Abstract
Hearing loss (HL) is common among individuals aged 50 and older and is associated with increased healthcare costs. Whether HL is associated with less access to healthcare is unclear. In this study, we examined the association between HL and access to medical care and prescription drugs among individuals 50+ with and without HL. We used nationally representative 2013–2014 Medical Expenditure Panel Survey data, consisting of 1,977 adults with HL and 17,399 without. We applied an inverse propensity score weighting and regression modeling to adjust for any potential differences in health and socioeconomic conditions between the two groups. Adults with HL were more likely to be white, less educated, poorer, and with public insurance ( p < 0.001). They were also likely to have hypertension, heart disease, stroke, emphysema, high cholesterol, diabetes, joint pain, and arthritis ( p < 0.001). The odds of reporting unmet medical needs (odds ratio [OR] = 1.85; 95% confidence interval [CI] = 1.29–2.66), delay in getting medical needs met (OR = 1.37; 95% CI = 1.00–1.87), and having unfilled prescriptions (OR = 1.81; 95% CI = 1.27–2.59) were higher among individuals with HL compared with their counterparts without HL. Individuals with HL have less access to care and prescription drugs. To ensure equitability in access, public health policies should address specific needs of people with HL.
Keywords: hearing loss, healthcare access, medical expenditure panel survey
Hearing loss (HL) is the third most common chronic condition among older adults, with over 50% of those 75 years and older already living with a significant HL. Compared with other older adults, individuals with HL are at a higher risk for a variety of health conditions, including cardiovascular disease, diabetes, 1 dementia, 2 3 4 and falls. 5 HL is associated with higher healthcare use and costs. On average, total healthcare cost over a 10-year period among adults with HL is estimated to be $22,000 more than what it is among those without HL. Furthermore, the prevalence of adverse health events is more pronounced for individuals with HL compared with their counterparts without HL. For instance, a 30-day hospital readmission is estimated to be 32 to 44% higher for those with HL compared with the general patient population. 6 7
Despite their higher disease burdens and healthcare needs, individuals with HL frequently encounter a variety of healthcare barriers, especially patient–provider communication breakdowns and poorer physician understanding of HL' impact on health. 8 Elevated disease burden and difficulty with patient–provider communication put people with HL at a particularly high risk for disease complications and higher healthcare costs. Timely access to care is a particularly salient factor for these individuals. Although there has been a large body of research on disparities in access to care among various subpopulations of adults, there is a paucity of research examining access to care among people with HL. It is of paramount public-health policy importance to examine access to care among adults with HL in comparison to those without.
The objective of this study was to examine whether HL is associated with poorer access to care. Using a nationally representative sample of adults 50 years of age and older, access to care was assessed for those with and without HL. We hypothesized that HL is associated with less access to medical care and prescription drugs.
Methods
Data and Sample Used
We examined the association between HL and access to medical care and prescription drugs among individuals 50 and older with and without HL. We used the 2013–2014 Medical Expenditure Panel Survey (MEPS) data, a nationally representative sample of noninstitutionalized individuals in the United States. 9 The pooled 2013 and 2014 MEPS Household Component files were used for this analysis. Our final sample included all adults 50 years of age or older ( n = 19,736). Our control group consisted of 1,977 people who responded yes to the following question in MEPS: “Are you deaf, or do you have serious difficulty hearing?” The comparison group consisted of those who did not self-report a significant HL ( n = 17,399). Fig. 1 shows the schematic flow diagram of our sample. Our study was exempted from review by an institutional review board because the MEPS dataset is publicly available, and individuals are de-identified.
Figure 1.
Schematic flow diagram of the sample size. Source: 2013–2014 Medical Expenditure Panel Survey.
Dependent and Explanatory Variables
Our main outcome of interest was access to care, which was measured by the following four binary variables (yes = 1; no = 0): (1) unmet medical needs; (2) delays in getting medical care; (3) unmet prescription drug needs; and (4) delays in getting needed prescriptions. The main exposure variable was HL. All models were adjusted for age, sex, race/ethnicity, marital status, physical limitations, the presence of certain chronic conditions (including hypertension, any heart problem, stroke, emphysema, high cholesterol, cancer, diabetes, joint pain, arthritis, and asthma), interview language, region of the country, educational level, federal poverty level (FPL), and if the individual reported having a usual source of care. The presence of HL was self-reported, not based on objective audiogram measures. As a result, the severity, duration, type, and laterality of HL were not known.
Age was measured as a continuous variable in years (range: 50–85). Dichotomous variables measured include sex (male/female); marital status (married/unmarried); physical limitations based on any self-reported difficulty with standing, walking, climbing stairs, bending, reaching, and grasping; whether the individual was ever diagnosed with each of the 10 chronic conditions—hypertension, any heart disease (including coronary heart disease, angina, heart attack, and other heart diseases), stroke, emphysema, high cholesterol, cancer, diabetes, joint pain, arthritis, and asthma—and interview language (English/other). We also controlled for race/ethnicity (white, Hispanic, African American, Asian, and other minority or mixed race), educational attainment (less than high school, high school/general education diploma, some college education, and college degree), and residential region (Northeast, Midwest, South, and West) as categorical variables. Finally, household income was measured according to the FPL. We used five mutually exclusive categories (poor or <100% of the FPL; near poor or 100–125% of the FPL; low or 125–199% of the FPL; middle or 200–399% of the FPL; and high or ≥400% of the FPL).
Statistical Analysis
Our target population comprised adults (50+) who reported having moderate to severe HL. To adjust for a potential selection bias between adults with and without HL, we applied an inverse propensity score weighting (IPSW). We used age, sex, marital status, functional limitation, physical and mental health conditions, and chronic conditions to calculate IPSW (Appendix Table A1). We examined the association between HL and access to medical care and prescription drugs using multivariable regression analysis ( Appendix Tables A2 A3 A4 A5 ).
Appendix Table A2. Regression results using IPSW for unmet medical care.
| Odds ratio | SE | p | 95% Confidence interval | |
|---|---|---|---|---|
| Hearing loss | 1.85 | 0.34 | 0.001 | 1.29–2.66 |
| Race/Ethnicity | ||||
| Black | 0.44 | 0.13 | 0.007 | 0.25–0.80 |
| Hispanic | 0.66 | 0.17 | 0.110 | 0.40–1.10 |
| Asian | 0.36 | 0.17 | 0.028 | 0.15–0.89 |
| Other | 0.73 | 0.38 | 0.546 | 0.26–2.03 |
| Poverty | ||||
| Near poor | 0.53 | 0.19 | 0.069 | 0.26–1.05 |
| Low income | 0.56 | 0.17 | 0.052 | 0.31–1.00 |
| Middle income | 0.46 | 0.13 | 0.007 | 0.26–0.81 |
| High income | 0.27 | 0.07 | 0.000 | 0.15–0.46 |
| Education | ||||
| High school | 0.91 | 0.27 | 0.749 | 0.51–1.63 |
| Some degree | 1.22 | 0.38 | 0.519 | 0.66–2.26 |
| Graduate school | 2.31 | 0.73 | 0.009 | 1.24–4.30 |
| Insurance coverage | ||||
| Public | 2.52 | 0.57 | 0.000 | 1.62–3.93 |
| No insurance | 8.75 | 2.43 | 0.000 | 5.06–15.12 |
| Have usual source of care | 0.42 | 0.12 | 0.002 | 0.24–0.73 |
| Region | ||||
| Midwest | 0.86 | 0.31 | 0.679 | 0.42–1.75 |
| Northeast | 0.97 | 0.28 | 0.905 | 0.54–1.72 |
| South | 1.10 | 0.37 | 0.773 | 0.57–2.14 |
| Intercept | 0.06 | 0.03 | 0.000 | 0.02–0.16 |
Source: 2013–2014 Medical Expenditure Panel Survey.
Appendix Table A3. Regression results using IPSW for delayed in getting needed medical care.
| Odds ratio | SE | p | 95% Confidence interval | |
|---|---|---|---|---|
| Hearing loss | 1.37 | 0.22 | 0.048 | 1.00–1.87 |
| Race/Ethnicity | ||||
| Black | 0.50 | 0.14 | 0.014 | 0.29–0.87 |
| Hispanic | 1.15 | 0.25 | 0.527 | 0.74–1.78 |
| Asian | 0.53 | 0.20 | 0.098 | 0.25–1.13 |
| Other | 0.96 | 0.39 | 0.917 | 0.43–2.13 |
| Poverty | ||||
| Near poor | 0.64 | 0.17 | 0.097 | 0.38–1.08 |
| Low income | 0.63 | 0.16 | 0.063 | 0.39–1.03 |
| Middle income | 0.51 | 0.13 | 0.008 | 0.31–0.83 |
| High income | 0.49 | 0.12 | 0.006 | 0.30–0.81 |
| Education | ||||
| High school | 1.02 | 0.22 | 0.942 | 0.67–1.55 |
| Some degree | 1.15 | 0.26 | 0.534 | 0.74–1.79 |
| Graduate school | 1.48 | 0.35 | 0.100 | 0.93–2.37 |
| Insurance coverage | ||||
| Public | 1.80 | 0.34 | 0.002 | 1.23–2.61 |
| No insurance | 3.48 | 0.85 | 0.000 | 2.15–5.62 |
| Have usual source of care | 1.08 | 0.31 | 0.795 | 0.61–1.91 |
| Region | ||||
| Midwest | 0.76 | 0.24 | 0.370 | 0.41–1.40 |
| Northeast | 0.72 | 0.18 | 0.195 | 0.43–1.19 |
| South | 0.80 | 0.25 | 0.476 | 0.43–1.48 |
| Intercept | 0.06 | 0.02 | 0.000 | 0.03–0.13 |
Source: 2013–2014 Medical Expenditure Panel Survey.
Appendix Table A4. Regression results using IPSW for unmet medication needs.
| Odds ratio | SE | p | 95% Confidence interval | |
|---|---|---|---|---|
| Hearing loss | 1.81 | 0.33 | 0.001 | 1.27–2.59 |
| Race/Ethnicity | ||||
| Black | 0.71 | 0.22 | 0.281 | 0.38–1.32 |
| Hispanic | 0.99 | 0.27 | 0.979 | 0.58–1.69 |
| Asian | 0.47 | 0.28 | 0.204 | 0.15–1.50 |
| Other | 0.83 | 0.54 | 0.779 | 0.23–2.98 |
| Poverty | ||||
| Near poor | 0.53 | 0.18 | 0.061 | 0.27–1.03 |
| Low income | 0.92 | 0.26 | 0.780 | 0.53–1.61 |
| Middle income | 0.68 | 0.23 | 0.243 | 0.35–1.31 |
| High income | 0.59 | 0.22 | 0.153 | 0.29–1.21 |
| Education | ||||
| High school | 1.01 | 0.29 | 0.982 | 0.57–1.78 |
| Some degree | 1.56 | 0.52 | 0.187 | 0.80–3.02 |
| Graduate school | 1.15 | 0.45 | 0.717 | 0.54–2.47 |
| Insurance coverage | ||||
| Public | 2.39 | 0.66 | 0.002 | 1.39–4.11 |
| No insurance | 7.93 | 3.18 | 0.000 | 3.60–17.47 |
| Have usual source of care | 0.62 | 0.16 | 0.063 | 0.37–1.03 |
| Region | ||||
| Midwest | 0.65 | 0.25 | 0.263 | 0.31–1.38 |
| Northeast | 0.73 | 0.23 | 0.329 | 0.39–1.37 |
| South | 0.55 | 0.16 | 0.046 | 0.30–0.99 |
| Intercept | 0.04 | 0.02 | 0.000 | 0.01–0.13 |
Source: 2013–2014 Medical Expenditure Panel Survey.
Appendix Table A5. Regression results using IPSW for delayed in getting medication needs.
| Odds ratio | SE | p | 95% Confidence interval | |
|---|---|---|---|---|
| Hearing loss | 1.36 | 0.22 | 0.058 | 0.99–1.87 |
| Race/Ethnicity | ||||
| Black | 0.60 | 0.14 | 0.027 | 0.38–0.94 |
| Hispanic | 1.03 | 0.21 | 0.875 | 0.69–1.55 |
| Asian | 1.41 | 0.59 | 0.410 | 0.62–3.24 |
| Other | 1.17 | 0.45 | 0.682 | 0.55–2.49 |
| Poverty | ||||
| Near poor | 0.53 | 0.15 | 0.028 | 0.30–0.93 |
| Low income | 0.86 | 0.22 | 0.545 | 0.52–1.41 |
| Middle income | 0.49 | 0.12 | 0.005 | 0.30–0.80 |
| High income | 0.37 | 0.10 | 0.000 | 0.22–0.62 |
| Education | ||||
| High school | 1.03 | 0.24 | 0.881 | 0.66–1.63 |
| Some degree | 1.49 | 0.34 | 0.081 | 0.95–2.32 |
| Graduate school | 1.11 | 0.30 | 0.708 | 0.65–1.90 |
| Insurance coverage | ||||
| Public | 1.22 | 0.23 | 0.293 | 0.84–1.78 |
| No insurance | 1.72 | 0.54 | 0.086 | 0.93–3.19 |
| Have usual source of care | 1.58 | 0.40 | 0.068 | 0.97–2.59 |
| Region | ||||
| Midwest | 0.73 | 0.21 | 0.277 | 0.41–1.29 |
| Northeast | 0.74 | 0.19 | 0.247 | 0.44–1.24 |
| South | 0.60 | 0.17 | 0.065 | 0.35–1.03 |
| Intercept | 0.06 | 0.03 | 0.000 | 0.03–0.14 |
Source: 2013–2014 Medical Expenditure Panel Survey.
As a sensitivity analysis, we also conducted our analyses without applying IPSW. Throughout, we adjusted for the survey design of MEPS using AHRQ-supplied primary sampling unit, stratified sampling, and weights. Statistical testing was two-tailed, with a 0.05 significance level. We used Stata 15 for all analyses.
Appendix Table A1. Regression results for inverse propensity score weighting (IPSW).
| Hearing loss | Odds ratio | SE | p -Value | 95% confidence interval |
|---|---|---|---|---|
| Age | 1.07 | 0.00 | 0.000 | 1.07–1.08 |
| Female | 0.45 | 0.02 | 0.000 | 0.41–0.50 |
| Married | 1.04 | 0.06 | 0.440 | 0.94–1.16 |
| Good health | 0.90 | 0.06 | 0.110 | 0.79–1.02 |
| Good mental health | 0.67 | 0.04 | 0.000 | 0.59–0.76 |
| Physical limitation | 1.69 | 0.11 | 0.000 | 1.49–1.92 |
| Hypertension | 1.08 | 0.07 | 0.212 | 0.96–1.22 |
| Any heart condition | 1.11 | 0.06 | 0.069 | 0.99–1.24 |
| Stroke | 1.20 | 0.09 | 0.015 | 1.04–1.39 |
| Emphysema | 1.42 | 0.14 | 0.000 | 1.18–1.73 |
| Hypercholesterolemia | 1.26 | 0.07 | 0.000 | 1.12–1.41 |
| Cancer | 1.07 | 0.07 | 0.264 | 0.95–1.21 |
| Diabetes | 1.00 | 0.06 | 0.988 | 0.89–1.13 |
| Joint pain | 1.22 | 0.07 | 0.001 | 1.08–1.37 |
| Arthritis | 1.36 | 0.08 | 0.000 | 1.20–1.53 |
| Asthma | 1.13 | 0.09 | 0.133 | 0.96–1.33 |
Source: 2013–2014 Medical Expenditure Panel Survey.
Results
Table 1 presents descriptive characteristics of adults with and without HL. Adults with HL were more likely to be older (by 8.7 years; 95% CI: 8.01–9.38), male (by 14%; 95% CI: 0.11–0.17), white (by 12%; 95% CI: 0.09–0.14), less educated (by 6% with no high school degree; 95% CI: 0.04–0.09), poorer (by 4% in low-income category; 95% CI: 0.01–0.06), and covered by public health insurance (by 13%; 95% CI: 0.10–0.16). On the other hand, people with HL were less likely to be married (by 6%; 95% CI: −0.09 to −0.03), and less likely to have good physical (by 16%; 95% CI: −0.19 to −0.13) or mental health (by 14%; 95% CI: −0.17 to −0.11).
Table 1. Descriptive Characteristics of Adults 50+ with and without Hearing Loss.
| Variables | Without hearing loss | With hearing loss | Difference | p -Value of the difference | 95% confidence intervals | |||
|---|---|---|---|---|---|---|---|---|
| Mean | SE | Mean | SE | |||||
| Dependent variables | ||||||||
| Unmet medical needs | 0.03 | 0.00 | 0.04 | 0.01 | 0.01 | 0.048 | 0.00 | 0.03 |
| Delays in medical care | 0.04 | 0.00 | 0.06 | 0.01 | 0.02 | 0.014 | 0.00 | 0.04 |
| Unmet prescription | 0.03 | 0.00 | 0.04 | 0.01 | 0.01 | 0.030 | 0.00 | 0.02 |
| Delays in prescription | 0.04 | 0.00 | 0.06 | 0.01 | 0.02 | 0.006 | 0.01 | 0.03 |
| Independent variables | ||||||||
| Age | 62.82 | 0.16 | 71.52 | 0.38 | 8.70 | 0.000 | 8.01 | 9.38 |
| Female | 0.55 | 0.00 | 0.41 | 0.02 | −0.14 | 0.000 | −0.17 | −0.11 |
| Married | 0.62 | 0.01 | 0.56 | 0.02 | −0.06 | 0.000 | −0.09 | −0.03 |
| Good physical health | 0.73 | 0.01 | 0.57 | 0.02 | −0.16 | 0.000 | −0.19 | −0.13 |
| Good mental health | 0.83 | 0.00 | 0.69 | 0.01 | −0.14 | 0.000 | −0.17 | −0.11 |
| Physical limitation | 0.34 | 0.01 | 0.62 | 0.02 | 0.27 | 0.000 | 0.24 | 0.31 |
| White | 0.73 | 0.01 | 0.84 | 0.01 | 0.12 | 0.000 | 0.09 | 0.14 |
| Black | 0.10 | 0.01 | 0.07 | 0.01 | −0.03 | 0.000 | −0.05 | −0.02 |
| Hispanic | 0.11 | 0.01 | 0.05 | 0.01 | −0.05 | 0.000 | −0.07 | −0.04 |
| Asian | 0.05 | 0.01 | 0.02 | 0.00 | −0.03 | 0.000 | −0.04 | −0.02 |
| Race mix/others | 0.02 | 0.00 | 0.02 | 0.00 | 0.00 | 0.521 | −0.01 | 0.01 |
| English | 0.94 | 0.00 | 0.96 | 0.01 | 0.02 | 0.000 | 0.01 | 0.03 |
| North east | 0.19 | 0.01 | 0.16 | 0.01 | −0.03 | 0.020 | −0.06 | 0.00 |
| West | 0.22 | 0.01 | 0.21 | 0.01 | −0.01 | 0.399 | −0.04 | 0.01 |
| Midwest | 0.22 | 0.01 | 0.22 | 0.02 | 0.00 | 0.931 | −0.03 | 0.03 |
| South | 0.36 | 0.01 | 0.41 | 0.02 | 0.04 | 0.020 | 0.01 | 0.08 |
| Less than high school | 0.16 | 0.01 | 0.23 | 0.01 | 0.06 | 0.000 | 0.04 | 0.09 |
| High school | 0.24 | 0.01 | 0.29 | 0.01 | 0.04 | 0.002 | 0.02 | 0.07 |
| Some college | 0.29 | 0.01 | 0.27 | 0.01 | −0.03 | 0.083 | −0.05 | 0.00 |
| Graduate school | 0.30 | 0.01 | 0.22 | 0.01 | −0.08 | 0.000 | −0.11 | −0.06 |
| Poor | 0.10 | 0.00 | 0.12 | 0.01 | 0.02 | 0.011 | 0.01 | 0.04 |
| Near poor | 0.04 | 0.00 | 0.07 | 0.01 | 0.03 | 0.001 | 0.01 | 0.04 |
| Low income | 0.12 | 0.00 | 0.16 | 0.01 | 0.04 | 0.002 | 0.01 | 0.06 |
| Middle income | 0.26 | 0.01 | 0.28 | 0.01 | 0.02 | 0.084 | 0.00 | 0.05 |
| High income | 0.47 | 0.01 | 0.36 | 0.02 | −0.11 | 0.000 | −0.14 | −0.07 |
| Private insurance | 0.67 | 0.01 | 0.59 | 0.02 | −0.08 | 0.000 | −0.12 | −0.05 |
| Public insurance | 0.25 | 0.01 | 0.37 | 0.02 | 0.13 | 0.000 | 0.10 | 0.16 |
| No insurance | 0.08 | 0.00 | 0.03 | 0.00 | −0.04 | 0.000 | −0.06 | −0.03 |
| Usual source of care | 0.87 | 0.00 | 0.93 | 0.01 | 0.06 | 0.000 | 0.04 | 0.07 |
Source: 2013–2014 Medical Expenditure Panel Survey.
Furthermore, individuals with HL reported more chronic conditions. Fig. 2 represents prevalence of hypertension, heart disease, stroke, emphysema, high cholesterol, diabetes, cancer, joint pain, arthritis, and asthma among people with and without HL. Except for asthma, people with HL compared with those without had higher prevalence of reported chronic conditions (all p < 0.001).
Figure 2.
Prevalence of certain chronic conditions among those with and without hearing loss. Source: 2013–2014 Medical Expenditure Panel Survey.
Risk-adjusted and propensity score–weighted odds of access to care are presented in Table 2 . Odds of unmet medical needs (OR = 1.85; 95% CI: 1.29–2.67), delays in getting medical care (OR = 1.37; 95% CI: 1.00–1.87), unmet prescription drug needs (OR = 1.81; 95% CI: 1.27–2.59), and delays in getting needed prescription drugs (OR = 1.36; 95% CI: 0.99–1.87) were higher among adults with HL compared with their counterparts without.
Table 2. Risk-Adjusted Odds of Having Lower Access to Care: Comparing Adults with Hearing Loss to their Counterparts without.
| Access measures | Odds ratio | p -Value | 95% confidence interval |
|---|---|---|---|
| Unable to get needed medical care | 1.85 | <0.001 | 1.29–2.67 |
| Delayed in getting a needed medical care | 1.37 | 0.048 | 1.00–1.87 |
| Unable to get needed prescription drug | 1.81 | 0.001 | 1.27–2.58 |
| Delayed in getting a needed prescription drug | 1.36 | 0.043 | 0.99–1.87 |
Source: 2013–2014 Medical Expenditure Panel Survey.
Notes: All models are adjusted for demographics and health needs (age, sex, marital status, mental and physical health, certain chronic conditions, income, education, insurance status, interview language, region, and having a usual source of care. Regression results are presented in Appendix Tables A2 A3 A4 A5 .
Fig. 3 shows differences in predicted risk-adjusted access measures between adults with and without HL. Being unable to get needed medical care (by 2 percentage points; 95% CI: 0.01–0.04) and prescription drugs (by 2 percentage points; 95% CI: 0.01–0.03) was lower among people with HL compared with those without.
Figure 3.
Predicted risk-adjusted access to care: adults 50+ with and without hearing loss. Source: 2013–2014 Medical Expenditure Panel Survey. Note: Regression results are reported in Tables A2–A5 in Appendix.
Sensitivity Analysis
We conducted our regression analyses to measure access to care without any propensity score weighting. Our results did not change qualitatively; individuals with HL continued to have lower access to care and prescription drugs.
Discussion
In this cross-sectional study of a nationally representative sample of adults 50 years and older with and without HL, significant HL was associated with having lower access to care. Our findings suggest that individuals with HL experience more barriers in obtaining necessary care and prescriptions to help manage their higher health burdens. To our knowledge, this is the first analysis of the association between HL with access to care and prescription drugs.
The association between HL and access to medical care and prescriptions remains largely unexplained. There are several studies that have demonstrated higher health burdens and hospitalization among individuals with HL. For example, those with both mild HL and moderate HL had a 16% (OR, 1.16; 95% CI, 1.04–1.29) and a 21% (OR, 1.21; 95% CI, 1.06–1.38) greater risk, respectively, of hospitalization compared with those without HL. 10 Prior work using MEPS data found increased odds of emergency department and hospital visits among those with untreated HL. 11 Individuals with HL also are connected to a variety of health burdens that may increase their need for additional health care services and prescriptions. 1 2 3 4 12 The increased health burden may create additional medical needs that they feel unable to adequately obtain from their health care providers and pharmacies.
Difficulties in obtaining medications may be related to the higher out-of-pocket expenses that individuals with HL frequently encounter. Despite clear benefits of using hearing aids, 13 they are generally not covered by most insurance plans. 14 15 16 17 Hearing aids, averaging anywhere from $2,200 to $7,000 for a fitted pair, often are out-of-pocket expenses. 11 13
Adults with HL face significant communication barriers in healthcare settings. 7 These communication struggles appear to affect their ability to obtain the appropriate information to manage their health. This may result in lower quality provider–patient interactions that may lead to lower adherence to recommended treatments. 18 19 Hospital readmission rates were higher among those with HL and reported communication difficulties with health care staff and providers. 7 It is plausible that lower quality interactions with providers may increase the need for additional medical care appointments to manage their health conditions. In addition, adults with HL report lower health literacy. 20 Poor health literacy is connected with worse health outcomes and inappropriate health care utilization. 21 22 This may impact their navigational ability in healthcare, including their ability to seek additional care or find economical ways to obtain prescriptions.
Limitations
This study has multiple limitations. First, MEPS relies on self-reported information. Although no objective HL measures were obtained, self-reported HL has been shown to be reliable proxy for objectively measured HL. 23 24 25 Second, this study was based on cross-sectional data. Thus, no causal inference can be concluded from the study. Further longitudinal analyses are warranted to establish causality.
Conclusion
Individuals aged 50 years and older with HL struggle with gaining appropriate access to medical care and prescription drugs. Additional steps are needed to ensure that health care is accessible and equitable for this growing population given that their risk of chronic conditions and adverse health events is elevated.
Acknowledgments
The authors have no financial or nonfinancial conflicts relevant to this article.
Footnotes
Conflict of Interest None declared.
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