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Health Literacy and Hospital Length of Stay: An Inpatient Cohort Study
Health literacy (HL), defined as patients’ ability to understand health information and make health decisions,1 is a prevalent problem in the outpatient and inpatient settings.2,3 In both settings, low HL has adverse implications for self-care including interpreting health labels4 and taking medications correctly.5 Among outpatient cohorts, HL has been associated with worse outcomes and acute care utilization.6 Associations with low HL include increased hospitalizations,7 rehospitalizations,8,9 emergency department visits,10 and decreased preventative care use.11 Among the elderly, low HL is associated with increased mortality12 and decreased self-perception of health.13
A systematic review revealed that most high-quality HL outcome studies were conducted in the outpatient setting.6 There have been very few studies assessing effects of low HL in an acute-care setting.7,14 These studies have evaluated postdischarge outcomes, including admissions or readmissions,7-9 and medication knowledge.14 To the best of our knowledge, there are no studies evaluating associations between HL and hospital length of stay (LOS).
LOS has received much attention as providers and payers focus more on resource utilization and eliminating adverse effects of prolonged hospitalization.15 LOS is multifactorial, depending on clinical characteristics like disease severity, as well as on sociocultural, demographic, and geographic factors.16 Despite evidence that LOS reductions translate into improved resource allocation and potentially fewer complications, there remains a tension between the appropriate LOS and one that is too short for a given condition.17
Because low HL is associated with inefficient resource utilization, we hypothesized that low HL would be associated with increased LOS after controlling for illness severity. Our objectives were to evaluate the association between low HL and LOS and whether such an association was modified by illness severity and sociodemographics.
METHODS
Study Design, Setting, Participants
An in-hospital, cohort study design of patients who were admitted or transferred to the general medicine service at the University of Chicago between October 2012 and November 2015 and screened for inclusion as part of a large, ongoing study of inpatient care quality was conducted.18 Exclusion criteria included observation status, age under 18 years, non-English speaking, and repeat participants. Those who died during hospitalization or whose discharge status was missing were excluded because the primary goal was to examine the association of HL and time to discharge, which could not be evaluated among those who died. We excluded participants with LOS >30 days to limit overly influential effects of extreme outliers (1% of the population).
Variables
HL was screened using the Brief Health Literacy Screen (BHLS), a validated, 3-question verbal survey not requiring adequate visual acuity to assess HL.19,20 The 3 questions are as follows: (1) “How confident are you filling out medical forms by yourself?”, (2) “How often do you have someone help you read hospital materials?”, and (3) “How often do you have problems learning about your medical condition because of difficulty understanding written information?” Responses to the questions were scored on a 5-point Likert scale in which higher scores corresponded to higher HL.21,22 The scores for each of the 3 questions were summed to yield a range between 3 and 15. On the individual questions, prior work has demonstrated improved test performance with a cutoff of ≤3, which corresponds to a response of “some of the time” or “somewhat”; therefore, when the 3 questions were summed together, scores of ≤9 were considered indicative of low HL.21,23
For severity of illness adjustment, we used relative weights derived from the 3M (3M, Maplewood, MN) All Patient Refined Diagnosis Related Groups (APR-DRG) classification system, which uses administrative data to classify the severity. The APR-DRG system assigns each admission to a DRG based on principal diagnosis; for each DRG, patients are then subdivided into 4 severity classes based on age, comorbidity, and interactions between these variables and the admitting diagnosis.24 Using the base DRG and severity score, the system assigns relative weights that reflect differences in expected hospital resource utilization.
LOS was derived from hospital administrative data and counted from the date of admission to the hospital. Participants who were discharged on the day of admission were counted as having an LOS of 1. Insurance status (Medicare, Medicaid, no payer, private) also was obtained from administrative data. Age, sex (male or female), education (junior high or less, some high school, high school graduate, some college, college graduate, postgraduate), and race (black/African American, white, Asian or Pacific Islander [including Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asian, Native Hawaiian, Guam/Chamorro, Samoan, other Pacific], American Indian or Alaskan Native, multiple race) were obtained from administrative data based on information provided by the patient. Participants with missing data on any of the sociodemographic variables or on the APR-DRG score were excluded from the analysis.
Statistical Analysis
χ2 and 2-tailed t tests were used to compare categorical and continuous variables, respectively. Multivariate linear regressions were employed to measure associations between the independent variables (HL, illness severity, race, gender, education, and insurance status) and the dependent variable, LOS. Independent variables were chosen for clinical significance and retained in the model regardless of statistical significance. The adjusted R2 values of models with and without the HL variable included were reported to provide information on the contribution of HL to the overall model.
Because LOS was observed to be right skewed and residuals of the untransformed regression were observed to be non-normally distributed, the decision was made to natural log transform LOS, which is consistent with previous hospital LOS studies.16 Regression coefficients and confidence intervals were then transformed into percentage estimates using the following equation: 100(eβ–1). Adjusted R2 was reported for the transformed regression.
The APR-DRG relative weight was treated as a continuous variable. Sociodemographic variables were dichotomized as follows: female vs male; high school graduates vs not; African American vs not; Medicaid/no payer vs Medicare/private payer. Age was not included in the multivariate model because it has been incorporated into the weighted APR-DRG illness severity scores.
Each of the sociodemographic variables and the APR-DRG score were examined for effect modification via the same multivariate linear equation described above, with the addition of an interaction term. A separate regression was performed with an interaction term between age (dichotomized at ≥65) and HL to investigate whether age modified the association between HL and LOS. Finally, we explored whether effects were isolated to long vs short LOS by dividing the sample based on the mean LOS (≥6 days) and performing separate multivariate comparisons.
Sensitivity analyses were performed to exclude those with LOS greater than the 90th percentile and those with APR-DRG score greater than the 90th percentile; age was added to the model as a continuous variable to evaluate whether the illness severity score fully adjusted for the effects of age on LOS. Furthermore, we compared the participants with missing data to those with complete data across both dependent and independent variables. Alpha was set at 0.05; analyses were performed using Stata Version 14 (Stata, College Station, TX).
RESULTS
A total of 5983 participants met inclusion criteria and completed the HL assessment; of these participants, 75 (1%) died during hospitalization, 9 (0.2%) had missing discharge status, and 79 (1%) had LOS >30 days. Two hundred eighty (5%) were missing data on sociodemographic variables or APR-DRG score. Of the remaining (n = 5540), the mean age was 57 years (standard deviation [SD] = 19 years), over half of participants were female (57%), and the majority were African American (73%) and had graduated from high school (81%). The sample was divided into those with private insurance (25%), those with Medicare (46%), and those with Medicaid (26%); 2% had no payer. The mean APR-DRG score was 1.3 (SD = 1.2), and the scores ranged from 0.3 to 15.8.
On the BHLS screen for HL, 20% (1104/5540) had inadequate HL. Participants with low HL had higher weighted illness severity scores (average 1.4 vs 1.3; P = 0.003). Participants with low HL were also more likely to be 65 or older (55% vs 33%; P < 0.001), non-high school graduates (35% vs 15%; P < 0.001), and African American (78% vs 72%; P < 0.001), and to have Medicare or private insurance (75% vs 71%; P = 0.02). There was no significant difference with respect to gender (54% male vs 57% female; P = 0.1)
Finally, we compared the group with missing data (n = 280) to the group with complete data (n = 5540). The participants with missing data were more likely to have low HL (31% [86/280] vs 20%; P < 0.001) and to have Medicare or private insurance (82% [177/217] vs 72%; P = 0.002); however, they were not more likely to be 65 or older (40% [112/280] vs 37%; P = 0.3), high school graduates (88% [113/129] vs 81%; P = 0.06), African American (69% [177/256] vs 73%; P = 0.1), or female (57% [158/279] vs 57%; P = 1), nor were they more likely to have longer LOS (5.7 [n = 280] vs 5.5 days; P = 0.6) or higher illness severity scores (1.3 [n = 231] vs 1.3; P = 0.7).
DISCUSSION
To our knowledge, this study is the first to evaluate the association between low HL and an important in-hospital outcome measure, hospital LOS. We found that low HL was associated with a longer hospital LOS, a result which remained significant when controlling for severity of illness and sociodemographic variables and when testing the model for sensitivity to the highest values of LOS and illness severity. Additionally, the association of HL with LOS appeared concentrated among participants with shorter LOS. Relative to other predictors, the contribution of HL to the overall LOS model was small, as evidenced by the change in adjusted R2 values with HL excluded.
Among the covariates, only gender modified the association between HL and LOS; the findings suggested that men were more susceptible to the effect of low HL on increased LOS. Illness severity and other sociodemographics, including age ≥65, did not appear to modify the association. We also found that being African American and having Medicaid or no insurance were associated with a significantly shorter LOS in multivariate analysis.
Previous work suggested that the adverse health effects of low HL may be mediated through several pathways, including health knowledge, self-efficacy, health skills, and illness stigma.25-27 The finding of a small but significant relationship between HL and LOS was not surprising given these known associations; nevertheless, there may be an additional patient-dependent effect of low HL on LOS not discovered here. For instance, patients with poor health knowledge and self-efficacy might stay in the hospital longer if they or their providers do not feel comfortable with their self-care ability.
This finding may be useful in developing hospital-based interventions. HL-specific interventions, several of which have been tested in the inpatient setting,14,28,29 have shown promise toward improving health knowledge,30 disease severity,31 and health resource utilization.32
Those with low HL may lack the self-efficacy to participate in discharge planning; in fact, previous work has related low HL to posthospital readmissions.8,9 Conversely, patients with low HL might struggle to engage in the inpatient milieu, advocating for shorter LOS if they feel alienated by the inpatient experience.
These possibilities show that LOS is a complex measure shown to depend on patient-level characteristics and on provider-based, geographical, and sociocultural factors.16,33 With these forces at play, additional effects of lower levels of HL may be lost without phenotyping patients by both level of HL and related characteristics, such as self-efficacy, health skills, and stigma. By gathering these additional data, future work should explore whether subpopulations of patients with low HL may be at risk for too-short vs too-long hospital admissions.
For instance, in this study, both race and Medicaid insurance were associated with shorter LOS. Being African American was associated with shorter LOS in our study but has been found to be associated with longer LOS in another study specifically focused on diabetes.34 Prior findings found uninsured patients have shorter LOS.35 Therefore, these findings in our study are difficult to explain without further work to understand whether there are health disparities in the way patients are cared for during hospitalization that may shorten or lengthen their LOS because of factors outside of their clinical need.
The finding that gender modified the effect of low HL on LOS was unexpected. There were similar proportions of men and women with low HL. There is evidence to support that women make the majority of health decisions for themselves and their familes36; therefore, there may be unmeasured aspects of HL that provide an advantage for female vs male inpatients. Furthermore, omitted confounders, such as social support, may not fully capture potential gender-related differences. Future work is needed to understand the role of gender in relationship to HL and LOS.
Limitations of this study include its observational, single-centered design with information derived from administrative data; positive and negative confounding cannot be ruled out. For instance, we did not control for complex aspects affecting LOS, such as discharge disposition and goals of care (eg, aggressive care after discharge vs hospice). To address this limitation, multivariate analyses were performed, which were adjusted for illness severity scores and took into account both comorbidity and severity of the current illness. Additionally, although it is important to study such populations, our largely urban, minority sample is not representative of the U.S. population, and within our large sample, there were participants with missing data who had lower HL on average, although this group represented only 5% of the sample. Finally, different HL tools have noncomplete concordance, which has been seen when comparing the BHLS with more objective tools.20,37 Furthermore, certain in-hospital clinical scenarios (eg, recent stroke or prolonged intensive care unit stay) may present unique challenges in establishing a baseline HL level. However, the BHLS was used in this study because of its greater feasibility.
In conclusion, this study is the first to evaluate the relationship between low HL and LOS. The findings suggest that HL may play a role in shaping outcomes in the inpatient setting and that targeting interventions toward screened patients may be a pathway toward mitigating adverse effects. Our findings need to be replicated in larger, more representative samples, and further work understanding subpopulations within the low HL population is needed. Future work should measure this association in diverse inpatient settings (eg, psychiatric, surgical, and specialty), in addition to assessing associations between HL and other important in-hospital outcome measures, including mortality and discharge disposition.
Acknowledgments
The authors thank the Hospitalist Project team for their assistance with data collection. The authors especially thank Chuanhong Liao and Ashley Snyder for assistance with statistical analyses; Andrea Flores, Ainoa Coltri, and Tom Best for their assistance with data management. The authors would also like to thank Nicole Twu for her help with preparing and editing the manuscript.
Disclosures
Dr. Jaffee was supported by a Calvin Fentress Research Fellowship and NIH R25MH094612. Dr. Press was supported by a career development award (NHLBI K23HL118151). This work was also supported by a seed grant from the Center for Health Administration Studies. All other authors declare no conflicts of interest.
1. U.S. Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. Washington, DC: U.S. Government Printing Office; 2000.
2. “What Did the Doctor Say”? Improving Health Literacy to Protect Patient Safety. The Joint Commission; 2007.
3. Kutner M, Greenberg E, Jin Y, Paulsen C. The Health Literacy of America’s Adults: Results from the 2003 National Assessment of Adult Literacy. National Center for Education Statistics; 2006.
4. Davis TC, Wolf MS, Bass PF, et al. Literacy and misunderstanding prescription drug labels. Ann Intern Med. 2006;145(12):887-894. PubMed
5. Kripalani S, Henderson LE, Chiu EY, Robertson R, Kolm P, Jacobson TA. Predictors of medication self-management skill in a low-literacy population. J Gen Intern Med. 2006;21(8):852-856. PubMed
6. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97-107. PubMed
7. Baker DW, Parker RM, Williams MV, Clark WS. Health literacy and the risk of hospital admission. J Gen Intern Med. 1998;13(12):791-798. PubMed
8. Mitchell SE, Sadikova E, Jack BW, Paasche-Orlow MK. Health literacy and 30-day postdischarge hospital utilization. J Health Commun. 2012;17(Suppl 3):325-338. PubMed
9. Jaffee EG, Arora VM, Matthiesen MI, Hariprasad SM, Meltzer DO, Press VG. Postdischarge Falls and Readmissions: Associations with Insufficient Vision and Low Health Literacy among Hospitalized Seniors. J Health Commun. 2016;21(sup2):135-140. PubMed
10. Hope CJ, Wu J, Tu W, Young J, Murray MD. Association of medication adherence, knowledge, and skills with emergency department visits by adults 50 years or older with congestive heart failure. Am J Health Syst Pharm. 2004;61(19):2043-2049. PubMed
11. Bennett IM, Chen J, Soroui JS, White S. The contribution of health literacy to disparities in self-rated health status and preventive health behaviors in older adults. Ann Fam Med. 2009;7(3):204-211. PubMed
12. Baker DW, Wolf MS, Feinglass J, Thompson JA. Health literacy, cognitive abilities, and mortality among elderly persons. J Gen Intern Med. 2008;23(6):723-726. PubMed
13. Cho YI, Lee SY, Arozullah AM, Crittenden KS. Effects of health literacy on health status and health service utilization amongst the elderly. Soc Sci Med. 2008;66(8):1809-1816. PubMed
14. Paasche-Orlow MK, Riekert KA, Bilderback A, et al. Tailored education may reduce health literacy disparities in asthma self-management. Am J Respir Crit Care Med. 2005;172(8):980-986. PubMed
15. Soria-Aledo V, Carrillo-Alcaraz A, Campillo-Soto Á, et al. Associated factors and cost of inappropriate hospital admissions and stays in a second-level hospital. Am J Med Qual. 2009;24(4):321-332. PubMed
16. Lu M, Sajobi T, Lucyk K, Lorenzetti D, Quan H. Systematic review of risk adjustment models of hospital length of stay (LOS). Med Care. 2015;53(4):355-365. PubMed
17. Clarke A, Rosen R. Length of stay. How short should hospital care be? Eur J Public Health. 2001;11(2):166-170. PubMed
18. Meltzer D, Manning WG, Morrison J, et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866-874. PubMed
19. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36(8):588-594. PubMed
20. Press VG, Shapiro MI, Mayo AM, Meltzer DO, Arora VM. More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18 Suppl 1:197-204. PubMed
21. Willens DE, Kripalani S, Schildcrout JS, et al. Association of brief health literacy screening and blood pressure in primary care. J Health Commun. 2013;18 Suppl 1:129-142. PubMed
22. Peterson PN, Shetterly SM, Clarke CL, et al. Health literacy and outcomes among patients with heart failure. JAMA. 2011;305(16):1695-1701. PubMed
23. Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561-566. PubMed
24. Averill RF, Goldfield N, Hughes JS, et al. All Patient Refined Diagnosis Related Groups (APR-DRGs): Methodology Overview. 3M Health Information Systems; 2003.
25. Waite KR, Paasche-Orlow M, Rintamaki LS, Davis TC, Wolf MS. Literacy, social stigma, and HIV medication adherence. J Gen Intern Med. 2008;23(9):1367-1372. PubMed
26. Paasche-Orlow MK, Wolf MS. The causal pathways linking health literacy to health outcomes. Am J Health Behav. 2007;31 Suppl 1:S19-26. PubMed
27. Berkman ND, Sheridan SL, Donahue KE, et al. Health literacy interventions and outcomes: an updated systematic review. Evid Rep Technol Assess (Full Rep). 2011;(199):1-941. PubMed
28. Kripalani S, Roumie CL, Dalal AK, et al. Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1-10. PubMed
29. Press VG, Arora VM, Shah LM, et al. Teaching the use of respiratory inhalers to hospitalized patients with asthma or COPD: a randomized trial. J Gen Intern Med. 2012;27(10):1317-1325. PubMed
30. Sobel RM, Paasche-Orlow MK, Waite KR, Rittner SS, Wilson EAH, Wolf MS. Asthma 1-2-3: a low literacy multimedia tool to educate African American adults about asthma. J Community Health. 2009;34(4):321-327. PubMed
31. Rothman RL, DeWalt DA, Malone R, et al. Influence of patient literacy on the effectiveness of a primary care-based diabetes disease management program. JAMA. 2004;292(14):1711-1716. PubMed
32. DeWalt DA, Malone RM, Bryant ME, et al. A heart failure self-management
program for patients of all literacy levels: a randomized, controlled trial [ISRCTN11535170].
BMC Health Serv Res. 2006;6:30. PubMed
33. Hasan O, Orav EJ, Hicks LS. Insurance status and hospital care for myocardial
infarction, stroke, and pneumonia. J Hosp Med. 2010;5(8):452-459. PubMed
34. Cook CB, Naylor DB, Hentz JG, et al. Disparities in diabetes-related hospitalizations:
relationship of age, sex, and race/ethnicity with hospital discharges, lengths
of stay, and direct inpatient charges. Ethn Dis. 2006;16(1):126-131. PubMed
35. Hadley J, Steinberg EP, Feder J. Comparison of uninsured and privately insured
hospital patients. Condition on admission, resource use, and outcome. JAMA.
1991;265(3):374-379. PubMed
36. Women’s Health Care Chartbook: Key Findings From the Kaiser Women’s
Health Survey. May 2011. https://kaiserfamilyfoundation.files.wordpress.
com/2013/01/8164.pdf. Accessed August 1, 2017.
37. Louis AJ, Arora VM, Matthiesen MI, Meltzer DO, Press VG. Screening Hospitalized Patients for Low Health Literacy: Beyond the REALM of Possibility? PubMed
Health literacy (HL), defined as patients’ ability to understand health information and make health decisions,1 is a prevalent problem in the outpatient and inpatient settings.2,3 In both settings, low HL has adverse implications for self-care including interpreting health labels4 and taking medications correctly.5 Among outpatient cohorts, HL has been associated with worse outcomes and acute care utilization.6 Associations with low HL include increased hospitalizations,7 rehospitalizations,8,9 emergency department visits,10 and decreased preventative care use.11 Among the elderly, low HL is associated with increased mortality12 and decreased self-perception of health.13
A systematic review revealed that most high-quality HL outcome studies were conducted in the outpatient setting.6 There have been very few studies assessing effects of low HL in an acute-care setting.7,14 These studies have evaluated postdischarge outcomes, including admissions or readmissions,7-9 and medication knowledge.14 To the best of our knowledge, there are no studies evaluating associations between HL and hospital length of stay (LOS).
LOS has received much attention as providers and payers focus more on resource utilization and eliminating adverse effects of prolonged hospitalization.15 LOS is multifactorial, depending on clinical characteristics like disease severity, as well as on sociocultural, demographic, and geographic factors.16 Despite evidence that LOS reductions translate into improved resource allocation and potentially fewer complications, there remains a tension between the appropriate LOS and one that is too short for a given condition.17
Because low HL is associated with inefficient resource utilization, we hypothesized that low HL would be associated with increased LOS after controlling for illness severity. Our objectives were to evaluate the association between low HL and LOS and whether such an association was modified by illness severity and sociodemographics.
METHODS
Study Design, Setting, Participants
An in-hospital, cohort study design of patients who were admitted or transferred to the general medicine service at the University of Chicago between October 2012 and November 2015 and screened for inclusion as part of a large, ongoing study of inpatient care quality was conducted.18 Exclusion criteria included observation status, age under 18 years, non-English speaking, and repeat participants. Those who died during hospitalization or whose discharge status was missing were excluded because the primary goal was to examine the association of HL and time to discharge, which could not be evaluated among those who died. We excluded participants with LOS >30 days to limit overly influential effects of extreme outliers (1% of the population).
Variables
HL was screened using the Brief Health Literacy Screen (BHLS), a validated, 3-question verbal survey not requiring adequate visual acuity to assess HL.19,20 The 3 questions are as follows: (1) “How confident are you filling out medical forms by yourself?”, (2) “How often do you have someone help you read hospital materials?”, and (3) “How often do you have problems learning about your medical condition because of difficulty understanding written information?” Responses to the questions were scored on a 5-point Likert scale in which higher scores corresponded to higher HL.21,22 The scores for each of the 3 questions were summed to yield a range between 3 and 15. On the individual questions, prior work has demonstrated improved test performance with a cutoff of ≤3, which corresponds to a response of “some of the time” or “somewhat”; therefore, when the 3 questions were summed together, scores of ≤9 were considered indicative of low HL.21,23
For severity of illness adjustment, we used relative weights derived from the 3M (3M, Maplewood, MN) All Patient Refined Diagnosis Related Groups (APR-DRG) classification system, which uses administrative data to classify the severity. The APR-DRG system assigns each admission to a DRG based on principal diagnosis; for each DRG, patients are then subdivided into 4 severity classes based on age, comorbidity, and interactions between these variables and the admitting diagnosis.24 Using the base DRG and severity score, the system assigns relative weights that reflect differences in expected hospital resource utilization.
LOS was derived from hospital administrative data and counted from the date of admission to the hospital. Participants who were discharged on the day of admission were counted as having an LOS of 1. Insurance status (Medicare, Medicaid, no payer, private) also was obtained from administrative data. Age, sex (male or female), education (junior high or less, some high school, high school graduate, some college, college graduate, postgraduate), and race (black/African American, white, Asian or Pacific Islander [including Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asian, Native Hawaiian, Guam/Chamorro, Samoan, other Pacific], American Indian or Alaskan Native, multiple race) were obtained from administrative data based on information provided by the patient. Participants with missing data on any of the sociodemographic variables or on the APR-DRG score were excluded from the analysis.
Statistical Analysis
χ2 and 2-tailed t tests were used to compare categorical and continuous variables, respectively. Multivariate linear regressions were employed to measure associations between the independent variables (HL, illness severity, race, gender, education, and insurance status) and the dependent variable, LOS. Independent variables were chosen for clinical significance and retained in the model regardless of statistical significance. The adjusted R2 values of models with and without the HL variable included were reported to provide information on the contribution of HL to the overall model.
Because LOS was observed to be right skewed and residuals of the untransformed regression were observed to be non-normally distributed, the decision was made to natural log transform LOS, which is consistent with previous hospital LOS studies.16 Regression coefficients and confidence intervals were then transformed into percentage estimates using the following equation: 100(eβ–1). Adjusted R2 was reported for the transformed regression.
The APR-DRG relative weight was treated as a continuous variable. Sociodemographic variables were dichotomized as follows: female vs male; high school graduates vs not; African American vs not; Medicaid/no payer vs Medicare/private payer. Age was not included in the multivariate model because it has been incorporated into the weighted APR-DRG illness severity scores.
Each of the sociodemographic variables and the APR-DRG score were examined for effect modification via the same multivariate linear equation described above, with the addition of an interaction term. A separate regression was performed with an interaction term between age (dichotomized at ≥65) and HL to investigate whether age modified the association between HL and LOS. Finally, we explored whether effects were isolated to long vs short LOS by dividing the sample based on the mean LOS (≥6 days) and performing separate multivariate comparisons.
Sensitivity analyses were performed to exclude those with LOS greater than the 90th percentile and those with APR-DRG score greater than the 90th percentile; age was added to the model as a continuous variable to evaluate whether the illness severity score fully adjusted for the effects of age on LOS. Furthermore, we compared the participants with missing data to those with complete data across both dependent and independent variables. Alpha was set at 0.05; analyses were performed using Stata Version 14 (Stata, College Station, TX).
RESULTS
A total of 5983 participants met inclusion criteria and completed the HL assessment; of these participants, 75 (1%) died during hospitalization, 9 (0.2%) had missing discharge status, and 79 (1%) had LOS >30 days. Two hundred eighty (5%) were missing data on sociodemographic variables or APR-DRG score. Of the remaining (n = 5540), the mean age was 57 years (standard deviation [SD] = 19 years), over half of participants were female (57%), and the majority were African American (73%) and had graduated from high school (81%). The sample was divided into those with private insurance (25%), those with Medicare (46%), and those with Medicaid (26%); 2% had no payer. The mean APR-DRG score was 1.3 (SD = 1.2), and the scores ranged from 0.3 to 15.8.
On the BHLS screen for HL, 20% (1104/5540) had inadequate HL. Participants with low HL had higher weighted illness severity scores (average 1.4 vs 1.3; P = 0.003). Participants with low HL were also more likely to be 65 or older (55% vs 33%; P < 0.001), non-high school graduates (35% vs 15%; P < 0.001), and African American (78% vs 72%; P < 0.001), and to have Medicare or private insurance (75% vs 71%; P = 0.02). There was no significant difference with respect to gender (54% male vs 57% female; P = 0.1)
Finally, we compared the group with missing data (n = 280) to the group with complete data (n = 5540). The participants with missing data were more likely to have low HL (31% [86/280] vs 20%; P < 0.001) and to have Medicare or private insurance (82% [177/217] vs 72%; P = 0.002); however, they were not more likely to be 65 or older (40% [112/280] vs 37%; P = 0.3), high school graduates (88% [113/129] vs 81%; P = 0.06), African American (69% [177/256] vs 73%; P = 0.1), or female (57% [158/279] vs 57%; P = 1), nor were they more likely to have longer LOS (5.7 [n = 280] vs 5.5 days; P = 0.6) or higher illness severity scores (1.3 [n = 231] vs 1.3; P = 0.7).
DISCUSSION
To our knowledge, this study is the first to evaluate the association between low HL and an important in-hospital outcome measure, hospital LOS. We found that low HL was associated with a longer hospital LOS, a result which remained significant when controlling for severity of illness and sociodemographic variables and when testing the model for sensitivity to the highest values of LOS and illness severity. Additionally, the association of HL with LOS appeared concentrated among participants with shorter LOS. Relative to other predictors, the contribution of HL to the overall LOS model was small, as evidenced by the change in adjusted R2 values with HL excluded.
Among the covariates, only gender modified the association between HL and LOS; the findings suggested that men were more susceptible to the effect of low HL on increased LOS. Illness severity and other sociodemographics, including age ≥65, did not appear to modify the association. We also found that being African American and having Medicaid or no insurance were associated with a significantly shorter LOS in multivariate analysis.
Previous work suggested that the adverse health effects of low HL may be mediated through several pathways, including health knowledge, self-efficacy, health skills, and illness stigma.25-27 The finding of a small but significant relationship between HL and LOS was not surprising given these known associations; nevertheless, there may be an additional patient-dependent effect of low HL on LOS not discovered here. For instance, patients with poor health knowledge and self-efficacy might stay in the hospital longer if they or their providers do not feel comfortable with their self-care ability.
This finding may be useful in developing hospital-based interventions. HL-specific interventions, several of which have been tested in the inpatient setting,14,28,29 have shown promise toward improving health knowledge,30 disease severity,31 and health resource utilization.32
Those with low HL may lack the self-efficacy to participate in discharge planning; in fact, previous work has related low HL to posthospital readmissions.8,9 Conversely, patients with low HL might struggle to engage in the inpatient milieu, advocating for shorter LOS if they feel alienated by the inpatient experience.
These possibilities show that LOS is a complex measure shown to depend on patient-level characteristics and on provider-based, geographical, and sociocultural factors.16,33 With these forces at play, additional effects of lower levels of HL may be lost without phenotyping patients by both level of HL and related characteristics, such as self-efficacy, health skills, and stigma. By gathering these additional data, future work should explore whether subpopulations of patients with low HL may be at risk for too-short vs too-long hospital admissions.
For instance, in this study, both race and Medicaid insurance were associated with shorter LOS. Being African American was associated with shorter LOS in our study but has been found to be associated with longer LOS in another study specifically focused on diabetes.34 Prior findings found uninsured patients have shorter LOS.35 Therefore, these findings in our study are difficult to explain without further work to understand whether there are health disparities in the way patients are cared for during hospitalization that may shorten or lengthen their LOS because of factors outside of their clinical need.
The finding that gender modified the effect of low HL on LOS was unexpected. There were similar proportions of men and women with low HL. There is evidence to support that women make the majority of health decisions for themselves and their familes36; therefore, there may be unmeasured aspects of HL that provide an advantage for female vs male inpatients. Furthermore, omitted confounders, such as social support, may not fully capture potential gender-related differences. Future work is needed to understand the role of gender in relationship to HL and LOS.
Limitations of this study include its observational, single-centered design with information derived from administrative data; positive and negative confounding cannot be ruled out. For instance, we did not control for complex aspects affecting LOS, such as discharge disposition and goals of care (eg, aggressive care after discharge vs hospice). To address this limitation, multivariate analyses were performed, which were adjusted for illness severity scores and took into account both comorbidity and severity of the current illness. Additionally, although it is important to study such populations, our largely urban, minority sample is not representative of the U.S. population, and within our large sample, there were participants with missing data who had lower HL on average, although this group represented only 5% of the sample. Finally, different HL tools have noncomplete concordance, which has been seen when comparing the BHLS with more objective tools.20,37 Furthermore, certain in-hospital clinical scenarios (eg, recent stroke or prolonged intensive care unit stay) may present unique challenges in establishing a baseline HL level. However, the BHLS was used in this study because of its greater feasibility.
In conclusion, this study is the first to evaluate the relationship between low HL and LOS. The findings suggest that HL may play a role in shaping outcomes in the inpatient setting and that targeting interventions toward screened patients may be a pathway toward mitigating adverse effects. Our findings need to be replicated in larger, more representative samples, and further work understanding subpopulations within the low HL population is needed. Future work should measure this association in diverse inpatient settings (eg, psychiatric, surgical, and specialty), in addition to assessing associations between HL and other important in-hospital outcome measures, including mortality and discharge disposition.
Acknowledgments
The authors thank the Hospitalist Project team for their assistance with data collection. The authors especially thank Chuanhong Liao and Ashley Snyder for assistance with statistical analyses; Andrea Flores, Ainoa Coltri, and Tom Best for their assistance with data management. The authors would also like to thank Nicole Twu for her help with preparing and editing the manuscript.
Disclosures
Dr. Jaffee was supported by a Calvin Fentress Research Fellowship and NIH R25MH094612. Dr. Press was supported by a career development award (NHLBI K23HL118151). This work was also supported by a seed grant from the Center for Health Administration Studies. All other authors declare no conflicts of interest.
Health literacy (HL), defined as patients’ ability to understand health information and make health decisions,1 is a prevalent problem in the outpatient and inpatient settings.2,3 In both settings, low HL has adverse implications for self-care including interpreting health labels4 and taking medications correctly.5 Among outpatient cohorts, HL has been associated with worse outcomes and acute care utilization.6 Associations with low HL include increased hospitalizations,7 rehospitalizations,8,9 emergency department visits,10 and decreased preventative care use.11 Among the elderly, low HL is associated with increased mortality12 and decreased self-perception of health.13
A systematic review revealed that most high-quality HL outcome studies were conducted in the outpatient setting.6 There have been very few studies assessing effects of low HL in an acute-care setting.7,14 These studies have evaluated postdischarge outcomes, including admissions or readmissions,7-9 and medication knowledge.14 To the best of our knowledge, there are no studies evaluating associations between HL and hospital length of stay (LOS).
LOS has received much attention as providers and payers focus more on resource utilization and eliminating adverse effects of prolonged hospitalization.15 LOS is multifactorial, depending on clinical characteristics like disease severity, as well as on sociocultural, demographic, and geographic factors.16 Despite evidence that LOS reductions translate into improved resource allocation and potentially fewer complications, there remains a tension between the appropriate LOS and one that is too short for a given condition.17
Because low HL is associated with inefficient resource utilization, we hypothesized that low HL would be associated with increased LOS after controlling for illness severity. Our objectives were to evaluate the association between low HL and LOS and whether such an association was modified by illness severity and sociodemographics.
METHODS
Study Design, Setting, Participants
An in-hospital, cohort study design of patients who were admitted or transferred to the general medicine service at the University of Chicago between October 2012 and November 2015 and screened for inclusion as part of a large, ongoing study of inpatient care quality was conducted.18 Exclusion criteria included observation status, age under 18 years, non-English speaking, and repeat participants. Those who died during hospitalization or whose discharge status was missing were excluded because the primary goal was to examine the association of HL and time to discharge, which could not be evaluated among those who died. We excluded participants with LOS >30 days to limit overly influential effects of extreme outliers (1% of the population).
Variables
HL was screened using the Brief Health Literacy Screen (BHLS), a validated, 3-question verbal survey not requiring adequate visual acuity to assess HL.19,20 The 3 questions are as follows: (1) “How confident are you filling out medical forms by yourself?”, (2) “How often do you have someone help you read hospital materials?”, and (3) “How often do you have problems learning about your medical condition because of difficulty understanding written information?” Responses to the questions were scored on a 5-point Likert scale in which higher scores corresponded to higher HL.21,22 The scores for each of the 3 questions were summed to yield a range between 3 and 15. On the individual questions, prior work has demonstrated improved test performance with a cutoff of ≤3, which corresponds to a response of “some of the time” or “somewhat”; therefore, when the 3 questions were summed together, scores of ≤9 were considered indicative of low HL.21,23
For severity of illness adjustment, we used relative weights derived from the 3M (3M, Maplewood, MN) All Patient Refined Diagnosis Related Groups (APR-DRG) classification system, which uses administrative data to classify the severity. The APR-DRG system assigns each admission to a DRG based on principal diagnosis; for each DRG, patients are then subdivided into 4 severity classes based on age, comorbidity, and interactions between these variables and the admitting diagnosis.24 Using the base DRG and severity score, the system assigns relative weights that reflect differences in expected hospital resource utilization.
LOS was derived from hospital administrative data and counted from the date of admission to the hospital. Participants who were discharged on the day of admission were counted as having an LOS of 1. Insurance status (Medicare, Medicaid, no payer, private) also was obtained from administrative data. Age, sex (male or female), education (junior high or less, some high school, high school graduate, some college, college graduate, postgraduate), and race (black/African American, white, Asian or Pacific Islander [including Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asian, Native Hawaiian, Guam/Chamorro, Samoan, other Pacific], American Indian or Alaskan Native, multiple race) were obtained from administrative data based on information provided by the patient. Participants with missing data on any of the sociodemographic variables or on the APR-DRG score were excluded from the analysis.
Statistical Analysis
χ2 and 2-tailed t tests were used to compare categorical and continuous variables, respectively. Multivariate linear regressions were employed to measure associations between the independent variables (HL, illness severity, race, gender, education, and insurance status) and the dependent variable, LOS. Independent variables were chosen for clinical significance and retained in the model regardless of statistical significance. The adjusted R2 values of models with and without the HL variable included were reported to provide information on the contribution of HL to the overall model.
Because LOS was observed to be right skewed and residuals of the untransformed regression were observed to be non-normally distributed, the decision was made to natural log transform LOS, which is consistent with previous hospital LOS studies.16 Regression coefficients and confidence intervals were then transformed into percentage estimates using the following equation: 100(eβ–1). Adjusted R2 was reported for the transformed regression.
The APR-DRG relative weight was treated as a continuous variable. Sociodemographic variables were dichotomized as follows: female vs male; high school graduates vs not; African American vs not; Medicaid/no payer vs Medicare/private payer. Age was not included in the multivariate model because it has been incorporated into the weighted APR-DRG illness severity scores.
Each of the sociodemographic variables and the APR-DRG score were examined for effect modification via the same multivariate linear equation described above, with the addition of an interaction term. A separate regression was performed with an interaction term between age (dichotomized at ≥65) and HL to investigate whether age modified the association between HL and LOS. Finally, we explored whether effects were isolated to long vs short LOS by dividing the sample based on the mean LOS (≥6 days) and performing separate multivariate comparisons.
Sensitivity analyses were performed to exclude those with LOS greater than the 90th percentile and those with APR-DRG score greater than the 90th percentile; age was added to the model as a continuous variable to evaluate whether the illness severity score fully adjusted for the effects of age on LOS. Furthermore, we compared the participants with missing data to those with complete data across both dependent and independent variables. Alpha was set at 0.05; analyses were performed using Stata Version 14 (Stata, College Station, TX).
RESULTS
A total of 5983 participants met inclusion criteria and completed the HL assessment; of these participants, 75 (1%) died during hospitalization, 9 (0.2%) had missing discharge status, and 79 (1%) had LOS >30 days. Two hundred eighty (5%) were missing data on sociodemographic variables or APR-DRG score. Of the remaining (n = 5540), the mean age was 57 years (standard deviation [SD] = 19 years), over half of participants were female (57%), and the majority were African American (73%) and had graduated from high school (81%). The sample was divided into those with private insurance (25%), those with Medicare (46%), and those with Medicaid (26%); 2% had no payer. The mean APR-DRG score was 1.3 (SD = 1.2), and the scores ranged from 0.3 to 15.8.
On the BHLS screen for HL, 20% (1104/5540) had inadequate HL. Participants with low HL had higher weighted illness severity scores (average 1.4 vs 1.3; P = 0.003). Participants with low HL were also more likely to be 65 or older (55% vs 33%; P < 0.001), non-high school graduates (35% vs 15%; P < 0.001), and African American (78% vs 72%; P < 0.001), and to have Medicare or private insurance (75% vs 71%; P = 0.02). There was no significant difference with respect to gender (54% male vs 57% female; P = 0.1)
Finally, we compared the group with missing data (n = 280) to the group with complete data (n = 5540). The participants with missing data were more likely to have low HL (31% [86/280] vs 20%; P < 0.001) and to have Medicare or private insurance (82% [177/217] vs 72%; P = 0.002); however, they were not more likely to be 65 or older (40% [112/280] vs 37%; P = 0.3), high school graduates (88% [113/129] vs 81%; P = 0.06), African American (69% [177/256] vs 73%; P = 0.1), or female (57% [158/279] vs 57%; P = 1), nor were they more likely to have longer LOS (5.7 [n = 280] vs 5.5 days; P = 0.6) or higher illness severity scores (1.3 [n = 231] vs 1.3; P = 0.7).
DISCUSSION
To our knowledge, this study is the first to evaluate the association between low HL and an important in-hospital outcome measure, hospital LOS. We found that low HL was associated with a longer hospital LOS, a result which remained significant when controlling for severity of illness and sociodemographic variables and when testing the model for sensitivity to the highest values of LOS and illness severity. Additionally, the association of HL with LOS appeared concentrated among participants with shorter LOS. Relative to other predictors, the contribution of HL to the overall LOS model was small, as evidenced by the change in adjusted R2 values with HL excluded.
Among the covariates, only gender modified the association between HL and LOS; the findings suggested that men were more susceptible to the effect of low HL on increased LOS. Illness severity and other sociodemographics, including age ≥65, did not appear to modify the association. We also found that being African American and having Medicaid or no insurance were associated with a significantly shorter LOS in multivariate analysis.
Previous work suggested that the adverse health effects of low HL may be mediated through several pathways, including health knowledge, self-efficacy, health skills, and illness stigma.25-27 The finding of a small but significant relationship between HL and LOS was not surprising given these known associations; nevertheless, there may be an additional patient-dependent effect of low HL on LOS not discovered here. For instance, patients with poor health knowledge and self-efficacy might stay in the hospital longer if they or their providers do not feel comfortable with their self-care ability.
This finding may be useful in developing hospital-based interventions. HL-specific interventions, several of which have been tested in the inpatient setting,14,28,29 have shown promise toward improving health knowledge,30 disease severity,31 and health resource utilization.32
Those with low HL may lack the self-efficacy to participate in discharge planning; in fact, previous work has related low HL to posthospital readmissions.8,9 Conversely, patients with low HL might struggle to engage in the inpatient milieu, advocating for shorter LOS if they feel alienated by the inpatient experience.
These possibilities show that LOS is a complex measure shown to depend on patient-level characteristics and on provider-based, geographical, and sociocultural factors.16,33 With these forces at play, additional effects of lower levels of HL may be lost without phenotyping patients by both level of HL and related characteristics, such as self-efficacy, health skills, and stigma. By gathering these additional data, future work should explore whether subpopulations of patients with low HL may be at risk for too-short vs too-long hospital admissions.
For instance, in this study, both race and Medicaid insurance were associated with shorter LOS. Being African American was associated with shorter LOS in our study but has been found to be associated with longer LOS in another study specifically focused on diabetes.34 Prior findings found uninsured patients have shorter LOS.35 Therefore, these findings in our study are difficult to explain without further work to understand whether there are health disparities in the way patients are cared for during hospitalization that may shorten or lengthen their LOS because of factors outside of their clinical need.
The finding that gender modified the effect of low HL on LOS was unexpected. There were similar proportions of men and women with low HL. There is evidence to support that women make the majority of health decisions for themselves and their familes36; therefore, there may be unmeasured aspects of HL that provide an advantage for female vs male inpatients. Furthermore, omitted confounders, such as social support, may not fully capture potential gender-related differences. Future work is needed to understand the role of gender in relationship to HL and LOS.
Limitations of this study include its observational, single-centered design with information derived from administrative data; positive and negative confounding cannot be ruled out. For instance, we did not control for complex aspects affecting LOS, such as discharge disposition and goals of care (eg, aggressive care after discharge vs hospice). To address this limitation, multivariate analyses were performed, which were adjusted for illness severity scores and took into account both comorbidity and severity of the current illness. Additionally, although it is important to study such populations, our largely urban, minority sample is not representative of the U.S. population, and within our large sample, there were participants with missing data who had lower HL on average, although this group represented only 5% of the sample. Finally, different HL tools have noncomplete concordance, which has been seen when comparing the BHLS with more objective tools.20,37 Furthermore, certain in-hospital clinical scenarios (eg, recent stroke or prolonged intensive care unit stay) may present unique challenges in establishing a baseline HL level. However, the BHLS was used in this study because of its greater feasibility.
In conclusion, this study is the first to evaluate the relationship between low HL and LOS. The findings suggest that HL may play a role in shaping outcomes in the inpatient setting and that targeting interventions toward screened patients may be a pathway toward mitigating adverse effects. Our findings need to be replicated in larger, more representative samples, and further work understanding subpopulations within the low HL population is needed. Future work should measure this association in diverse inpatient settings (eg, psychiatric, surgical, and specialty), in addition to assessing associations between HL and other important in-hospital outcome measures, including mortality and discharge disposition.
Acknowledgments
The authors thank the Hospitalist Project team for their assistance with data collection. The authors especially thank Chuanhong Liao and Ashley Snyder for assistance with statistical analyses; Andrea Flores, Ainoa Coltri, and Tom Best for their assistance with data management. The authors would also like to thank Nicole Twu for her help with preparing and editing the manuscript.
Disclosures
Dr. Jaffee was supported by a Calvin Fentress Research Fellowship and NIH R25MH094612. Dr. Press was supported by a career development award (NHLBI K23HL118151). This work was also supported by a seed grant from the Center for Health Administration Studies. All other authors declare no conflicts of interest.
1. U.S. Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. Washington, DC: U.S. Government Printing Office; 2000.
2. “What Did the Doctor Say”? Improving Health Literacy to Protect Patient Safety. The Joint Commission; 2007.
3. Kutner M, Greenberg E, Jin Y, Paulsen C. The Health Literacy of America’s Adults: Results from the 2003 National Assessment of Adult Literacy. National Center for Education Statistics; 2006.
4. Davis TC, Wolf MS, Bass PF, et al. Literacy and misunderstanding prescription drug labels. Ann Intern Med. 2006;145(12):887-894. PubMed
5. Kripalani S, Henderson LE, Chiu EY, Robertson R, Kolm P, Jacobson TA. Predictors of medication self-management skill in a low-literacy population. J Gen Intern Med. 2006;21(8):852-856. PubMed
6. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97-107. PubMed
7. Baker DW, Parker RM, Williams MV, Clark WS. Health literacy and the risk of hospital admission. J Gen Intern Med. 1998;13(12):791-798. PubMed
8. Mitchell SE, Sadikova E, Jack BW, Paasche-Orlow MK. Health literacy and 30-day postdischarge hospital utilization. J Health Commun. 2012;17(Suppl 3):325-338. PubMed
9. Jaffee EG, Arora VM, Matthiesen MI, Hariprasad SM, Meltzer DO, Press VG. Postdischarge Falls and Readmissions: Associations with Insufficient Vision and Low Health Literacy among Hospitalized Seniors. J Health Commun. 2016;21(sup2):135-140. PubMed
10. Hope CJ, Wu J, Tu W, Young J, Murray MD. Association of medication adherence, knowledge, and skills with emergency department visits by adults 50 years or older with congestive heart failure. Am J Health Syst Pharm. 2004;61(19):2043-2049. PubMed
11. Bennett IM, Chen J, Soroui JS, White S. The contribution of health literacy to disparities in self-rated health status and preventive health behaviors in older adults. Ann Fam Med. 2009;7(3):204-211. PubMed
12. Baker DW, Wolf MS, Feinglass J, Thompson JA. Health literacy, cognitive abilities, and mortality among elderly persons. J Gen Intern Med. 2008;23(6):723-726. PubMed
13. Cho YI, Lee SY, Arozullah AM, Crittenden KS. Effects of health literacy on health status and health service utilization amongst the elderly. Soc Sci Med. 2008;66(8):1809-1816. PubMed
14. Paasche-Orlow MK, Riekert KA, Bilderback A, et al. Tailored education may reduce health literacy disparities in asthma self-management. Am J Respir Crit Care Med. 2005;172(8):980-986. PubMed
15. Soria-Aledo V, Carrillo-Alcaraz A, Campillo-Soto Á, et al. Associated factors and cost of inappropriate hospital admissions and stays in a second-level hospital. Am J Med Qual. 2009;24(4):321-332. PubMed
16. Lu M, Sajobi T, Lucyk K, Lorenzetti D, Quan H. Systematic review of risk adjustment models of hospital length of stay (LOS). Med Care. 2015;53(4):355-365. PubMed
17. Clarke A, Rosen R. Length of stay. How short should hospital care be? Eur J Public Health. 2001;11(2):166-170. PubMed
18. Meltzer D, Manning WG, Morrison J, et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866-874. PubMed
19. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36(8):588-594. PubMed
20. Press VG, Shapiro MI, Mayo AM, Meltzer DO, Arora VM. More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18 Suppl 1:197-204. PubMed
21. Willens DE, Kripalani S, Schildcrout JS, et al. Association of brief health literacy screening and blood pressure in primary care. J Health Commun. 2013;18 Suppl 1:129-142. PubMed
22. Peterson PN, Shetterly SM, Clarke CL, et al. Health literacy and outcomes among patients with heart failure. JAMA. 2011;305(16):1695-1701. PubMed
23. Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561-566. PubMed
24. Averill RF, Goldfield N, Hughes JS, et al. All Patient Refined Diagnosis Related Groups (APR-DRGs): Methodology Overview. 3M Health Information Systems; 2003.
25. Waite KR, Paasche-Orlow M, Rintamaki LS, Davis TC, Wolf MS. Literacy, social stigma, and HIV medication adherence. J Gen Intern Med. 2008;23(9):1367-1372. PubMed
26. Paasche-Orlow MK, Wolf MS. The causal pathways linking health literacy to health outcomes. Am J Health Behav. 2007;31 Suppl 1:S19-26. PubMed
27. Berkman ND, Sheridan SL, Donahue KE, et al. Health literacy interventions and outcomes: an updated systematic review. Evid Rep Technol Assess (Full Rep). 2011;(199):1-941. PubMed
28. Kripalani S, Roumie CL, Dalal AK, et al. Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1-10. PubMed
29. Press VG, Arora VM, Shah LM, et al. Teaching the use of respiratory inhalers to hospitalized patients with asthma or COPD: a randomized trial. J Gen Intern Med. 2012;27(10):1317-1325. PubMed
30. Sobel RM, Paasche-Orlow MK, Waite KR, Rittner SS, Wilson EAH, Wolf MS. Asthma 1-2-3: a low literacy multimedia tool to educate African American adults about asthma. J Community Health. 2009;34(4):321-327. PubMed
31. Rothman RL, DeWalt DA, Malone R, et al. Influence of patient literacy on the effectiveness of a primary care-based diabetes disease management program. JAMA. 2004;292(14):1711-1716. PubMed
32. DeWalt DA, Malone RM, Bryant ME, et al. A heart failure self-management
program for patients of all literacy levels: a randomized, controlled trial [ISRCTN11535170].
BMC Health Serv Res. 2006;6:30. PubMed
33. Hasan O, Orav EJ, Hicks LS. Insurance status and hospital care for myocardial
infarction, stroke, and pneumonia. J Hosp Med. 2010;5(8):452-459. PubMed
34. Cook CB, Naylor DB, Hentz JG, et al. Disparities in diabetes-related hospitalizations:
relationship of age, sex, and race/ethnicity with hospital discharges, lengths
of stay, and direct inpatient charges. Ethn Dis. 2006;16(1):126-131. PubMed
35. Hadley J, Steinberg EP, Feder J. Comparison of uninsured and privately insured
hospital patients. Condition on admission, resource use, and outcome. JAMA.
1991;265(3):374-379. PubMed
36. Women’s Health Care Chartbook: Key Findings From the Kaiser Women’s
Health Survey. May 2011. https://kaiserfamilyfoundation.files.wordpress.
com/2013/01/8164.pdf. Accessed August 1, 2017.
37. Louis AJ, Arora VM, Matthiesen MI, Meltzer DO, Press VG. Screening Hospitalized Patients for Low Health Literacy: Beyond the REALM of Possibility? PubMed
1. U.S. Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. Washington, DC: U.S. Government Printing Office; 2000.
2. “What Did the Doctor Say”? Improving Health Literacy to Protect Patient Safety. The Joint Commission; 2007.
3. Kutner M, Greenberg E, Jin Y, Paulsen C. The Health Literacy of America’s Adults: Results from the 2003 National Assessment of Adult Literacy. National Center for Education Statistics; 2006.
4. Davis TC, Wolf MS, Bass PF, et al. Literacy and misunderstanding prescription drug labels. Ann Intern Med. 2006;145(12):887-894. PubMed
5. Kripalani S, Henderson LE, Chiu EY, Robertson R, Kolm P, Jacobson TA. Predictors of medication self-management skill in a low-literacy population. J Gen Intern Med. 2006;21(8):852-856. PubMed
6. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97-107. PubMed
7. Baker DW, Parker RM, Williams MV, Clark WS. Health literacy and the risk of hospital admission. J Gen Intern Med. 1998;13(12):791-798. PubMed
8. Mitchell SE, Sadikova E, Jack BW, Paasche-Orlow MK. Health literacy and 30-day postdischarge hospital utilization. J Health Commun. 2012;17(Suppl 3):325-338. PubMed
9. Jaffee EG, Arora VM, Matthiesen MI, Hariprasad SM, Meltzer DO, Press VG. Postdischarge Falls and Readmissions: Associations with Insufficient Vision and Low Health Literacy among Hospitalized Seniors. J Health Commun. 2016;21(sup2):135-140. PubMed
10. Hope CJ, Wu J, Tu W, Young J, Murray MD. Association of medication adherence, knowledge, and skills with emergency department visits by adults 50 years or older with congestive heart failure. Am J Health Syst Pharm. 2004;61(19):2043-2049. PubMed
11. Bennett IM, Chen J, Soroui JS, White S. The contribution of health literacy to disparities in self-rated health status and preventive health behaviors in older adults. Ann Fam Med. 2009;7(3):204-211. PubMed
12. Baker DW, Wolf MS, Feinglass J, Thompson JA. Health literacy, cognitive abilities, and mortality among elderly persons. J Gen Intern Med. 2008;23(6):723-726. PubMed
13. Cho YI, Lee SY, Arozullah AM, Crittenden KS. Effects of health literacy on health status and health service utilization amongst the elderly. Soc Sci Med. 2008;66(8):1809-1816. PubMed
14. Paasche-Orlow MK, Riekert KA, Bilderback A, et al. Tailored education may reduce health literacy disparities in asthma self-management. Am J Respir Crit Care Med. 2005;172(8):980-986. PubMed
15. Soria-Aledo V, Carrillo-Alcaraz A, Campillo-Soto Á, et al. Associated factors and cost of inappropriate hospital admissions and stays in a second-level hospital. Am J Med Qual. 2009;24(4):321-332. PubMed
16. Lu M, Sajobi T, Lucyk K, Lorenzetti D, Quan H. Systematic review of risk adjustment models of hospital length of stay (LOS). Med Care. 2015;53(4):355-365. PubMed
17. Clarke A, Rosen R. Length of stay. How short should hospital care be? Eur J Public Health. 2001;11(2):166-170. PubMed
18. Meltzer D, Manning WG, Morrison J, et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866-874. PubMed
19. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36(8):588-594. PubMed
20. Press VG, Shapiro MI, Mayo AM, Meltzer DO, Arora VM. More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18 Suppl 1:197-204. PubMed
21. Willens DE, Kripalani S, Schildcrout JS, et al. Association of brief health literacy screening and blood pressure in primary care. J Health Commun. 2013;18 Suppl 1:129-142. PubMed
22. Peterson PN, Shetterly SM, Clarke CL, et al. Health literacy and outcomes among patients with heart failure. JAMA. 2011;305(16):1695-1701. PubMed
23. Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561-566. PubMed
24. Averill RF, Goldfield N, Hughes JS, et al. All Patient Refined Diagnosis Related Groups (APR-DRGs): Methodology Overview. 3M Health Information Systems; 2003.
25. Waite KR, Paasche-Orlow M, Rintamaki LS, Davis TC, Wolf MS. Literacy, social stigma, and HIV medication adherence. J Gen Intern Med. 2008;23(9):1367-1372. PubMed
26. Paasche-Orlow MK, Wolf MS. The causal pathways linking health literacy to health outcomes. Am J Health Behav. 2007;31 Suppl 1:S19-26. PubMed
27. Berkman ND, Sheridan SL, Donahue KE, et al. Health literacy interventions and outcomes: an updated systematic review. Evid Rep Technol Assess (Full Rep). 2011;(199):1-941. PubMed
28. Kripalani S, Roumie CL, Dalal AK, et al. Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1-10. PubMed
29. Press VG, Arora VM, Shah LM, et al. Teaching the use of respiratory inhalers to hospitalized patients with asthma or COPD: a randomized trial. J Gen Intern Med. 2012;27(10):1317-1325. PubMed
30. Sobel RM, Paasche-Orlow MK, Waite KR, Rittner SS, Wilson EAH, Wolf MS. Asthma 1-2-3: a low literacy multimedia tool to educate African American adults about asthma. J Community Health. 2009;34(4):321-327. PubMed
31. Rothman RL, DeWalt DA, Malone R, et al. Influence of patient literacy on the effectiveness of a primary care-based diabetes disease management program. JAMA. 2004;292(14):1711-1716. PubMed
32. DeWalt DA, Malone RM, Bryant ME, et al. A heart failure self-management
program for patients of all literacy levels: a randomized, controlled trial [ISRCTN11535170].
BMC Health Serv Res. 2006;6:30. PubMed
33. Hasan O, Orav EJ, Hicks LS. Insurance status and hospital care for myocardial
infarction, stroke, and pneumonia. J Hosp Med. 2010;5(8):452-459. PubMed
34. Cook CB, Naylor DB, Hentz JG, et al. Disparities in diabetes-related hospitalizations:
relationship of age, sex, and race/ethnicity with hospital discharges, lengths
of stay, and direct inpatient charges. Ethn Dis. 2006;16(1):126-131. PubMed
35. Hadley J, Steinberg EP, Feder J. Comparison of uninsured and privately insured
hospital patients. Condition on admission, resource use, and outcome. JAMA.
1991;265(3):374-379. PubMed
36. Women’s Health Care Chartbook: Key Findings From the Kaiser Women’s
Health Survey. May 2011. https://kaiserfamilyfoundation.files.wordpress.
com/2013/01/8164.pdf. Accessed August 1, 2017.
37. Louis AJ, Arora VM, Matthiesen MI, Meltzer DO, Press VG. Screening Hospitalized Patients for Low Health Literacy: Beyond the REALM of Possibility? PubMed
© 2017 Society of Hospital Medicine
Inpatients With Poor Vision
Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.
METHODS
From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).
Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.
Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).
RESULTS
Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).
Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).
One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).
Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)
DISCUSSION
We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.
Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.
Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.
Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]
Acknowledgements
The authors thank several individuals for their assistance with this project. Andrea Flores, MA, Senior Programmer, helped with programming and data support. Kristin Constantine, BA, Project Manager, helped with developing and implementing the database for this project. Edward Kim, BA, Project Manager, helped with management of the database and data collection. The authors also thank Ainoa Coltri and the Hospitalist Project research assistants for assistance with data collection, Frank Zadravecz, MPH, for assistance with the creation of figures, and Nicole Twu, MS, for assistance with the project. The authors thank other students who helped to collect data for this project, including Allison Louis, Victoria Moreira, and Esther Schoenfeld.
Disclosures: Dr. Press is supported by a career development award from the National Heart Lung and Blood Institute (NIH K23HL118151). A pilot award from The Center on the Demography and Economics of Aging (CoA, National Institute of Aging P30 AG012857) supported this project. Dr. Matthiesen and Ms. Ranadive received support from the Summer Research Program funded by the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795). Dr. Matthiesen also received funding from the Calvin Fentress Fellowship Program. Dr. Hariprasad reports being a consultant or participating on a speaker's bureau for Alcon, Allergan, Regeneron, Genentech, Optos, OD‐OS, Bayer, Clearside Biomedical, and Ocular Therapeutix. Dr. Meltzer received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795), and from the Agency for Healthcare Quality and Research through the Hospital Medicine and Economics Center for Education and Research in Therapeutics (U18 HS016967‐01), and from the National Institute of Aging through a Midcareer Career Development Award (K24 AG031326‐01), from the National Cancer Institute (KM1 CA156717), and from the National Center for Advancing Translational Science (2UL1TR000430‐06). Dr. Arora received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795) and National Institutes on Aging (K23AG033763).
- , , , . Risk factors and risk assessment tools for falls in hospital in‐patients: a systematic review. Age Ageing. 2004;33(2):122–130.
- , , , , . More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197–204.
- , , , , , . Risk factors for delirium at discharge: development and validation of a predictive model. Arch Intern Med. 2007;167(13):1406–1413.
- , , , et al. Misuse of respiratory inhalers in hospitalized patients with asthma or COPD. J Gen Intern Med. 2011;26(6):635–642.
- , , . Can elderly people take their medicine? Patient Educ Couns. 2005;59(2):186–191.
- , , , et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866–874.
- . Prospective evaluation of visual acuity assessment: a comparison of Snellen versus ETDRS charts in clinical practice (An AOS Thesis). Trans Am Ophthalmol Soc. 2009;107:311–324.
- , , , et al. Delirium. The occurrence and persistence of symptoms among elderly hospitalized patients. Arch Intern Med. 1992;152(2):334–340.
- , , , et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340(9):669–676.
- , , , , . One‐year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):27–32.
- , , , et al. A low‐vision rehabilitation program for patients with mild cognitive deficits. JAMA Ophthalmol. 2013;131(7):912–919.
Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.
METHODS
From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).
Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.
Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).
RESULTS
Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).
Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).
One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).
Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)
DISCUSSION
We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.
Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.
Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.
Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]
Acknowledgements
The authors thank several individuals for their assistance with this project. Andrea Flores, MA, Senior Programmer, helped with programming and data support. Kristin Constantine, BA, Project Manager, helped with developing and implementing the database for this project. Edward Kim, BA, Project Manager, helped with management of the database and data collection. The authors also thank Ainoa Coltri and the Hospitalist Project research assistants for assistance with data collection, Frank Zadravecz, MPH, for assistance with the creation of figures, and Nicole Twu, MS, for assistance with the project. The authors thank other students who helped to collect data for this project, including Allison Louis, Victoria Moreira, and Esther Schoenfeld.
Disclosures: Dr. Press is supported by a career development award from the National Heart Lung and Blood Institute (NIH K23HL118151). A pilot award from The Center on the Demography and Economics of Aging (CoA, National Institute of Aging P30 AG012857) supported this project. Dr. Matthiesen and Ms. Ranadive received support from the Summer Research Program funded by the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795). Dr. Matthiesen also received funding from the Calvin Fentress Fellowship Program. Dr. Hariprasad reports being a consultant or participating on a speaker's bureau for Alcon, Allergan, Regeneron, Genentech, Optos, OD‐OS, Bayer, Clearside Biomedical, and Ocular Therapeutix. Dr. Meltzer received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795), and from the Agency for Healthcare Quality and Research through the Hospital Medicine and Economics Center for Education and Research in Therapeutics (U18 HS016967‐01), and from the National Institute of Aging through a Midcareer Career Development Award (K24 AG031326‐01), from the National Cancer Institute (KM1 CA156717), and from the National Center for Advancing Translational Science (2UL1TR000430‐06). Dr. Arora received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795) and National Institutes on Aging (K23AG033763).
Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.
METHODS
From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).
Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.
Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).
RESULTS
Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).
Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).
One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).
Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)
DISCUSSION
We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.
Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.
Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.
Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]
Acknowledgements
The authors thank several individuals for their assistance with this project. Andrea Flores, MA, Senior Programmer, helped with programming and data support. Kristin Constantine, BA, Project Manager, helped with developing and implementing the database for this project. Edward Kim, BA, Project Manager, helped with management of the database and data collection. The authors also thank Ainoa Coltri and the Hospitalist Project research assistants for assistance with data collection, Frank Zadravecz, MPH, for assistance with the creation of figures, and Nicole Twu, MS, for assistance with the project. The authors thank other students who helped to collect data for this project, including Allison Louis, Victoria Moreira, and Esther Schoenfeld.
Disclosures: Dr. Press is supported by a career development award from the National Heart Lung and Blood Institute (NIH K23HL118151). A pilot award from The Center on the Demography and Economics of Aging (CoA, National Institute of Aging P30 AG012857) supported this project. Dr. Matthiesen and Ms. Ranadive received support from the Summer Research Program funded by the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795). Dr. Matthiesen also received funding from the Calvin Fentress Fellowship Program. Dr. Hariprasad reports being a consultant or participating on a speaker's bureau for Alcon, Allergan, Regeneron, Genentech, Optos, OD‐OS, Bayer, Clearside Biomedical, and Ocular Therapeutix. Dr. Meltzer received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795), and from the Agency for Healthcare Quality and Research through the Hospital Medicine and Economics Center for Education and Research in Therapeutics (U18 HS016967‐01), and from the National Institute of Aging through a Midcareer Career Development Award (K24 AG031326‐01), from the National Cancer Institute (KM1 CA156717), and from the National Center for Advancing Translational Science (2UL1TR000430‐06). Dr. Arora received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795) and National Institutes on Aging (K23AG033763).
- , , , . Risk factors and risk assessment tools for falls in hospital in‐patients: a systematic review. Age Ageing. 2004;33(2):122–130.
- , , , , . More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197–204.
- , , , , , . Risk factors for delirium at discharge: development and validation of a predictive model. Arch Intern Med. 2007;167(13):1406–1413.
- , , , et al. Misuse of respiratory inhalers in hospitalized patients with asthma or COPD. J Gen Intern Med. 2011;26(6):635–642.
- , , . Can elderly people take their medicine? Patient Educ Couns. 2005;59(2):186–191.
- , , , et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866–874.
- . Prospective evaluation of visual acuity assessment: a comparison of Snellen versus ETDRS charts in clinical practice (An AOS Thesis). Trans Am Ophthalmol Soc. 2009;107:311–324.
- , , , et al. Delirium. The occurrence and persistence of symptoms among elderly hospitalized patients. Arch Intern Med. 1992;152(2):334–340.
- , , , et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340(9):669–676.
- , , , , . One‐year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):27–32.
- , , , et al. A low‐vision rehabilitation program for patients with mild cognitive deficits. JAMA Ophthalmol. 2013;131(7):912–919.
- , , , . Risk factors and risk assessment tools for falls in hospital in‐patients: a systematic review. Age Ageing. 2004;33(2):122–130.
- , , , , . More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197–204.
- , , , , , . Risk factors for delirium at discharge: development and validation of a predictive model. Arch Intern Med. 2007;167(13):1406–1413.
- , , , et al. Misuse of respiratory inhalers in hospitalized patients with asthma or COPD. J Gen Intern Med. 2011;26(6):635–642.
- , , . Can elderly people take their medicine? Patient Educ Couns. 2005;59(2):186–191.
- , , , et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866–874.
- . Prospective evaluation of visual acuity assessment: a comparison of Snellen versus ETDRS charts in clinical practice (An AOS Thesis). Trans Am Ophthalmol Soc. 2009;107:311–324.
- , , , et al. Delirium. The occurrence and persistence of symptoms among elderly hospitalized patients. Arch Intern Med. 1992;152(2):334–340.
- , , , et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340(9):669–676.
- , , , , . One‐year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):27–32.
- , , , et al. A low‐vision rehabilitation program for patients with mild cognitive deficits. JAMA Ophthalmol. 2013;131(7):912–919.
