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Table of Contents
ORIGINAL ARTICLE
Year : 2021  |  Volume : 4  |  Issue : 3  |  Page : 244-249

Driving distance and glycemic control in patients with insulin-treated diabetes mellitus: Results from the diabetes and driving study


Department of Family and Community Medicine, College of Medicine; King Saud University Medical City, King Saud University; Vision College of Medicine, Vision Colleges in Riyadh, Saudi Arabia

Date of Submission21-Nov-2020
Date of Decision27-Feb-2021
Date of Acceptance18-Mar-2021
Date of Web Publication26-Jul-2021

Correspondence Address:
Turky H Almigbal
Department of Family and Community Medicine, College of Medicine, King Saud University, Riyadh; King Saud University Medical City, King Saud University, Riyadh; Vision College of Medicine, Vision Colleges in Riyadh
Saudi Arabia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jnsm.jnsm_147_20

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  Abstract 


Context: Driving distance to health-care facilities has been associated with suboptimal glycemic control in patients with diabetes. The data pertaining to the driving burden on patients with diabetes in Saudi Arabia is lacking. Aims: This study aims to assess the driving distance to healthcare facilities and the glycemic control of patients with insulin-treated diabetes mellitus (ITDM) in Saudi Arabia. Setting and Design: This study is part of the diabetes and driving study–a cross-sectional project conducted on individuals with ITDM in Saudi Arabia. Materials and Methods: Data collection was performed from August 2016 to February 2017 from the designated clinics every alternate day, for 4-h intervals. We included men aged more than 18 years, with at least 1 year of follow-up with the clinic, and using a car as the main mode of transportation. Results: A total of 429 individuals were included in the study, they were mostly from Riyadh (95.3%, n = 409) with an average age of 49.54 ± 15.20 years. The distance driven was on average 32.09 ± 115.23 km. The average duration of diabetes was 14.36 ± 8.44 years. Most (80.4%; n = 345) had uncontrolled diabetes and were almost equally distributed between those driving <10 km (55.48% n = 238) and more. We found statistically significant associations between driving for more than 10 km to access healthcare (odds ratio [OR] = 1.47; confidence interval [CI] 1.127–1.92); P = 0.004) and lower age (OR = 0.97; CI = 0.949, 1.00; P = 0.029) with uncontrolled diabetes. Conclusion: Patients with ITDM in Saudi Arabia have a driving burden if the healthcare facilities located far, which also might be associated with poor glycemic control. A thorough study of healthcare facilities and location of diabetes centers needs to be implemented on a national level.

Keywords: Diabetes, driving, glycemic control, Saudi Arabia


How to cite this article:
Almigbal TH. Driving distance and glycemic control in patients with insulin-treated diabetes mellitus: Results from the diabetes and driving study. J Nat Sci Med 2021;4:244-9

How to cite this URL:
Almigbal TH. Driving distance and glycemic control in patients with insulin-treated diabetes mellitus: Results from the diabetes and driving study. J Nat Sci Med [serial online] 2021 [cited 2023 Jan 28];4:244-9. Available from: https://www.jnsmonline.org/text.asp?2021/4/3/244/322321




  Introduction Top


Diabetes mellitus (DM) is a global health issue, affecting an estimated 463 million adults in 2019. This figure is predicted to grow by 51% and affect an estimated 700 million individuals by 2045.[1] Among Gulf countries, estimates from 2019 placed the prevalence of diabetes among Saudi Arabian adults at 15.8%, second only to the United Arab Emirates (16.3%).[1] This alarming increase is a cause for concern because DM is a chronic metabolic disease associated with a lower quality of life, high disease burden, and significant morbidity and mortality.[2]

Optimal glycemic control is a common approach in diabetes care and management, and is known to prevent serious complications such as microvascular (retinopathy, nephropathy, and neuropathy) and macrovascular diseases (coronary artery disease, peripheral arterial disease, and stroke).[3] Conversely, poor glycemic control can be harmful to patients with diabetes and is associated with higher diabetes-related healthcare costs.[4],[5] To that end, both the American Association for Diabetes and the National Institute for Health and Care Excellence (NICE) have guidelines to measure hemoglobin A1c (HbA1c) levels every 3–6 months with targets to be adjusted accordingly.[6],[7],[8] However, while there are benefits related to achieve a good glycemic control, the results remain suboptimal.[9],[10]

Complex multifactorial reasons are associated with poor glycemic control among patients with diabetes.[11] Ease of access to healthcare providers that offer patients with specialized diabetes services has been documented as one of the factors that enable patients to adequately control their glycemic levels.[12] In this regard, the geographic location of the patients is particularly important because diabetes can be prevented and controlled through different interventions.[12] A study in 2006 by Strauss and colleagues concluded that driving distance was significantly associated with glycemic control while studying older subjects in a rural area.[13] They concluded that for every 35 kilometers, there is a 0.25% increase in HbA1c levels independent of sex, income, insurance coverage, and diabetes complications. This effect was more pronounced among insulin-treated diabetes mellitus (ITDM) patients.[13] They further speculated that patients far from healthcare centers visited them less frequently due to the distance burden. This may be perceived to be at a greater risk for hypoglycemic complications leading to less aggressive care.[13] The role of driving burden on uncontrolled diabetes was studied by Littenberg et al., who examined the role of travel burden as a barrier to insulin use among adult diabetes patients, and reported a significant relationship between driving distance to the primary source of care and insulin use; they controlled for confounders such as age, sex, race, education, income, health insurance, body mass index, duration of diabetes, use of oral agents, glycemic control, or frequency of care.[14] Among children with type 1 diabetes, Fox et al., concluded that children who travelled for >2 h to their clinic had significantly higher HbA1C levels.[15]

In Saudi Arabia, a national study failed to establish a direct correlation between driving distance to healthcare facilities and diagnosis of both diabetes and hypertension.[16] Per the Ministry of Health statistics, there is a geographic maldistribution of health-care services, which is accompanied by an inequality in access to health-care facilities by disadvantaged groups (e.g., older patients) and those living in remote areas.[17],[18]

At present, the literature on geographical inequality in access to health-care facilities and its effect on patients with diabetes is lacking in Saudi Arabia. In this study, we assess the impact of the driving distance to health-care facilities on glycemic control of patients with ITDM in Saudi Arabia.


  Materials and Methods Top


Study design and setting

This study is part of the diabetes and driving (DAD) study–a cross-sectional research project conducted on individuals with ITDM in Saudi Arabia. Data were collected from two specialized diabetes clinics, each in a different tertiary care hospital (5–6 clinics in each hospital) in Riyadh, Saudi Arabia.[19]

Sample collection

Data collection was performed by four medical students with established experience as research assistants (RA). From August 2016 to February 2017, the RAs collected data from the designated clinics every alternate day for randomly chosen 4-h intervals to ensure that the collected sample is representative of the population. The inclusion criteria included being a male over the age of 18, with at least 1 year of follow-up with the clinic, and using a car as their main mode of transportation. Written informed consent was obtained.

Sample size

This study is part of a registry: The DAD study. The registry was set for the inclusion of 499 participants with full data captured for 429 participants.[19]

Ethical consideration

We believe that the results of our research can help students from a variety of majors with identifying the factors that contribute to self-esteem and imposter syndrome.

Questionnaire used

The questionnaire was developed and validated in a previous study published by the research group in 2018.[19]

Distance of 10 km was used as a cut-off to allow more comprehensive comparison with the available literature. All reference levels related to glycemic control used in this paper follow the American Diabetes Association and NICE guidelines.[6],[7],[8]

Statistical analysis

Categorical data were summarized with absolute numbers and percentages, whereas continuous data were summarized with mean and standard deviation or median and interquartile range. Comparisons between the different groups were performed using the Chi-squared test or Fisher's exact for categorical variables and independent sample t-test or Mann–Whitney U test for continuous variables. A multiple logistic regression model was used to identify independent risk factors for uncontrolled diabetes. All analyses were performed using the (SAS/STAT) software, Version (9.2) (SAS Institute Inc., Cary, NC, USA.) and (R foundation for Statistical Computing, Vienna, Austria). A two-sided P < 0.05 was considered statistically significant.


  Results Top


A total of 429 subjects agreed to participate in the study out of the initial 480 eligible patients; their socio-demographic data are presented in [Table 1]. The participants were mostly from Riyadh (95.3%, n = 409), and 56.9% (n = 244) stated having never smoked before. The average age of the participants was 49.54 ± 15.20 years, and most of them had type 2 diabetes (73.89% n = 317). The distance driven by participants was on average 32.09 ± 115.23 km [Table 1].
Table 1: Sociodemographic factors of the participants

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The average duration of diabetes was 14.36 ± 8.44 years. Most (80.4%; n = 345) had uncontrolled diabetes, and the participants were almost equally distributed between those driving less than 10 km (55.48% n = 238) and those driving more than 10 km (44.52%; n = 191). Age (P = 0.028), insulin total daily dosage (P = 0.032), location and driving distance (P = 0.005) to health-care facilities were all statistically significant in relation to diabetes control as shown in [Table 2].
Table 2: Comparison of different socio-demographic and environmental factors with diabetes control levels

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The multivariate logistic regression model presented in [Table 3] found statistically significant associations between driving for more than 10 km to access healthcare (odds ratio [OR] = 1.47; confidence interval [CI] 1.127–1.92); P = 0.004) and lower age (OR = 0.97; CI = 0.949, 1.00; P = 0.029) and uncontrolled diabetes.
Table 3: Multivariate logistic regression model for associations with uncontrolled diabetic levels

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  Discussion Top


The evidence on the effects of geographical inaccessibility to health-care facilities on diabetes patients in Saudi Arabia remains unclear. With around 80.4% of the participants suffering from uncontrolled diabetes, this study suggests a positive association between driving burden and uncontrolled glycemic levels. The age of patients and driving >10 km to their healthcare facility was associated with poor glycemic control for ITDM patients.

Several reasons might explain the association between increased driving distance and poor glycemic control. A longer driving distance could lead to fewer visits, limiting the control and monitoring of the glycemic levels of these patients.[12] Our results are consistent with previous studies such as that reported by Zgibor et al., who concluded longer driving distance and younger age to be correlated with HBA1c levels of >7.0%.[12] A strong relationship between glycemic control and driving distance (b = 10.07%/10 km, P < 0.001, 95% [CI] = 10.03, 10.11) was also found in a study conducted by Strauss in 2006.[13]

Driving burden may be related to the general healthcare-seeking behavior and not limited to patients with diabetes alone;[20],[21] therefore, our conclusion may be justified in light of a paper by Mansour wherein spatial data were analyzed to determine the distribution of public health-care facilities in the districts of Riyadh.[22] It was found that the districts located at the center had more facilities compared to the outlying districts. Moreover, it was reported that only half of the population lived <1 km from the nearest health-care facility, while for people living in 38 districts, there was a very high distance variation observed, concluding that the pattern of distribution of public healthcare facilities in Riyadh was significantly clustered (P < 0.001).[22]

Furthermore, Hosler and Michaels assessed the travel distance to the location of a faith-based diabetes intervention program as a potential barrier to participation among Guyanese immigrants in New York, and concluded short driving distances to be beneficial, resulting in regular utilization of the program.[23] From this perspective, driving distance may be considered as an important barrier to access of healthcare facilities for treatment and management among diabetes patients.

It is important to note that our study indicated lower age to be significantly associated with uncontrolled diabetes, and this may be substantiated by findings from international studies wherein young adults have cited time constraints and inconvenience as barriers to controlling diabetes.[24],[25] Longer driving distance could presumably compound this barrier among young adults. Moreover, a Saudi Arabian study found that individuals mostly sought the health-care system when sick,[16] which aligns with our findings of individual-level factors such as possible negligence in addition to driving burden among the ITDM patients in our study.

Our study implicates the inaccessibility to healthcare in terms of driving distance to the healthcare facility as an underlying reason for uncontrolled diabetes in the general Saudi Arabian population. This is a useful criterion to be considered while planning prevention and treatment programs initiated by the government. Also, implementing miniature specialized diabetes centers with highly specialized personnel and services at primary care centers may improve the quality of care for diabetic patients in Saudi Arabia, where a high prevalence of diabetes is observed. Such a project needs to be undertaken extensively in the future to determine its impact on the care provided to patients with diabetes. There were several limitations to the present study, particularly a recall bias as it was a questionnaire-based study. Furthermore, the study population included insulin-dependent individuals suffering from diabetes; therefore, the results cannot be generalized to people with diabetes and noninsulin-dependent. No similar studies have been previously performed in Saudi Arabia, and our results were compared to similar studies conducted elsewhere; therefore, the cultural and socio-demographic differences among these could be an added limitation.

Driving burden is suggested to be a barrier for patients with insulin-dependent diabetes in Saudi Arabia. A thorough study of health-care facilities and location of diabetes centers needs to be implemented on a national level to encourage and facilitate seeking healthcare among Saudi Arabians. In addition, since the Saudi Arabian government has uplifted the ban on women driving, it is recommended to include both sexes in future studies.

Financial support and sponsorship

This project was supported by the College of Medicine Research Centre, Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
International Diabetes Federation – Facts & Figures; 2019. Available from: https://www.idf.org/aboutdiabetes/what-is-diabetes/facts-figures.html. [Last accessed on 2020 Jun 09].  Back to cited text no. 1
    
2.
Shi BY. The importance and strategy of diabetes prevention. Chronic Dis Transl Med 2016;2:204-7.  Back to cited text no. 2
    
3.
Fowler M. Microvascular and macrovascular complications of diabetes. Clin Diabetes 2008;26:77-82.  Back to cited text no. 3
    
4.
Huhtaniemi I, Martini L, Subramanian S, Chait A. Dyslipidemia in diabetes. In: Huhtaniemi I, Martini L, editors. Encyclopedia of Endocrine Diseases. 2nd ed. Oxford, U.K: Academic Press; 2019. p. 186-98.  Back to cited text no. 4
    
5.
Degli Esposti L, Saragoni S, Buda S, Sturani A, Degli Esposti E. Glycemic control and diabetes-related health care costs in type 2 diabetes; retrospective analysis based on clinical and administrative databases. Clinicoecon Outcomes Res 2013;5:193-201.  Back to cited text no. 5
    
6.
Jude EB, Nixon M, O'Leary C, Myland M, Gooch N, Shaunik A, et al. Evaluating glycemic control in patients with type 2 diabetes suboptimally controlled on basal insulin: UK ATTAIN real-world study. Diabetes Ther 2019;10:1847-58.  Back to cited text no. 6
    
7.
American Diabetes Association. 6. glycemic targets: Standards of Medical Care in Diabetes-2021. Diabetes Care 2021;44:S73-84.  Back to cited text no. 7
    
8.
National Institute for Health and Care Excellence. Type 2 Diabetes in Adults: Management (NG28). NICE Guideline. Published: 2 December 2015, Updated: May, 2017 2015. Available from: https://www.nice.org.uk/guidance/ng28/resources/type-2-diabetes-in-adults-management-pdf-1837338615493. [Last accessed on 2020 Jun 01].  Back to cited text no. 8
    
9.
Khunti K, Millar-Jones D. Clinical inertia to insulin initiation and intensification in the UK: A focused literature review. Prim Care Diabetes 2017;11:3-12.  Back to cited text no. 9
    
10.
Blak BT, Smith HT, Hards M, Curtis BH, Ivanyi T. Optimization of insulin therapy in patients with type 2 diabetes mellitus: Beyond basal insulin. Diabet Med 2012;29:e13-20.  Back to cited text no. 10
    
11.
Wallace TM, Matthews DR. Poor glycemic control in type 2 diabetes, conspiracy of disease, suboptimal therapy and attitude. Q J Med 2000;93:369-74.  Back to cited text no. 11
    
12.
Zgibor JC, Gieraltowski LB, Talbott EO, Fabio A, Sharma RK, Hassan K. The association between driving distance and glycemic control in rural areas. J Diabetes Sci Technol 2011;5:494-500.  Back to cited text no. 12
    
13.
Strauss K, MacLean C, Troy A, Littenberg B. Driving distance as a barrier to glycemic control in diabetes. J Gen Intern Med 2006;21:378-80.  Back to cited text no. 13
    
14.
Littenberg B, Strauss K, MacLean CD, Troy AR. The use of insulin declines as patients live farther from their source of care: Results of a survey of adults with type 2 diabetes. BMC Public Health 2006;6:198.  Back to cited text no. 14
    
15.
Fox DA, Islam N, Amed S. Type 1 diabetes outcomes: Does distance to clinic matter? Pediatr Diabetes 2018;19:1331-6.  Back to cited text no. 15
    
16.
El Bcheraoui C, Tuffaha M, Daoud F, Kravitz H, AlMazroa MA, Al Saeedi M, et al. Access and barriers to healthcare in the Kingdom of Saudi Arabia, 2013: Findings from a national multistage survey. BMJ Open 2015;5:e007801.  Back to cited text no. 16
    
17.
Health Statistical Year Book. Riyadh, Saudi Arabia: Ministry of Health; 2009.  Back to cited text no. 17
    
18.
Almalki M, FitzGerald G, Clark M. Health care system in Saudi Arabia: An overview. EMHJ 2011;17:784-93.  Back to cited text no. 18
    
19.
Almigbal TH, Alfaifi AA, Aleid MA, Billah B, Alramadan MJ, Sheshah E, et al. Safe driving practices and factors associated with motor-vehicle collisions among people with insulin-treated diabetes mellitus: Results from the Diabetes and Driving (DAD) study. J Safety Res 2018;65:83-8.  Back to cited text no. 19
    
20.
Syed ST, Gerber BS, Sharp LK. Traveling towards disease: Transportation barriers to health care access. J Community Health 2013;38:976-93.  Back to cited text no. 20
    
21.
Kelly C, Hulme C, Farragher T, Clarke G. Are differences in travel time or distance to healthcare for adults in global north countries associated with an impact on health outcomes? A systematic review. BMJ Open 2016;6:e013059.  Back to cited text no. 21
    
22.
Mansour S. Spatial analysis of public health facilities in Riyadh Governorate, Saudi Arabia: A GIS-based study to assess geographic variations of service provision and accessibility. Geo Spat Inf Sci 2016;19:26-38.  Back to cited text no. 22
    
23.
Hosler AS, Michaels IH. Spatial access to faith-based diabetes intervention for Guyanese adults in Schenectady, New York. Diabetes Educ 2014;40:526-32.  Back to cited text no. 23
    
24.
Kibbey KJ, Speight J, Wong JL, Smith LA, Teede HJ. Diabetes care provision: Barriers, enablers and service needs of young adults with Type 1 diabetes from a region of social disadvantage. Diabet Med 2013;30:878-84.  Back to cited text no. 24
    
25.
Tong T, Myers AK, Bissoonauth AA, Pekmezaris R, Kozikowski A. Identifying the barriers and perceptions of non-Hispanic black and Hispanic/Latino persons with uncontrolled type 2 diabetes for participation in a home Telemonitoring feasibility study: A quantitative analysis of those who declined participation, withdrew or were non-adherent. Ethn Health 2020;25:485-94.  Back to cited text no. 25
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

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