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Diabetes mellitus, Cardiovascular diseases, Comorbidity, Healthcare resources, What&,rsquo s Known Cardiovascular disease (CVD) is the most prevalent comorbidity among diabetic patients. In developing countries, data on diabetes with CVD comorbidity and the associated healthcare resource utilization and expenditures (HRUE) are scarce. What&,rsquo s New Overall, 63% of patients treated for diabetes received concomitant cardiovascular medications. In all categories of healthcare services, comorbid conditions in patients with CVD substantially increase the HRUE of patients with diabetes. IntroductionDiabetes mellitus (DM) is the most prevalent disorder with a high cost of care and a major public health issue worldwide. Globally, in 2013, more than 380 million adults suffered from DM resulting in approximately 1.3 million deaths. 1, , 2, It is projected that the number of DM patients will rise to more than 590 million by 2035. 1, The highest prevalence of DM is reported in the Middle East and North Africa. 1, Iran has the second-highest number of DM patients in the Middle East. An Iranian national survey conducted in 2011 reported that DM had affected over four million Iranians, 2, a two-fold increase in 40 years. 3, By 2030, it is estimated that around 9.2 million Iranians will suffer from DM. 4, In 2009, the total health care costs for Iranian DM patients were about 3.64 billion U.S. dollars (USD), consisting of direct (1.71 billion USD) and indirect (1.93 billion USD) costs. 4, Cardiovascular disease (CVD) has a strong association with DM and is very common among people with diabetes. 5, During recent decades, there has been a substantial increase in the incidence of CVD with diabetes. A systematic review reported that, globally, CVD has affected about 32.2% of all people with type-2 diabetes, but the true estimate could be as high as 60%. 5, It has also been reported that CVD is the main cause of mortality in people with diabetes. 6, Some studies have shown that the incidence of CVD with diabetes has strongly contributed to the need to assess healthcare utilization and costs incurred by patients with diabetes. 7, , 8, Understanding various aspects of healthcare resource utilization and expenditures (HRUE) is important for healthcare policymaking, resource allocation process, and reducing the burden of this comorbidity. There have been few studies, in various medical settings, examining incremental HRUE in comorbid conditions in diabetic patients with CVD. 7, , 9, However, very few have addressed this issue in the context of low- and middle-income countries, where 85% of deaths are due to non-communicable diseases. Since HRUE is country-specific information, its generalizability is questionable. A previous study estimated incremental HRUE in diabetic patients with CVD complications based on the direct attributable costs of the disease. 10, Such an approach underestimates the true incremental HRUE associated with comorbidity. 8, It is therefore important to use the net incremental cost of all HRUE irrespective of the underlying disease.Despite the high prevalence of CVD in people with diabetes, only a limited number of studies have been conducted in Iran. Available studies have primarily investigated multimorbidity using self-report and epidemiological data rather than its association with HRUE. 11, In the present study, for the first time, the prevalence of CVD with diabetes in Iran during the fiscal year 2014 is examined. In addition, a comparison is made between HRUE over three years in diabetic patients with and without CVD comorbidity in a large outpatient population. Materials and MethodsIn a cross-sectional study, the database of the Iran Health Insurance Organization (IHIO) was used to identify patients with diabetes in 2014, and the frequency of HRUE during 2014-2016 was examined. The study protocol was approved by the Research Ethics Committee of Tabriz University of Medical Sciences, Tabriz, Iran (code, IR.TBZMED.REC.1397.559). With a specific focus on the population of East Azerbaijan province (Iran), the records of all medical claims related to outpatient services were retrieved. East Azerbaijan is the fifth most populous province in Iran with approximately four million people, representing 5% of the total population of Iran. 12, The East Azerbaijan Health Insurance Organization (EAHIO) serves approximately 2.3 million people and has a business contract with roughly 90% of healthcare providers in the province. 13, As part of their service, they refund over 50% of the costs incurred by patients for physician services, laboratory tests, medical imaging, and prescription drugs. All patients registered as diabetic in the IHIO database during 2014, prescribed hyperglycemic agents (e.g., insulin) twice within the year, and aged &,ge 18 years were included in the study. Pharmacy claims data were also used as a measure of the population&,rsquo s chronic disease status. 14, , 15, Using the stratified sampling method, diabetic patients were assigned to two groups, namely patients receiving and those not receiving CVD medications. Outcome Measures The primary outcome of interest was the mean difference in direct HRUE per capita, both overall and by the type of healthcare service, between the two groups during 2014-2016 adjusted for demographic characteristics of patients. Outpatient services included services rendered in a physician&,rsquo s office, emergency department, medical imaging center, medical laboratory, pharmacy, other outpatient services covered by the insurance, and costs incurred by patients. Total expenditure was adjusted for 2016 inflation using the overall medical care component of the consumer price index (MCPI). 16, To compare expenditure data with other studies, the average US dollar to Iranian rial exchange rate in 2016 (1 USD equal to 36,000 IRR) was used [http,//old.nasimonline.ir/Content/Detail/2066919]). Key Explanatory Variables Explanatory variables included age, sex, types of insurance funds, and chronic conditions. The most prevalent chronic conditions included diabetes, CVD, inflammatory bowel disease (IBD), chronic obstructive pulmonary disease (COPD) acid-related disorders, cancer, dementia, hyperlipidemia, migraine, thyroid disorders, schizophrenia and bipolar disorders and depression, anxiety, and sleep-related disorders. For each of these conditions, pharmacy claims data from the insurance database were used to identify the corresponding patients. This approach has been proposed as a reliable indicator of underlying chronic conditions, for which patients are being treated. 14, In consultation with a panel of medical experts, specific drugs prescribed at least twice to patients for a specific chronic condition were used as an indicator of comorbidity. The other explanatory variable was related to the internal categorization of funds by IHIO, namely health insurance policy for government employees (fund two), the general public (fund three), private sector and specific public institutions (fund four), self-employed individuals (fund six), and rural and tribal populations (fund nine). Each category is designed to cover certain groups of people with specific reimbursement arrangements. Statistical Analysis Data analysis was performed using STATA/MP 14 (StataCorp LP, College Station, TX, USA). All continuous and categorical covariates were presented descriptively and expressed as means&,plusmn SD, and proportions. The difference in baseline characteristics between diabetic patients with and without CVD comorbidity was examined using t test for continuous variables and the Chi squared test for categorical variables. Unadjusted difference in HRUE between the cohorts was determined using t test. The generalized linear regression model (GLM) was used to adjust for the potential confounding variables and to account for the common highly skewed distribution of the HRUE data. 17, This model is an effective alternative to ordinary least squares regression that corrects for heteroscedasticity and avoids re-transformation bias on log-transformed expenditures. 18, A negative binomial distribution was defined for the frequency of healthcare resource utilization. In order to analyze expenditure data, a two-part regression model was applied consisting of logistic models of all incurred costs and of a gamma model with log-link in the second part to model positive costs. 19, STATA software was used to execute the two-part regression model allowing the calculation of predictions and marginal effects and their standard errors from the combination of the first and second parts. 20, We reported the exponents of regression coefficients from the models representing a factor by which the groups differed in terms of costs (eform function in stata). For example, a covariate with an exponentiated coefficient of 1.25 is associated with a 25% increase in expenditure. 21, The method of recycled predictions (also called predictive margins) was then used to obtain the incremental arithmetic mean cost/utilization between cohorts, rather than simply comparing the costs incurred by patients with diabetes with and without CVD. This approach is gaining increasing attention and is commonly used to calculate differences in absolute costs and utilization between groups in generalized linear models, 12, , 22, as it presents the results of regression in a meaningful scale. We did not apply a matched control design because the method of recycled prediction controls for covariates. The predictive margin technique avoids the problem of covariate imbalance through the counterfactual prediction technique. Confidence intervals were calculated based on 1000 bootstrap replications using the percentile method. The goodness of fit of the regression models was examined using the Hosmer-Lemeshow test (STATA command, estat gof).ResultsBased on the IHIO database, during 2014, a total of 481,733 people filed insurance claims for the prescription of at least one drug. Of these, 34,176 cases were identified as diabetic patients, indicating a prevalence of 5.15% (95% CI, 5.09-5.20) of the total outpatient population. Among these, 21,659 (63.37%) were identified with CVD comorbidity. Characteristics of all diabetic patients with and without CVD comorbidity are presented in table 1,. Overall, compared to diabetic patients without CVD, those with CVD were older with significant variations in acid-related disorders, schizophrenia and bipolar disorders, COPD, dementia hyperlipidemia, thyroid disorders, and depression, anxiety, and sleep-related disorders. VariablesDiabetic patients without CVD (n=12,517)Diabetic patients with CVD (n=2,659)P value Demographic characteristicsAge (years mean&,plusmn SD)50.59&,plusmn 15.9963.48&,plusmn 11.80&,lt 0.001a,Female (n, %)7,841 (62.64)13,975 (64.52)&,lt 0.001b,Male (n, %)4,676 (37.36)7,684 (35.48)&,lt 0.001b,Insurance fund (n, %)Insurance fund 25,367 (42.88)11,810 (54.53)&,lt 0.001b,Insurance fund 3782 (6.25)1,887 (8.71) &,lt 0.001b,Insurance fund 4711 (5.68)784 (3.62)&,lt 0.001b,Insurance fund 63,117 (24.90)4,442 (20.51)&,lt 0.001b,Insurance fund 92,540 (20.29)2,736 (12.63)&,lt 0.001b,Other comorbidities (%)IBD0.210.320.052b,Acid-related disorders11.0719.81&,lt 0.001b,Schizophrenia and bipolar disorders4.355.070.002b,Cancer0.410.480.307b,COPD5.088.67&,lt 0.001b,Dementia0.220.76&,lt 0.001b,Hyperlipidemia22.4058.01&,lt 0.001b,Migraine0.060.030.197b,Depression, anxiety, and sleep disorders15.5826.86&,lt 0.001b,Thyroid disorders 3.214.03&,lt 0.001b,at test, bPearson&,rsquo s Chi squared test, IBD, Inflammatory bowel disease, COPD, Chronic obstructive pulmonary disease, Statistical significance (P&,lt 0.005) |