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. Author manuscript; available in PMC: 2025 Apr 29.
Published in final edited form as: ANS Adv Nurs Sci. 2024 Sep 5;48(2):166–176. doi: 10.1097/ANS.0000000000000526

Construction of a Theoretical Model of Chronic Disease Self-Management Self-Management Within a Syndemic

Julie Zuñiga 1, Whitney Thurman 2, Chelsi West Ohueri 3, Emma Cho 4, Praise Chineyemba 5, C Andrew Martin 6, William Christopher Mathews 7, Katerina Christopoulos 8, Thibaut Davy-Mendez 9, Alexandra A García 10
PMCID: PMC11880358  NIHMSID: NIHMS2052895  PMID: 39235280

Abstract

The purpose of this article is to describe a model of chronic disease self-management that incorporates the complexity of social and environmental interactions experienced by people who self-manage chronic conditions. This study combines quantitative data from a large national research cohort and qualitative interviews to test and refine a self-management model. The self-management within a syndemic model depicts the contextual, psychological, and social factors that predict self-management behaviors and clinical and long-term outcomes.

Keywords: diabetes, HIV, social determinants of health, syndemic model

Chronic disease self-management can be arduous for patients who must engage in extensive maintenance of healthy behaviors, monitor their symptoms, take medications, perform disease-specific treatments, and attend to and prevent physical and psychosocial consequences.1,2 Chronic disease self-management also requires significant cognitive work, including problem identification, consideration of alternatives, decision-making, and monitoring outcomes.1 Patients must partner with their health care providers and support systems to make and maintain their lifestyle changes, motivation, and health.2,3

An individual’s adherence to prescribed medications and a general treatment plan is regarded as successful self-management and is often used as a proxy for health status.4 Nonadherence or noncompliance to health care providers’ recommendations is often attributed to the individual’s lack of knowledge, resources, motivation, or competing demands and preferences.5,6 Interventions to improve patient self-management usually focus on the individual patient and sometimes a family member and aim to equip patients with skills to help them change their behavior. However, patient education and individual behavior change strategies strongly emphasize individual autonomy without addressing macrolevel structures, including social determinants of health, that significantly shape people’s lives and choices.

People with 2 or more co-occurring health conditions are especially challenged in their self-management because each chronic condition requires a different, sometimes competing set of behaviors.7,8 People with a cluster of chronic conditions often experience significantly worse health, decreased quality of life, and more lost workdays than do those without 2 or more chronic conditions,9 in part because self-management decisions and tasks increase in difficulty, burden, and complexity for people with multiple conditions. Furthermore, having multiple chronic conditions often means that patients face increased health care needs and, subsequently, higher medical costs that can result in significant financial burdens for patients, their caregivers, and the health care system.10

A cluster of conditions interacting with a harmful social environment exacerbating each condition is called a syndemic, meaning a synergistic epidemic where epidemic-level biological interactions and social inequities intersect.11 A syndemic is more than merely having comorbid conditions dependent upon each other and physiologically associated. The co-occurring conditions in syndemics include harmful social situations and more varied pathophysiologic complications.12 Approximately 42% of adults in the United States have been diagnosed with 2 chronic conditions. African Americans have an even higher prevalence of multiple chronic conditions than non-Hispanic Whites.13 Almost all people with diabetes have at least 1 additional diagnosis, with 88% having 2 additional diagnoses.14 People affected by adverse social determinants of health and chronic comorbid conditions are more likely to experience them as a syndemic. Despite the high prevalence of people with multiple chronic conditions, there is scant research about how people self-manage multiple health conditions in the context of their social, environmental, and economic environments. Thus, we needed to develop a new model to guide future research.

People living with the dual diagnoses of HIV and type 2 diabetes (diabetes) exemplify a population that must manage 2 highly complex health conditions.15 People with HIV are at a somewhat higher risk for diabetes than the general population due in part to being in a chronic inflammatory state even when their viral loads are undetectable.7,16 The HIV immune response can increase levels of pro-inflammatory biomarkers, which can lead to insulin resistance and the development or exacerbation of diabetes.17 Beyond the pathophysiologic responses, people with HIV may be at risk of developing diabetes due to detrimental social determinants of health and personal factors.18 People of color are overrepresented among those with both HIV and diabetes.19 Thus, people living with HIV and diabetes are an exemplar of managing chronic conditions within a syndemic.

Traditional self-management approaches are predicated on the notion that individuals have choice, independence, and competence, making these approaches well-aligned with the Western value of individual autonomy.20 The predominant self-management paradigm is to provide information to people with a chronic illness who are uncritically assumed to need instruction,20 often without regard for the influences of their complex environments on their decisions, resources, and priorities. However, traditional models of disease-specific self-management have been insufficient to predict outcomes for people with both HIV and diabetes8 because they do not adequately capture the synergistic and cumulative biological consequences of living with HIV and diabetes along with the social and environmental factors that impact self-management and health outcomes.11 The purpose of this article is to describe the development process and the resulting new theoretical model, the self-management within a syndemic (SMwS) model.

OVERVIEW OF MODEL DEVELOPMENT

The integrative approach to theory development includes checking assumptions, using multiple sources to explore the phenomena, and theorizing.21 We used this approach by checking the assumptions of an established model, then checking the assumptions using quantitative and qualitative data. We based our study on the HIV and diabetes self-management model (HDSM; see Figure 1), which we derived from the diabetes self-management model.22 Brown and her team22 developed the diabetes self-management model after a meta-analysis of interventions to improve diabetes-related outcomes. The diabetes self-management model posits that contextual (eg, demographic), psychological (eg, depression), and behavioral (eg, adherence) factors associated with diabetes self-management behaviors (eg, diet and physical activity) predict diabetes control (eg, hemoglobin A1C levels) and quality of life. Because we wanted to understand self-management for people with both diabetes and HIV, we adapted Brown et al’s diabetes-specific model. We assumed that the phenomenon of SMwS is defined by social and environmental factors. The HDSM incorporated the diabetes self-management model concepts and added concepts associated with HIV control (viral load and CD4 count).8,23,24 We examined quantitative data from a large national research cohort to compare people who had dual diagnoses of HIV and diabetes (n = 1777) with people with HIV alone (n = 9412) using the cross-sectional and longitudinal lag analysis. After the cross-sectional analyses, we collected qualitative data composed of ethnographic interviews and observations (ie, multiple sources) from a local sample of people with the dual diagnoses of HIV and diabetes (n = 22).23 Our quantitative analyses showed us that simply adding HIV-related variables was not sufficient to model the complexities of self-managing both HIV and diabetes, as evidenced by relationships that we hypothesized would be significant were not. When we compared and contrasted the qualitative findings to the quantitative findings, we identified context and variables to add to the model (ie, theorizing). Therefore, using the quantitative and qualitative findings as a foundation, we created the SMwS model.

Figure 1.

Figure 1.

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Self-management with a syndemic model.

When exploring social determinants of health, it is essential to recognize the role of the researchers and their positions in relation to the power of research participants. The research team is composed predominantly of people of color or from other marginalized groups from different levels of economic status (eg, faculty, students, and health care providers). To address possible power differentials between researchers and participants, who may be of lower education or income levels, participants were considered experts in their health and were consulted to validate findings.25 In addition, in research team meetings, inherent perceived power differences between researchers, students, and participants were acknowledged, discussed, and examined in relation to data interpretation and publication to encourage all team members to share their insights. As part of the research process, team members reflected on their privilege and biases before and during data collection and analysis. The research team drew upon their personal and clinical experiences to inform the design, interpretation, and conclusions presented in this article.

Quantitative analyses that guided development of the SMwS model

Development of the SMwS began with analyses of data from the Center for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS), which captures a broad range of information associated with HIV disease management, including diagnoses, medications, routine biomarkers, and self-reported variables from patients at 8 large clinic practices around the United States.26 Approximately every 4 to 6 months, outpatients at CNICS clinics complete an assessment of patient-reported outcomes before their routine clinical visits with health care providers. To match concepts to the HDSM, we chose the following variables from the CNICS dataset: depression, measured with the Patient Health Questionnaire (PHQ); anxiety, measured with the PHQ Anxiety screener; the Alcohol, Smoking, Substance Involvement Screening Test (ASSIST) to assess drugs and alcohol use; adherence, measured with the Adult AIDS Clinical Trials Group scale; hemoglobin A1C, a biomarker indicating level of diabetes control; CD4 level, a biomarker indicating HIV control; and health-related quality of life (HRQoL), measured by the EuroHRQoL. We conducted cross-sectional multiple regression (N = 11 189, n = 1777 with dual diagnosis of HIV and diabetes, n = 9412 with HIV alone) and longitudinal lag analysis (N = 5897) to test relationships and predictions proposed by the HDSM model.8,24 The cross-sectional analyses found 2 variables that predicted HRQoL, anxiety (B = −0.736, P < .001), and depression (B = −0.070, P < .001; R2 = 0.270, P = .001) for people with both HIV and diabetes.24 The regression model explained 27% of the variance, meaning additional variables were missing. The longitudinal lag analyses found that medication adherence, CD4, A1C, and depression did not significantly predict HRQoL.8 More details about the methods and findings of the cross-sectional and longitudinal lag analyses have been published.8,24 These findings led us to believe that additional variables influenced self-management and quality of life and were likely not captured in the CNICS dataset.

Qualitative analyses that guided development of the SMwS model

We conducted a focused ethnographic study to collect qualitative data from a local sample of people with HIV and diabetes (n = 22) to examine concepts or variables related to health and self-management of HIV and diabetes from the patients’ perspectives.23 Focused ethnography is a methodological framework for collecting specific data within a narrower scope of inquiry that is well-suited for investigating the patient’s perspective.24,27 Focused ethnography is an applied methodology well-suited to the clinical application of nursing research findings.28 We used open-ended interview questions and direct observation to learn what was necessary for self-management from the patients’ perspectives.

The qualitative sample was similar to the CNICS dataset sample in many demographic characteristics (eg, gender, age, marital status, and education). However, more participants in the qualitative sample identified as Hispanic, reflecting the local ethnic distribution.29 The interview questions were designed to explore participant experiences and perceptions of the concepts in the HDSM model; additional questions addressed promising concepts identified in a review of quantitative (including descriptive, correlational, and intervention research) and qualitative literature about self-management of diabetes and HIV. For example, we asked participants how they took their prescribed medications, made decisions, what food they ate, and about their relationships with health care providers. Probing questions were generated during the interviews to explore participants’ in-depth responses. All participants were interviewed 1 to 3 times and were observed as they engaged in self-management tasks, such as taking medications or grocery shopping. The number of interviews per person varied based on participants’ availability, research needs, and participants’ desire to take part in additional interviews. Data saturation was assessed during team meetings, and we recruited participants and conducted interviews until there was a consensus that saturation had been reached.30 We re-interviewed a subset of participants during the first 4 months of the coronavirus disease of 2019 (COVID-19) pandemic when quarantine and isolation restrictions were widely used to explore how the pandemic impacted self-management of their chronic conditions.31 All interviews were audio-recorded, professionally transcribed, and coded by 2 researchers using NVIVO software (released March 2020). We conducted thematic analysis. The codes were then synthesized, assessed for patterns, and compared to the HDSM model. More details about the methods and findings of the qualitative arm have been published.8,23,31

Synthesis of quantitative and qualitative arms

We examined the quantitative statistical analyses as informed by the qualitative thematic analysis to depict better self-management of the syndemic of 2 chronic conditions managed by people with adverse social determinants of health. We found that the synthesis of findings was richer than either analysis alone and strengthened the validity of our conclusions about the inadequacy of the HDSM model to predict the quality of life for people in the syndemic.32 The qualitative interviews occurred after the cross-sectional analyses and before the longitudinal quantitative analyses, with the order approximately being 1) cross-sectional data analysis, 2) qualitative data collection and simultaneous analysis, and 3) longitudinal gap analysis, with some overlap in the timing of the quantitative and qualitative analyses.31,33 We compared and contrasted the quantitative findings of the cross-sectional and longitudinal relationships for the whole sample and by subgroups for the concepts in the HDSM. We then used qualitative findings to provide context and identify concepts not included in the HDSM.

Based on the quantitative findings presented in detail elsewhere,8 the HDSM did not adequately describe the factors that affect self-management and outcomes for people with the dual diagnosis of HIV and diabetes who are largely affected by adverse social determinants of health. The overall model was not statistically significant; all variables failed to predict long- and short-term outcomes in lag analysis. Although the people in the 2 subsamples, people with HIV alone and people with the dual diagnosis of HIV and diabetes, were clinically similar, the variables in the HDSM were only significant predictors of outcomes for people with HIV alone. After synthesizing quantitative and qualitative findings, we identified variables missing from the quantitative that were salient in the qualitative interviews, including social isolation, social determinants of health, trauma, and discrimination. We added these variables to make the SMwS and posit that the SMwS will better predict self-management and outcomes in future studies.

RESULTS THAT INFORMED THE NEW SMwS MODEL

During the qualitative interviews, we learned that participants experienced high loneliness and social isolation levels. Loneliness is a subjective feeling, and social isolation is the objective state.34 For example, many participants had not disclosed their HIV diagnosis to friends and family. One participant asked us to whisper the word “HIV” during our interview for fear that her neighbors could hear it through the shared walls.23 She and other participants described feeling lonely because they did not have people they could trust close to them. Among the subset of participants interviewed during the COVID-19 quarantine, many stated that the early days of social distancing during the pandemic did not change their lives because they had already lived in relative social isolation. They did not see many people before the quarantine, so they did not feel their life was more isolated because of the COVID-19 restrictions.31

The qualitative data revealed discrimination that most of the qualitative participants were negatively impacted by social determinants of health, including housing, food access, emotional trauma, and racism/discrimination. Although many of the participants lived in permanent and supportive housing, some lived in less stable housing, such as halfway houses, or had experienced homelessness, which caused stress and detracted from their self-management activities. Most participants reported being reliant on food banks and other helpful but unstable nutritional assistance programs, which required frequent applications for renewal and inconvenient access to food.23 Only 2 participants were fully employed, with several underemployed or unemployed due to disabilities and other circumstances but desiring more work.

Most of the qualitative participants revealed they had experienced past and current trauma. Many reported being subjected to personal violence and significant losses that negatively impacted their ability to manage their health.23 Participants talked of their children being removed from their home by social services and remembering multiple loved ones (partners and family members) who had died. The lasting effects of complex grief made disease self-management challenging even if participants were motivated.23 Life trauma negatively impacts health outcomes and puts people at risk for worse physical and mental health over time.35

Participants who were people of color spoke more directly about racism when interviewed during the COVID-19 pandemic than they did in the prepandemic interviews. Their outspokenness coincided with the protests against police violence that occurred in the spring and summer of 2020.31 Although the descriptions of their experiences of racism and discrimination were generally subtle, they were pervasive.

These qualitative themes were so common that we added them to create the SMwS model (Figure 2), which we believe will better represent self-management for people experiencing chronic diseases within adverse social determinants of health. The SMwS depicts the contextual, psychological, and social factors that predict self-management behaviors and clinical and long-term outcomes for people with HIV and diabetes. Contextual factors are the same HDSM model variables, including age, gender, race/ethnicity, and diagnoses. The list of Psychological factors originally included stress, depression, and self-efficacy, to which we added substance use, social isolation, and loneliness. Social factors are a new category in the model, also resulting from our qualitative analyses, and are composed of social determinants of health, trauma, discrimination, and racism. Short-term outcomes remain the same as the HDSM and include self-management behaviors such as medication-taking, eating healthier foods, and engaging in physical activity, as well as clinical outcomes (eg, A1C and CD4). Long-term outcomes also remain the same as the HDSM and include quality of life and disease complications, such as stroke.

Figure 2.

Figure 2.

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A syndemic self-management model.

DISCUSSION

We posit that the combination of HIV and diabetes is a syndemic because both conditions are associated with social inequities, which are also epidemic.36 Self-management of a cluster of chronic conditions that are part of a syndemic is fundamentally different from managing one chronic condition for people not impacted by adverse social conditions. Thus, chronic disease SMwS can only be explained by models that include social context variables. In the syndemic of HIV, diabetes, and social inequalities, adverse social conditions such as poverty, violence, or discrimination affect people’s physiological response systems and behaviors and lead to a cluster of disease and self-management challenges.15,36 People with both HIV and diabetes are more likely to be people of color than the general population and are also more likely to experience social inequities.36,37 The syndemics perspective reveals that variables associated with self-management behaviors, such as adverse social determinants of health (ie, poverty, housing and food instability, transportation limitations, current or past substance use, complex grief, and racism), contribute to physiologic dysregulation.38

Social isolation and loneliness were identified as key missing variables because people with multiple chronic conditions are more likely to experience physical symptoms (eg, pain and fatigue), apathy, and stigma, as well as have restricted social networks—all of which contribute to social isolation and feelings of loneliness.39 Social isolation and loneliness are associated with depression, cardiovascular disease, cognitive decline, reduced physical activity, and stress.34,40 Thus, social isolation and loneliness are known to affect self-management, health, and quality of life negatively and are represented in the SMwS.

Social determinants of health are the physical and social environmental conditions that affect health risks and outcomes and refer to access to quality health care, education, housing, and food; discrimination, incarceration, workplace conditions, and economic stability.41 Social determinants of health also affect disease self-management.42,43 Many clinics routinely screen for social needs, such as housing, and refer patients to social services, which indicates the importance of attending to social needs.44 In particular, housing insecurity is highly associated with HIV diagnosis and HIV outcomes.45 However, researchers often use variables, such as income or level of education, as proxies for social need, which do not capture the breadth of social determinants and their effect on health. Measurements of housing or food insecurity are rarely collected in large datasets.37 There are few validated measures of social determinants, and most are used clinically by providers to identify patients who need referrals or case management services.46 A simple measure with a summative score could help researchers and clinicians to adequately quantify and analyze the effect of social determinants of health on self-management, health status, and quality of life. In order to better address barriers to care, clinicians should assess for patients’ social determinants of health and other variables included in the SMwS. Providers can use this information to link to care and referral.

Racism and discrimination negatively impact all health outcomes for people of color.47 Perceived racial and ethnic discrimination among people of color are associated with less frequent A1C testing, foot examinations, and blood pressure testing.48 Chronic condition self-management behaviors drop by half when people experience racial or ethnic discrimination.48 Black people commonly experience the most damaging health effects from discrimination, but other racialized, marginalized, and underrepresented groups experience detriments to their health from discrimination to a lesser extent. However, self-management studies rarely measure, report, or address discrimination or racism.22,49 Discrimination and bias should be measured in future research, and intervention should be created to address this at the policy and provider levels. Health care professionals should take measures to address discrimination and racism at the system level. This can be achieved through multilevel approaches with policy change, accountability, and training.50

The purpose of developing this model was to reflect the self-management experience of the population living in a syndemic. Without the social context, we posit that any model will not predict health outcomes sufficiently.

FUTURE RESEARCH AND THEORETICAL DEVELOPMENT

Further testing of the whole model is the required next step. In order to proceed, the newly added variables will need to be operationalized. Reliable and valid measures must be congruent with the cultural needs of the population. The relationships in the SMwS model should be tested with a sample of people living with the dual diagnoses of HIV and diabetes. A comparison with data from a sample of people with only one of the conditions could be used to validate the model’s ability to predict self-management outcomes in people experiencing a syndemic. Researchers should consider using the SMwS model with people experiencing other chronic conditions in different syndemics, such as people with asthma experiencing climate change, to guide variable selection and predict relationships.

CONCLUSION

Traditional self-management theoretical models designed for people with a single chronic condition still need to address the complexity of factors that impact people with multimorbidities. Using an integrated approach to theory development, we identified the syndemic perspective as a key component, examined qualitative and quantitative data, and theorized how these assumptions and empirical evidence guide our understanding of self-management of multiple chronic conditions. We identified variables that may be key to self-management and thus lead to improved health outcomes for people with the dual diagnosis of HIV and diabetes, and other multimorbidities. Specifically, the impacts of social determinants of health, racism, and discrimination, and complex grief are often overlooked in clinical care and research, yet significantly affect the patients’ ability to engage in healthy self-management behaviors and other health outcomes. Furthermore, self-management expectations of each chronic disease add complexity to people’s daily routines. Health care professionals must address these challenges of syndemics to improve self-management behaviors, clinical outcomes, and quality of life. The development of the SMwS model filled a gap in theoretical approaches to self-management.

Statements of Significance.

What is known or assumed to be true about this topic?

Chronic disease self-management can be complicated and difficult for patients, especially those with multiple chronic conditions.

Social determinants of health are the conditions in which people live that affect health outcomes. They include domains such as housing, education, economics, and environment.

What this article adds:

This article proposes a new model of self-management that is built upon the lens of social determinants of health. The model includes personal factors, such as depression and adherence, as well as macrolevel social factors such as housing, food, trauma, and discrimination. These social factors are not usually assessed in self-management research, yet are highly predictive of health outcomes.

Acknowledgments

The research was funded by the National Institute of Health (NIH), National Institute of Nursing Research (R15NR017579; PI Zuñiga). The CFAR Network of Integrated Clinical Systems (CNICS) is an NIH-funded program (R24 AI067039) made possible by the National Institute of Allergy and Infectious Diseases (NIAID). The CFAR sites involved in the CNICS include University of Alabama at Birmingham (P30 AI027767), University of Washington (P30 AI027757), University of California San Diego (P30 AI036214), University of California San Francisco (P30 AI027763), Case Western Reserve University (P30 AI036219), Johns Hopkins University (P30 AI094189, U01 DA036935), Fenway Health/Harvard (P30 AI060354), and University of North Carolina Chapel Hill (P30 AI50410).

Footnotes

The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.

Contributor Information

Julie Zuñiga, The University of Texas at Austin, Austin.

Whitney Thurman, The University of Texas at Austin, Austin.

Chelsi West Ohueri, The University of Texas at Austin, Austin.

Emma Cho, The University of Texas at Austin, Austin.

Praise Chineyemba, The University of Texas at Austin, Austin.

C. Andrew Martin, Regis College, Weston, Massachusetts.

William Christopher Mathews, University of California San Diego, San Diego.

Katerina Christopoulos, University of San Francisco, San Francisco, California.

Thibaut Davy-Mendez, University of North Carolina, Chapel Hill.

Alexandra A. García, The University of Texas at Austin, Austin.

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