasthan, India

DOI:

https://doi.org/10.18203/2394-6040.ijcmph20261410

Keywords:

AR-GWR, Bandwidth selection, COVID-19, γ-divergence, GWR, OLS, Predictive modelling, Spatial regression

Abstract

Background: Out-of-pocket expenditure (OOPE) on health care especially with non-communicable diseases forces households into poverty. The study was conducted with the aim of understanding the out-of-pocket health expenditure and their coping mechanisms in patients suffering from hypertension and diabetes mellitus in the rural field practice area.
Methods: A community-based cross-sectional study was conducted among 230 study participants suffering from hypertension or diabetes mellitus or both in the rural field practice area of Andhra Medical College, Visakhapatnam.
Results: The total mean OOPE among hypertensives was ₹ 501.69, among diabetics it was ₹ 1522.27, and among both hypertensives and diabetics, it was ₹1352.93. Age<50 years, being literate, belonging to upper-middle and lower-middle socioeconomic status, being married, suffering from diabetes mellitus and receiving treatment from a private hospital were associated with the presence of OOPE.
Conclusions: The current study highlights the rising economic burden of hypertension and diabetes mellitus. There is a significant impact of OOPE on household finances. Many households resort to coping mechanisms such as family members providing the money, borrowing money, selling assets or spending from savings or pension.

References

Chaurasia R, Singh S, Singh V. Progression of COVID-19 in India: a linear regression analysis. medRxiv. 2021.

Kumar A, Awasthi A. Piecewise linear regression for predicting COVID-19 cases in India. SpringerLink. 2020.

Kang D, Choi H, Kim JH, Choi J. Spatial epidemic dynamics of the COVID-19 outbreak in China. Int J Infect Dis. 2020;94:96-102.

Mollalo A, Vahedi B, Rivera KM. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Sci Total Environ. 2020;728:138884.

Fotheringham AS, Brunsdon C, Charlton M. Geographically weighted regression: The analysis of spatially varying relationships. Wiley; 2002.

LeSage JP. A family of geographically weighted regression models. In: Anselin L, Florax RJM, Rey SJ, eds. Advances in spatial econometrics. 1st edn. Springer Berlin, Heidelberg; 2004:241-264.

Zhang J, Mei CL. Local least absolute deviation estimation of spatially varying coefficients models: Robust geographically weighted regression approaches. Int J Geogr Inf Sci. 2011;25(9):1417-38.

Salvati N, Pratesi M, Giusti C. The use of M-quantile models as an alternative to random effect models in small area estimation. J R Stat Soc Ser A. 2012;175(1):267-86.

Chen Y, Okada N, Zhou Y. Geographically weighted regression based on the asymmetric loss function. Stoch Environ Res Risk Assess. 2012;26(6):799-810.

Sugasawa S, Murakami D. Adaptively robust geographically weighted regression. J Spat Sci. 2021;66(2):285-98.

Hunter DR, Lange K. A tutorial on MM algorithms. Am Stat. 2004;58(1):30-7.

Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. BMJ. 2020;369.

Downloads

Published

2026-04-30

How to Cite

Sharma, M., & Chandra, S. (2026). Out of pocket expenditure on hypertension and diabetes mellitus among people residing in the rural field practice area of a medical college: a community based cross-sectional study. International Journal Of Community Medicine And Public Health, 13(5), 2282–2289. https://doi.org/10.18203/2394-6040.ijcmph20261410

Issue

Section

Original Research Articles