HIV patient biopsychosocial complexity

Abstract

Background

Biopsychosocial comorbidities are common in chronic illnesses like HIV, often resulting in complex clinical presentations. The Clinical Complexity Rating Scale for HIV (CCRS-HIV) was developed to identify these factors. This study examined whether complexity, as measured by the CCRS-HIV, could change over time.

Methods

Patients at The Albion Centre in Sydney were assessed at two time points (T₁ and T₂) using the CCRS-HIV. Changes in total and subscale scores were analysed, along with the odds of scoring in the complex range (40+ or 45+). The impact of age and time between assessments was also evaluated.

Results

Results showed a significant decrease in total complexity scores from T₁ to T₂, with improvements across four subscales—two psychological/behavioural, one social, and one biomedical. The proportion of complex cases declined, and participants were nearly twice as likely to be complex at T₁ compared to T₂. Longer intervals between assessments were linked to greater improvements, while age and sex had no effect.

Conclusions

These findings suggest that complexity in people living with HIV (PLHIV) is modifiable and support the CCRS-HIV as a valuable tool for screening and tracking changes in clinical complexity.

Keywords

Comorbidity HIV Biopsychosocial

Introduction

“To cope with escalating complexity in health care we must abandon linear models, accept unpredictability, respect (and utilise) autonomy and creativity, and respond flexibly to emerging patterns and opportunities.” ¹

The success of antiretroviral therapy (ART) has increased the life expectancy of people living with HIV (PLHIV). The subsequent impact on clinical complexity due to comorbidities associated with aging, long term infection, and medication toxicity cannot be discounted.² Additionally, complexity associated with non-AIDS medical (e.g., cardiovascular, hepatic), psychological (e.g., anxiety, major depression, drug and alcohol concerns), and social (e.g., homelessness, unemployment) comorbidities has been previously identified.^3,4 The current gold standard of care, which focuses on viral load and CD4 count, effective management of HIV as a chronic illness must include assessment and treatment of psychosocial factors in addition to these factors.⁴

From the early days of the HIV epidemic patient-centred therapy has been the mainstay of treatment/management.⁵ Holistic, patient-centred and multidisciplinary care are integral elements in the ongoing development of the biopsychosocial model (BPSM) of medicine, psychiatry, and health care,^6–9 patient complexity research¹⁰ and HIV treatment and care.^11,12 The BPSM was first conceptualized in 1977 to address the limitations of viewing patients and their presentations through the lens of the biomedical model (BM) alone, and is considered a more holistic framework that accounts for the complex interaction between biological, psychological, behavioural, and social factors impacting on illness.^13,14

Considering HIV through a biopsychosocial lens accounts for the complex interplay of psychological, social and medical factors which often interact to increase the burden of illness. What is less understood is whether these factors are modifiable and amenable to change. The development of the Clinical Complexity Rating Scale for HIV (CCRS-HIV) at our service has provided clinicians with a specific HIV risk-prediction complexity screening tool developed to capture comorbidity across the biopsychosocial spectrum.¹⁵ Recent research using the CCRS-HIV reported good concordance/agreement between Attending Medical Officer (AMO) assessment and patient self-assessment using the original clinician version CCRS-HIV^C and patient versions CCRS-HIV^P of the scale.¹⁶ That study also demonstrated that a variety of demographic/lifestyle (psychosocial) and physical/treatment (biomedical) factors were associated with high complexity scores.¹⁶ These findings provide further evidence of the validity of the scale and reinforce the importance of holistic, patient-centred and interdisciplinary HIV care; to assess, treat and refer patients in line with the biopsychosocial factors impacting complexity.¹⁶

As increasingly complex clinical presentations were identified within our service we redesigned our response to complexity, incorporating interdisciplinary care through a multidisciplinary lens; a more proactive and comprehensive way to identify and respond to complex clinical presentations with the aim of retaining patients in care and improving support by harnessing interdisciplinary clinical resources.

Additional enhanced interdisciplinary care provided specifically to patients with high CCRS-HIV scores were introduced and include:

(1) Weekly Complex Care Planning meetings where patients whose needs were identified as complex by the CCRS-HIV were reviewed by small interdisciplinary teams (comprising medical, nursing, psychology and other allied health clinicians as identified) to develop and/or adjust coordinated care plans and refer as needed.

(2) Monthly Interdisciplinary Comprehensive Care meetings with all clinical staff (AMOs, medical registrars, nurses, clinical psychologists, pharmacists, social workers and dietitians)together with community case management teams (nurses, social workers) to discuss the specific needs of patients with identified complex care needs and maximize coordinated interdisciplinary care.

The current study was designed to further assess the clinical utility of the original clinician version of CCRS-HIV as a measure of change in complexity over time, in the context of service changes to our interdisciplinary response to manage complex clinical presentations.

Methods

Participants

The cohort of 1125 PLHIV clients from The Albion Centre (Albion), a multidisciplinary HIV treatment centre in Sydney, Australia, who were investigated in the original CCRS-HIV study¹⁵ were the participants assessed in the current follow-up study. Data for the present study were collected via a review of electronic medical records (eMR) to re-assess patients evaluated in the original CCRS-HIV study.¹⁵

Measures

The CCRS-HIV includes the following psychosocial variables: problematic crystal methamphetamine (CMA) use; mental health/other problematic substance use; social isolation; financial instability; and the following physical/biomedical health variables: cognitive/neurological concerns; polypharmacy (≥5 regular medications); active Hepatitis C (HCV) and/or a cancer diagnosis; and other physical health comorbidities. Full information regarding its development and initial validation is provided elsewhere.¹⁵ The original version of the CCRS-HIV was developed to assess functional impairment rather than the presence of a factor alone.¹⁵

Procedure

Ethics approval was obtained from South-Eastern Sydney Local Helath District (SESLHD; HREC 2022/ETH01421). AMOs who had earlier assessed their patients using the CCRS-HIV as part of the original study¹⁵ and were continuing to see those patients for ongoing care were asked, after the introduction of the scale into routine care, to review and re-score all patients (including those not initially assessed as complex) using the CCRS-HIV. A review of eMR of all patients receiving medical care at Albion was later undertaken to identify basic demographic variables (age and sex) and CCRS-HIV scores at the initial scoring Time 1 (T₁) and follow-up scoring Time 2 (T₂).

Statistical analyses

The total and subscale (psychosocial and physical health) scores at T₁ and T₂ were compared using both the Wilcoxon signed-rank test and paired t-tests to assess medians and means respectively. Together, these tests provide a comprehensive evaluation of the changes in total scores, both in terms of central tendency measures. Logistic regression models were used to estimate the impact of the total complexity score for 40+ and 45+ cut-offs at T₁ compared to T_2. Similar methodology was used to examine if age or sex were associated with improvement; that is, a decrease in complexity scores over time. After calculating the total complexity score by assigning the estimated weights to each item, we compared the proportion of complexity at T₁ and T₂ using the previously identified cut-off CCRS-HIV scores of 40 and 45, employing McNemar’s test. Spearman coefficients were used to assess the correlation between HIV complexity scores and the duration of time between T₁ and T₂ assessments as a potential proxy measure of retention in care.

Results

A total of 1125 participants were involved in the original study¹⁵ with the initial ratings (T₁) occurring between 2018 and 2021. Changes to routine care at the service involved re-scoring patients as needed. For purpose of the present dataset, updated (T₂) CCRS-HIV ratings for 765 (67.82%) patients were collected between four (4) and 67 months after T₁ scores. The mean age of participants was 48.53 years; 77 identified as female, and 688 identified as male. Reasons for attrition were as follows: AMOs from the original study not participating in this follow-up study (35.77%); patients transferring their HIV care elsewhere (16.5%); difficulty accessing original CCRS-HIV data (14.67 %); patient not seen by AMO recently enough to reliably complete follow-up CCRS-HIV ratings (23.85%); patients lost-to-follow-up (LTFU; 8.25%); and deceased patients (0.91%).

A significant reduction in mean total complexity scores was observed between T₁ and T₂, suggesting a reduction in complexity in the overall sample (Table 1).

Table 1.

Change in CCRS-HIV total score over time (T₁ vs T₂).

CCRS-HIV total score	T₁ (N = 765)	T₂ (N = 765)	P value
Median (IQR) CCRS-HIV total scores	14 (0-32)	0 (0-13)	<0.001
Mean (SD) CCRS-HIV total scores	21 (28)	18 (25)	<0.001

We investigated the impact of time between T₁ and T₂ assessments, using the Spearman correlation coefficient for the full sample. The results demonstrated a weak negative association between the score at T₂ and the length of time between assessments (rs = −35%, p value <0.000), demonstrating a decrease in complexity and suggesting the longer patients are retained in care, the lower their complexity score.

The analyses also compared changes in subscales on the CCRS-HIV between T₁ and T₂. Four of the eight biopsychosocial variables showed significant improvement between T₁ and T₂. Those included: problematic crystal methamphetamine (CMA) use (p = 0.0055 median, p = 0.0054 mean), mental health/other problematic substance use (p = 0.0429 median, p = 0.0428 mean), financial instability (p = 0.005 median, p = 0.0048 mean) and other physical health factors (p = 0.016 median, p = 0.0163 mean) (Table 2).

Table 2.

Change in CCRS-HIV subscale scores over time (T_{1 vs} T₂).

CCRS-HIV subscale	T₁ (N = 765)	T₂ (N = 765)	P Value
Problematic CMA use
Median (IQR)	0 (0-0)	0 (0-0)	0.0055
Mean (SD)	1.28 (4.9)	0.81 (3.94)	0.0054
Mental health/other problematic substance use
Median (IQR)	0 (0-14)	0 (0-14)	0.0428
Mean (SD)	3.78 (6.22)	3.37 (6.0)	0.0430
Financial instability
Median (IQR)	0 (0-0)	0 (0-0)	0.005
Mean	3.53 (9.1)	2.61 (8.0)	0.0048
Social isolation
Median (IQR)	0 (0-0)	0 (0.0)	0.1432
Mean (SD)	3.57 (8.6)	3.11 (8.1)	0.1433
Cognitive/neurological impairment
Median (IQR)	0 (0-0)	0 (0-0)	0.7773
Mean (SD)	1.22 (4.5)	1.27 (4.6)	0.7775
Polypharmacy
Median (IQR)	0 (0-0)	0 (0-0)	0.1083
Mean (SD)	1.70 (4.8)	1.43 (4.4)	0.1083
Hep C infection &/or cancer
Median (IQR)	0 (0-0)	0 (0-0)	0.999
Mean (SD)	0.51 (2.90)	0.51 (2.90)	0.999
Other physical comorbidity
Median (IQR)	0 (0-17)	0 (0-17)	0.0164
Mean (SD)	5.35 (8.0)	4.70 (7.6)	0.0163

To assess if there was a significant change in complexity ratings between those assessed as complex versus non-complex at T₁, the analysis compared the proportion of participants with risk scores above the cut-off points of 40 and 45 at T₁ and T₂ using McNemar’s test. For the 40+ score, 21% of participants had scores ≥40 at T₁, while 16.7% had scores ≥40 at T₂. The p-value from McNemar’s test (<0.001) indicates a statistically significant reduction in the proportion of participants with scores ≥40 between T₁ and T₂. For the 45+ score, 19.2% of participants had scores ≥45 at T₁, which decreased to 14.8% at T₂. Again, the p-value (<0.001) suggests a statistically significant reduction in the proportion of participants with scores ≥45 over time. The odds ratio (OR = 1.94) for cut-off #1 (40+ score) indicates that participants were almost twice as likely to score in the complex range at T₁ compared to T₂. Similarly, the odds ratio (OR = 2.02) for cut-off #2 (45+) indicates that participants were about twice as likely to have scored in the complex range at T₁ compared to T₂ (Table 3). There was no significant association of age or sex identified between T₁ and T₂ for both cut-offs (scores of 40+ and 45+).

Table 3.

CCRS-HIV Cut-off complexity scores 40+ and 45+ (T₁ vs T₂).

CCRS-HIV total score	T₁ (N = 765)	T₂ (N = 765)	Proportional difference between T₁ & T₂ (95% CI)	Odds ratio (95% CI) of complexity at T₁ & T₂	P value
CCRS-HIV cut-off #1 (40+)	160 (21%)	128 (16.7%)	4% (1.5%, 6.8%)	1.9 (1.26, 3.03)	<0.001
CCRS-HIV cut-off #2 (45+)	147 (19.2%)	113 (14.8%)	4% (1.8%, 7.1%)	2.03 (1.32, 3.18)	<0.001

Discussion

Complexity has been a hallmark of HIV since the first cases of unexpected infections and cancers presented in gay men in the USA in 1981.¹⁷ Those events heralded the development of a medical, psychological, social and public health crisis. Much has changed since then, including HIV infection changing from a terminal illness to an effectively managed chronic condition. However, HIV complexity remains a hallmark that impacts PLHIV and their clinical management.

The current results demonstrate that HIV related complexity is modifiable and can decrease over time, demonstrated by the significant reduction in CCRS-HIV complexity scores from T₁ to T₂ in the present study. Additionally, the results show that there was a significantly greater likelihood of improvement over time in those patients initially assessed as complex by the CCRS-HIV, compared to those identified as non-complex. These findings suggest that the additional enhanced interdisciplinary clinical interventions provided to patients assessed as having complex care needs at T₁ (enhanced interdisciplinary assessment, intervention, case management and referral), may contribute to the decrease in complexity ratings as a similar improvement was not observed in the non-complex group (i.e., non-enhanced standard multidisciplinary intervention group). Neither age nor sex were associated with improvement over time.

Of note, specific factors across the biopsychosocial domain were assessed by AMOs as significantly improving over time: two psychological/behavioural factor (problematic CMA use, mental health/other problematic substance use), one social factor (financial instability), and one biomedical (other physical health) factor. As such, the results reveal that a variety of factors are modifiable and amenable to change and suggest the implementation of the enhanced care model, incorporating enhanced interdisciplinary care for complex clients, is a likely factor in that improvement. However, improvement was only noted in four of the eight variables, indicating the importance of further investment in the enhanced interdisciplinary care process to demonstrate improvement across the board.

For the full sample, both complex and non-complex, results showed a negative association between the duration between assessments (time in care) and reduction in complexity. That is, the longer the time between assessments, the lower the complexity score. The importance of retention in care has been demonstrated as a critical factor in the success and efficacy of HIV treatment and care,¹⁸ as has the use of interdisciplinary assessment, intervention, referral and case management strategies.^11,12,19,20 The current findings provide additional evidence of this, highlighting the value of assessing beyond medical factors alone for biopsychosocial complexity, and incorporating individualised interdisciplinary intervention strategies into HIV clinical care accordingly.

Our findings extend those from previous studies,^6–9 and provide further evidence of the value of the BPSM in assessing complex clinical presentations and the provision of appropriate patient care. It has been suggested that the concept of patient complexity was developed to better understand the intricate interaction between biological, psychological, behavioural and social factors described by Engel.²¹ General Systems Theory (GST), which Engel proposed underpinned the BPSM, has developed over time from a hierarchical organisation¹³ to a circular (feedback loops) causality and structural¹⁴ and more recently to a biosemiotics systemic approach to better understand the BPSM and describe complex causality.²² In this latter framework, both top down and bottom-up regulatory circuits are suggested as introducing sufficient flexibility to maintain hierarchical systemic integrity in the presence of stress.²² The capacity of the BPSM to be reimagined and refined in line with the development of the GST and increasing complexity in healthcare settings demonstrates that the model is non-linear, respects creativity and responds flexibly to emerging patterns and opportunities, as described by Plsek and colleagues.¹ The current results provide evidence of the value of the model in the measurement of change in HIV complexity and its capacity to guide intervention.

Our findings build on earlier research on the clinical use and validity of the scale as a measure of complexity^15,16 and demonstrate its use as a measure of change in complexity over time. Further, they build on earlier research at this centre, where HIV complexity was shown to be associated with biopsychosocial factors including non-HIV medical comorbidities as well as psychological and social factors.^3,4

This study does have limitations. First, the present study collected data from a clinical population accessing public health services and thus the results may not be representative of the broader Australian HIV cohort. Second, factors other than the enhanced care interventions may contribute to the reduction in complexity ratings over time. Third, the absence of some demographic data is limiting. This was consequence of restraints imposed by the eMR-review methodology, meaning information on age and sex were the only reliable demographic data that could be extracted. Fourth, it is possible the results were skewed by patients who were LTFU who, potentially, presented with more comorbidity/increased clinical complexity. Last, the use of the original (clinician) version of the scale CCRS-HIV^C may be interpreted as being less valid and reliable that using the patient version CCRS-HIV^P. While there is merit in this argument, as reported earlier, recent research using both versions of the scale demonstrated good concordance/agreement between AMO assessment and patient self-assessment. We suggest further research with the CCRS-HIV, including: 1) Replication of the current investigation using the patient version of the scale CCRS-HIV^P to determine if it is more effective in assessing change in complexity over time, 2) Investigating the psychometric properties and efficacy of the CCRS-HIV in GP clinics that have high case load HIV presentations & 3) Assess the efficacy of appropriately modified enhanced HIV interdisciplinary care model in high case load GP clinics that employ multidisciplinary staff (psychologists, nurses, social workers etc.,).

From the early days of the HIV epidemic patient-centred therapy has been an integral aspect of treatment and management.⁵ Holistic, patient-centred, and multidisciplinary care are also elements in the ongoing development of the BPSM,⁹ patient complexity research¹⁰ and HIV treatment and care.^11,12 The findings from this study reinforce the importance of the holistic, patient-centred, interdisciplinary model of care in HIV and demonstrate the value of interdisciplinary enhanced care in effective management and treatment.

Footnotes

Acknowledgements

The authors would like to thank patients and staff at Albion for their participation in this research.

ORCID iDs

Kim Begley

Don Smith

Handan Wand

Derek Chan

Melissa Louise Kelly

Isaac Sabel

Lia Purnomo

Shiraze M Bulsara

Ethical Consideration

Ethics approval was obtained from South-Eastern Sydney Local Helath District (SESLHD; HREC 2022/ETH01421).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by an unrestricted grant from Gilead Sciences used to purchase grocery vouchers to thank participants for their participation.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Plsek

Greenhalgh

. Complexity science: the challenge of complexity in health care. BMJ 2001; 323: 625–628.

Guaraldi

Malagoli

Calcagno

, et al. The increasing burden and complexity of multi-morbidity and polypharmacy in geriatric HIV patients: a cross sectional study of people aged 65 - 74 years and more than 75 years. BMC Geriatr 2018; 18: 99.

Chan

Furner

Smith

, et al. Non-AIDS complexity amongst patients living with HIV in Sydney: risk factors and health outcomes. AIDS Res Ther 2018; 15: 6.

Bulsara

Wainberg

Rogers

, et al. The role of comorbidity on retention in HIV care. AIDS Behav 2020. DOI: 10.1007/s10461-020-02992-1.

Cooke

. Expert patients: learning from HIV. BMJ Qual Saf 2011; 20 Suppl 1(Suppl 1): i67–i68.

Wade

Halligan

. The biopsychosocial model of illness: a model whose time has come. Clin Rehabil 2017; 31: 995–1004.

Kusnanto

Agustian

Hilmanto

. Biopsychosocial model of illnesses in primary care: a hermeneutic literature review. J Family Med Prim Care 2018; 7: 497–500.

Hardie

. Three aspects of health and healing: the biopsychosocial model in medicine. Washington University, Dept of Surgery.

Farre

Rapley

. The new old (and old new) medical model: four decades navigating the biomedical and psychosocial understandings of health and illness. Healthcare (Basel) 2017; 5: 20171118.

10.

Grant

Ashburner

Hong

, et al. Defining patient complexity from the primary care physician's perspective: a cohort study. Ann Intern Med 2011; 155: 797–804.

11.

Broeckaert

Challacombe

. Does multidisciplinary care improve health outcomes among people living with HIV and or HCV? A review of the evidence. Prevention in Focus 2015. Online First.

12.

Sherer

Stieglitz

Narra

, et al. HIV multidisciplinary teams work: support services improve access to and retention in HIV primary care. AIDS Care 2002; 14 Suppl 1(Suppl 1): S31–S44.

13.

Engel

. The need for a new medical model: a challenge for biomedicine. Science 1977; 196: 129–136.

14.

Borrell-Carrio

Suchman

Epstein

. The biopsychosocial model 25 years later: principles, practice, and scientific inquiry. Ann Fam Med 2004; 2: 576–582.

15.

Bulsara

Begley

Smith

, et al. The development of an HIV-specific complexity rating scale. Int J STD AIDS 2019; 30: 1265–1274.

16.

Begley

Smith

Wand

, et al. “How well do we know our patients?”: further validation of a complexity rating scale for HIV. Int J STD AIDS 2024. Online First. https://doi.org/10.1177/09564624241279

17.

Centres for Disease Control and Prevention . Kaposi's sarcoma and pneumocystis pneumonia among homosexual men-New York City and California. Morb Mortal Wkly Rep 1981.

18.

Geng

Nash

Kambugu

, et al. Retention in care among HIV-infected patients in resource-limited settings: emerging insights and new directions. Curr HIV AIDS Rep 2010; 7: 234–244.

19.

Messeri

Abramson

Aidala

, et al. The impact of ancillary HIV services on engagement in medical care in New York City. AIDS Care 2002; 14 Suppl 1(Suppl 1): S15–S29.

20.

Chinyandura

Jiyane

Tsalong

, et al. Supporting retention in HIV care through a holistic, patient-centred approach: a qualitative evaluation. BMC Psychol 2022; 10: 17.

21.

Mutai

Sugiyama

Aoki

, et al. Key characteristics of patient complexity and patient complexity conceptual models/measurement tools: a scoping review protocol. BMJ Open 2023; 13: e063982.

22.

Schubert

Sulis

De La Torre-Luque

, et al. Editorial: biopsychosocial complexity research. Front Psychiatry 2023; 14: 1157217.

HIV patient biopsychosocial complexity - Fixed or modifiable?

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Participants

Measures

Procedure

Statistical analyses

Results

Discussion

Footnotes

Acknowledgements

ORCID iDs

Ethical Consideration

Funding

Declaration of conflicting interests

References