Assessment of Cost and Diabetes Management Due to Polypharmacy in Iran Based on Haemoglobin A1c Levels, A Cross Sectional National Study

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Assessment of Cost and Diabetes Management Due to Polypharmacy in Iran Based on Haemoglobin A1c Levels, A Cross Sectional National Study

https://doi.org/10.21203/rs.3.rs-2505848/v1

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Background

Patients with diabetes often receive multiple drug regimens termed ‘polypharmacy’. Since polypharmacy imposes excessive cost, we evaluated the prescriptions of patients with diabetes regarding polypharmacy, and its association with HbA1c levels and cost.

Method

102 million prescriptions of Iranian health insurance data in 2015 and 2016 were overlapped with STEPS Iran 2016 survey database. Then, 2075 Metabolic syndrome patients were selected. Among the patients who consumed anti diabetes medications; individual and sociodemographic factors, and polypharmacy were assessed and association between these factors and HbA1c levels were quantified using logistic regression. Each patient’s prescription costs in 2015 and 2016 and its association with individual and sociodemographic factors as well as polypharmacy was assessed using linear regression.

Results

Out of 2075 metabolic syndrome patients, 205 patients had diabetes according to their fasting blood glucose in 2015 and 2016. HbA1c was significantly more controlled (≤7%) in urban areas. No significant association existed between polypharmacy and HbA1c levels. Medication cost was significantly higher in male patients, patients above 80 years, and patients with >12 years of education (p< 0.05). Area of living and wealth index had no significant association with cost.

Conclusion

Although diabetic patients with polypharmacy significantly pay more money for their prescriptions, no significant difference was observed between their HbA1c levels.

Metabolic syndrome

polypharmacy

diabetes

HbA1c

cost

Iran.

Metabolic syndrome (MetS), which is a constellation of dyslipidaemia, hypertension and diabetes, is a worldwide challenge. Until 2020, the prevalence of MetS was 4.8% of the world adult population [1]. Also according to the center for disease control and prevention in the United States, more than one third of US adults have metabolic syndrome [2, 3]. In 2020, Iran was among the top three countries with highest prevalence of metabolic syndrome ranking third with 9.0% of its adults population involved after Spain and United Arab Emirates [1]. Since this syndrome is accompanied by various complications, MetS patients often use multiple drugs simultaneously. Concomitant use of 5 drugs or more in a prescription regardless of their clinical indication is called polypharmacy [4–7].

Polypharmacy increases the risk of drug-drug interactions, adverse drug reactions, patient incompliance and medication error. These untoward consequences lead to poor quality of life, and increased morbidity and mortality, and also impose a financial burden on both patients and health system [8]. Meanwhile, it is often hard to distinguish the adverse drug reactions from symptoms of the disease, and this confusion, increases the risk of prescription cascade in which patients take more drugs to treat the untoward reactions of their previous medication [9].

MetS prevalence ranges from 8% in type1 diabetes mellitus to 80% in type 2 diabetes mellitus according to different populations and criteria [10]. Diabetes often needs multiple drug regimen therapy which imposes all the polypharmacy related problems. The prevalence of diabetes was 536.6 million patients in 20–79 year-olds worldwide in 2021 and it is predicted to reach 783.2 million patients in 2045 [11]. Also in 2017, 5 million deaths related to diabetes were recorded worldwide between 18–99 years old, and 850 billion dollars were spent for diabetes worldwide in the same year, expected to rise to 958 billion dollars by 2045 [12]. In 2021, 14.15% of patients 18 years and above In Iran diabetes had and it is predicted to rise to 9.2 million patients until 2030 [13, 14]. Physicians usually prescribe multiple drugs to treat diabetes due to: a) obligation for precise control of blood glucose which necessitates multiple medications, and b) comorbidities such as dyslipidaemia, cardiovascular diseases, renal failure, neuropathy, retinopathy, diabetic ulcers, obesity and gastro intestinal diseases [15]. Therefore, these patients face various problems in taking their medications such as adverse reaction, drug-drug interactions and also heavy costs. These costs are both in short term in obtaining the prescribed medications and in long term following untoward consequences of polypharmacy and multiple referrals to health care facilities and hospital admissions. All these factors impose huge costs on the health system as well. Furthermore, in a study between 2006 and 2009 in Iran, the amount of money spent on over the counter (OTC) antidiabetic drugs was 100 times more than amount of money spent for prescribed antidiabetic drugs. This shows the importance of education for patients in learning the correct patterns of medicine consumption [16]. Moreover, considering the decreasing trend in insurance coverage of drugs and steep increase of drug cost due to inflation in Iran, it is extremely important to assess the necessity of polypharmacy in patients with diabetes in Iran [17–20].

We performed a cross-sectional, multi-level study. In this study, MetS was defined based on American Heart Association (AHA) criteria. According to AHA, any patient who has 3 or more conditions of high levels of triglycerides, hypertension, hyperglycaemia, low levels of HDL, and waist circumference more than > 102 cm in men, and > 89 cm in women is considered as a MetS patient [21, 22]. In this study, due to lack of data regarding patients’ waist circumference, this item was not considered in defining criteria.

In current study, according to European view, patients whose prescriptions included 5 or more drugs were considered in the polypharmacy group [5–7]. We only included the drugs which have been repeated 3 times or more in the prescriptions of one year [23]. Only the patients who had blood sugar lowering agents in their prescriptions were considered as patients with diabetes.

Data was gathered from 102 million prescriptions related to Iranians Health Insurance Service records in 2015 and 2016, which was then overlapped with STEPS Iran 2016 project data. STEPS is a national, large scale cross sectional study which assessed risk factors of 4 non-communicable diseases (cardiovascular, diabetes, cancers, chronic respiratory diseases) in Iran in 389 districts. About 31,000 patients were enrolled in STEPS 2016 study and all their demographic and epidemiologic data as well as risky behaviours were recorded in the form of laboratory data, physical examinations, and questionnaire [24]. At first, approximately 16,000 patients, whose prescriptions overlapped with STEPS data were selected from insurance data and 2075 patients, 25 year olds and above with MetS were chosen according to AHA criteria (Fig. 1). All patients have been categorized based on gender, and area of living (urban and rural). Also we defined 4 age groups; 25 to 39 years, 40 to 59 years, 60 to 80 years and over 80 years old. Also based on their education level; we had illiterate, one to 6 years of education, 7 to 12 years of education and more than 12 years. Furthermore, Principle Component Analysis (PCA) was used to define the wealth index of the patients [25]. The data regarding asset ownership and housing characteristics information are collected which are then combined into a proxy indicator such as the wealth index, which is created using PCA. Patients were accordingly divided into 5 groups; 0-<20% as poorest, 20-<40% as poor, 40-<60% as average, 60-<80% as rich and 80–100% as richest [26]. All drugs in the prescriptions were recorded as ATC codes [27]. Then, we assessed polypharmacy among the prescriptions of patients with diabetes. In this step, we separated the patients who consumed blood sugar lowering drugs three times or more during one year and evaluated HbA1c levels and polypharmacy amongst them. According to American Diabetes Association (ADA), 7% is the standard cut off for HbA1c [28]. Patients with HbA1c level equal or less than 7% were placed in controlled group and those with HbA1c higher than 7% were considered uncontrolled. Then we calculated the cost of prescribed drugs used at least once a year in 2015 and 2016, and subsequently assessed whether polypharmacy is effective among these patients in controlling their blood sugar levels based on HbA1c levels. Cost was defined as the actual price of the prescribed medications at least one time in a period of one year.

Analysis was done based on both individual factors (gender and age) and sociodemographic factors (education level, area of living, and wealth index). Correlation of HbA1c levels was evaluated with these factors and polypharmacy using adjusted and unadjusted logistic regression models. In one approach HbA1c was considered as a dependent variable and individual and socio-demographic factors and polypharmacy were all considered independent variables and regression was performed. In another approach, cumulative cost of drugs was calculated in 2015 and 2016 amongst prescriptions of patients with diabetes and then the cumulative cost was considered as dependant variable and individual and sociodemographic factors as well as polypharmacy, were all considered independent variables and linear regression was applied. Since the cost did not have normal distribution, we calculated the logarithm of the cost in our assessments. SPSS version 22 software was used for analysis of data. P value ≤ 0.05 was considered as statistically significant.

Among 2075 MetS patients in 2015 and 2016, 510 patients had diabetes mellitus type 1 and 2, among whom 205 patients (9.9%) consumed blood sugar lowering agents. Among these, 98 patients (47.8%) had polypharmacy, 137 (66.8%) were females and 68 (33.2%) were males. HbA1c of 74 patients (36.1%) was equal to or less than 7 which was considered as controlled. HbA1c was more controlled in male patients but difference was not significant. Among different age groups, 25–39 year-olds were the best controlled one with 42.9%, and 40–59 year-olds were the least controlled ones with 32.5%, but the difference was not significant. Urban areas significantly had more controlled HbA1c (40.4%) compared to rural areas (p: 0.025). Patients with more than 12 years of education had insignificantly the most controlled HbA1c levels (51.7%) and patients with 7–12 years of education were significantly the least controlled ones (23.1%), (p: 0.038). Richest patients were the best controlled individuals (50%) among wealth groups, but the difference was not significant. Controlled HbA1c was 36.7% in diabetic patients who had polypharmacy and 35.5% in without polypharmacy patients, and no significant association was observed between HbA1c levels and polypharmacy (Table 1).

Table 1

Association between individual, and sociodemographic factors, polypharmacy and HbA1c in diabetes mellitus patients using unadjusted multivariate logistic regression
Factor	N(%)	Controlled HbA1c N(%)	Crude OR^a (95% C.I)	P value	Adjusted OR^a (95% CI)	P value
Sex
Female	137(66.8)	44(32.1)
Male	68(33.2)	30(44.1)	1.669 (0.918–3.035)	0.093	1.405 (0.694–2.844)	0.344
Age group
25–39	7(3.4)	3(42.9)
40–59	80(39.0)	26(32.5)	0.642 (0.134–3.081)	0.580	0.554 (0.062–4.917)	0.596
60–80	108(52.7)	41(38.0)	0.816 (0.174–3.831)	0.797	0.666 (0.075–5.872)	0.714
> 80	10(4.9)	4(40.0)	0.889 (0.125–6.310)	0.906	1.237 (0.092–16.538)	0.873
Area of living
Rural	44(21.5)	9(20.5)
Urban	161(78.5)	65(40.4)	2.633 (1.186–5.844)	0.017*	2.667 (1.132–6.282)	0.025*
Years of schooling
0	60(29.3)	24(40.0)
1–6	64(31.2)	23(35.9)	0.841 (0.407–1.740)	0.641	0.887 (0.404–1.947)	0.765
7–12	52(25.4)	12(23.1)	0.450 (0.197–1.028)	0.058	0.378 (0.151–0.947)	0.038*
> 12	29(14.1)	15(51.7)	1.607 (0.658–3.925)	0.298	1.440 (0.548–3.785)	0.459
Wealth Index
Poorest	51(24.9)	17(33.3)
Poor	42(20.5)	16(38.1)	1.231 (0.525–2.887)	0.633	1.446 (0.576–3.628)	0.433
Average	34(16.6)	11(32.4)	0.957 (0.379–2.412)	0.925	1.325 (0.477–3.683)	0.589
Rich	48(23.4)	15(31.3)	0.909 (0.391–2.113)	0.825	0.919 (0.360–2.346)	0.860
Richest	28(13.7)	14(50.0)	2.000 (0.780–5.131)	0.149	1.655 (0.605–4.524)	0.326
Polypharmacy
No	107(52.2)	38(35.5)
Yes	98(47.8)	36(36.7)	1.054 (0.596–1.865)	0.856	0.856 (0.448–1.637)	0.639

* Indicates significance of the P value

^aOR: odds ratio

Among 205 patients consuming blood sugar lowering agents, the cost of patients prescriptions was calculated in 2015 and 2016 cumulatively, which was 58,800,442 Rials in patients without polypharmacy and 69,381,530 Rials in patients with polypharmacy and the difference was significant (p < 0.001), (Table 2).

Male patients paid almost double average cost compared to females, (p: 0.036). The patients above 80 years old significantly paid more cost compared to other age groups (p: 0.003). No significant difference was observed between urban and rural areas. The cost was significantly higher in patients with more than 12 years of schooling (p: 0.035). No significant difference was observed between different wealth groups. Patients with polypharmacy paid almost 4.7 times more average cost compared to without polypharmacy group (p < 0.001), (Table 2).

Table 2

Association of prescriptions cost with individual and sociodemographic factors of patient, and polypharmacy using linear regression
Factor	P value	Odds ratio	CI (95%)
Sex
Female	0.036*	0.559	0.324–0.964
Male
Age group
25–39	0.228	0.647	0.318–1.314
40–59	0.001*	0.336	0.173–0.653
60–80	0.003*	0.502	0.319–0.791
> 80
Area of living
Rural	0.357	0.792	0.481–1.302
Urban
Years of schooling
0	0.251	0.694	0.372–1.295
1–6	0.011*	0.320	0.133–0.769
7–12	0.050*	0.439	0.192–1.001
> 12
Wealth Index
Poorest	0.191	1.386	0.850–2.281
Poor	0.061	0.439	0.186–1.036
Average	0.795	0.933	0.550–1.581
Rich	0.666	0.888	0.519–1.520
Richest
Polypharmacy
No	< 0.001*	0.211	0.106–0.423
Yes

* Indicates significance of the P value

In this cross-sectional, multi-level, epidemiological, national study, we assessed whether the polypharmacy, individual and sociodemographic factors were related to the control of HbA1c levels in patients taking antidiabetic medications in 2015 and 2016. HbA1c was significantly more controlled in urban areas. This maybe because of the more accessibility to health care facilities and pharmacies in urban areas. The same result was observed in a study by L.Govan et al. in Scotland on 23 thousand diabetes type 1 patients, in which significant association was seen between HbA1c levels and area of living. Their results showed that patients in more deprived areas had higher levels of HbA1c and higher hospital admissions due to diabetic ketoacidosis [29]. No significant relation was seen between age, gender, education level, wealth index and control of HbA1c of the patients. We also assessed the associaton between wealth index and HbA1c levels among the patients of urban areas and the patients among rural areas separately and still no significant relation was seen. This means that the area of living and maybe culture and ethinicity are more determinative factors in the control of HbA1c rather than the purchasing power of the patients. This implies that the focal point for improving health in rural areas should be educational, not financial or monetary. Similar to our results, In a study by Emelia Mellergard et al. in Sweeden, no significant association was observed between levels of HbA1c with age, and education level of the patients but male patients had significantly more variability in their HbA1c levels [30]. In our study, no significant difference was observed between patients with and without polypharmacy regarding the levels of their HbA1c. Likewise, in a systematic review and meta-analysis by Labib Al-Musawee et al. on over 1 million patients in Portugal in 2019, no significant association was observed between polypharmacy and control of HbA1c meanwhile, significant positive association was observed between polypharmacy and mortality [17]. But, in another study by Akshar Y. Patel et al. in 2012 in the US, 324 patients ≥ 65 years were divided into three groups: low medication group with less than 5 drugs, average group with 5–10 drugs and high medication group with more than 10 drugs in their prescriptions. Their results showed that after 2 years of monitoring, control of HbA1c was significantly better in the average group [31]. Our results were not consistent with the findings in this study.

Expectedly the cost of medications of patients with diabetes was significantly higher in patients with polypharmacy. On the other hand, we observed that polypharmacy does not necessarily cause better control of HbA1c. Therefore, we can conclude that patients with diabetes who have polypharmacy and pay more, do not necessarily have a better control on their HbA1c levels. Our results also depicted that male patients with diabetes significantly paid more money compared to women. This may be because men have more job opportunities than women, and consequently have more income to pay for their medications. Although the association is significant, but the population of our study was not big, therefore the confidence interval was wide, and it seems that studies on larger samples are required. Logically, patients with diabetes above 80 years old significantly paid more for their medications. It may be because they have more comorbidities and therefore need to take more medications. From the education point of view, patients with more than 12 years of education significantly paid more money for their medications but wealth index had no association with the average cost. This may again indirectly denote that intellectual knowledge is a more important determinant of money expenditure policy compared to wealth.

Considering financial point of view, it is important to monitor the prescriptions with polypharmacy using online monitoring platforms and references such as Beers criteria and Lexi-interact to minimize the unnecessary polypharmacy and enhance patients’ quality of care in Iran [15, 32, 33].

According to our study, although polypharmacy does not positively affect HbA1c levels, it imposes unnecessary cost on these patients. Moreover, wealth status of the diabetic patients is not as determinative as imagined for their HbA1c levels.

MetS, OTC, AHA, PCA, ADA, ATC

Ethics approval and consent to participate

The Ethical Committee of Shahid Beheshti University of Medical Sciences approved the study of the Medical School Pharmacology under reference number IR.SBMU.MSP.REC.1400.004.

Consent for publication

The data used for this research is derived from STEPS 2016 survey, and the aforementioned consent is already obtained by that survey authorities at Non Communicable Disease Research Center (NCDRC), Tehran, Iran.

Availability of data and materials

The data that support the findings of this study are available from Non Communicable Research Center (NCDRC), Tehran, Iran, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Non Communicable Research Center (NCDRC), Tehran, Iran.

Competing interests

The authors declare that they have no competing interests.

Funding

The authors received no source of funding for this study.

Authors contributions

MD: Performed the statistical analysis and prepared the manuscript.

FF: Provided data, aided in statistical analysis.

HJ: Suggested the main idea and topic of the research.

SM: Gave comments on manuscript, Supervised statistical analysis.

MS: Gave comments on manuscript, Supervised statistical analysis.

MHF: Supervised manuscript preparation and reviewed the manuscript.

Acknowledgment

Special thanks to Dr. Mohamadreza Malekpour for helping in providing data and the statistical analysis of this article.

Noubiap JJ, Nansseu JR, Lontchi-Yimagou E, Nkeck JR, Nyaga UF, Ngouo AT, et al. Global, regional, and country estimates of metabolic syndrome burden in children and adolescents in 2020: a systematic review and modelling analysis. The Lancet Child & adolescent health. 2022;6(3):158–70. 10.1016/s2352-4642(21)00374-6.
Gurka MJ, Filipp SL, DeBoer MD. Geographical variation in the prevalence of obesity, metabolic syndrome, and diabetes among US adults. Nutr diabetes. 2018;8(1):1–8. https://doi.org/10.1038/s41387-018-0024-2.
Denson JL, Gillet AS, Zu Y, Brown M, Pham T, Yoshida Y, et al. Metabolic syndrome and acute respiratory distress syndrome in hospitalized patients with COVID-19. JAMA Netw open. 2021;4(12):e2140568–e. 10.1001/jamanetworkopen.2021.40568.
Grundy SM. Drug therapy of the metabolic syndrome: minimizing the emerging crisis in polypharmacy. Nat Rev Drug Discovery. 2006;5(4):295–309. https://doi.org/10.1038/nrd2005.
Haider SI, Johnell K, Weitoft GR, Thorslund M, Fastbom J. The influence of educational level on polypharmacy and inappropriate drug use: a register-based study of more than 600,000 older people. J Am Geriatr Soc. 2009;57(1):62–9. https://doi.org/10.1111/j.1532-5415.2008.02040.x.
Jyrkkä J, Enlund H, Lavikainen P, Sulkava R, Hartikainen S. Association of polypharmacy with nutritional status, functional ability and cognitive capacity over a three-year period in an elderly population. Pharmacoepidemiology and drug safety. 2011;20(5):514 – 22. DOI: https://doi.org/10.1002/pds.2116.
Gnjidic D, Hilmer S, Blyth F, Naganathan V, Cumming R, Handelsman D, et al. High-risk prescribing and incidence of frailty among older community‐dwelling men. Clin Pharmacol Ther. 2012;91(3):521–8. https://doi.org/10.1038/clpt.2011.258.
Rodrigues MCS. Oliveira Cd. Drug-drug interactions and adverse drug reactions in polypharmacy among older adults: an integrative review. Rev Latinoam Enferm. 2016;24. 10.1590/1518-8345.1316.2800.
Guthrie B, Makubate B, Hernandez-Santiago V, Dreischulte T. The rising tide of polypharmacy and drug-drug interactions: population database analysis 1995–2010. BMC Med. 2015;13(1):1–10. https://doi.org/10.1186/s12916-015-0322-7.
Chillarón JJ, Le-Roux JAF, Benaiges D, Pedro-Botet J. Type 1 diabetes, metabolic syndrome and cardiovascular risk. Metabolism. 2014;63(2):181–7. https://doi.org/10.1016/j.metabol.2013.10.002.
Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. Diabetes Res Clin Pract. 2022;183:109119. https://doi.org/10.1016/j.diabres.2021.109119. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045.
Cho N, Shaw J, Karuranga S, Huang Y, da Rocha Fernandes J, Ohlrogge A et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes research and clinical practice. 2018;138:271–81. DOI: https://doi.org/10.1016/j.diabres.2018.02.023.
Moradpour F, Rezaei S, Piroozi B, Moradi G, Moradi Y, Piri N, et al. Prevalence of prediabetes, diabetes, diabetes awareness, treatment, and its socioeconomic inequality in west of Iran. Sci Rep. 2022;12(1):1–11. https://doi.org/10.1038/s41598-022-22779-9.
Esteghamati A, Larijani B, Aghajani MH, Ghaemi F, Kermanchi J, Shahrami A, et al. Diabetes in Iran: prospective analysis from first nationwide diabetes report of National Program for Prevention and Control of Diabetes (NPPCD-2016). Sci Rep. 2017;7(1):1–10. https://doi.org/10.1038/s41598-017-13379-z.
Good CB. Polypharmacy in elderly patients with diabetes. Diabetes Spectr. 2002;15(4):240–8. https://www.researchgate.net/profile/Chester-Good/publication/244936233_Polypharmacy_in_Elderly_Patients_With_Diabetes/links/56d99a6808aee1aa5f828ac8/Polypharmacy-in-Elderly-Patients-With-Diabetes.pdf.
Soleymani F, Farshchi A, Haerizadeh M. PDB69 Prescription Pattern Study of Type 2 Diabetes Mellitus in Iran. Value in Health. 2011;14(7):A484. https://doi.org/10.1016/j.jval.2011.08.1372.
Labib A-M, Martins AP, Raposo JF, Torre C. The association between polypharmacy and adverse health consequences in elderly type 2 diabetes mellitus patients; a systematic review and meta-analysis. Diabetes Res Clin Pract. 2019;155:107804. 10.1016/j.diabres.2019.107804.
Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17(1):230. https://doi.org/10.1186/s12877-017-0621-2.
Kojima G, Bell C, Tamura B, Inaba M, Lubimir K, Blanchette PL, et al. Reducing cost by reducing polypharmacy: the polypharmacy outcomes project. J Am Med Dir Assoc. 2012;13(9):818. https://doi.org/10.1016/j.jamda.2012.07.019. e11-. e15.
Baruth JM, Gentry MT, Rummans TA, Miller DM, Burton MC. Polypharmacy in older adults: the role of the multidisciplinary team. Hospital practice (1995). 2020;48(sup1):56–62. DOI: https://doi.org/10.1080/21548331.2019.1706995.
Espósito RC, de Medeiros PJ, de Souza Silva F, Oliveira AG, Aragão CFS, Rocha HAO et al. Prevalence of the metabolic syndrome according to different criteria in the male population during the Blue November Campaign in Natal, RN, Northeastern Brazil. Diabetes, metabolic syndrome and obesity: targets and therapy. 2018;11:401. DOI: 10.2147/DMSO.S168430.
Yamagishi K, Iso H. The criteria for metabolic syndrome and the national health screening and education system in Japan. Epidemiol health. 2017;39. 10.4178/epih.e2017003.
Cahir C, Fahey T, Teeling M, Teljeur C, Feely J, Bennett K. Potentially inappropriate prescribing and cost outcomes for older people: a national population study. Br J Clin Pharmacol. 2010;69(5):543–52. https://doi.org/10.1111/j.1365-2125.2010.03628.x.
Djalalinia S, Modirian M, Sheidaei A, Yoosefi M, Zokaiee H, Damirchilu B et al. Protocol design for large–scale cross–sectional studies of surveillance of risk factors of non–communicable diseases in Iran: STEPs 2016. Archives of Iranian medicine. 2017;20(9):-. DOI, https://pubmed.ncbi.nlm.nih.gov/29048923/.
Abdi H, Williams LJ. Principal component analysis. Wiley interdisciplinary reviews: computational statistics. 2010;2(4):433–59. DOI: https://doi.org/10.1002/wics.101.
Lisa Hjelm AM, Miller D, Wadhwa A. VAM Guidance Paper, creation of a wealth index. 2017. DOI, https://docs.wfp.org/api/documents/WFP-0000022418/download/.
WorldHealthOrganization(WHO), February 5. AGEING AND HEALTH. DOI; 2018. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health2018 update, available.
Association AD. 6. Glycemic Targets: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2020;44(Supplement1):73–S84. 10.2337/dc21-S006.
Govan L, Maietti E, Torsney B, Wu O, Briggs A, Colhoun H, et al. The effect of deprivation and HbA1c on admission to hospital for diabetic ketoacidosis in type 1 diabetes. Diabetologia. 2012;55(9):2356–60. https://doi.org/10.1007/s00125-012-2601-6.
Mellergård E, Johnsson P, Eek F. Sociodemographic factors associated with HbA1c variability in type 2 diabetes: a prospective exploratory cohort study. BMC Endocr Disorders. 2020;20(1):1–8. https://doi.org/10.1186/s12902-020-00585-6.
Patel AY, Shah P, Flaherty JH. Number of medications is associated with outcomes in the elderly patient with metabolic syndrome. J geriatric cardiology: JGC. 2012;9(3):213. 10.3724/SP.J.1263.2011.12011.
Wojtczak D, Kasznicki J, Drzewoski J. Pros and cons of polypharmacy in elderly patients with diabetes. Clin Diabetol. 2017;6(1):34–8. 10.5603/DK.2017.0006.
Panel AGSBCUE, Fick DM, Semla TP, Steinman M, Beizer J, Brandt N, et al. American Geriatrics Society 2019 updated AGS Beers Criteria® for potentially inappropriate medication use in older adults. J Am Geriatr Soc. 2019;67(4):674–94. 10.1111/jgs.15767.

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Assessment of Cost and Diabetes Management Due to Polypharmacy in Iran Based on Haemoglobin A1c Levels, A Cross Sectional National Study

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