Background

Approximately 425 million adults worldwide are affected by diabetes, and this number is expected to increase to 629 million people by the year 2045 [1]. In Chile, Argentina, and Uruguay, the prevalence of diabetes is 12.4% [2]. Diabetes is the eighth leading cause of years lived with disability in these three countries [3].

Health-related quality of life (HRQoL) is one of the most frequently used patient-reported outcomes and complements the common public health measures mortality and morbidity [4]. Due to the specific characteristics of diabetes, the assessment of HRQoL of patients with diabetes is important for planning processes, making decisions about interventions, and enhancing patients’ outcomes [5, 6].

Few studies have investigated the association between health care access and HRQoL of people with diabetes. Some studies have examined the association between barriers to health care and HRQoL for example in dental care [7], in patients with acute coronary heart syndrome [8], in men with prostate cancer [9], in adults with asthma [10], and in the general population [11].

Existing studies showed inconsistent results regarding this relationship. This may be due to small sample sizes and differences in operationalizing health care access [12,13,14,15].

When examining health care access, one should consider two aspects: the usability of health care services through coverage and the actual use of these services by the patient. Even in patients who possess health coverage, the access to health care may still be restricted because of organizational and financial barriers. These barriers include long waiting times for appointments, living remotely from health care facilities, and compulsory copayments, among others [16]. Current studies examining the association between health care access and HRQoL in people with diabetes do not consider both aspects.

Knowing whether there is an association between health care access and HRQoL in patients with diabetes would enable the implementation of adequate interventions. These interventions could reduce health inequalities and improve HRQoL of patients with diabetes.

Most research on HRQoL of people with diabetes is from high-income countries. Especially in South America, there is a lack of research. The impact of barriers to health care access could be more prominent in low- and middle-income countries than in developed countries. The aims of this study were to examine the general HRQoL of people with diabetes in the Southern Cone of Latin America (including Chile, Argentina, and Uruguay) and to investigate the association with health care access.

Methods

Study design and sample

We used cross-sectional data from CESCAS I, a prospective cohort study with baseline data collected from February 2011 to December 2012. The aim of this cohort study is to examine the prevalence and incidence of certain risk factors and their prospective association with cardiovascular disease [24]. The sample consists of people from the general population from four mid-sized cities. Two cities are located in Argentina (Bariloche and Marcos Paz), one in Chile (Temuco), and one in Uruguay (Barros Blancos). The sample consists of 7524 men and women. The age of the participants ranges from 35 to 74 years. Detailed descriptions of the study design were published elsewhere [2, 17]. Within this sample, 1061 participants either had a self-reported previous diagnosis of diabetes or a value of fasting blood glucose above 126 mg/dl. Self-reported cases as well as cases diagnosed within the context of our study were included in the analyses. We excluded cases with missing exposure or outcome information, resulting in a final sample of 1025 participants (Fig. 1). The sample included people with type 1 and type 2 diabetes.

Fig. 1
figure 1

Sample selection flow chart

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HRQoL measure

Participants completed the Argentine-Spanish version of the SF-12 questionnaire in their homes accompanied by a trained researcher [18]. The SF-12 is a self-administered questionnaire derived from the Medical Outcome Study SF-36 questionnaire [19]. The SF-12 consists of 12 items. These include general health, mental health, physical function, role limitations due to physical problems (role physical), role limitations due to emotional problems (role emotional), bodily pain, vitality, and social function. The items can be grouped into two summary scores: the physical (PCS-12) and the mental (MCS-12) component scores. We used factor weights from the general population of the United States [20] and transformed the scores to a mean of 50 with a standard deviation of 10. Scores above 50 indicate better functioning than the general population. Scores below 50 indicate worse functioning than the general population. The United States-derived scoring algorithms represent a standard benchmark and allow comparing and interpreting the SF-12 scores across studies from different countries [21]. The SF-36, the PCS-12, and MCS-12 were validated in Argentina. Cronbach’s alpha ranged from 0.69 to 0.85 in the different subscales of the SF-36 [18]. Due to highly corresponding summary scores, the SF-12 is a practical alternative to the SF-36. The SF-12 has good reliability, construct validity, and internal consistency and was tested in different population and for different diseases [22].

Barriers to health care access

We measured health care utilization using questionnaires from the Hispanic Community Health Study/Study of Latinos with adaptions made for the population living in the Southern Cone of Latin America [17]. The cities Bariloche, Marcos Paz, Temuco, and Barros Blancos are located in rather rural areas. General practitioners in primary care settings rather than specialists treat patients with diabetes in these cities. We examined insurance status and self-reported barriers to health care access. The health care system in Chile, Argentina, and Uruguay has three sectors: the public, the social security, and the private sector. The public sector provides health care to the uninsured population, while the social security sector covers all workers of the formal economy and their families. The private sector covers individuals paying premiums to private insurances.

Uninsured people with diabetes attend public primary care clinics and hospitals and receive medication free of charge for diabetes, hypertension, and dyslipidemia through public essential drug programs (Remediar in Argentina, Fondo de Farmacia (FOFAR) in Chile and Sistema Integrado de de Salud in Uruguay). However, this medical care does not include all services, especially for the uninsured population. This results in copayments or out of pocket expenditure [23,24,25]. Hence, there are equity gaps in access and quality of services. Effective universal coverage is not optimal for chronic diseases in these countries [26,27,28].

Regarding insurance coverage, there were three options in the questionnaire from which the participants had to choose one:

  1. 1.

    Attending public hospitals and public primary health care centers (public hospitals and health care centers in Argentina and Uruguay, hospitals of Fondo Nacional de Salud (FONASA) in Chile.).

  2. 2.

    Covered by social security, deducted from own income or the income of a family member (Obra Social in Argentina, FONASA or Instituciones de Salud Previsional (ISAPRE) in Chile, Mutualista or Administración de los Servicios de Salud del Estado (ASSE) in Uruguay).

  3. 3.

    Covered by private insurance, paying out of pocket.

We considered people who chose the first option as uninsured and people who chose the second or third option as insured.

The following question determined self-reported barriers to health care access: “In the last 12 months, has there been any moment where you needed health care services, but were not able to obtain these services?” The answer options were “yes,” “no,” and “does not apply/do not know.” We did not consider the last answer option in the analyses. In the questionnaire, people could specify the reasons why they were not able to obtain/ access the services.

With these two questions (insurance status and self-reported barriers), we created a composite variable with four groups: (a) insured people without self-reported barriers to health care access, (b) uninsured people without self-reported barriers to health care access, (c) insured people with self-reported barriers to health care access and (d) uninsured people with self-reported barriers to health care access.

Statistical analysis

We conducted all analyses with SPSS 20. We compared the above-mentioned groups regarding the PCS-12 and the MCS-12. We calculated three models with different adjustment sets using linear regression for each, the PCS-12 and the MCS-12, as outcome. In total, we performed 6 models. Table 1 provides details on the operationalization of the variables included in the models. We adjusted the first model for sex, age, occupational status, and low educational level. The second model was adjusted for the variables in model 1 and additionally for body mass index, physical inactivity, self-reported diabetes (not diagnosed within the context of the study), age at diagnosis of diabetes, insulin therapy, fasting blood glucose, hospitalization during the last year, chronic kidney disease, and cardiovascular disease. We adjusted the third model for the variables in model 1 and 2 and additionally for having access to a primary physician. We chose the variables for the models based on a thorough literature review. Socioeconomic, sociodemographic, diabetes-related factors, as well as comorbidities and having access to a primary physician could be associated with access to health care and HRQoL [7,8,9,10,11, 16, 29,30,31,32,33,34,35]. By choosing different adjustment sets, it is possible to gain an impression of the impact the adjustment has on the estimates. We excluded cases pairwise, as pairwise deletion allows for using cases that contain missing data for some variables. This procedure does not include variables with missing values but still uses the overall case for analyzing the variables without missing values. In contrast to complete case analysis, a case is not omitted completely because of a missing value in one variable [36, 37].

Table 1 Measurement of the variables included in the regression models
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For all models, we tested multicollinearity of the included variables. The variance inflation factor values were below five, indicating low multicollinearity [38]. Hence, we kept all variables in the models.

We hypothesized that the strength of the relationship between health care access and HRQoL could differ depending on whether someone requires insulin or not. Since insulin is vital, the impact of barriers to health care access on HRQoL could be stronger for a person needing insulin than for a person treated with diet or oral medication. We tested whether there was an interaction effect between insulin therapy and the four groups of health care access. We performed a two-factorial analysis of variance (ANOVA) and adjusted for the variables included in the third model.

In sensitivity analysis, we performed a complete case analysis. In the complete case analysis, we calculated estimated marginal means and standard errors for PCS-12 and MCS-12 for all four groups using ANOVA. We compared the results of the complete case analysis with the results of the pairwise deletion analysis to examine whether the exclusion of missing values changes the results.

We present self-reported barriers to health care access as total numbers and percentages.

Results

Of the 1025 participants, 407 were insured and reported no barriers to health care access, 471 participants were uninsured and reported no barriers to health care access, 44 participants were insured and reported barriers to health care access, and 103 participants were uninsured and reported barriers to health care access.

The majority of the sample were females, ranging from about 70% to about 59% in the four groups. Compared to participants with barriers to health care access, participants without barriers were older, more often retired, less often housewives and more often had access to a primary physician (Table 2).

Table 2 Sample characteristics
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Insured people without self-reported barriers to health care had the highest PCS-12 and MCS-12 scores, followed by uninsured people without self-reported barriers and insured people with self-reported barriers to health care. Uninsured people with self-reported barriers to health care had the lowest PCS-12 and MCS-12 score (Fig. 2). In linear regression analyses, we compared insured people without self-reported barriers to health care access with the other three groups. For the first model, we found that the PCS-12 was 1.22 points lower for uninsured people without self-reported barriers, 4.66 points lower for insured people with self-reported barriers, and 5.19 points lower for uninsured people with self-reported barriers. We observed this tendency also for the other two models with additional adjustments. The adjustment slightly altered the estimates. In the third model, we observed the largest difference for uninsured people with self-reported barriers (− 6.14 points). We observed the smallest difference for uninsured people without self-reported barriers (− 1.90 points) (Table 3).

Fig. 2
figure 2

Health-related quality of life in people with different access to health care. HRQoL, health-related quality of life; PCS-12, Physical Component Summary-12; MCS-12, Mental Component Summary

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Table 3 Association between HRQoL and access to health care
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The MCS-12 in the first model was 0.70 points lower for uninsured people without self-reported barriers, 5.21 points lower for insured people with self-reported barriers, and 5.82 points lower for uninsured people with self-reported barriers. As with the PCS-12, we observed this tendency also for the other two models with additional adjustments. Again, the adjustments slightly altered the estimates. In the third model, we also observed the largest difference for uninsured people with self-reported barriers (− 5.78 points) and the smallest difference for uninsured people without self-reported barriers (− 0.63) (Table 3).

We found no interaction effect between insulin therapy and the four groups of health care access and hence did not include an interaction term in the third model (p-value = 0.13 for PCS-12 and 0.44 for MCS-12). However, the proportion of participants using insulin was low across all four groups, and this might explain why we did not find an interaction effect. Sensitivity analysis using only complete cases yielded similar results as the pairwise deletion analysis. Effect estimates were slightly higher in sensitivity analysis and showed generally low PCS-12 and MCS-12 adjusted mean scores for the participants (Supplemental Table 1).

Table 4 shows the certain barriers to health care that people have reported. Self-reported barriers to health care access were mainly long waiting times for appointments (n = 65) and high costs of care (n = 52). Other reasons (n = 30) included no doctor being available at the health care facility, having asked for medical attendance at home, but not receiving it, the available doctor not wanting to treat the patient, the health care facility being closed, and personnel being on strike.

Table 4 Self-reported barriers to health care access
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Supplemental Table 2 shows the effect estimates and confidence intervals for the other variables in model 3 for both, the PCS-12 and the MCS-12.

Discussion

This study examined the association between barriers to health care access and general HRQoL in people with diabetes in four cities of the Southern Cone of Latin America.

People with barriers to health care access had lower HRQoL compared to people without barriers. A high number of the people in our study had barriers to health care access. Only about 40% of the participants were insured and did not report barriers to health care access. Given the fact that limited health care access in people with diabetes is associated with negative health outcomes, such as poor glycemic control, this finding is particularly alarming [39].

The PCS-12 and the MCS-12 were low in the four groups. This indicates that there are disparities by the type of insurance and access to care regarding HRQoL in people with diabetes. In all groups, the scores for the PCS-12 were below 50, which indicate worse HRQoL than the general population. After adjustment, the PCS-12 was even lower in all four groups. For the MCS-12, scores were below 50 for people with self-reported barriers to health care (insured and uninsured). However, the adjusted mean scores were slightly higher in all four groups for the MCS-12. Overall, the results show that people with diabetes in the Southern Cone of Latin America have rather low physical functioning. This physical functioning is even worse in people reporting barriers to health care.

Previous research in the general population of the United States showed that people without health insurance had lower HRQoL compared to people with health insurance. In the study from the United States, the-PCS-12 was almost 6 point lower in uninsured people [11]. We could not observe such large differences for the PCS-12 when comparing insured and uninsured people with diabetes. The PCS-12 was 1.9 points lower in uninsured people without self-reported barriers to care as when compared to insured people without self-reported barriers. Although there are no clear guidelines for interpreting differences between mean scores as clinically meaningful, a difference between 2 points on a scale from 0 to 100 can be considered as rather small [40]. No insurance status means that people only have access to public health care centers. These public health care centers do not include all services. Compared to the insured population, people attending public health care might not receive the optimal treatment and hence experience lower HRQoL when compared to insured people that have access to a higher amount of different services. However, health insurance status seems to play a minor role in determining HRQoL in people with diabetes in this sample. Even though people are uninsured, people in Chile, Argentina, and Uruguay have the possibility to receive basic medical treatment in the public hospitals. Nevertheless, when they might need more than just basic treatment, they might experience barriers to health care access and lower HRQoL, for example, due to high costs of care and long waiting times for appointments.

Low HRQoL of people with self-reported barriers to access (insured or not) could be of clinical relevance. Compared to insured people without barriers, differences in HRQoL ranged from 4 to 6 points. These differences are of moderate to large magnitude [29, 40]. Hence, self-reported barriers to access may be an important factor in determining HRQoL in people with diabetes in the Southern Cone of Latin America. Zimbudzi et al. [12] investigated the association between personal, health system, and clinician-related barriers and HRQoL in people with diabetes and chronic kidney disease. They found that the odds of having low mental health status increased with increasing number of reported barriers. This is in line with our result that people with self-reported barriers to access had lower MCS-12 scores than people without barriers. Pinchevsky et al. [15] found that people with diabetes treated in private hospitals experienced fewer barriers to health care than people treated in public sector facilities in South Africa. However, HRQoL did not differ between the private and the public sector. The small sample size and the higher burden of diabetes in the public sector may have contributed to this result. Lontchi-Yimagou et al. [13] found that the implementation of free diabetes care did not improve HRQoL in children and adolescents with type 1 diabetes in Cameroon after one year. Besides free care and insurance status, the organization of care may determine HRQoL and may explain this finding. In our analysis, we found that self-reported barriers to access, including organization of care, rather than insurance status, were important in determining HRQoL. Konerding et al. [14] showed that increased travel and waiting time to and in the physician’s practice were associated with lower HRQoL in people with type 2 diabetes in six European countries. The participants in our study also mentioned waiting time as a barrier.

Several studies have shown an association between health care access and HRQoL in other non-communicable diseases. In the United States, Erskine et al. [8] found that the PCS-12 score was 4 to 6 points lower for people with acute coronary syndrome who reported to have financial and transportation barriers. Hoffmann et al. [10] examined barriers to health care and HRQoL in adults with asthma in the United States. As in our study, long waiting times for appointments and cost of care were frequently mentioned barriers in the study by Hoffmann et al.

Strengths and limitations

Previous research rarely investigated the association between access to health care and HRQoL in people with diabetes. To the authors’ knowledge, this is the first study regarding this association in the Southern Cone of Latin America.

Strengths of the study include the use of CESCAS I data. The sociodemographic distribution in CESCAS I is comparable to national surveys of each country included. In addition, the association between certain risk factors and diabetes was consistent in all three countries. However, diabetes prevalence differed among the countries and ranged between 8.4% in Bariloche (Argentina) and 14.3% in Temuco (Chile) [41].

Another strength is that we investigated insurance status and self-reported barriers to health care as two important aspects of health care access.

The participants filled in the questionnaires at home with trained researchers and not at health care facilities or with their primary physicians. This minimized social desirability bias.

A limitation of the study is that there was no distinction between type 1 and type 2 diabetes. Previous research showed that determinants of HRQoL as well as HRQoL may differ between people with type 1 and type 2 diabetes [42].

For measuring HRQoL, we used a generic instrument. The lack of a disease-specific instrument represents a limitation of the study. Despite the great practicability and the high validity of the SF-12, this instrument may not be sensitive enough to evaluate factors that are of particular interest for people with diabetes. Hence, it may underestimate the health limitations associated with diabetes [43]. However, Huang et al. [44] indicated, that the SF-36 was superior to the diabetes-specific D-39 regarding the distinction between complication and well-being groups. Most studies on HRQoL in people with diabetes used generic instruments, especially the SF-12. The broad use of this questionnaire in previous research allows for comparison between the results of this study with previous research.

The cross-sectional nature of the data represents a limitation of the study. Whether barriers to health care access actually cause lower HRQoL cannot be determined. People with low HRQoL might have more care needs and contact with the health care system. Hence, they may be more likely to experience barriers than people with few care needs.

Participants in our study mentioned different barriers to health care. Due to the small sample size, we did not analyze the association between HRQoL and each barrier separately. It remains unclear, which barrier is the main contributor to lower HRQoL in our sample. Knowing which barriers effect HRQoL would allow implementing interventions that tackle these specific barriers. Barrier-specific interventions would not only improve access to health care but also HRQoL of people with diabetes.

Conclusion

We found that HRQoL is generally low in people with diabetes in the Southern Cone of Latin America. Interventions should especially target the physical functioning in this vulnerable group.

We also found that impeded health care access is a common experience in this population. Especially self-reported barriers to access may impede HRQoL in people with diabetes.

Our results indicate that self-reported barriers to health care may be more important than insurance status for determining HRQoL. Self-reported barriers to access are associated with notably lower HRQoL even in insured people.

More research is necessary for exploring which barriers contribute mostly to lower HRQoL in people with diabetes.