EVALUATION OF THE SOCIOECONOMIC STATUS OF PATIENTS RECEIVED AT RADIOLOGY DEPARTMENT, HAYATABAD MEDICAL COMPLEX PESHAWAR
Article Sidebar
Main Article Content
Abstract
Objectives: To determine the socio-economic status of patients availing Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) at the Radiology Department, Hayatabad Medical Complex.
Methodology: This cross-sectional study was conducted in Hayatabad Medical Complex, Peshawar, between August 2018 and January 2019. The study group included 355 patients availing of CT and MRI facilities in the hospital. A systematic random sampling method was used to collect data. Data was collected through an open-ended questionnaire. The cost & type of the investigation was documented. Modified Kuppuswamy's Scale was used to collect the data and was analyzed appropriately.
Results: The majority of the patients belonged to the lower-middle socio-economic class representing 38.87% of the study population. Second, the highest percentage of patients were from the upper lower stratum constituting 21.69%, while 19.44% were from upper-middle households whereas 11.83% were from lower socioeconomic classes. whereas, 8.17% belonged to the high socio-economic stratum. The average monthly household income in the lower-middle-class group was calculated to be approximately Rs. 15,000/-. 98% of patients used out-of-pocket methods for payment exposing them to the risk of increased expenditure. 5.92% of patients utilized the government-funded facility or ‘poor-free’ facility for free cost scan; or relied on donations from other sources.
Conclusion: Most of the patients were restricted in terms of their financial resources with the largest percentage of patients belonging to lower-middle-class households. Low socioeconomic status was found to be associated with missed appointments & delays in availing the CT and MRI scans.
Article Details
Work published in JPMI is licensed under a
Creative Commons Attribution-NonCommercial 2.0 Generic License.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
References
Health Care Spending and the Medicare Program. [Internet 2016]. [cited 2021 Jun 15]; Available from: www.medpac. gov.
Data Book-Rates of low-value service use and spending in Medicare [Internet 2016]. [cited 2021 Jun 23]. Available from URL: https://www.medpac.gov/ document-type/data-book/
Smith BR, Miglioretti DL, Larson EB. Ris¬ing use of diagnostic medical imaging in a large integrated health system. Health Aff (Millwood). 2008;27(6):1491-502. DOI: 10.1377/hlthaff.27.6.1491.
World bank data help desk- World bank country and lending groups [Internet 2021]. [cited 2020 Jun 23]. Avail¬able from URL:https://datahelpdesk. worldbank.org/knowledgebase/arti¬cles/90659-world-bank-country-and-lending-groups
Manzoor I, Hashmi NR, Mukhtar F. De¬terminants And Pattern Of Health Care Services Utilisation In Post Graduate Students. J Ayub Med Coll Abbottabad. 2009;21(3):100-5.
Shaikh BT, Hatcher J. Health seeking be¬haviour and health service utilization in Pakistan: challenging the policy makers. J Public Health (Oxf). 2005;27(1):49- 54. DOI: 10.1093/pubmed/fdh207.
Home|Pakistan Bureau of Statistics [In¬ternet]. [cited 2021 Jun 15]. Available from: https://www.pbs.gov.pk/
Gottret P, Schieber G. Health financing revisited: A practitioner’s guide. World Bank Publications; 2006.
Health expenditure comparisons: low, middle and high income countries. [cited 2022 Jun 15]. Available from URL: https://espace.curtin.edu.au/han¬dle/20.500.11937/5184
McIntyre D, Thiede M, Dahlgren G, Whitehead M. What are the eco¬nomic consequences for house¬holds of illness and of paying for health care in low-and middle-in¬come country contexts? Soc Sci Med. 2006;62(4):858-65. DOI:10.1016/j. socscimed.2005.07.001.
Kurji Z, Premani ZS, Mithani Y. analysis of the health care system of Pakistan: Lessons learnt and way forward. J Ayub Med Coll Abbottabad. 2016;28(3):601- 604.
Sharma R. Revised Kuppuswamy's Socioeconomic Status Scale: ex¬plained and updated. Indian Pediatr. 2017;54(10):867-70.
Kumar G, Panda N, Roy R, Bhattacharjee G. An observational study to assess the socioeconomic status and demographic profile of advanced cancer patients re¬ceiving palliative care in a tertiary-level cancer hospital of eastern India. Indian J Palliat Care. 2018;24(4):496-9. DOI: 10.4103/IJPC.IJPC_72_18.
Mughal AR, Sadiq M, Hyder SN, Qureshi AU, A Shah SS, Khan MA, Nasir JA. So¬cioeconomic status and impact of treat¬ment on families of children with con¬genital heart disease. J Coll Physicians Surg Pak. 2011;21(7):398-402.
Saleem SM. Modified Kuppuswamy scale updated for Year 2018. Indian J Appl Res. 2018;7(3)1-2.
Khan N, Nadeem RA, Hussain M. Influ¬ence of Education and Socio-Economic Factors on Stage of Cancer Diagnosis: A Study in Pakistani Population. Ann Pak Inst Med Sci. 2016;1815:225-28.
Chiragh S, Rehman A, Ahmad S. View of financial toxicity of cancer treatment and its impact on quality of life in ter¬tiary care public sector hospital. J Med Sci. 2017;25(3):313-8.
Beg DA, Younas DM, Asma C. DT. Rheumatic heart disease (RHD); So¬cio-economic and environmental risk factors for acute rheumatic fever (ARF) and rheumatic heart disease (RHD) patients in Pakistan. Prof Med J. 2016;23(03):324-7.
S Hashmi. Socio-economic evaluation of the parents of patients under treat¬ment at the oncology unit of the Nation¬al Institute of Child Health (NICH). Pak Paed J. 2006;30(4):179-83.
Pearce MS, Salotti JA, McHugh K, Pyo Kim K, Craft AW, Lubin J, et al. Socio-economic variation in CT scan¬ning in Northern England, 1990-2002. BMC Health Serv Res. 2012;12(1):1-6. DOI:10.1186/1472-6963-12-24.
Shah M, Parmar A, Chan KKW. Socio¬economic disparity trends in diagnostic imaging, treatments, and survival for non-small cell lung cancer 2007-2016. Cancer Med. 2020;9(10):3407-16. DOI:10.1002/cam4.2978.
Okoro CA, Strine TW, Young SL, Bal¬luz LS, Mokdad AH. Access to health care among older adults and re¬ceipt of preventive services. Results from the Behavioral Risk Factor Sur¬veillance System, 2002. Prev Med. 2005;40(3):337-43. DOI: 10.1016/j. ypmed.2004.06.009.
Dey S, Nambiar D, Lakshmi JK, Sheikh K, Srinath Reddy K. Health of the elderly in India: Challenges of access and af¬fordability. Washington, D.C. National Academies Press; 2012.
Doetsch J, Pilot E, Santana P, Krafft T. Potential barriers in healthcare access of the elderly population influenced by the economic crisis and the troika agreement: a qualitative case study in Lisbon, Portugal. Int J Equity Health. 2017;16(1):184-201. DOI:10.1186/ s12939-017-0679-7.
Wang N, Cao F, Liu F, Jia Y, Wang J, Bao C, et al. The effect of socioeconomic status on health-care delay and treat¬ment of esophageal cancer. J Transl Med. 2015;13(1):1-5. DOI:10.1186/ s12967-015-0579-9.
Ouasmani F, Hanchi Z, Haddou Rahou B, Bekkali R, Ahid S, Mesfioui A. De¬terminants of patient delay in seeking diagnosis and treatment among Moroc¬can women with cervical cancer. Ob¬stet Gynecol Int. 2016:4840762. DOI: 10.1155/2016/4840762.
Ma J, Zhu Q, Han S, Zhang Y, Ou W, Wang H, et al. Effect of socio-econom¬ic factors on delayed access to health care among Chinese cervical cancer patients with late rectal complications after radiotherapy. Gynecol Oncol. 2012;124(3):395-8. DOI:10.1016/j. ygyno.2011.11.040.
Dougherty B, Cooley S, Deffler R, Da¬vidorf F. Socioeconomic status and missed clinical appointments in age-re¬lated macular degeneration. Innov Ag¬ing. 2018;2:500-1.
Alhamad Z. Reasons for missing ap¬pointments in general clinics of primary health care center in Riyadh Military Hospital, Saudi Arabia. Int J Med Sci Public Heal. 2013;2(2):258-67.
Daye D, Carrodeguas E, Glover M 4th, Guerrier CE, Harvey HB, Flores EJ. Impact of delayed time to advanced imaging on missed appointments across different demographic and so¬cioeconomic factors. J Am Coll Radiol. 2018;15(5):713-20. DOI: 10.1016/j. jacr.2018.01.023.
Harvey HB, Liu C, Ai J, Jaworsky C, Guerrier CE, Flores E, Pianykh O. Pre¬dicting no-shows in radiology using regression modeling of data available in the electronic medical record. J Am Coll Radiol. 2017;14(10):1303-9. DOI: 10.1016/j.jacr.2017.05.007.
Cronin PR, DeCoste L, Kimball AB. A multivariate analysis of dermatology missed appointment predictors. JAMA Dermatology. 2013;149(12):1435-7.
Glover M 4th, Daye D, Khalilzadeh O, Pi¬anykh O, Rosenthal DI, Brink JA, Flores EJ. Socioeconomic and demographic predictors of missed opportunities to provide advanced imaging services. J Am Coll Radiol. 2017;14(11):1403-11. DOI: 10.1016/j.jacr.2017.05.015.