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. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: Nurs Res. 2019 Sep-Oct;68(5):339–347. doi: 10.1097/NNR.0000000000000357

Demographics, Psychological Distress, and Pain from Pressure Injury

Junglyun Kim 1, Debra Lyon 2, Michael T Weaver 3, Gail Keenan 4, Joyce Stechmiller 5
PMCID: PMC6989099  NIHMSID: NIHMS1066960  PMID: 30829837

Abstract

Background:

There is a knowledge gap regarding factors that influence the intensity of pain associated with pressure injuries.

Objectives:

We examined the influence of age, sex, race, and comorbidity on the relationships between pressure injuries, psychological distress, and pain intensity in hospitalized adults.

Methods:

This study was a cross-sectional, retrospective secondary analysis using data from a regional acute hospital’s electronic health records from 2013– 2016. A sample of 454 cases met the inclusion criteria and were analyzed using path analysis.

Results:

The hypothesized model (A) and two alternative models (B and C) were tested and demonstrated adequate model fit. All tested models demonstrated statistically significant independent, direct effects of age on the severity of pressure injury (p < .001) and pain intensity (p = .001), as well as independent, direct effects of sex (p ≤ .005), race (p < .001), and comorbidity (p = .001) on psychological distress.

Discussion:

Pain management for individuals with pressure injuries should include not only the treatment of wounds, but also the individual characteristics of the patient such as demographics, comorbidity, and psychological status that may affect pain. Given the limitations of secondary analyses, further studies are suggested to validate these findings.

Keywords: demographics, pain intensity, pressure ulcer, psychological distress, electronic health records, secondary analysis

Pressure ulcers, also known as pressure injuries, are highly prevalent in the United States, developing in more than 2.5 million people each year (Agency for Healthcare Research and Quality, 2014). Pressure injuries cause constant pain that may differ in intensity by stage (Ahn, Stechmiller, Fillingim, Lyon, & Garvan, 2015; Gorecki et al., 2009; Gunes, 2008).

Factors associated with the perception of pain include psychological distress and conditions such as anxiety and depression (Beesdo et al., 2009; El-Gabalawy, Mackenzie, Shooshtari, & Sareen, 2011). Pain is prevalent in depressed individuals, and reciprocal relationships have been identified between pain and depression (Failde et al., 2013; Kroenke et al., 2011; Nicholl et al., 2014). Sleep disturbance has also been identified as having a direct relationship with pain (Schrimpf et al., 2015; Sivertsen et al., 2015); in fact, reciprocal relationships between sleep disturbance, depression, and pain have been found (Finan & Smith, 2013).

In addition to psychological distress, demographic factors (e.g., age, sex, race, etc.) affect pain perception. Differences in the prevalence of pain among racial groups (Riskowski, 2014), variability of pain perception by age group (Cole, Farrell, Gibson, & Egan, 2010), and sex (Johannes, Le, Zhou, Johnston, & Dworkin, 2010) have been reported. Further, researchers have reported associations between demographic factors and rates of pressure injuries. For example, Lyder et al. (2012) found that patients between 75 and 84 years of age had the highest rates of pressure injury among those over the ages of 65, while Baumgarten et al. (2006) found that African American patients had higher rates of pressure injuries than White patients ones.

Additionally, the occurrence of pressure injuries and pain has been directly associated with the presence of comorbidities (Dugaret et al., 2014; Lyder et al., 2012). In fact, people who have comorbidities are more likely to experience pain than people without comorbidities (Grimby-Ekman, Gerdle, Bjork, & Larsson, 2015).

While many researchers have explored associations between pain and psychological distress, none have yet used a model to specifically examine the effect of the severity of pressure injury on pain and psychological distress. Further, studies where the influence of demographic factors on pain associated with pressure injuries were examined are scarce, and, in our review of the literature, we identified no studies where the influence of comorbidities on pressure injury pain was examined.

The aim of our study was to examine the influence of comorbidities and demographic factors such as age, sex, and race on the relationship between psychological distress and pain from pressure injuries. Our theoretical framework was based on the biopsychosocial model of pain (Fillingim, 2005), which explains the effects of demographics, psychosocial factors, and biological factors (e.g., tissue damage) on the variability of individual pain perception. Specifically, we examined how factors in the model influence pain intensity in hospitalized patients with pressure injuries. We hypothesized that the selected demographic factors and comorbidities have independent direct effects on the relationships between psychological distress and pain from pressure injuries.

METHODS

Design

The study was a cross-sectional, retrospective secondary analysis that used data from electronic health records (EHRs) of a regional acute hospital.

Sample

The sample was selected from among patients who were over the age of 18, diagnosed with a pressure injury, and completed a stay of at least four days at a regional acute hospital between February 2, 2013 and December 31, 2016. m, we excluded patients who had been diagnosed with other wounds, received any type of surgery in an operating room before the third day of admission, or had a verbal response below 4 on the Glasgow coma scale (GCS). We developed these exclusion criteria to limit the sample to patientts who experienced pain exclusively from pressure injuries and to increase the validity of pain assessment.

The preliminary sample size was 886 identified through the Integrated Data Repository (IDR). The IDR stores pertinent clinical and administrative health data retrieved from a university health system’s electronic data sources. Among the preliminary sample, 177 cases of consecutive admission were excluded to maintain an independent sample; 225 cases whose relevant data elements were unavailable were also excluded.

The first author selected the final sample (N = 454). This sample size was adequate for path analysis using the structural equation modeling approach, which requires a certain number of participants per an estimated parameter (20:1) (Pituch & Stevens, 2016).

Data Collection

The IDR staff extracted the data elements for the initial sample. These elements included demographic information, diagnostic codes for the stage of the pressure injury, comorbidities, pain scores, administered medication names, dose, route, and administered time. To generate a dataset that kept missing information to a minimum, we used data elements recorded on the third day of admission. Data cleaning was performed using SAS software.

Measures

The following measures were used in the analsyis.

The severity of pressure injury.

The severity of pressure injury was measured in stages defined by the National Pressure Ulcer Advisory Panel (NPUAP) (National Pressure Ulcer Advisory Panel, 2016).

Pain intensity.

Pain intensity was defined by the mean of pain scores collected over 24 hours on the participant’s third day of admission. Pain scores were measured by the Defense and Veterans Pain Rating Scale (DVPRS) (Office of the Army Surgeon General, 2010), which has an acceptable internal consistency reliability (Cronbach’s alpha = .871) and test-retest reliability (r = .637 to r = .774) (Buckenmaier et al., 2013).

Psychological distress.

Psychological distress was defined by a participant’s having symptoms of depression, anxiety, insomnia, and lack of energy (Drapeau, Marchand, & Beaulieu-Prévot, 2011). Since direct measures of psychological distress were not available in the EHRs, we used a medication proxy, a process in which we counted the types of prescribed and administered medications (antidepressants, anxiolytic agents, and hypnotics) on the medication administration record over 24 hours on the third day of admission. A proxy measure using medication, such as medication possession ratio, is common in pharmacology when measuring target symptoms or treatment effects (Fortney, Pyne, Edlund, & Mittal, 2010; Sikka, Xia, & Aubert, 2005).

Opioid medication.

The total doses of administered opioid medications on the third day of admission over 24 hours were computed and converted into opioid morphine milligram equivalents (MME) doses (Centers for disease control and prevention, 2016). Meanwhile, antidepressants, anxiolytic agents, hypnotics, and opioid medications were classified by MICROMEDEX, a medication reference for health professionals.

Comorbidity.

Comorbidity was a patient’s total number of coexisting medical problems identified as “ongoing medical problems” during hospitalization. Comorbidities were identified by either primary diagnoses or secondary diagnoses and did not include the diagnosis of pressure injuries.

Age, sex, and race.

These data were extracted from the electronic health records.

Ethical Considerations

The study was reviewed and approved by the University Institutional Review Board for expedited review for both the electronic health record data and chart review.

Data Analysis

We used Mplus 7.4 for path analysis. In this study, the level of significance in the analysis was p ≤ .05, which was adjusted by a false discovery rate (Benjamini & Hochberg, 1995). Missing mechanisms were checked by the MCAR test using SAS software (Little, 1998), and listwise deletion was applied to treat missing data, which were missing completely at random (Chi-Square = 24.165, Degree of Freedom (DF) = 20, p = .235). Outliers were checked, and no influential ones were identified.

We then applied path analysis using weighted least square mean and variance adjusted (WLSMV) estimators, which is an efficient estimator for data with categorical variables (Muthén & Muthén, 2017). Finally, we evaluated overall model fit using the Chi square goodness of fit test, the Comparative fit index (CFI > .95), the Root mean square error of approximation (RMSEA < .05), and Tucker-Lewis Index (TLI > .90) (Pituch & Stevens, 2016).

RESULTS

Table 1 shows data about the sample (N = 454). The typical subject was male (57.27%), White (77.09%), had stage 2 pressure injury (40.31%). A total of 50.44% of the subjects took medication related to psychological distress. Other characteristics included an average age of 65.33 (SD = 16.97), average number of comorbidities of 16.63 (SD=6.67), average opioid MME doses of 37.35 (SD = 92.88), and average pain intensity of 3.94 (SD = 3.27).

Table 1.

Description of sample used in the analysis (N=454)

Characteristics Class n % Mean SD Minimum-Maximum
Age 65.33 16.97 18–97
Sex Male 260 57.27
Female 194 42.73
Race White 350 77.09
Other 104 22.91
Psychological distress None 225 49.56
1 type 134 29.52
1 more type 95 20.93
Severity of pressure injury Stage 1 90 19.82
Stage 2 183 40.31
Stage 3 85 18.72
Stage 4 96 21.15
Number of comorbidities 16.63 6.67 0–39
Pain intensity 3.94 3.27 0–10
Opioid MME doses 37.35 92.88 0–702
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Note. Psychological distress = the number of treated medication types among anxiolytic, antidepressant and hypnotics; MME = morphine milligram equivalents

We performed path analysis to examine model fit and path coefficients for each direct and indirect path of the hypothesized model (Model A in Figure 1), while also exploring two alternative models B (Figure 2) and C (Figure 3). All three models (A, B, and C) adequately fit the data; Table 2 shows model fits and direct and indirect path coefficients of all three models.

Figure 1.

Figure 1.

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Model A. Estimated path coefficients (standard error). *p ≤ .005. Dotted lines are nonsignificant path coefficients. Indirect effect from severity of pressure injury to pain intensity = −0.012 (0.018), p = 0.481. pustage = severity of pressure injury; psydis = psychological distress; pain = pain intensity; comor = comorbidity; opioid = opioid morphine milligram equivalents doses.

Figure 2.

Figure 2.

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Model B. Estimated path coefficients (standard error). * p ≤ .005. Dotted lines are nonsignificant path coefficients. pustage = severity of pressure injury; psydis = psychological distress; pain = pain intensity; comor = comorbidity; opioid = opioid morphine milligram equivalents doses.

Figure 3.

Figure 3.

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Model C. Estimated path coefficients (standard error). * p ≤ .005. Dotted lines are nonsignificant path coefficients. Indirect effect from severity of pressure injury to pain intensity = −0.019 (0.034), p = 0.582. pustage = severity of pressure injury; psydis = psychological distress; pain = pain intensity; comor = comorbidity; opioid = opioid morphine milligram equivalents doses.

Table 2.

Model fits and path coefficients of A, B, and C models

Model A Model B Model C
NP 17 17 18
Chi-Square 12.468 14.204 11.705
DF 8 8 7
p-value 0.1315 0.0766 0.1107
RMSEA PRMSEA 0.035 0.714 0.041 0.614 0.038 0.645
CFI 0.957 0.941 0.955
TLI 0.904 0.867 0.884
Direct Path
from to Estimate p value Estimate p value Estimate p value
Age Pustage −0.013 .000 −0.013 .000 −0.013 .000
Age Pain −0.033 .001 −0.034 .001 −0.032 .001
Sex Psydis 0.326 .004 0.323 .005 0.323 .005
Race Psydis −0.681 .000 −0.664 .000 −0.685 .000
Comor Psydis 0.028 .001 0.028 .001 0.029 .001
Opioid Psydis 0.002 .000 0.002 .002 0.003 .000
Opioid Pain 0.008 .000 0.009 .000 0.007 .000
Pustage Psydis −0.044 .433 −0.049 .389 −0.032 .577
Pustage pain 0.180 .236 0.164 .279 0.192 .216
Psydis pain 0.279 .073 n/a n/a 0.592 .138
Pain Psydis n/a n/a 0.023 .204 −0.039 .361
Indirect path
from to
Pustage Pain −0.012 .481 n/a n/a −0.019 .582
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Note. NP = number of free parameters; Chi-Square = chi-square test of model fit; DF = degrees of freedom; RMSEA = root mean square error of approximation; PRMSEA = probability root mean square error of approximation; CFI = confirmatory fit index; TLI = tucker lewis index; Comor = comorbidity; Opioid = opioid morphine milligram equivalents doses; Pustage = severity of pressure injury; Psydis = psychological distress; Pain = pain intensity.

The study results were consistent with our initial hypothesis that demographic factors and comorbidity have independent direct effects on the relationships between psychological distress and pain from pressure injuries.

Direct Effect of Age on the Severity of Pressure Injury and Pain Intensity

All three models (A, B, and C) showed that age has significant independent direct effects on the severity of pressure injury (γ11 = −0.013, p < .001) and pain intensity (γ31 = −0.033,−0.034, −0.032, respectively A, B, and C, p = .001).

Direct Effect of Sex, Race, and Comorbidity on Psychological Distress

All three models (A, B, and C) showed that sex (γ22 = 0.326, 0.323, 0.323, respectively A, B, and C, p ≤ .005), race (γ23 = −0.681, −0.664, −0.685 respectively A, B, and C, p < .001), and comorbidity (γ24 = 0.028, 0.028, 0.029, respectively A, B, and C, p = .001) have significant independent direct effects on psychological distress.

Direct Effect of Opioid MME Doses on Psychological Distress and Pain Intensity

All three models (A, B, and C) showed that opioid MME doses have significant independent direct effects on psychological distress (γ25 = 0.002, 0.002, 0.003, respectively A, B, and C, p ≤ .002) and pain intensity (γ35 = 0.008, 0.009, 0.007, respectively A, B, and C, p < .001).

Direct Effects or Indirect Effect between the Severity of Pressure Injury, Psychological Distress, and Pain Intensity

All three models (A, B, and C) did not demonstrate significant independent direct or indirect effects in the relationships between the severity of pressure injury, psychological distress, and pain intensity.

DISCUSSION

Our findings show that sex, race, and comorbidity have independent direct effects on psychological distress and that age has independent inverse-direct effects on the severity of pressure injury and pain intensity in patients who have pressure injuries. We found that females with more comorbidities experienced more psychological distress than males with fewer comorbidities. These results were consistent with existing literature (Barnett et al., 2012; Eaton et al., 2012; X. Huang, Liu, & Yu, 2017; Roberts, Abbott, & McKee, 2010; Seedat et al., 2009; Tedstone Doherty & Kartalova-O’Doherty, 2010). Further, we found that younger adults were more likely to have greater pain intensity than older adults, which is also consistent with existing literature (Cole et al., 2010; Lautenbacher, Peters, Heesen, Scheel, & Kunz, 2017). Notably, we found that younger adults were more likely to have advanced pressure injuries than older adults. Finally, we found that White patients had more psychological distress than non-White patients.

Effects of Age on the Severity of Pressure Injury and Pain Intensity

In all three models, we observed inverse-direct effects of age on pain intensity. In other words, among patients who had pressure injuries, older patients had lower pain intensity than younger patients. This result is consistent with current reports suggesting that aging lowers pain intensity (Cole et al., 2010; Lautenbacher et al., 2017).

The negative coefficient for age on the severity of pressure injury further indicates that younger patients also experience more advanced stages of pressure injury than older patients. Although it has been reported that age is related to the prevalence of pressure injury (Leijon, Bergh, & Terstappen, 2013), to date, no researchers have reported that younger patients have more advanced stages of pressure injury than older patients. Our finding may reflect the sample characteristics of an acute care setting or other existing covariates that we did not include in our study. Thus, further research is required to validate this finding.

Effects of Sex, Race, and Comorbidity on Psychological Distress

In this study, hospitalized female patients with pressure injuries had more psychological distress (i.e. took more anti-depressants, anxiolytic agents, and hypnotics) than hospitalized male patients. This result is consistent with current epidemiological research a showing higher prevalence of anxiety (Eaton et al., 2012; Seedat et al., 2009) and higher psychological distress (Roberts et al., 2010; Tedstone Doherty & Kartalova-O’Doherty, 2010) in women than in men.

We also found that White patients exhibited more psychological distress (i.e. took more anti-depressants, anxiolytic agents, and hypnotics) than non-White patients. In contrast, McVeigh et al. (2006) found that White patients had less psychological distress than patients who were Black and other races. Since our study measured psychological distress through medications, the racial differences in access to and utilization of healthcare services may have affected the results. Specifically, it’s been shown that, among those who have psychological distress, non-White patients use mental health services less frequently than White patients do (Marko, Linder, Tullar, Reynolds, & Estes, 2015), including Asian, Black, and Hispanic patients who suffer from depression (Lee, Xue, Spira, & Lee, 2014). Regardless, the association between race and service utilization is not uniform; researchers have also reported no associations (Decoux, Chafetz, & White, 2010). Inconsistencies in findings among among studies may have resulted from differing cultural, financial, and educational factors. Indeed, psychological distress is strongly associated with financial difficulty (Marko et al., 2015) and lower income levels (Chittleborough, Winefield, Gill, Koster, & Taylor, 2011; McVeigh et al., 2006). Thus, when interpreting our study’s results, one must consider the indirect measurement of psychological distress, since confounding factors included the influence of racial differences on the utilization of health services and the prevalence of psychological distress.

Finally, we found that patients with more comorbidities had more psychological distress (i.e. took more medications for anti-depressants, anxiolytic agents, and hypnotics). Several researchers have reported that chronic comorbidities are associated with psychological distress (Barnett et al., 2012; C. Q. Huang, Dong, Lu, Yue, & Liu, 2010).

The Effect of Opioid MME Doses on Psychological Distress and Pain Intensity

The independent effects of opioid MME doses on psychological distress and pain intensity were significant. However, this finding requires additional testing, since, given their bidirectional association (Burns et al., 2017; Scherrer, Salas, Lustman, Burge, & Schneider, 2015), opioid medication may blunt psychological distress. Further, anxiolytics, hypnotics, and antidepressants may affect pain and alter the effects of opioid medications.

The Severity of Pressure Injury, Psychological Distress, and Pain Intensity

Relationships between the severity of pressure injury and pain were consistent with several previous studies that found that pain is not associated with the severity of pressure injury (Briggs et al., 2013; McGinnis et al., 2014). However, this finding is inconsistent with those of other researchers (Ahn et al., 2015; Gunes, 2008) who reported that pain is more severe in those with more advanced-staged pressure injuries. Inconsistency in findings may have resulted from the use of different measures for pain and the severity of pressure injury, inclusion or exclusion of different covariates, and the use of different analytic methods.

We found that, after controlling other factors, there were no statistically significant independent effects between the severity of pressure injury and psychological distress. However, we did find that 50.44% of patients with pressure injuries suffered from psychological distress (i.e., they were prescribed one or more of the target medications).

To our knowledge, no studies have been published where psychological distress in those with pressure injuries has been examined. We recommend that additional studies using more precise measures of psychological distress further validate the relationships between the severity of pressure injury and psychological distress, as well as those between the severity of pressure injury, psychological distress, and pain intensity.

Strengths and Limitations

This study addressed the relationships of demographics and comorbidity on psychological distress and pain in an understudied population using data from a large sample of hospitalized patients with pressure injuries. In addition, we used a validated tool to measure pain in subjects with intact verbal communication abilities (GCS of 4 or 5) to express their pain. However, the study has several limitations. First, it has the inherent limitations of secondary analyses using EHR data, and, since it is a cross-sectional design, we cannot ascribe causality among variables. Second, we used a proxy measure for psychological distress rather than a validated measure for distress; therefore, untreated psychological distress would have been missed, and the effect of medication to reduce experienced distress may not have been fully tested. Third, this study has limited generalizability since the sample was collected from a single regional hospital; due to a small sample size of varying racial groups, racial diversity had to be limited to White patients and others.

In addition, we only examined the coefficients of variables with pressure injury stages 1 through 4 and, due to their rarity, did not include deep tissue pressure injuries and unstageable pressure injuries. We also did not account for the number of pressure injuries or their location, and these factors may have influenced the study variables. Finally, we did not measure the potential influences of different types of comorbidities (e.g. cardiovascular diseases, diabetes mellitus, etc.), since we only counted the number of comorbidities. Finally, Mplus does not allow for estimations to test indirect effects of censored variables. In this study, 34.88% of participants had 0 pain intensity, which was a censored variable with an inflated number of 0 values; censored variables inflated effects (Rigobon & Stoker, 2007).

These limitations together may have hidden relationships between the severity of pressure injury, psychological distress, and pain. We offer several recommendations for future studies. To further validate our findings, researchers should incorporate more precise measures of psychological distress. Additionally, since pain medication had a significant effect in this study, further studies are suggested to examine the effects of pain medication on the relationships between psychological distress and pain in patients with pressure injuries. To validate the effects of comorbidity and race on pain and the effect of sex on psychological distress in those with pressure injuries, researchers should note types of comorbidities, incorporate racially and ethnically diverse groups, and compare the degree of psychological distress in the general population to that of the population with pressure injuries.

Finally, EHRs have emerged as an important resource to generate data for nursing research. However, because nursing documentation lacks a standardized format, retrievable and relevant data elements are still extremely limited. Therefore, researchers should carefully confirm retrievable data elements through the clinical data repository prior to conducting their studies.

CONCLUSION

In this study we identified independent effects that select demographic factors and comorbidity have on pain from pressure injury and psychological distress. Although we failed to demonstrate direct and indirect effects between the severity of pressure injury, psychological distress, and pain intensity, our findings enhance understanding of pain and psychological distress in those who suffer from pressure injuries. Using this evidence, healthcare providers are encouraged to personalize patients’ pain management by not only treating their wounds, but also carefully considering factors as age, sex, race, comorbidities, psychological status, and medications.

Acknowledgement:

This publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under University of Florida Clinical and Translational Science Awards UL1TR001427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

We appreciate Dr. Xinguang Chen, PhD, MD, Professor, Department of Epidemiology, College of Public Health and Health Profession & College of Medicine, University of Florida for advising the proposal of this publication’s research. We also thank the University of Florida Integrated Data Repository and the UF Health Office of the Chief Data Officer for providing us with the necessary data sets.

Footnotes

Ethical Conduct of Research: IRB approval was obtained from the University of Florida Institutional Review Board prior to study commencement.

The authors have no conflicts of interest to report.

Contributor Information

Junglyun Kim, University of Florida College of Nursing, Gainesville, FL.

Debra Lyon, University of Florida College of Nursing, Gainesville, FL.

Michael T. Weaver, University of Florida College of Nursing, Gainesville, FL.

Gail Keenan, University of Florida College of Nursing, Gainesville, FL.

Joyce Stechmiller, University of Florida College of Nursing, Gainesville, FL.

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