A Contextual Analysis to Explore Barriers and Facilitators of Pain Management in Swiss Nursing Homes

Organizational Context and Quality Indicators in Nursing Homes: A Microsystem Look

The association of organizational context with quality of care in nursing homes is not well understood at the clinical microsystem (care unit) level. This cross-sectional study examined the associations of unit-level context with 10 unit-level quality indicators derived from the Minimum Data Set 2.0. Study settings comprised 262 care units within 91 Canadian nursing homes. We assessed context using unit-aggregated care-aide-reported scores on the 10 scales of the Alberta Context Tool. Mixed-effects regression analysis showed that structural resources were negatively associated with antipsychotics use (B = -.06; p = .001) and worsened late-loss activities of daily living (B = -.03, p = .04). Organizational slack in time was negatively associated with worsened pain (B = -.04, p = .01). Social capital was positively associated with delirium symptoms (B = .12, p = .02) and worsened depressive symptoms (B = .10, p = .01). The findings suggested that targeting interventions to modifiable contextual elements and unit-level quality improvement will be promising.

Algorithm-based pain management for people with dementia in nursing homes

BACKGROUND

People with dementia in nursing homes often experience pain, but often do not receive adequate pain therapy. The experience of pain has a significant impact on quality of life in people with dementia, and is associated with negative health outcomes. Untreated pain is also considered to be one of the causes of challenging behaviour, such as agitation or aggression, in this population. One approach to reducing pain in people with dementia in nursing homes is an algorithm-based pain management strategy, i.e. the use of a structured protocol that involves pain assessment and a series of predefined treatment steps consisting of various non-pharmacological and pharmacological pain management interventions.

OBJECTIVES

To assess the effects of algorithm-based pain management interventions to reduce pain and challenging behaviour in people with dementia living in nursing homes. To describe the components of the interventions and the content of the algorithms.

SEARCH METHODS

We searched ALOIS, the Cochrane Dementia and Cognitive Improvement Group's register, MEDLINE, Embase, PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science Core Collection (ISI Web of Science), LILACS (Latin American and Caribbean Health Science Information database), ClinicalTrials.gov and the World Health Organization's meta-register the International Clinical Trials Registry Portal on 30 June 2021.

SELECTION CRITERIA

We included randomised controlled trials investigating the effects of algorithm-based pain management interventions for people with dementia living in nursing homes. All interventions had to include an initial pain assessment, a treatment algorithm (a treatment plan consisting of at least two different non-pharmacological or pharmacological treatment steps to reduce pain), and criteria to assess the success of each treatment step. The control groups could receive usual care or an active control intervention. Primary outcomes for this review were pain-related outcomes, e.g. the number of participants with pain (self- or proxy-rated), challenging behaviour (we used a broad definition that could also include agitation or behavioural and psychological symptoms assessed with any validated instrument), and serious adverse events.

DATA COLLECTION AND ANALYSIS

Two authors independently selected the articles for inclusion, extracted data and assessed the risk of bias of all included studies. We reported results narratively as there were too few studies for a meta-analysis. We used GRADE methods to rate the certainty of the results.

MAIN RESULTS

We included three cluster-randomised controlled trials with a total of 808 participants (mean age 82 to 89 years). In two studies, participants had severe cognitive impairment and in one study mild to moderate impairment. The algorithms used in the studies varied in the number of treatment steps. The comparator was pain education for nursing staff in two studies and usual care in one study. We judged the risk of detection bias to be high in one study. The risk of selection bias and performance bias was unclear in all studies. Self-rated pain (i.e. pain rated by participants themselves) was reported in two studies. In one study, all residents in the nursing homes were included, but fewer than half of the participants experienced pain at baseline, and the mean values of self-rated and proxy-rated pain at baseline and follow-up in both study groups were below the threshold of pain that may require treatment. We considered the evidence from this study to be very low-certainty and therefore are uncertain whether the algorithm-based pain management intervention had an effect on self-rated pain intensity compared with pain education (MD -0.27, 95% CI -0.49 to -0.05, 170 participants; Verbal Descriptor Scale, range 0 to 3). In the other study, all participants had mild to moderate pain at baseline. Here, we found low-certainty evidence that an algorithm-based pain management intervention may have little to no effect on self-rated pain intensity compared with pain education (MD 0.4, 95% CI -0.58 to 1.38, 246 participants; Iowa Pain Thermometer, range 0 to 12). Pain was rated by proxy in all three studies. Again, we considered the evidence from the study in which mean pain scores indicated no pain, or almost no pain, at baseline to be very low-certainty and were uncertain whether the algorithm-based pain management intervention had an effect on proxy-rated pain intensity compared with pain education. For participants with mild to moderate pain at baseline, we found low-certainty evidence that an algorithm-based pain management intervention may reduce proxy-rated pain intensity in comparison with usual care (MD -1.49, 95% CI -2.11 to -0.87, 1 study, 128 participants; Pain Assessment in Advanced Dementia Scale-Chinese version, range 0 to 10), but may not be more effective than pain education (MD -0.2, 95% CI -0.79 to 0.39, 1 study, 383 participants; Iowa Pain Thermometer, range 0 to 12). For challenging behaviour, we found very low-certainty evidence from one study in which mean pain scores indicated no pain, or almost no pain, at baseline. We were uncertain whether the algorithm-based pain management intervention had any more effect than education for nursing staff on challenging behaviour of participants (MD -0.21, 95% CI -1.88 to 1.46, 1 study, 170 participants; Cohen-Mansfield Agitation Inventory-Chinese version, range 7 to 203). None of the studies systematically assessed adverse effects or serious adverse effects and no study reported information about the occurrence of any adverse effect. None of the studies assessed any of the other outcomes of this review.

AUTHORS' CONCLUSIONS

There is no clear evidence for a benefit of an algorithm-based pain management intervention in comparison with pain education for reducing pain intensity or challenging behaviour in people with dementia in nursing homes. We found that the intervention may reduce proxy-rated pain compared with usual care. However, the certainty of evidence is low because of the small number of studies, small sample sizes, methodological limitations, and the clinical heterogeneity of the study populations (e.g. pain level and cognitive status). The results should be interpreted with caution. Future studies should also focus on the implementation of algorithms and their impact in clinical practice.

A Systematic Review of Interventions to Improve Analgesic Use and Appropriateness in Long-Term Care Facilities

OBJECTIVES

To systematically review the effectiveness of interventions to improve analgesic use and appropriateness in long-term care facilities (LTCFs).

DESIGN

Systematic review.

SETTING AND PARTICIPANTS

MEDLINE, Embase, PsycINFO, and CINAHL Plus were searched from inception to June 2021. Randomized controlled trials (RCTs), controlled and uncontrolled prospective interventions that included analgesic optimization, and reported postintervention change in analgesic use or appropriateness in LTCFs were included.

METHODS

Screening, data extraction, and quality assessment were performed independently by 2 review authors.

RESULTS

Eight cluster RCTs, 2 controlled, and 6 uncontrolled studies comprising 9056 residents across 9 countries were included. The 16 interventions included education (n = 13), decision support (n = 7), system modifications (n = 6), and/or medication review (n = 3). Six interventions changed analgesic use or appropriateness, all of which included prescribers, 5 involved multidisciplinary collaboration, and 5 included a component of education. Education alone changed analgesic use and appropriateness in 1 study. Decision support was effective when combined with education in 3 interventions. Overall, 13 studies reported analgesic optimization as part of pain management interventions and 3 studies focused on medication optimization. Two pain management interventions reduced the percentage of residents reporting pain not receiving analgesics by 50% to 60% (P = .03 and P < .001, respectively), and 1 improved analgesic appropriateness (P = .03). One reduced nonsteroidal anti-inflammatory drugs (NSAIDs) (P < .001) and another resulted in 3-fold higher odds of opioid prescription in advanced dementia [95% confidence interval (CI) 1.1-8.7]. One medication optimization intervention reduced NSAID prescription (P = .036), and another reduced as-needed opioid (95% CI 8.6-13.8) and NSAID prescription (95% CI 1.6-4.2).

CONCLUSIONS AND IMPLICATIONS

Interventions involving prescribers and enhanced roles for pharmacists and nurses, with a component of education, are most effective at changing analgesic use or appropriateness. Interventions combining education and decision support are also promising. Medication review interventions can change analgesic prescription, although there is currently minimal evidence in relation to possible corresponding improvements in resident-related outcomes.

Implementation of the Multidisciplinary Guideline on Chronic Pain in Vulnerable Nursing Home Residents to Improve Recognition and Treatment: A Qualitative Process Evaluation

The recognition and treatment of pain in nursing home residents presents challenges best addressed by a multidisciplinary approach. This approach is also recommended in the applicable Dutch guideline; however, translating guidelines into practical strategies is often difficult in nursing homes. Nevertheless, a better understanding of guideline implementation is key to improving the quality of care. Here we describe and qualitatively evaluate the implementation process of the multidisciplinary guideline 'Recognition and treatment of chronic pain in vulnerable elderly' in a Dutch nursing home. The researchers used interviews and document analyses to study the nursing home's implementation of the guideline. The project team of the nursing home first filled out an implementation matrix to formulate goals based on preferred knowledge, attitudes, and behaviors for the defined target groups. Together with experts and organizations, pharmacotherapy audit meetings were organized, an expert pain team was appointed, a policy document and policy flowchart were prepared, and 'anchor personnel' were assigned to disseminate knowledge amongst professionals. Implementation was partially successful and resulted in a functioning pain team, a pain policy, the selection of preferred measurement instruments, and pain becoming a fixed topic during multidisciplinary meetings. Nevertheless, relatively few professionals were aware of the implementation process.

Use of Pain Management Champions to Enhance Guideline Implementation by Care Workers in Nursing Homes

BACKGROUND

Underutilization of evidence-based pain management in nursing homes (NHs) is common. Evidence toward effective approaches to improve adoption of evidence-based practices in NHs is limited. Application of theory in evaluation approaches can increase understanding of implementation challenges.

AIM

To get a better understanding of the impact of implementation strategies by exploring the underlying mechanisms using behavioral theory.

METHODS

This mixed-methods study is embedded in an implementation-effectiveness study of a pain management guideline in four Swiss NHs. To evaluate our implementation strategies, training workshops were held, and trained pain champions were introduced. We also developed a conceptual framework. Based on Bandura's self-efficacy theory, we hypothesized how our implementation strategies might affect changes in care workers' behavior. Care workers' questionnaire surveys were conducted at baseline (n = 136), after 3 months (n = 99), and after 6 months (n = 83) to assess self-efficacy in pain management and self-reported guideline adoption. We computed linear mixed-effect models to assess changes over time in self-efficacy and logistic regressions to assess associations between self-efficacy and guideline adoption. Concurrently, we conducted focus groups with care workers (n = 8) to explore their response to the implementation strategies.

RESULTS

Overall, there was a significant increase in self-efficacy at both time points (p < .001). We found significant associations between self-efficacy and adoption of two guideline components, that is, performing a comprehensive pain assessment and using observational pain assessment tools in cognitively impaired residents. Qualitative findings showed that implementation strategies were received positively by care workers. Focus group participants reported more attentiveness to residents' pain experience. The participants also reported increases in assessment and documentation of pain with more detail than before.

LINKING EVIDENCE TO ACTION

Our findings highlighted that the training and use of pain champions increased self-efficacy and thereby induced behavior change leading to guideline adoption. Regarding persistent implementation challenges, a theory-based conceptual model contributes to the overall understanding.

The Mediating Effect of Model-Based Learning on Attitude and Pain Management Awareness Among Nurses During COVID 19 Pandemic in Saudi Arabia

Background

All hospitals are required to provide high-quality pain management; one of the most critical issues in achieving high-quality pain management is that the hospitals have a clear plan to manage the patients’ pain and improve the nurses’ awareness of pain management during the COVID 19 pandemic. However, there is a significant gap in the literature that this study can cover.

Aim

This study aimed to investigate the mediating effect of model-based learning on nurses’ attitudes toward nurses’ pain management awareness during the COVID 19 pandemic in Saudi Arabia government hospitals.

Method

The Heath Beliefs Model was used, with a quasi-experimental design, with per experimental one group pre-test post-test design, and a quantitative approach using self-administered questionnaires obtained from 330 nurses working with patients suffering from pain. IBM SPSS V23 and Analysis of Moment Structures (AMOS)V23 were applied to analyze the causal relationships between the variables.

Results

A two-step approach to analyze the study: the first step was to test the measurement models’ constructs’ reliability and validity. The second step was to test research hypotheses in the structural models. The results show a significant positive relationship between model-based learning and pain management awareness among nurses during COVID 19 pandemic. Furthermore, model-based learning fully mediated the relationship between nurses’ attitudes and pain management awareness.

Conclusions

The study successfully improved nurses’ attitudes toward pain management awareness among nurses during COVID 19 pandemic. These findings will help strengthen the debate in the existing literature, and this is a new development window in the pain management area.