A Population Health Model in the Context of Emerging Biological Threats:The Case of COVID-19

Abstract

BACKGROUND: The consequences of emerging biological threats, such as COVID-19, for population life and health remain insufficiently studied. A particularly pressing scientific challenge is the development of an adaptive population health model. By integrating the lessons from the COVID-19 pandemic, such a model would enable a systematic reduction of risks from similar threats in the future.

AIM: This study aims to develop and validate a health model by identifying key biopsychosocial factors affecting physical and mental health in the post-COVID period.

METHODS: For the collection of empirical data (N=337), we employed E. Diener's Satisfaction with Life Scale (SWLS) and an original questionnaire. The latter included subjective assessments of physical and mental health in the post-COVID period, levels of physical and communicative activity, perceptions of the pandemic's consequences for various life domains, as well as demographic characteristics and chronic disease status.

RESULTS: The results indicate that all three types of factors—namely biological, social, and psychological – influence subjective status of both physical and mental health, with psychological factors having the strongest impact. The study highlights the significant contribution of psychological variables to the assessment of individuals' health status in the post-COVID period. Specifically, emotional stability and communicative activity were the most significant variables for maintaining both physical and mental health during this time.

CONCLUSION: The model developed and validated in this study enhances the framework for understanding post-COVID health by integrating biological, social, and psychological factors. Its findings offer a foundation for revising clinical protocols and formulating targeted rehabilitation strategies.

Full Text

BACKGROUND

The global community, including Russia, has been profoundly impacted by the coronavirus (SARS-CoV-2) pandemic and its aftermath. This phenomenon has exerted a substantial influence on population-wide psychological well-being and physical health; however, systematic research into the predictors of health status in the post-COVID period remains limited. The present study seeks to address this gap by identifying key biopsychosocial factors associated with physical and mental health outcomes.

In the context of emerging biological threats such as COVID-19, the development of a robust health model has become a research priority. This is driven by the pervasive risks and long-term sequelae of the pandemic. Even after the lifting of quarantine restrictions, societies worldwide are confronted with the challenge of adapting to new, post-traumatic realities that have precipitated profound changes across all life domains, particularly health. Documented consequences encompass impairments in physical and mental health [1-3], the emotional sphere [4], cognitive functioning [5], social interactions [6], and personal life [7]. Given this multifaceted impact, the development of novel, comprehensive strategies for safeguarding population health is imperative.

The complex dynamics, structure, and manifestations of post-COVID syndrome necessitate a systemic perspective on health. Health is a multifactorial construct, the components of which are often studied in isolation. The biopsychosocial model, first proposed by Engel [8], provides a framework to overcome this fragmentation. It conceptualizes health as an interplay of biological, psychological, and social factors, and it serves as the conceptual foundation for the model developed in this study.

AIM

This study aims to develop and validate a biopsychosocial model of health by identifying significant factors affecting physical and mental well-being in the post-COVID period.

METHODS

STUDY DESIGN

This study employed a cross-sectional, correlational design to develop a regression model. The model treated biological, psychological, and social indicators as independent variables, with assessments of mental and physical health serving as the dependent variables. Separate regression analyses were conducted to evaluate the contribution of each factor group (biological, psychological, and social) to the respondents' physical and mental health in the post-COVID period. This approach, resulting in six distinct models, allowed for a direct comparison of the explanatory power of each domain for both health outcomes.CONDITIONS OF THE STUDY

Participants were selected through random voluntary sampling. Data were collected online via Google Forms between April and December 2024. The sample comprised 337 participants (102 men, 235 women) aged 18 to 60 years (M = 32.1, Ϭ = 11.7). All participants were urban residents of the Russian Federation, representing both large cities (with populations exceeding one million) and smaller towns.

ELIGIBILITY CRITERIA

Inclusion criteria were: age 18 – 60 years, internet access, and no prior psychodiagnostic diagnosis of mental pathology.

Exclusion criteria were: minors or individuals over 60 years of age – to minimize the potentially heightened influence of biological factors and reduced impact of social factors on health in these age groups and a lack of internet access.

Group Allocation. No group allocation was performed, as the primary method of analysis was the construction of regression models for the entire sample.

DIAGNOSTIC METHODS UNDER INVESTIGATION

Data were collected using two primary instruments: an original, author-developed questionnaire and the Satisfaction with Life Scale (SWLS) by Diener.

The author-developed questionnaire was designed to capture key variables of interest and consisted of three thematic sections.

The first section collected data on the dependent variables: subjective assessments of physical and mental health during the post-COVID period. Respondents rated their physical and mental health on a single-item scale from 0 to 10.

The second section gathered data on health predictors and covariates, including demographic variables (gender, age, marital status, parenthood, place of residence, education level, employment status, and income level), objective health factors (personal history of COVID-19 and the presence of chronic diseases), behavioral variables (self-reported levels of physical and communicative activity).

The third section incorporated an author-developed scale assessing perceptions of the pandemic's impact on various life domains, which demonstrated good internal consistency (α = .835).

Psychometric tests of the questionnaire showed good structural correspondence. All the statements were included in one factor (KMO=.859; Chi-square =1666.617 p=.000), explaining 60.95% of the total dispersion. The confirmatory factor analysis was used to verify the factorial structure of the scale, with one latent factor for all variables. The model was tested with the AMOS SPSS module and demonstrated satisfactory compliance with the original data: CMIN = 17.521; df = 6; p = 0.008; GFI = 0.993; CFI = 0.991; RMSEA = 0.056; Pclose = 0.329. Besides, the questionnaire showed an acceptable level of reliability: Cronbach’s Alpha = 0.748, with none of the items being removed significantly improving the reliability of the questionnaire.

Furthermore, subjective well-being was measured using Diener's Satisfaction with Life Scale (SWLS) [9]. The Russian version of the scale, adapted and validated by Leontiev and Osin (2008), was used in this study [10]. The SWLS is a short, validated screening instrument designed to assess an individual's global judgment of life satisfaction. Respondents indicate their agreement with five statements on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree).

STATISTICAL PROCEDURES

Planned Sample Size

The sample size of 337 was deemed sufficient for the regression analysis, as it exceeded the minimum requirement of 300 respondents, which was calculated based on the approximately 30 variables under consideration.

Statistical methods

Prior to conducting the regression analysis, a comprehensive diagnostic check was performed to ensure the data met the key assumptions. The analysis confirmed that the assumptions of linearity, independence of predictors (absence of multicollinearity), normality of residuals, and homoscedasticity were satisfactorily met [11].

All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 24.0.

Results

Predictors in the Theoretical Health Model

Biological Factors

Biological factors represent a category of endogenous variables, encompassing primary individual characteristics (e.g., sex, age) and objective medical indicators related to an individual's clinical status and comorbidities. These factors determine the organism's general resistance to biological threats, including coronaviruses.

Empirical evidence consistently identifies age as the most significant risk factor for severe COVID-19 and its adverse physical and mental health sequelae [12, 13]. Individuals over 65 years of age constitute a high-risk group for mortality from this virus [12]. This is attributed to immunosenescence – the age-related decline in immune function—which heightens vulnerability to severe COVID-19 outcomes [14].

COVID-19 severity and mortality also vary by sex. Studies have empirically demonstrated that men exhibit higher mortality rates and are more susceptible to severe consequences of COVID-19 than women [15], though this association is often moderated by age and comorbidities [16]. The mechanisms underlying this sex-based disparity are not yet fully elucidated. Some explanations posit a combination of chromosomal differences, reproductive biology, and sex steroid hormones [17], while others emphasize behavioral patterns influenced by sociocultural roles [16, 18].

Preexisting health conditions are another critical determinant. Common comorbidities associated with an increased risk of severe COVID-19 include hypertension, diabetes, coronary heart disease, and chronic obstructive pulmonary disease [15]. Furthermore, lifestyle factors, such as the presence of harmful habits, physical activity levels, and diet, are also significant predictors [19, 20].

Social Factors

Social factors are classified as exogenous variables, external to the individual, that influence the health of those affected by COVID-19. These include marital status, education, income level, and place of residence. Such variables shape health-related behaviors, including the level of health consciousness and the propensity to adopt healthy practices [21].

The coronavirus pandemic has profoundly impacted daily social and personal life. Research has revealed negative consequences for families, where disruptions in services and routines have contributed to domestic stress and a deterioration in the mental health of both parents and children—a situation often exacerbated by caring for a family member with a disability [22].

Conversely, the pandemic has also fostered zones of positive change. Some studies report that it has brought families closer together, fostering better mutual understanding and a slower pace of life, which has, in turn, improved psychological well-being [22]. Cohabiting with family is associated with reduced social isolation and lower levels of loneliness during the pandemic [23]. Furthermore, familial support has been shown to mitigate the adverse psychological and physiological consequences of the pandemic while promoting positive affect [24].

Education and Economic Factors

A substantial body of research examines the impact of education on health during and after the pandemic. The evidence indicates that education exerts a dual influence on COVID-19-related risks. On one hand, higher educational attainment, which is associated with greater awareness of health risks and preventive measures, fosters social solidarity and the adoption of positive health behaviors, such as hand hygiene, social distancing, and mask-wearing [25]. On the other hand, higher education has also been identified as a factor associated with vaccine hesitancy [26]. It can be hypothesized that this indecision, despite compliance with other sanitary norms, may contribute to a wider prevalence of post-COVID syndrome by forgoing one of the most effective protective measures.

Economic stability, encompassing employment type, income level, and living conditions, is another critical determinant. Financial stress during the COVID-19 pandemic has been linked to a marked deterioration in social and emotional well-being [27]. Income level directly affects living conditions, which in turn influence comfort during isolation, access to resources for health maintenance, and the ability to secure vaccinations, additional medical services, rehabilitation programs, and medications. Studies confirm that identification with a social minority, homelessness, housing insecurity, lower income, and residence in areas with poor air quality were all correlated with higher COVID-19 morbidity and mortality [28].

Psychological Factors

Psychological factors encompass emotional and cognitive appraisals of events, as well as stable personal characteristics. During the pandemic, fear of the disease, lifestyle changes, and severe restrictions acted as chronic stressors, adversely affecting mood, mental health, and overall well-being. The capacity for emotion regulation and cognitive reappraisal has proven critical for health outcomes. For instance, a study of a Chinese sample using stepwise linear regression identified the ability to estimate one's own emotions as a key predictor of psychological disorders [29]. Maladaptive emotion regulation strategies, such as rumination, catastrophizing, intolerance of uncertainty, and excessive worry, were risk factors for depressive mood, anxiety, loneliness, and sleep disturbances. Conversely, adaptive strategies like positive reappraisal served as protective factors against the negative consequences of a prolonged crisis [24]. Consequently, individuals adept at positive cognitive reappraisal were more likely to maintain better health during and after the pandemic.

Subjective well-being—a construct reflecting a positive emotional and cognitive evaluation of one's life and experiences [9, p.34] – is a significant psychological resource. Research has demonstrated that subjective well-being and the ability to perceive positive aspects amid the pandemic played a key role in responses to this threatening event [30]. Identifying manageable and positive elements within the crisis can function as a psychological buffer, reducing vulnerability to the biological threat (Ibid.).

Alongside cognitive reappraisal and well-being, personality traits, particularly sociability, have been identified as important correlates of COVID-19 susceptibility [31]. The social distancing mandates of the pandemic era posed a specific challenge for highly sociable individuals, who reported experiencing a pronounced lack of social contacts, severe loneliness, psychological stress, and depression during periods of isolation [32]. Furthermore, a direct association was established between higher sociability and an increased risk of contact infection with COVID-19 [24, 31].

Synthesis of the Theoretical Model

In summary, the theoretical health model integrates predictors from three domains:

Biological: sex, age, chronic diseases, severity of COVID-19 illness, and physical activity level.

Psychological: perception of the pandemic's impact, sociability, and life satisfaction.

Social: marital status, parenthood, place of residence, education level, employment status, and income.

Verification of the Theoretical Health Model

Based on the collected data, biopsychosocial models were constructed to assess the impact of various predictors on individuals' physical and mental health.

The results of the regression analysis indicate that psychological factors were the most significant contributors to the variance in both physical health (R2 = .238) and mental health (R2 = .250). The findings for each model are detailed below.

Impact on Physical Health
The regression model for physical health was statistically significant (F=49.952 p=.000). As shown in Table 1, higher levels of physical health were significantly predicted by: lower perceived negative impact of the COVID-19 pandemic on one's emotional state (i.e., greater emotional stability) (β = -.386), higher satisfaction with life (β = .382), higher degree of sociability (β = .353).

Among biological factors, the presence of chronic diseases (β = -.224), physical activity level (β = .243), and age (β = -.147) were significant predictors of physical health ratings. The overall regression model was statistically significant (F = 34.347, p < .001), as detailed in Table 2.

The regression model for social factors was statistically significant in predicting physical health in the post-COVID period (F = 15.632, p < .001, R² = .115). As shown in Table 3, higher physical health was significantly associated with: lower perceived negative impact of the pandemic on one's social life (β = -.190), lower perceived negative impact on one's professional life (β = -.122), lower level of educational attainment (β = -.133), higher satisfaction with one's income (β = .133).

In summary, individuals who reported better physical health were those who felt less disruption from the pandemic in their social and professional spheres, had a lower formal education level, and were more content with their financial situation.

The regression model for social factors was statistically significant in predicting physical health in the post-COVID period (F = 15.632, p < .001, R2 = .115). As shown in Table 3, higher physical health was significantly associated with: lower perceived negative impact of the pandemic on one's social life (β = -.190), lower perceived negative impact on one's professional life (β = -.122), lower level of educational attainment (β = -.133), higher satisfaction with one's income (β = .133).

In summary, individuals who reported better physical health were those who felt less disruption from the pandemic in their social and professional spheres, had a lower formal education level, and were more content with their financial situation.

The final model for psychological predictors of mental health was statistically significant (F=53.311 p=.000; R2=0.250). As detailed in Table 4, the significant predictors were: lower perceived negative impact of the COVID-19 pandemic on one's emotional state, higher level of sociability, greater satisfaction with life.

This indicates that individuals who were less emotionally affected by the pandemic, more sociable, and more satisfied with their lives reported better mental health. It can be concluded that emotional resilience, a propensity for social engagement, and overall life satisfaction are pivotal factors that contribute to both improved physical and mental well-being in the post-COVID period.

The analysis of biological factors affecting mental health (Table 5) revealed a model with fewer significant predictors and a substantially lower total explanatory power (F = 14.781, p < .001, R2 = .058) compared to the model for physical health. Age was not a significant predictor in this model. The absence of chronic diseases and a higher level of physical activity were found to positively, though modestly, influence the assessment of mental health.

The analysis of social determinants of mental health (Table 6) identified a slightly narrower set of predictors compared to the model for physical health, with educational level being excluded. The overall model was statistically significant (F = 19.213, p < .001, R2 = .107), indicating that individuals who perceived a lower negative impact of the COVID-19 pandemic on their social and personal lives –suggesting an ability to maintain stable social connections and family relationships – as well as those reporting higher satisfaction with their material status, tended to have better mental health outcomes.

The final comprehensive model, incorporating all variable types, was highly significant in predicting mental health in the post-COVID period (F = 34.723, *p* < .001, R2 = .271). The most salient predictors, in descending order of influence, were: lower perceived impact of the pandemic on emotional state (β = -.341), higher level of sociability (β = .202), greater life satisfaction (β = .148), higher income satisfaction (β = .089), absence of chronic diseases (β = -.088).

Thus, the integrated model confirms that psychological factors are the most potent predictors of mental health, with significant contributions from social and biological domains.

DISCUSSION

SUMMARY OF THE MAIN FINDING

The results demonstrate that biological, social, and psychological factors all impact subjective physical and mental health, with the psychological component being the most significant. In the post-COVID period, psychological variables—particularly emotional stability and communicative activity—are the key contributors to an individual's assessment of their health status.

DISCUSSION OF THE MAIN FINDING

This study highlights the substantial contribution of psychological variables to the assessment of both physical and mental health in the post-COVID period. Specifically, emotional stability and communicative activity emerged as the most critical factors for maintaining health. These findings align with a growing body of research investigating the role of emotions and emotion regulation strategies in preserving mental health during the pandemic [24, 29].

While previous research has identified sociability as a potential risk factor during the pandemic—linking it to increased loneliness, psychological stress, and depression during social isolation [32] and higher susceptibility to COVID-19 infection [24, 31]—our study reveals a contrasting protective effect. The present findings demonstrate that the ability to maintain social relationships, even at a distance, contributes to less severe disease perception and reduced perceived impact of the illness.

Life satisfaction was found to contribute to both mental and physical health maintenance. This conclusion extends Diener's (1998) concepts of subjective well-being, confirming that a positive emotional-cognitive evaluation of one's life and the presence of diverse positive experiences increase the likelihood of better health outcomes.

The study further demonstrates that an individual's perception of the COVID-19 pandemic and its impact on various life domains significantly determines both physical and mental health status. This crucial factor must be incorporated into future planning for protection against new biological threats.

STUDY LIMITATIONS

The generalizability of these findings is constrained by the sample composition, which exclusively represents one country, despite including urban and rural residents. Although gender-balanced, the sample predominantly comprises middle-aged participants who may demonstrate different vulnerability patterns to COVID-19. Additionally, the study did not account for participants' professional backgrounds and employment types, which might influence pandemic perception.

The exclusive reliance on self-report methodologies presents inherent limitations regarding data objectivity. Future investigations would benefit from incorporating objective measures, including clinical diagnoses and medical records.

CONCLUSION

This investigation of post-COVID health predictors enhances understanding of the mechanisms through which COVID-19 affects population quality of life. The findings support the development of evidence-based clinical guidelines and optimization of service delivery by healthcare professionals for SARS-CoV-2 survivors. The proposed health model facilitates forecasting global health trends, though subsequent multinational studies are necessary to identify cultural specificities and validate the model's cross-cultural applicability.

SUPPLEMENTARY INFORMATION

AUTHOR CONTRIBUTIONS

Danilova A.A.: Literature search and analysis, manuscript preparation and writing, manuscript revision and editing

Kuba E.A.: Data collection, literature search and analysis, manuscript revision and editing

Zabelina E.V.: Study conceptualization, data curation, interpretation of empirical results, translation, manuscript revision and editing

Trushina I.A.: Data collection, literature search and analysis, manuscript revision and editing

All authors have approved the final version of the manuscript for publication and agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

ACKNOWLEDGMENTS
Not applicable.

ETHICS APPROVAL

Not applicable.

CONSENT FOR PUBLICATION

Not applicable.

FUNDING

The study was carried out with the financial support of the Russian Science Foundation within the framework of scientific project No. 24-28-20200 https://rscf.ru/project/24-28-20200/%20.

ETHICAL REVIEW

The study was approved by the Local Ethics Committee of the Institute of Education and Practical Psychology at Chelyabinsk State University (Protocol No. 3, dated January 14, 2024). All study participants voluntarily signed the informed consent form approved by the Ethics Committee as part of the study protocol.

COMPETING INTERESTS

The authors declare that they have no competing interests, relationships, or activities with third parties (individuals or organizations) over the past 36 months that could be influenced by the content of this article.

ORIGINALITY STATEMENT

The authors confirm that no previously published materials were used in the creation of this work.

DATA AVAILABILITY

The authors provide full open (unrestricted) access to data stored on an external repository.

GENERATIVE ARTIFICIAL INTELLIGENCE

Generative AI (DeepSeek) was used during manuscript preparation for auxiliary purposes (improving text style and readability, formatting references).

PEER REVIEW

This article is submitted to the journal editorial office on the authors' own initiative.

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