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Forecasting multimorbidity in aging people with HIV using a microsimulation model

Communications Health volume 1, Article number: 7 (2026) Cite this article

Abstract

Background

While life expectancy has significantly increased among people living with HIV (PLWH) due to effective antiretroviral therapy, aging-related non-communicable diseases and multimorbidity have emerged as critical challenges. Understanding the evolving health needs of PLWH is essential to guiding healthcare planning and interventions.

Methods

We developed a new microsimulation model that integrates longitudinal clinical and administrative data to project the health trajectories of PLWH in British Columbia from 2019 to 2034. High prevalence conditions, such as cardiovascular disease (CVD), non-AIDS-related cancers, chronic liver disease (CLD), hypertension, chronic obstructive pulmonary disease (COPD), and mood and anxiety disorders (MANX), were included in the model. Results were stratified by age and sex.

Results

Here we show that by 2034, nearly half of PLWH will be aged 60 years or older, and more than one-quarter will live with at least three additional chronic conditions. CVD is projected to nearly double (8.4–14.8%), cancers to rise from 8.9 to 17.2%, while MANX will remain the most common comorbidity, affecting about half of PLWH. Sex differences are pronounced; females show higher rates of multimorbidity involving MANX, CLD, and COPD, whereas males show higher prevalence of non-AIDS-related cancers and CVD.

Conclusions

These findings highlight the urgency of preparing for a sharp increase in multimorbidity and demonstrate the value of forecasting tools to guide integrated models of care for aging PLWH.

Introduction

As in many high-income settings, British Columbia (BC), Canada is experiencing a demographic shift among people living with HIV (PLWH). In BC, the median age of PLWH has increased dramatically mainly due to the success of antiretroviral therapy (ART), from 38 (25th–75th percentiles: 33–44) years in 1996 to 55 (44–63) years in 2023. Consequently, the prevalence of age-related non-communicable diseases (NCDs) has increased during this period, with 16% of PLWH aged 50 years or older now living with at least two NCDs (24% among those 60 years and older), with metabolic, cardiovascular and lung diseases, and non-AIDS related cancers representing the highest burden of illness1. Studies have demonstrated that PLWH, in comparison to those without HIV, consistently experience a higher number of comorbid NCDs at an earlier age, and in some studies, onset occurs at least 12 years earlier2. Additionally, premature mortality from non-HIV/AIDS related causes are on the rise, representing 92% of all deaths among PLWH in 20223.

It is well established that as individuals age, their immune system experiences senescence, which can be exacerbated by HIV infection which causes immunological damage and chronic inflammation4,5. Whether HIV infection is accelerating the onset of NCDs is yet to be shown, but clinical progression of these diseases can be attributed, in part, to the syndemic effect of psychosocial factors (e.g., social support and/or isolation), structural factors (e.g., access to healthcare resources, housing, food, and medications), behavioral factors (e.g., unregulated drug, alcohol, and/or tobacco use), and the cumulative toxicity of HIV/non-HIV medications. In addition, older PLWH are increasingly suffering from mental health issues, which may be associated with cognitive impairment, which in turn may impair their ability to adhere to complex treatment regimens needed to optimally treat both HIV and NCDs. This is further compounded by the paucity of services and programs specifically designed to support older PLWH.

Older PLWH are a highly heterogeneous subgroup6, reflecting differences in sex, stages of aging and an array of HIV-associated and aging-linked NCDs, which complicates their care7,8. Life expectancy for female PLWH aged 55 and older remained lower compared to their male counterparts, which makes female PLWH as a priority for care optimization9. Prioritizing on the basis of symptoms can lead to undermanaging serious asymptomatic cases with potentially devastating consequences7,10. Until a cure for HIV is found, there is an urgent need for new research on policy and program interventions to promote healthy aging among PLWH11,12. As such, empirical evidence is needed to direct resources and care appropriately for this population, and inform new interventions and programs to address the needs of PLWH. Knowledge of the impact of NCDs on HIV treatment response and the use of healthcare resources is lacking in an aging PLWH population13. Dynamic modeling of disease and outcome trends has been widely used to forecasting future healthcare needs of PLWH and assessing the impact of multiple interventions, especially when dealing with complex data from multiple, varied sources14.

Individual-based microsimulation models have been extensively used to predict NCDs among PLWH in different settings. However, previous studies either focused on one specific NCD or did not incorporate mental health factors15,16,17. Althoff et al. predicted trends in mental and physical NCDs as well as multimorbidity among PLWH in the United States stratified by race/ethnicity, sex and HIV exposure category18. However, specific patterns of multimorbidity for improvement of healthcare delivery were not identified.

Therefore, in this study our aim was to develop a dynamic microsimulation model of HIV disease progression that incorporates incidence and prevalence trends of NCDs (and key determinants of health) as they change over time. We will use this model to forecast the impact of HIV and non-HIV prevention and treatment policies and interventions on health outcomes at the population level. The appeal of employing this modeling approach is that it can enable decision makers to make empirically-informed choices to: (1) identify different population segments as priority groups; (2) characterize which conditions are contributing most to the care/cost burden of NCDs in this population; (3) forecast health trajectories for key subgroups based on currently observed trends in determinants of health and NCDs; and (4) use counterfactual analysis to test the relative impact of different interventions with the aim of selecting the most effective combination of interventions for improving health outcomes and reducing the burden on the healthcare system.

Our study shows that by 2034, PLWH aged 60 and above will account for almost half of the population. In addition, the burden of comorbidities, especially cardiovascular disease (CVD) and non-AIDS-related cancers (henceforth referred to as cancers after excluding Kaposi sarcoma, non-Hodgkin’s lymphoma, and cervical cancer), will rise substantially, with more than a quarter living with at least three additional comorbidities. Our model also reveals different multimorbidity patterns by sex: females are disproportionally affected by mood and anxiety disorders (MANX), chronic liver disease (CLD), and chronic obstructive pulmonary disease (COPD), while males have a higher burden of CVD and cancers.

Methods

Model overview

To address the complex health trajectories of aging PLWH in BC, we developed an individual-based microsimulation model. This model captures the interactions between HIV disease progression, ART history, and the incidence of NCDs. By simulating individual life courses, the model forecasts the population-level impact of various health outcomes up to 2034.

Data sources

The data sources for this model included the provincial HIV Drug Treatment Program (DTP), which is a registry at the BC Centre for Excellence in HIV/AIDS (BC-CfE), and the Seek and Treat for Optimal Prevention of HIV/AIDS (STOP HIV/AIDS) cohort19,20. The DTP has provided universal ART at no cost to all medically eligible residents of BC since 1992 and, as such, provides historical clinical and therapeutic data on all persons with at least one viral load and/or ART dispensation in the province. The STOP HIV/AIDS cohort comprises all diagnosed PLWH in BC, which has been described at length previously21. In brief, these two linked data sources include detailed records on ART history and clinical markers for the vast majority of PLWH in BC, and additionally the STOP HIV/AIDS cohort includes administrative health data covering medical visits, hospitalizations, prescriptions, and mortality. Case-finding algorithms using International Classification of Diseases codes were applied to identify comorbidities in the linked datasets (details can be found in the Supplementary Information)22,23,24,25,26.

Model schematics

Figure 1 outlines the conceptual framework of the microsimulation model. The model captures HIV disease progression, ART history, and the development of age-related comorbidities. It integrates individual health trajectories with key determinants, including demographic, behavioral, and clinical factors, providing a robust foundation for forecasting health outcomes and evaluating policy interventions.

Fig. 1: Schematic representation of the health forecasting microsimulation model for aging PLWH in BC.

HIV human immunodeficiency virus, PLWH people living with HIV, BC British Columbia, ART antiretroviral therapy, CVD cardiovascular disease, HTN hypertension, OA osteoarthritis, CKD chronic kidney disease, Cancers Non-AIDS-related cancers, COPD chronic obstructive pulmonary disease, CLD chronic liver disease, MANX mood and anxiety disorders, SCZ schizophrenia, PD personality disorders. Note: Solid lines represent HIV disease progression with arrows indicating the direction of the transition, and dashed lines represent determinants of the probabilities with arrows indicating the direction of the relationship (e.g., the arrowed dash line between diabetes and cardiovascular disease indicates that diabetes is a determinant of the probability of the incidence of cardiovascular disease).

Population initialization

The model population was initialized to represent all individuals diagnosed with HIV and alive in BC as of January 1, 2008. The characteristics of each individual—including age, sex, HIV diagnosis date, ART history, CD4 count at baseline, and existing comorbidities—were extracted from the DTP and STOP HIV/AIDS cohort. New individuals were introduced into the model biannually, either through new diagnoses or migration into BC (Supplementary Figs. S1–S3).

Description of health states

The model included the following health states for individuals: (1) Aware but not on ART (i.e., HIV-diagnosed PLWH without ART initiation in BC); (2) On ART with unsuppressed viral load; (3) On ART with viral suppression; (4) Off ART with unsuppressed viral load.

Factors considered

The trajectory of each individual was simulated based on the following time-fixed or time-varying factors:

(1) demographic variables, such as age and sex at birth; (2) clinical markers, such as nadir CD4 count (<200 or ≥200 cells/mm3), viral suppression status, ART class (non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside reverse transcriptase inhibitors (nRTIs), protease inhibitors (PIs), and integrase strand transfer inhibitors (INSTIs)), year of HIV diagnosis and viral load suppression (<200 or ≥200 copies/mL); (3) prevalent physical and mental comorbidities: CVD, diabetes (mellitus), chronic kidney disease (CKD), CLD, COPD, hypertension (HTN), osteoarthritis (OA), cancers, MANX, schizophrenia (SCZ), and personality disorders (PD); (4) behavioral factors, such as drug-related substance use status (captured based on opioid use disorder or injection drug use), physical activity, smoking and alcohol use.

Disease progression and comorbidity development

The determination of state transitions for comorbidities was informed by a literature review and clinical assessments made by co-authors. Probabilities for health-state transitions and comorbidity development were modeled using different statistical methods as described in the Supplementary Information. The transition probability from one stage to another for each individual, accounting for heterogeneity at individual level, was updated every six months, based on different covariates as described in the Supplementary Information (Tables S1–S16).

Calibration and validation

Calibration was performed by fitting model outputs to historical data on ART initiation, comorbidity prevalence, and mortality from 2008 to 2016. Parameter sets were sampled using Latin Hypercube sampling method, which ensured that the sampled combinations provided balanced representation of the multidimensional parameter range27. The final parameter set was refined by minimizing the residual sum of squares between simulated and observed data. Model validation was achieved by comparing projections from 2017 to 2019 against real-world data, ensuring consistency with observed trends (Supplementary Figs. S4–S10). The same parameter set was used for model projection from 2020 to 2034. The results were summarized as medians (95% credible interval: 2.5th–97.5th percentiles) based on 1000 simulation runs.

Details on statistical analyses, parameter estimation, and additional modeling considerations are available in the Supplementary Information.

Ethics approval

This study received approval from the University of British Columbia Ethics Review Committee at St. Paul’s Hospital, Providence Health Care site (H18-02208). The use of administrative data was approved by data stewards. Due to the use of anonymized administrative data, informed consent was not required for this study.

Results

Population aging among diagnosed PLWH in BC

Our model estimated that the number of diagnosed PLWH in BC in 2022 was 9421 (2.5th–97.5th percentiles as 95% credible interval: 8787–9882), which was consistent with the estimation at 9691 from the BC-CfE, or at 8858 from the Public Health Agency of Canada28,29. The number of PLWH in BC is projected to peak at 9471 (8838–9920) in 2025, followed by a slight decline to 9182 (8630–9565) by 2034 (Fig. 2, Supplementary Tables S17 and S18). This trend is accompanied by a pronounced demographic shift toward older age groups. The proportion of PLWH aged ≥60 is expected to double, from 24.4% (23.4–25.5%) in 2019 to 48.9% (47.9–49.9%) in 2034, while those aged ≥70 will increase more than threefold, from 6.2% (5.7–6.7%) to 24.3% (23.3–25.1%). These results reflect the success of ART in extending life expectancy but underscore the growing complexity of managing aging-related health challenges in this population.

Fig. 2: Projected trends in the number and age distribution of diagnosed PLWH in BC (Median), 2007–2034.

A Number of Diagnosed PLWH; B Age Distribution of Diagnosed PLWH. PLWH people living with HIV, BC British Columbia.

Comorbidity and multimorbidity burden over time

Aging among PLWH is paralleled by an increase in both the prevalence and complexity of physical and mental comorbidities. By 2034, the number of PLWH with at least three comorbidities (including both physical and mental conditions, in addition to HIV) will rise from 1860 (1736–1967) or 20.2% (19.4–21.1%) of the population in 2019 to 2535 (2369–2681), representing 27.7% (26.8–28.6%) of the population (Fig. 3 and Supplementary Tables S19–S21). While physical comorbidities dominate the overall burden, mental health conditions remain a significant and growing concern. The proportion of PLWH experiencing at least one mental health condition will increase from 48.8% (47.8–49.8%) in 2019 to 51.7% (50.6–52.6%) in 2034, reflecting the persistent need for mental health care within this population. The proportion of PLWH with at least two physical comorbidities will rise from 21.5% (20.6–22.4%) to 29.1% (28.1–30.1%) during this period, further emphasizing the need for coordinated healthcare delivery systems that can address these overlapping health challenges.

Fig. 3: Trends in multimorbidity (number and distribution) among diagnosed PLWH in BC (median), 2007–2034.

A, D Both Mental and Physical Comorbidities; B, E Physical Comorbidities, C, F Mental Comorbidities. PLWH people living with HIV, BC British Columbia.

By 2034, the prevalence of NCDs among PLWH is projected to rise substantially (Fig. 4 and Supplementary Table S22). CVD prevalence is expected to nearly double, increasing from 8.4% (7.8–9.0%) in 2019 to 14.8% (14.1–15.5%) in 2034, and cancers will see a similar rise, from 8.9% (8.3–9.4%) to 17.2% (16.5–18.0%). CLD will decline slightly, from 14.1% (13.4–14.8%) in 2019 to 13.3% (12.7–14.1%) in 2034. MANX will remain the most prevalent mental health condition, affecting approximately half of all PLWH by 2034. Both SCZ and PD increase moderately between 2019 and 2034.

Fig. 4: Projected prevalence of key physical and mental comorbidities among PLWH, 2007–2034.

A Hypertension; B Cardiovascular Disease; C Diabetes; D Chronic Kidney Disease; E Chronic Liver Disease; F Chronic Obstructive Pulmonary Disease; G Osteoarthritis; H Non-AIDS-related Cancers; I Mood and Anxiety Disorders; J Schizophrenia; K Personality Disorders. The point represents the median, and the error bars correspond to the 95% credible interval based on 1000 simulation runs. PLWH people living with HIV.

Age-specific comorbidity patterns

The burden of comorbidities (i.e., comorbidity prevalence) is disproportionately concentrated in older age groups among PLWH, with significant growth projected in the coming decades (Fig. 5, Supplementary Figs. S11 and S12, and Tables S23–S28). From 2019 to 2034, the number of individuals aged 60–69 with at least two comorbidities will increase from 546 (498–596) or 5.9% (5.4–6.5%) of the population to 721 (656–782), representing 7.9% (7.3–8.4%) of the population. Among those aged ≥70, an even sharper rise will be seen, from 275 (242–309) or 3.0% (2.6–3.4%) of the population to 1089 (1019–1164), representing 11.9% (11.2–12.7%) of the population. The dramatic rise in multimorbidity in the ≥70 group points to the cumulative impact of aging with HIV and long-term ART exposure. Conditions like CVD, cancers, and HTN are expected to dominate this age group. This aligns with the natural progression of aging but may also reflect accelerated aging processes in PLWH. In contrast, younger PLWH (<50 years) exhibit a relatively stable prevalence of comorbidities, likely reflecting early intervention with ART and lower cumulative risk exposures.

Fig. 5: Age-specific trends in physical multimorbidity among PLWH (median), 2007–2034.

A PLWH aged <30; B PLWH aged 30–39; C PLWH aged 40–49; D PLWH aged 50–59; E PLWH aged 60–69; F PLWH aged ≥70. PLWH people living with HIV.

Sex-specific comorbidity patterns

Sex-based differences in comorbidity prevalence are pronounced, reflecting distinct patterns in health outcomes among male and female PLWH (Fig. 6 and Supplementary Fig. S13). COPD emerges as a significant comorbidity for older females, with prevalence reaching 13.6% (12.1–15.3%) by 2034, compared to only 9.6% (8.9–10.3%) among males, which might reflect the vulnerability of female PLWH due to substance use. CVD prevalence increases markedly among males, becoming the third most prevalent physical comorbidity by 2034 (up from fifth in 2024). Among females, however, CVD ranks lower, highlighting the need for tailored prevention strategies, including sex-specific cardiovascular risk assessments and management. Among males, the shift from HTN to cancers as the most prevalent physical comorbidity might reflect the growing impact of age-related malignancies in aging PLWH. Mental health conditions, particularly MANX, are more prevalent among females compared to males (56.5% [54.0–58.7%] vs. 48.5% [47.3–49.6%] in 2034), reflecting sex-specific differences in psychosocial stressors, structural barriers and behavioral factors which affect the development of comorbidities. CLD remains the most prevalent physical comorbidity among females, potentially linked to a higher prevalence of hepatitis co-infection or other behavioral and clinical factors.

Fig. 6: Sex-specific patterns of comorbidity prevalence among PLWH in 2024 and 2034.

Ranking for both physical and mental comorbidities (A: All PLWH; B: Male; C: Female); Ranking for physical comorbidities (D: All PLWH; E: Male; F: Female). The bar represents the median, and the error bars correspond to the 95% credible interval based on 1000 simulation runs. PLWH people living with HIV, CVD cardiovascular disease, HTN hypertension, OA osteoarthritis, CKD chronic kidney disease, Cancers Non-AIDS-related cancers, COPD chronic obstructive pulmonary disease, CLD chronic liver disease, MANX mood and anxiety disorders, SCZ schizophrenia, PD personality disorders.

Clustering and co-occurrence of comorbidities

The increasing prevalence of comorbidities highlights the complexity of managing overlapping health conditions among PLWH (Fig. 7). By 2034, mental health conditions, particularly MANX, will frequently co-occur with physical diseases such as HTN, cancers, and CVD. The most common co-occurring comorbidity dyad (other than HIV) involve MANX and SCZ, MANX and cancers, and MANX and HTN. For example, the prevalence of PLWH with both MANX and cancers will grow from 6.0% (5.5–6.5%) in 2024 to 9.3% (8.8–10.0%) in 2034. Among females, besides SCZ, MANX frequently co-occurs with CLD and COPD, highlighting distinct disease clustering in this subgroup. Conversely, males are more likely to experience combinations involving MANX, cancers, HTN and CVD. Older age groups show a shift in the clustering of comorbidities (Fig. 8 and Supplementary Fig. S14). Among those aged ≥70, MANX and cancers become the dominant co-occurring comorbidity dyad, followed by MANX combined with HTN or CVD, whereas the dominant co-occurring comorbidity dyad for those aged <50 was MANX and SCZ. This shift reflects the cumulative impact of aging and long-term ART exposure.

Fig. 7: Prevalence of co-occurring comorbidity dyad patterns among diagnosed PLWH by sex in BC, in 2024 and 2034.

A All PLWH in 2024; B Male in 2024; C Female in 2024; D All PLWH in 2034; E Male in 2034; F Female in 2034. PLWH people living with HIV, BC British Columbia, CVD cardiovascular disease, HTN hypertension, OA osteoarthritis, CKD chronic kidney disease, Cancers Non-AIDS-related cancers, COPD chronic obstructive pulmonary disease, CLD chronic liver disease, MANX mood and anxiety disorders, SCZ schizophrenia, PD personality disorders.

Fig. 8: Prevalence of co-occurring comorbidity dyad patterns among diagnosed PLWH by age in BC, in 2024 and 2034.

A PLWH aged <50 in 2024; B PLWH aged 50–59 in 2024; C PLWH aged 60–69 in 2024; D PLWH aged ≥70 in 2024; E PLWH aged <50 in 2034; F PLWH aged 50–59 in 2034; G PLWH aged 60–69 in 2034; H PLWH aged ≥70 in 2034. PLWH people living with HIV, BC British Columbia, CVD cardiovascular disease, HTN hypertension, OA osteoarthritis, CKD chronic kidney disease, Cancers Non-AIDS-related cancers, COPD chronic obstructive pulmonary disease, CLD chronic liver disease, MANX mood and anxiety disorders, SCZ schizophrenia, PD personality disorders.

Discussion

This study provides an in-depth analysis of the evolving health challenges faced by PLWH in BC as the population ages. By 2034, nearly half of PLWH will be aged ≥60, and approximately a quarter will be ≥70. This demographic shift is accompanied by a substantial rise in multimorbidity, with 27.7% of PLWH projected to have at least three additional diagnoses, including mental and physical health conditions. CVD and cancers are expected to see the largest increases, while CLD will slightly decline. MANX will remain highly prevalent, affecting approximately half of PLWH. Notably, older females disproportionately experience multimorbidity patterns involving MANX, CLD, and COPD, while older males exhibit higher prevalence of cancers and CVD. Unlike CVD, the prevalence of diabetes is projected to be stable by 2034, even though diabetes is associated with accelerated cardiovascular risk. In our model, while the development of CVD and diabetes shared the same risk factors such as HTN and MANX, there are divergent effects of the follow-up period, which indicates that diabetes is diagnosed earlier since the study baseline than CVD. These findings underscore the interplay between aging, sex-specific vulnerabilities, chronic inflammation, behavioral risks (e.g., smoking, alcohol, and unregulated substance use), and long-term ART exposure. For example, drug-related substance use, which is more prevalent among female PLWH in the STOP/HIV/AIDS cohort, is associated with the development of COPD, but not cancers (Supplementary Information).

The observed sex differences highlight distinct health challenges faced by male and female PLWH. Among females, the co-occurrence of MANX with CLD or COPD underscores a complex interaction of psychosocial stress, biological factors (e.g., hormonal changes, immune dysregulation), structural factors (e.g., access to primary care and preventive medications), and behavioral risks such as smoking, alcohol and injected drug use30,31,32,33,34,35,36. Hepatitis co-infection and systemic inflammation likely exacerbate these conditions37,38. Interventions for females should prioritize access to primary care, harm reduction programs, smoking cessation, liver health management, and mental health support to address these overlapping burdens. In contrast, males experience a higher prevalence of cancers and CVD, likely driven by longer ART exposure, metabolic syndrome, and lifestyle factors39,40,41,42,43. Prevention and management efforts should focus on lung, colorectal and anal cancer screening, as well as cardiovascular risk assessments, including management of HTN, dyslipidemia and diabetes. Comprehensive primary care access should be available to all PLWH with focus on primary prevention and psychosocial support services.

The rising burden of multimorbidity in older PLWH (≥60 years) reflects the cumulative effects of aging, chronic inflammation (or “inflammaging”), and long-term ART use44. Long-term ART exposure was shown to be associated with hypertension and dyslipidemia45. In addition, specific regimens in PIs and nRTIs, such as lopinavir and tenofovir disoproxil fumarate, were linked to renal stone formation and decline of renal function46. Conditions such as CVD, cancers, and diabetes highlight the accelerated aging experienced by PLWH, fueled by immune activation and oxidative stress47,48. In younger PLWH (<50 years), the relatively stable prevalence of comorbidities demonstrates the benefits of early ART initiation, sustained viral suppression, and access to newer ART with less metabolic and renal toxicities49,50,51. However, preventative efforts to promote healthy behaviors and mitigate long-term risks remain essential for this group.

The frequent clustering of MANX with physical conditions such as HTN, cancers, and CVD underscores the bidirectional relationship between mental and physical health. Chronic stress, depression, and anxiety exacerbate systemic inflammation, accelerating the progression of physical conditions, while physical comorbidities worsen mental health outcomes by increasing psychosocial stress and reducing quality of life52,53,54. Integrated care models addressing both mental and physical health simultaneously are critical to breaking this cycle and improving outcomes.

These projected trends demand strategic adaptations in healthcare systems and public health initiatives including but not limited to the following areas. Integrated care models which coordinated HIV care with mental health, geriatrics, and chronic disease care is urgently needed. Multidisciplinary teams led by primary care providers, possibly including HIV specialists, psychiatrists, cardiologists, and oncologists, should manage the complex health needs of aging PLWH. In addition, harm reduction programs for people who use drugs, treatment of opioid use disorder, and psychosocial support should also be accessible through the integrated care. It is critical to implement proactive screening for cancers, CVD, and mental health conditions, especially for older males and females with distinct disease patterns. Behavioral interventions such as smoking cessation, reduce alcohol use, and encourage physical activity are essential to mitigate key risk factors. Tailored sex- and gender-sensitive strategies should address the unique vulnerabilities of males (e.g., cancer prevention) and females (e.g., mental health and liver disease) to effectively reduce health disparities.

This study employs a dynamic microsimulation model that integrates HIV disease progression, ART, and the development of age-related comorbidities, offering a sophisticated framework for forecasting health outcomes. The model’s ability to capture sex-specific and age-specific trends provides valuable insights into the heterogeneous health needs of aging PLWH. The BC-CfE’s DTP and STOP HIV/AIDS cohort provides large-scale, longitudinal data for all diagnosed PLWH in BC, which ensures a representative sample and enhances the validity and relevance of the findings. Robust sensitivity analyses further reinforce the reliability of the projections, making the model a powerful tool for policy planning and intervention evaluation (Supplementary Fig. S15).

This study advances the field by providing a systematic and nuanced perspective on multimorbidity among PLWH. While previous models have often concentrated on individual comorbidities or overlooked mental health aspects, our approach captures the dynamic interplay between mental and physical health over time15,16,17. Notably, we identify sex-specific patterns, such as the clustering of MANX with CLD and COPD in females, and the association of cancers with CVD in males. These insights are seldom addressed in existing literature17,18. Furthermore, our focus on co-occurring comorbidity dyads enhances understanding of the complex care needs within this population, distinguishing our study from prior research.

For instance, a systematic review highlighted that women with HIV are more likely to report depressive symptoms compared to men, yet few studies have explored the underlying reasons for this disparity55. Additionally, research has indicated that women with HIV may experience a greater constellation of biopsychosocial factors, contributing to higher syndemic counts compared to men56. Our study builds upon these findings by integrating both mental and physical health comorbidities and examining their co-occurrence patterns, providing a more holistic understanding of the health challenges faced by PLWH.

Moreover, while previous studies have documented the rising prevalence of NCDs in PLWH, they often lacked detailed sex-specific or age-specific analyses17,18. Our findings expand on this by identifying distinct patterns of disease clustering, such as the higher prevalence of MANX and CLD in females and the predominance of cancers in males. Additionally, the incorporation of mental health conditions as both independent and co-occurring outcomes offers a more in-depth understanding of the health challenges faced by PLWH.

Our results are consistent with existing studies, including PEARL, CEPAC, ATHENA, and NA-ACCORD, which have also documented the increasing burden of multimorbidity among aging PLWH17,18,57,58. Similar to the PEARL and ATHENA models, our model projects significant rises in age-related comorbidities, such as CVD and cancers, as the PLWH population ages. Like CEPAC, our model emphasizes the importance of integrated care strategies, such as proactive screening and multidisciplinary management, to address the growing complexity of health needs in PLWH. Additionally, the inclusion of mental health conditions (e.g., MANX) in our analysis aligns with the PEARL model, which highlights the persistent prevalence of depression and anxiety. However, unlike these models, our work uniquely incorporates sex-specific analyses, identifying distinct multimorbidity patterns in males and females, such as the clustering of MANX with CLD and COPD in females, and the higher prevalence of cancers and CVD in males.

Despite its strengths, our model has some limitations. First, even though ART adherence and healthcare access are determined by baseline characteristics and time-varying mental health status, we did not consider potential disruptions or advancements in HIV care. Second, while behavioral factors such as smoking, alcohol, and drug-related substance use are considered, they are based on the distributions among the general population and are not dynamically modeled, which may underestimate their impact on disease trajectories, especially among female PLWH who are disproportionally affected by substance use59. Third, the model does not incorporate potential effects of emerging therapies that could change ART composition in the population and alter long-term outcomes. For example, the number of patients on newer ART regimens, such as tenofovir alafenamide (TAF) and bictegravir (BIC), started to increase in 2020, which is beyond the calibration period of the study60. The impact of TAF on dyslipidemia and the impact of TAF and BIC on weight gain could potentially exacerbate the burden of comorbidities, such as diabetes and CLD, among PLWH61,62,63,64. On the other hand, More BIC use with better lipid profiles and more TAF use with fewer renal toxicities could decrease the risk of dyslipidemia, obesity and renal disease19,50,51. Fourth, our model concentrates on key mental and physical comorbidities among PLWH. The incidence of substance use disorder and non-fatal overdose is not considered in our model since we focused on aging-related NCDs. Our model considers drug-related substance use and alcohol consumption as important risk factors for comorbidities such as CLD (Supplementary Information). Even though drug toxicity deaths are not separated from all-cause mortality in the model, the impact of drug toxicity is measured indirectly from the elevated risk of mortality due to drug-related substance use, as the ongoing drug toxicity crisis disproportionally affects PLWH65. In Fifth, the comorbidities were identified by implementing case-finding algorithms on administrative health data, which may underestimate or overestimate the comorbidity prevalence. However, the case-finding algorithms were previously used and validated to ascertain the presence of the comorbidities22,66,67. In addition, administrative health data are not sufficient to determine disease severity, which may affect the probability of comorbidity development. However, studies have shown that residual risk may exist due to subclinical disease burden or early damage, even though existing comorbidities are well controlled68,69. Sixth, due to data unavailability or the rarity of the disease, some aging-related comorbidities could not be included in the model, such as dyslipidemia, osteoporosis, and dementia. We tried to capture the bone-related comorbidity using OA instead. Seventh, the model was calibrated and validated based on available data up to 2019. The calibrated parameters, such as the annual number of migrated cases, were kept constant for the projection period. We did not consider the interruption of the healthcare system due to the COVID-19 pandemic, the worsening drug toxicity crisis since 2020, or the dramatic rise in immigration to Canada after the pandemic, due to lack of data or uncertainties related to their long-term impacts70,71,72,73,74. Similarly, we did not consider changes that might happen in the near future to affect the number of new HIV infections or the PLWH population in BC, such as the adoption of long-acting post-exposure prophylaxis, or the policy instability related to the United States President’s Emergency Plan for AIDS Relief (PEPFAR) funding74,75. Finally, the findings are specific to BC and may not be generalizable to regions with differing demographic or healthcare contexts.

In summary, our study provides a more detailed and nuanced understanding of multimorbidity in PLWH, emphasizing the importance of considering both mental and physical health, as well as sex-specific and age-specific differences, in future research and healthcare planning. Despite its limitations, our model offers valuable projections that can inform targeted interventions and healthcare strategies for aging PLWH in BC.

Our next steps aim to enhance the utility and scope of our microsimulation model by incorporating additional behavioral data and testing targeted interventions to improve care delivery for PLWH. We have ongoing surveys that will capture detailed information on behaviors such as drug abuse, smoking, alcohol use, physical activity, and mental health service utilization. These data will allow us to dynamically model the impact of these behaviors on comorbidity trajectories, providing a more accurate and nuanced understanding of risk factors. Additionally, we plan to test interventions designed to optimize care delivery within primary care settings, such as integrated mental and physical health services, enhanced access to preventive screenings (e.g., cardiovascular risk assessments and cancer screening), and telehealth programs tailored to aging PLWH. For instance, interventions that provide coordinated geriatric, mental health and HIV care through multidisciplinary teams will be evaluated for their ability to reduce multimorbidity burden and improve health outcomes. These advancements will not only refine our model’s predictive capabilities but also guide the development of actionable strategies to address the evolving health needs of PLWH.

This study underscores the complex and growing health challenges faced by aging PLWH in BC, driven by substantial increases in multimorbidity and distinct sex- and age-related differences in comorbidity patterns. These findings highlight the urgent need for integrated care models that address the dual burden of mental and physical health, with a particular focus on tailored interventions for males and females. Proactive prevention, screening, and behavioral interventions are essential to mitigate the rising prevalence of CVD, cancers, and mental health conditions.

The microsimulation model developed in this study offers a valuable tool for forecasting health trajectories and evaluating interventions, providing critical guidance for policymakers and healthcare providers. While further refinements are needed to incorporate behavioral dynamics and future therapeutic advancements, these findings underscore the importance of preparing healthcare systems to meet the challenges of an aging PLWH population. Addressing these challenges with innovative, inclusive, and data-driven strategies is essential to ensure equitable and effective care for this heterogeneous and highly vulnerable group.

Data availability

The BC-CfE is prohibited from making individual-level data available publicly due to provisions in our service contracts, institutional policy, and ethical requirements. To facilitate research, we make such data available via data access requests. Some BC-CfE data is not available externally due to prohibitions in service contracts with our funders or data providers. Institutional policies stipulate that all external data requests require collaboration with a BC-CfE researcher. For more information, please contact Mark Helberg, Senior Director, Internal and External Relations and Strategic Development: (mhelberg@bccfe.ca). For the STOP HIVA/AIDS cohort, access to data provided by the Data Stewards is subject to approval but can be requested for research projects through the Data Stewards or their designated service providers. The following data sets were used in this study: the BC Centre for Excellence in HIV/AIDS Drug Treatment Program (DTP), the DTP Drug Treatment and Laboratory Database, the BC Centre for Disease Control Provincial HIV/AIDS Surveillance Database, Medical Service Plan (MSP) Payment Information File, Consolidation files, Discharge Abstract Database (Hospital Separations) Database, PharmaNet and BC Vital Statistics and Events: Deaths. This Data was provisioned under ISP 13-062.

Code availability

The code for the microsimulation model is available in a public GitHub repository at https://github.com/jielinzhu/microsimulation_hiv_comorbidity.

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Acknowledgements

All inferences, opinions, and conclusions drawn in this publication are those of the author(s), and do not reflect the opinions or policies of the Data Steward(s). V.D.L is funded by a grant from the Canadian Institutes of Health Research (PJT-148595). The sponsors had no role in the design, data collection, data analysis, data interpretation, or writing of the report.

Author information

Authors and Affiliations

  1. British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada

    Viviane D. Lima, Jielin Zhu, Tian Shen, Jason Trigg, Silvia Guillemi, Robert S. Hogg, Kate A. Salters & Julio S. G. Montaner

  2. Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

    Viviane D. Lima, Kate A. Salters & Julio S. G. Montaner

  3. Department of Family Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

    Silvia Guillemi

  4. Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada

    Robert S. Hogg

Contributions

V.D.L.: Conceptualization, Methodology, Data curation, Supervision, Writing—original draft, Writing—review and editing, Project administration, Resources, Funding acquisition. J.Z.: Data curation, Formal analysis, Validation, Visualization, Writing—original draft, Writing—review and editing. T.S.: Data curation, Formal analysis, Validation, Writing—review and editing. J.T.: Data curation, Writing—review and editing. S.G.: Writing—review and editing. R.S.H: Writing—review and editing. K.A.S.: Writing—review and editing. J.S.G.M.: Resources; Writing—review and editing.

Corresponding author

Correspondence to Viviane D. Lima.

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Competing interests

J.S.G.M. is the Executive Director and Physician-in-Chief of the BC Centre for Excellence in HIV/AIDS, a provincial program serving all BC health authorities and based at St. Paul’s Hospital-Providence Health Care. J.S.G.M.’s Treatment as Prevention (TasP) research, paid to his institution, has received support from the BC Ministry of Health, Health Canada, Canadian Institutes of Health Research, Public Health Agency of Canada, Canadian Foundation for AIDS Research, Genome Canada, Genome BC, Vancouver Coastal Health, and Vancouver Hospital Foundation. Institutional grants have been provided by Gilead Sciences Inc, Janssen, Merck Sharp & Dohme LLC, and ViiV Healthcare. Other authors have no conflict to declare.

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Lima, V.D., Zhu, J., Shen, T. et al. Forecasting multimorbidity in aging people with HIV using a microsimulation model. Commun. Health 1, 7 (2026). https://doi.org/10.1038/s44528-026-00004-7

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  • Received05 December 2025

  • Accepted11 May 2026

  • Published02 July 2026

  • Version of record02 July 2026

  • DOIhttps://doi.org/10.1038/s44528-026-00004-7


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