Health literacy (HL) is required for successful navigation of any complex health care system. According to the U.S. Department of Health and Human Services (USDHHS), Office of Disease Prevention and Health Promotion, limited HL is a public health problem affecting nine of 10 English-speaking adults in the United States. It has now earned a place among the nation's targeted objectives (USDHHS, 2011). Limited HL contributes $106 billion to $236 billion to U.S. annual health care expenditures (USDHHS, 2011). However, The Center for Healthcare Strategies (2013) reported that the actual cost of limited HL was closer to $1.6 trillion to $3.6 trillion. The currency of the literature to contemporary society is limited, as can be noted by the number of citations in this manuscript over 10 years old. Despite the lack of current research on health literacy, patients' understanding of their health information is of even greater importance. This study examined the functional health literacy of adult patients diagnosed with bipolar disorder and investigated the relationship between these patients' ability to comprehend, access, and use health information and psychiatric hospital readmissions.
In a society with increasing diversity, the background of patients and providers is also more diverse. Patients report providers' bias and prejudice in their health care encounters (Gonzalez et al., 2019). Prioritization and implementation of a nursing-specific health literacy policy will result in patient empowerment, engagement, and activation; increased health care equity; and improved patient, population, organization, and system outcomes (Loan et al., 2018). Despite its importance, nursing is slow to move HL to the forefront of its research agenda (Sand-Jecklin et al., 2010).
The USDHHS (2011) defined HL as “the capacity to obtain, process and understand basic health information and services needed to make appropriate health decisions” (p. 1). In addition, The National Academy of Sciences (2004) and Berkman et al. (2011) stated that HL includes the ability to understand instructions on prescription drug bottles, appointment slips, medical education brochures, doctors' directions, and consent forms. HL also entails the ability to negotiate complex health care systems, including reading, listening, analytical, and decision making skills, (also described as functional cognition), as well as the ability to apply these skills to health and illness situations (The National Academy of Sciences, 2004). Each attribute in the HL definition represents some level of complexity and presents unique challenges for health care consumers, policy makers, and health services providers.
A large percentage of chronically mentally ill individuals (e.g., patients diagnosed with bipolar disorder, schizophrenia, or schizoaffective disorders) have low HL (Krishan et al., 2012). Due to their often poor self-management skills, chronically mentally ill individuals are at an increased risk for hospital readmission (Cloonan et al., 2013; Krishan et al., 2012; USDHHS, 2011); however, more needs to be known about these relationships.
According to a Substance Abuse and Mental Health Services Administration report (2016), approximately nine million adults in the United States (4%) had a mental illness that greatly affected their day-to-day living, including multiple financial implications. Individuals with a serious mental illness earned an average of $16,000 less than the general population, per year, accounted for an annual loss of 217 million work days, had more comorbidities, and died 25 years earlier than those in the general population (American Hospital Association [AHA], 2012). In 2013, expenditures for treatment of mental health disorders reached $201 billion, surpassing spending for heart conditions by $54 billion and cancer by $79 billion. According to recent estimates, spending for behavioral health treatments is expected to total $280.5 billion in 2020 (AHA, 2019).
With the advent of COVID-19 and its collateral increase in depression, substance abuse, lost wages, loneliness, anxiety, and fear of the unknown, Fair Health (2020) has reported an estimated cost ranging from a low of $362 billion in charges (for a population with low incidence rate and low inpatient stay) to a high of $1.449 trillion in charges (for a population with high incidence rate and high inpatient stay). Estimates are based on the number of Americans projected to be infected with the COVID-19, including mental health case increases and exacerbation. The estimated cost for behavioral health alone has not been isolated given the ongoing course of the virus (Fair Health, 2020). Mental illness has an enormous financial impact on both the individual and on health care in general and more so in a pandemic.
Mental Illness Statistics
A study conducted by the National Survey on Drug Use and Health (2017) reported that 47.6 million adults aged 18 years or older (18.9% of all adults in the United States) had experienced serious psychological distress within the past year. Montgomery and Kirkpatrick (2002) also reported that 30% to 40% of the mentally ill were rehospitalized within 6 months of discharge and 40% to 50% within 12 months of discharge. The USDHHS considers mental illness a disability and reported that 25% of all years of life lost to disability and early mortality were due to mental illness (Behavioral Health, 2012).
Hospital Readmission and Mental Illness
Hospital readmission in general has also earned attention due to the high associated costs. This phenomenon absorbs an estimated one third of the total U.S. health expenditures according to Cloonan et al. (2013). Approximately 35 million patients are discharged from U.S. hospitals annually; of this number, 2 million Medicare beneficiaries are readmitted within 30 days of discharge, at an annual cost of $17.5 billion (Cloonan et al., 2013). Readmission occurs when a patient is discharged from one acute care hospital (the index hospital) and is then admitted to the same or different hospital within a specified period after the time of discharge from the index hospital. The specified period after the time of discharge to readmission for the Centers for Medicare and Medicaid Services (CMS) is 30 days (CMS, 2013). Thirty-day readmission has been identified by the CMS as resulting from inadequate inpatient care and as a driver of high health care costs. Individuals with chronic mental illness are among the most common users of inpatient facilities according to the CMS (2013). The real issue with mental illness and hospital readmission is the overuse of services with no improvement in quality of life.
Population at Risk for Low HL
The USDHHS identifies those at risk for low HL as adults aged 65 years and older, racial and ethnic groups other than White, refugees and immigrants, those with less than a high school education, non-native English speakers, and those with annual incomes at or below the federal poverty level ($11,770 for an individual and $15,930 for a family of two) (USDHHS, 2011). According to The National Academy of Sciences (2004), individuals with low HL are unable to understand instructions on prescription drug bottles, appointment slips, medical education brochures, doctors' directions, and consent forms. These inabilities subject them to noncompliance, comorbidities, and complicated treatments that affect quality of life, employment, and mortality.
Cloonan et al. (2013) reported the population at greatest risk for readmission as those aged 75 years and older, ethnic minorities, individuals with comorbidities, and low socioeconomic status. Those with mental illness have more comorbidities than the general population and as reported previously, they die 25 years earlier than the general population (Everett et al., 2008). Mental illness complicates the treatment of another illness (AHA, 2012). The variables of comorbidity, low socioeconomic status, low educational attainment, and being a member of an ethnic minority seem to profile the chronically mentally ill and are the least likely to benefit from health interventions that are meant for the general population (Behavioral Health, 2012; Everett et al., 2008).
The CMS Solution
Recognizing the high costs associated with hospital re-admissions for mental illness, in 2012 the CMS instituted the Hospital Readmission Reduction Program, (Section 3025 of the Affordable Care Act). The program required the Secretary of the USDHHS to reduce payments to In-patient Prospective Payment System hospitals for excess readmissions (CMS, 2018).
The sample for this study (individuals diagnosed with bipolar disorder) experience multiple relapses and poor cognitive states even in remission. Combined with comorbidities, these factors negatively affect physical health and quality of life and increase hospital readmission rate for the patient diagnosed with bipolar disorder (Studart et al., 2015). For these reasons, bipolar disorder is a substantial contributor to direct and indirect health care and nonhealthy care costs (Cloutier et al., 2018). Patients with bipolar disorder types I and II according to the Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (American Psychiatric Association [APA], 2013) experience poor cognition throughout every phase of the illness.
Cognition in Patients With Bipolar Disorder
Cognition implies understanding the connection between cause and effect, between action and the consequences of action; cognitive strategies are mental plans that people use to understand themselves and the environment (Sadock & Sadock, 2007). This definition of cognition is similar to the definition of HL provided by the USDHHS for good reason, as functional cognition is a requisite condition for functional HL. In the absence of functional cognition, the patient diagnosed with bipolar disorder is at high risk for inadequate functional HL.
Episodes of mania and depression are diagnostic of bipolar disorder. These episodes are categorized as bipolar I and II, with bipolar I having more cognitive impairment than bipolar II (Torrent et al., 2006). Sparding et al. (2015) reported that patients with bipolar disorder type I were indistinguishable from bipolar disorder type II in 16 of 18 cognitive measures. This study is in agreement with the DSM-5 statement concerning bipolar disorder type II; that is, with the exception of memory and semantic fluency, individuals with bipolar type II disorder have similar cognitive impairment as do individuals with bipolar type I disorder (APA, 2013, p. 138). The DSM-5 reports that individuals with bipolar disorder types I and II may return to full functional levels after illness episodes; however, approximately 30% of patients with bipolar disorder type I remain impaired in work role function, and functional recovery is slower than symptom recovery, especially occupational recovery (APA, 2013). Neurocognitive deficits persist in every phase of the illness and have been identified as an impediment in numerous psychosocial domains (e.g., self-care, relationships, executive function) including HL.
In patients diagnosed with bipolar disorder, social support provided by family and friends is thought to prevent extreme responses associated with dysfunction. Prevention of extreme responses occur through communication of what is expected, of appropriate norms, of rewards and punishment, and through the provision of coping assistance. Social support is defined by Cohen (2004) as the provision of both psychological and material resources with the intention of helping the recipients cope with stress; it functions as a stress buffer or protective factor, by reinforcing self-esteem, self-efficacy, and problem-solving behaviors. Cohen also proposed that social support is related to well-being because it fosters positive emotions, a sense of self-worth, and predictability.
Social support is also thought to play a role in the risk for, progression of, and recovery from physical illness (Cohen, 2004); it also has been reported to play a significant role in stabilizing the patients diagnosed with bipolar disorder (Cohen, 2004; Studart et al., 2015). Perceived social support was measured as a variable of interest in this study.
Given existing mandates on psychiatric hospital readmission, attempts to provide optimum time and resources for inpatient stabilization may be difficult for clinicians and other health care providers to accomplish without eliciting social support from clients' family, friends, and communities. The literature suggests more current information is needed to impact the deficits in patients' understanding of their health. Considering the potential for cognitive deficits in patients with bipolar disorder and the role of cognition in functional HL, there is a gap in the current literature. Identification of the relationships between HL and this disorder can lead to the development of group specific interventions that can greatly improve quality of life and decrease psychiatric hospital readmission and related cost. The purpose of this pilot study is to explore the relationships between levels of HL as measured by the Short Test of Functional HL in Adults (S-TOFHLA; Parker et al., 1995), with readmission rates at 2 and 4 weeks postdischarge, in patients diagnosed with bipolar disorder.
This pilot study employed a prospective, exploratory, correlational, descriptive research design. Using the S-TOFHLA questionnaire, the study assessed HL of patients diagnosed with bipolar disorder, at discharge, and then tracked readmission rates at 2- and 4-week intervals, postdischarge. The study answered the questions: (a) What are the differences between functional HL groups (inadequate/marginal and adequate) in total number of readmissions for patients diagnosed with bipolar disorder, while controlling for age; and (b) What are the differences between functional HL groups (inadequate/marginal and adequate) in readmissions across two time intervals, 2 and 4 weeks postdischarge? The relationship between functional HL and readmission was also examined.
Prior approvals were obtained from the appropriate institutional review boards. After institutional review board approval, the university directly associated with the hospital served as the research oversight entity for the study. There were no concerns. Patients were recruited from the inpatient population of a major metropolitan mental health facility in the southwestern United States. Inclusion criteria consisted of patients who (a) were diagnosed with bipolar I and II disorders according to the DSM-5 criteria, (b) spoke and comprehended English, (c) were pending discharge, (d) reported a stable address (not living in a transient group home or other temporary facilities), (e) had a primary telephone with access to a secondary telephone that could have been that of a close relative or friend, and (f) scored ≥ 19 upon screening using the Mini Mental Status Examination (MMSE). Both sexes and all ethnic and racial groups were eligible for participation. Patients who participated were issued a gift card of $20 at the end of the study. A minimum sample size of 30 participants was determined by power analysis as sufficient to reach significance at effect sizes of r ≥ .49, p = .05 (two-tailed), and power = 0.80 ( http://biomath.info/power/corr.htm).
The instruments used in the current study were the MMSE, which assessed overall cognition, the S-TOFHLA, which measured functional HL and the Multidimensional Scale of Perceived Social Support (MSPSS) scale, which measured perceived social support. Demographic information was obtained using a data sheet compiled by the investigator and was complemented with additional information from participants' electronic medical records. These instruments were selected for their relevance to the study and their psychometric reliability.
The MMSE was developed by Folstein et al. (1975) and is one of the most widely used brief tests of cognition in clinical and research settings (Ridha & Rossor, 2005). The instrument was used in screening recruits for inclusion in this study. Recruits were required to score 19 or higher upon screening to participate. In this study, a cutoff of 19 was used based on the information provided in the original MMSE by Folstein (1975) that in validity studies, patients with “depression with mild cognitive disorder” had a mean score of 19.0 and patients with uncomplicated affective disorder had a mean score of 25.1. The test is divided into two sections, with 1 point awarded for each correct response. The first section requires verbal responses only, and covers orientation, memory, and attention (maximum score = 21). The second section tests ability to name, follow verbal and written commands, write a sentence spontaneously, and copy a complex polygon (maximum score = 9).
The maximum total combined score for both sections is 30, and a score greater than 23 is considered normal. Estimated time for completion is 5 to 10 minutes, although it may take up to 30 minutes in the specified population. It was validated by test–retest reliability at an interval of 2 months or less and had a correlation coefficient of .90 between participants' baseline and repeat MMSE scores (Folstein et al., 1975). With test–retest intervals of 2 years, a correlation coefficient of .80 was reported in nondemented patients. Folstein et al. (1975) also reported interrater reliability of .80, and Ridha and Rossor (2005) reported that at a cutoff score of 24 the MMSE is 87% sensitive and 82% specific in detecting dementia and delirium in patients in a general medical unit.
The S-TOFHLA (Parker et al., 1995) is used extensively in the assessment of functional HL and assesses a patient's comprehension of health-related material. The test is available in a full format (a 22-minute test with 50 reading comprehension items in three passages and 17 numeracy items); an abbreviated format (a 12-minute test with 36 reading comprehension items in two passages and four numeracy items); and a shortened version (a 7-minute test with 36 reading comprehension items in 2 passages). The shortened version was used in this study.
A literature search did not produce a reliability coefficient for the S-TOFHLA specific to mentally ill individuals; however, in a study testing the validity of the S-TOFHLA in multiple languages, Connor et al. (2013) reported a Cronbach's alpha of .73 for a German version of the S-TOFHLA, .88 for an Italian version, and .61 for a French version in a study sample of 651 Swiss residents aged 18 to 65 years. The 36-point scale of the S-TOFHLA is divided into three categories of functional literacy based on total score: inadequate (0–16), marginal (17–22), and adequate (23–36). However, given the small sample size, these categories were combined into two groups: inadequate/marginal (0–22) and adequate (23–36).
The MSPSS instrument was developed to assess three sources of perceived social support: family, friends, and significant others (Zimet et al., 1988; Zimet et al., 1990). The scale consists of 12 items measured on a 7-point Likert-type scale, with options ranging from 1 (very strongly disagree) to 7 (very strongly agree). Each of the three subscales (family, friends, significant others) consists of four items. Responses to items within each subscale were summed to obtain a total subscale score, and scores on all 12 items were summed to produce a composite MSPSS scale score (i.e., global perceived social support). Park et al. (2015) reported a Cronbach's alpha of .92 and factor loadings of .74 to .85 for the family subscale, .87 to .89 for the friends subscale, and .72 to .88 for the significant other subscale.
Data Collection Procedure
In phase one, consent form, individual demographic data sheet, and study instruments were numerically coded (01 to 30) and documented on a master list of identification codes that was maintained until all data had been collected, then subsequently destroyed at the end of data collection, effectively deidentifying the data. All scored tests were stored in a secured file cabinet at the study location. The data were then loaded onto a password-protected computer for data analysis. Data cleaning and entry began immediately after the first set of data were collected, although the recruitment process continued until the target sample size was reached.
The second phase of data collection included a baseline review of participants' medical records for dates of discharge and again at 2 and 4 weeks postdischarge to identify patients who had been readmitted. At each point, patients were also contacted by telephone and asked to self-report admissions to other hospitals. The information obtained during the follow-up was recorded using a follow-up information sheet. Data were analyzed using SPSS® Statistics for Windows, version 24.0.
The functional HL status (total score; levels: inadequate/marginal and adequate) of patients diagnosed with bipolar disorder using the S-TOFHLA was explored. For this study, a reliability coefficient of .89 was obtained with a sample of 30 on the 36-item S-TOFHLA. Distribution of functional HL levels in the group showed two participants having incomplete information on the S-TOFHLA; 28 participants completed the test. Assessment of the 28 participants with HL data showed that six (21.4%) had low HL (adequate/inadequate) and 22 (78.6%) had adequate HL (Table 1).
Demographic Group Differences on Total Functional Health Literacy
Overall, the mean total functional HL was 30.61 (SD = 7.40, n = 28). (Table 1 displays differences in functional HL between demographic groups.) Significant differences in functional HL were found for ethnic groups, with White participants having a higher group mean, compared with the combination of all other races.
The MSPSS examined perceived social support in the sample. The instrument was evaluated for this study and produced overall reliability of .96, and subscale reliabilities of .95 (family), .97 (friends), and .90 (significant others). When HL was dichotomized (low, adequate) there was a significant difference between low and adequate HL on perceived social support. Participants with low HL reported greater perceived social support than those with adequate HL. Men were readmitted more frequently than women. There were no significant differences in HL levels based on inpatient length of stay or participant age (Table 1).
Table 2 displays the distribution of HL levels across demographic variables. The data indicate there was no readmission of those with adequate HL at 4 weeks, compared with 16.7% of those with low HL (Table 2).
Distribution of Health Literacy Across Demographic Variables
The study also explored the relationship between HL total scores and readmission frequency at 2- and 4-week intervals postdischarge. The relationship between total HL scores and total number of readmissions was investigated using the Pearson product moment correlation coefficient. There was a strong negative correlation between the HL and total hospital readmission variables (r = −.73, n = 28, p < .01), representing a large effect size. This result indicates that HL contributes to 53% of the variance in total readmissions, with lower HL associated with higher readmission. There was also a strong significant relationship between HL scores and 2-week readmission (r = −.63, n = 28, p < .01), reflecting a moderate shared variance of 40%, with lower HL associated with higher 2-week readmission. HL and 4-week readmission showed a similar pattern, although not significant (r = −.34, n = 28, p = .08) with a smaller shared variance of 11.6%, lower HL was associated with higher 4-week readmission rates.
Examination of the differences between HL groups on total readmissions and readmissions across two time intervals, 2- and 4-weeks postdischarge, while controlling for age using a one-way analysis of covariance (ANCOVA) was conducted. There was a significant difference between the two HL groups on total number of readmissions (F [1, 25] = 10.36, p = .004, partial η2 = .293), reflecting a large effect size (Pallant, 2013). Those who had low HL showed higher readmission rates than those with adequate HL.
The HL factor accounted for 29% of the variance of the dependent variable (total readmission), holding constant the participants' age, although age was not a significant covariate.
The study again examined the differences between HL groups (inadequate/marginal [low], and adequate) in readmission across two time intervals, 2 and 4 weeks, controlling for age.
Of interest was change in readmission rates across the two intervals for the two HL groups while controlling for age. However, only one individual was readmitted at 4 weeks postdischarge, which precluded the opportunity to explore this aspect because at least two participants were needed at each point to calculate variance.
It was assumed that low HL might have an influence on psychiatric inpatient readmission. This assumption was supported by the correlations between HL and 2-week readmission, as well as between HL and total readmissions. There was a significant negative correlation between HL and total readmissions, in which HL total scores explained 53% of the variation in total readmissions. In addition, a significant negative correlation was found between HL and 2-week readmission, with a substantive percentage (40%) of the total variance in 2-week readmissions accounted for by HL scores. These results indicated that 2-week readmission and total readmissions increased with low HL. The number of cases in 4-week readmission was too small for meaningful analysis. Based on the analyses used in this study, younger and unemployed men who lived alone were at a high risk for readmission. They also had significantly more perceived social support, which could impede help and help-seeking behaviors in this at-risk group. Because they believed they had adequate social support, it could be difficult to motivate this group to achieve greater independence and self-care.
Results of the one-way ANCOVA partially supported the assumption that differences existed between low and adequate HL groups on total readmissions and that age was a cofactor (age trended as a factor but was not significant). There was a significant difference in total readmissions between low and adequate HL groups. Participants who scored low in HL showed higher total readmissions than those with high HL scores. These results affirmed the greater overall risk of readmission for patients with low HL; however, results did not support age as a risk factor.
The small sample size and the inclusion of only one psychiatric facility in a geographic area with numerous similar facilities, offering this patient population many treatment options, have limited the generalizability of the study. Collaboration with psychiatric facilities providing similar services is essential to optimize the results in a study of this nature and should be highly prioritized for future studies. Collaboration could ascertain whether multiple admissions to other psychiatric hospitals occurred between patients' discharge from the index hospital and their return. The convenience sampling method and inclusion of only English-speaking participants also limited these findings. The sample might not be representative of the population being studied and undermines the ability of the study to make generalizations from this sample to the population of interest. Because patients with unstable living situations could provide the most necessary and significant insight into the real impact of their illness, excluding patients with unstable living from participation may have been a mistake. The issue of self-reporting is a limitation related to honesty. Patients may have responded based on socially acceptable norms rather than the truth, or they may not have been able to assess themselves or recall information accurately. This bias may have been higher in this population, given their experience with stigma and rejection and their desire to please, in order to receive acceptance. Cognition may have been a limitation especially in a sample of patients diagnosed with bipolar disorder. It is postulated that even in euthymic states, patients with bipolar disorder may have cognitive deficits in areas of verbal learning and other areas (Zubieta et al., 2001). However, it is the belief of the investigator that even with the postulation of the potential for poor cognition in this population, poor cognition has not affected the validity and reliability of the study because participants were appropriately screened with the MMSE and met inclusion criteria prior to participation. Follow-up responses may have been limited based on patients' discharge locations. Some may have been discharged to rehabilitation facilities, others might have provided incorrect addresses and telephone numbers, whereas others may have returned to their old ways of life and homelessness.
Implications for Nursing
Significant findings from this study strongly support that functional HL is highly correlated with 2-week readmission and total readmissions in this sample of adult patients diagnosed with bipolar disorder. The findings also indicated that patients with low HL are more frequently readmitted for psychiatric treatment than are those with adequate HL, and that men are readmitted more frequently than women.
An assessment of HL by nurses upon admission, and possibly throughout patients' stay, may help nurses identify the best method and time for teaching this population. It may also be beneficial to initiate HL instruction when patients are stable, instead of advocating for discharge at this point, to decrease HL-related readmission. This may be the time when patients with bipolar disorder are most cognitively capable of comprehending health information. Therefore, it may be necessary to increase patients' length of stay or provide more comprehensive case management. Consequently, this study has implications for patients, nurse educators, and other stakeholders responsible for length of stay and discharge planning.
Policy Makers and Organizations
With 90% of this population unemployed, living arrangements should be a high priority. The high mean age of 39 years and 77% living in the residence of others is an indication of the lack of independence in the sample and is a call to action for nursing policy makers and facilities administration. The development and implementation of a simple HL assessment instrument at admission and predischarge could identify patients' HL levels and enable patient education that is appropriate to their HL levels. This is especially true considering cognitive load theory and the report by Zubieta et al. (2001) that even in euthymic states, patients with bipolar disorder have cognitive deficits in areas of verbal learning. Murphy and Sahakian (2001) and Sweeney et al. (2000) reported a deficit in executive functioning in patients with bipolar disorder, especially in planning, problem solving, concept formation, and set shifting. This could be a factor in the population studied and may support cognitively appropriate education and treatment to meet their HL needs. Policy makers could also make HL assessment prior to discharge a type of competency evaluation and develop partnerships agreements with other levels of care to ensure patients HL related self-care abilities and safety in the community. Staff nurses then could be trained to evaluate patients' pre-discharge capabilities and implement community-based partnerships protocols with families and other levels of care to decrease readmission and cost.
The implication for nurse educators lies in the development of curriculum across levels of education from entry to the doctoral level. In addition, they should provide local and online workshops and seminars, include HL topics in pre- and postclinical discussions and simulation exercises, and develop HL level-specific educational material for patients' education. Nurse educators could also implement HL as a small to large research project across the nursing curriculum.
Patients could be required to provide consents for discharge planners and patient educators to share educational information with approved surrogates. As a contribution to patient education, nursing administration should consider the implementation of a position designated “HL Educator” that is similar to the role of the contemporary diabetic educator, to whom individuals with low HL at admission could be referred at discharge and for follow up periodically.
For similar studies, it is suggested that individuals with unstable living arrangements be included. Because patients with unstable living situations could provide the most necessary and significant insight into the real influence of their illness, excluding patients with unstable living from participation may have been a mistake. Elimination of follow-up calls or implementation of more reliable methods to monitor readmission, such as electronic devices, case management, and institutional collaborations, may be necessary. Future studies could also include family members or significant others, with patient consent, or incorporate collaboration with institutions that accept Medicare or Medicaid payments to verify readmission to other institutions, using CMS common working files. The development of a new short, valid instrument for nurses to assess HL at the time of psychiatric admission or soon thereafter would provide mental health treatment teams with the necessary information to adequately prepare patients for discharge.
The current study provides vital information for nurses, considering the gravity of providing adequate care for this special population. Now is the opportune time for participation in programs to improve poor functional HL, decrease readmission rates, and reduce health care costs. This invitation to promote HL as an objective for future nursing agenda may be more applicable to nursing administration than staff nurses. However, both groups are stakeholders in the effort to decrease the cost of low HL and related readmission.
- American Hospital Association. (2012). Bringing behavioral health into the care continuum: Opportunities to improve quality, cost and outcomes. https://www.aha.org/system/files/research/reports/tw/12jan-tw-behavhealth.pdf
- American Hospital Association. (2019). Increasing access to behavioral health care advances value for patients, providers and communities. TrendWatch. https://www.aha.org/system/files/media/file/2019/05/aha-trendwatch-behavioral-health-2019.pdf
- American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (5th ed.). American Psychiatric Publishing.
- Behavioral Health, United States. (2012). Mental health and substance use disorders: Impairment in functioning. Substance Abuse and Mental Health Services Administration.
- Berkman, N., Sheridan, S., Donahue, K., Halpern, D. J., Viera, A. & Crotty, K. (2011). Health literacy interventions and outcomes: An update of the literacy and health outcomes: Systematic review of the literature (Evidence Report/Technology Assessment, Number 199. Prepared by RTI International-university of North Carolina Evidence-based Practice Center under Contract No. 290-2007-10056-I. AHRQ Publication Number 11-E006). Agency for Healthcare Research and Quality.
- Center for Healthcare Strategies, Inc. (2013). Health literacy fact sheet. U.S. Department of Health and Human Services. http://www.chcs.org
- Centers for Medicare and Medicaid Services. (2013). CMS final rule to improve quality of care during hospital in-patient stay. https://www.cms.gov/newsroom/mediareleasedatabase/fact-sheets/2013-fact-sheets-items/2013-08-02-3.html
- Centers for Medicare and Medicaid Services. (2018). Hospital readmission reduction. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program
- Cloonan, P., Wood, J. & Riley, J. B. (2013). Reducing 30-day readmissions: Health literacy strategies. The Journal of Nursing Administration, 43(7–8), 382–387 doi:10.1097/NNA.0b013e31829d6082 [CrossRef] PMID:23892303
- Cloutier, M., Greene, M., Guerin, A., Touya, M. & Wu, E. (2018). The economic burden of bipolar I disorder in the United States in 2015. Journal of Affective Disorders, 226, 45–51 www.elsevier.com doi:10.1016/j.jad.2017.09.011 [CrossRef] PMID:28961441
- Cohen, S. (2004). Social relationships and health. The American Psychologist, 59(8), 676–684 doi:10.1037/0003-066X.59.8.676 [CrossRef] PMID:15554821
- Connor, M., Mantwill, S. & Schulz, P. J. (2013). Functional health literacy in Switzerland—Validation of a German, Italian, and French health literacy test. Patient Education and Counseling, 90(1), 12–17 doi:10.1016/j.pec.2012.08.018 [CrossRef] PMID:23089240
- Everett, A., Mahler, J., Biblin, J., Ganguli, R. & Mauer, B. (2008). Improving the health of mental health consumers: Effective policies and practices. International Journal of Mental Health, 37(2), 8–48 doi:10.2753/IMH0020-7411370201 [CrossRef]
- Fair Health. (2020). The projected economic impact of the Covid-19 pandemic on the US healthcare system.
- Folstein, M. F., Folstein, S. E. & McHugh, P. R. (1975). “Mini-mental state.” A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198 doi:10.1016/0022-3956(75)90026-6 [CrossRef] PMID:1202204
- Gonzalez, C. M., Deno, M. L., Kintzer, E., Marantz, P. R., Lypson, M. L. & McKee, M. D. (2019). A qualitative study of New York medical student views on implicit bias instruction: Implications for curriculum development. Journal of General Internal Medicine, 34(5), 692–698 doi:10.1007/s11606-019-04891-1 [CrossRef] PMID:30993612
- Krishan, S., von Esenwein, S. A. & Druss, B. G. (2012). The health literacy of adults with severe mental illness. Psychiatric Services, 63(4), 397 doi:10.1176/appi.ps.20120p397 [CrossRef] PMID:22476312
- Loan, L. A., Parnell, T. A., Stichler, J. F., Boyle, D. K., Allen, P., Van-Fosson, C. A. & Barton, A. J. (2018). Call for action: Nurses must play a critical role to enhance health literacy. Nursing Outlook, 66(1), 97–100 doi:10.1016/j.outlook.2017.11.003 [CrossRef] PMID:29331444
- Montgomery, P. & Kirkpatrick, H. (2002). Understanding those who seek frequent psychiatric hospitalizations. Archives of Psychiatric Nursing, 16(1), 16–24 doi:10.1053/apnu.2002.30494 [CrossRef] PMID:11877602
- Murphy, F. C. & Sahakian, B. J. (2001). Neuropsychology of bipolar disorder. The British Journal of Psychiatry, 178(Suppl. 41), S120–S127 doi:10.1192/bjp.178.41.s120 [CrossRef] PMID:11388950
- The National Academy of Sciences. (2004). Health literacy: A prescription to end confusion. National Academies Press.
- National Survey on Drug Use and Health. (2017). https://www.datafiles.samhsa.gov/study/national-survey-drug-use-and-health-nsduh-2017-nid17938
- Pallant, J. (2013). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS (5th ed.). McGraw-Hill Education.
- Park, S., Sulaiman, A. H., Srisurapanont, M., Chang, S., Liu, C.-Y., Bautista, D., Ge, L., Chua, H. C. & Hong, J. P. (2015). The association of Suicide risk with negative life events and social support according to gender in Asian patients with major depressive disorder. Psychiatry Research, 228(3), 277–282.
- Parker, R. M., Baker, D. W., Williams, M. V. & Nurss, J. R. (1995). The test of functional health literacy in adults: A new instrument for measuring patients' literacy skills. Journal of General Internal Medicine, 10(10), 537–541 doi:10.1007/BF02640361 [CrossRef] PMID:8576769
- Ridha, B. & Rossor, M. (2005). The mini-mental state examination. Practical Neurology, 5(5), 298–303 doi:10.1111/j.1474-7766.2005.00333.x [CrossRef]
- Sadock, B. J. & Sadock, V. A. (2007). Synopsis of psychiatry (10th ed.). Lippincott Williams & Wilkins.
- Sand-Jecklin, K., Murray, B., Summers, B. & Watson, J. (2010). Educating nursing students about health literacy: From the classroom to the patient bedside. OJIN: The Online Journal of Issues in Nursing, 15(3). doi:10.3912/OJIN.Vol15No03PPT02 [CrossRef]
- Sparding, T., Silander, K., Pålsson, E., Östlind, J., Sellgren, C., Ekman, C. J., Joas, E., Hansen, S. & Landén, M. (2015). Cognitive functioning in clinically stable patients with bipolar disorder I and II. PLoS One, 10(1), e0115562 doi:10.1371/journal.pone.0115562 [CrossRef] PMID:25614986
- Studart, P. M., Bezerra Filho, S., Studart, A. B., Almeida, A. G. & Miranda-Scippa, A. (2015). Social support and bipolar disorder. Archives of Clinical Psychiatry, 42(4), 95–99 doi:10.1590/0101-60830000000057 [CrossRef]
- Substance Abuse and Mental Health Services Administration. (2016). Key substance use and mental health services indicators in the United States: Results from the 2016 National Survey on Drug Use and Health. Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services. https://www.samhsa.gov/data/
- Sweeney, J. A., Kmiec, J. A. & Kupfer, D. J. (2000). Neuropsychologic impairments in bipolar and unipolar mood disorders on the CANTAB neurocognitive battery. Biological Psychiatry, 48(7), 674–684 doi:10.1016/S0006-3223(00)00910-0 [CrossRef] PMID:11032979
- Torrent, C., Martinez-Aran, A., Daban, C., Sanchez-Moreno, J., Comes, M., Goikolea, M. J., Salamero, M. & Vieta, E. (2006). Cognitive impairment in bipolar II disorder. The British Journal of Psychiatry, 189, 254–259.
- U.S. Department of Health and Human Services. (2011). National action plan to improve health literacy.
- Zimet, G. D., Dahlem, N. W., Zimet, S. G. & Farley, G. K. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 30–41.
- Zimet, G. D., Powell, S. S., Farley, G. K., Werkman, S. & Berkoff, K. A. (1990). The multidimensional scale of perceived social support. Journal of Personality Assessment, 55(3–4), 610–617 doi:10.1207/s15327752jpa5201_2 [CrossRef] PMID:2280326
- Zubieta, J. K., Huguelet, P., Lajiness-O'Neill, R. & Giordani, B. J. (2001). Cognitive function in euthymic bipolar I disorder. Psychiatry Research, 102(1), 9–20 doi:10.1016/S0165-1781(01)00242-6 [CrossRef] PMID:11368835
Demographic Group Differences on Total Functional Health Literacya
|Demographic Group (n)||Mean (SD)||Significance|
|2-week readmission||t= 3.30; p= .003|
| Yes (3)||19.33 (12.06)|
| No (25)||31.96 (5.60)|
|Education||t= .970; p= .341|
| High school or less (15)||31.87 (5.68)|
| More than high school (13)||29.15 (8.97)|
|Employment||t= .230; p= .820|
| Employed (3)||29.67 (9.30)|
| Unemployed (25)||30.72 (7.34)|
|Ethnicity||t= 2.068; p= .033|
| White (12)||33.75 (3.93)|
| All other races (16)||28.25 (8.53)|
|Sex||t= 1.03; p= .310|
| Male (15)||29.27 (8.92)|
| Female (13)||32.15 (4.96)|
|Insurance status||t= .595; p= .557|
| Insured (6)||29.0 (7.54)|
| Uninsured (22)||31.0 (7.45)|
|Living arrangement||t= .329; p= .745|
| Lives alone (6)||31.50 (6.30)|
| Lives with others (22)||30.36 (7.76)|
|Marital status||t= .780; p= .442|
| Single (20)||31.3 (6.44)|
| Living with others (8)||28.88 (9.6)|
Distribution of Health Literacy Across Demographic Variablesa
|Demographic Group||Health Literacy||χ2||p|
|Low (n)||Adequate (n)|
| Insured||33.3% (2)||18.2% (4)||.64||.58|
| Single||66.7% (4)||72.7%(16)||.09||.77|
| Living with others||33.3% (2)||27.3% (6)|
| Yes||33.3% (2)||4.5% (1)||4.08||.043|
| No||66.7% (4)||95.5% (21)|
| Yes||16.7% (1)||22.7% (5)||3.80||.05|
| No||83.3% (5)||95.5% (21)|
| Lives alone||16.7% (1)||22.7% (5)||.64||.58|
| Lives with others||83% (5)||77.3% (17)|
| High school or less||33.3% (2)||54% (13)||1.30||.37|
| More than high school||66.7% (4)||41% (9)|
| White||16.7% (10)||50% (11)||2.14||.14|
| All other races||83.3% (5)||50% (11)|
| Male||6.7% (4)||50% (11)||.53||.66|
| Female||33.3% (2)||50% (11)|
| Employed||16.7% (1)||9.1% (2)||.28||1.00|
| Unemployed||83.3% (5)||90.9% (20)|