Demographic Factors
The study's findings provide a nuanced perspective on how demographic factors influence the prevalence of chronic diseases in older adults. Notably, age emerges as a significant determinant[9], with individuals in the 80–85 age group displaying the highest prevalence of multiple chronic conditions. This highlights the increasing demand for healthcare services and support for this aging population.
Furthermore, gender plays a vital role[21], with females consistently showing a lower risk of chronic diseases, even after adjusting for confounding variables. This observation aligns with existing literature suggesting potential gender-specific differences in health behaviors and susceptibility to specific chronic conditions.
Occupational status also exhibits a critical influence, as unemployed individuals report fewer chronic diseases, possibly due to various factors such as lifestyle differences or healthcare accessibility. In contrast, individuals classified as workers have the highest prevalence of multiple chronic conditions, possibly reflecting the physical and mental stress associated with certain types of labor.
Urban residents demonstrate a higher rate of chronic diseases compared to their rural counterparts, which could be attributed to lifestyle disparities[9], environmental factors, or differing access to healthcare facilities.
Marital status is another influential factor, with widowed individuals exhibiting a higher prevalence of multiple chronic conditions. This could be linked to the psychological and social impacts of losing a spouse, emphasizing the need for targeted support within this demographic.
Education level exhibits a complex relationship with chronic disease prevalence. While illiteracy is associated with fewer chronic diseases[22], higher education correlates with a higher rate of multiple conditions. This complexity may reflect varying lifestyle choices or occupational stresses associated with different education levels. Further research, as indicated in Table 3, highlights the significance of education[23], with participants having primary or junior middle education displaying a lower risk compared to illiterate individuals. These findings underscore the importance of education as a potential protective factor against chronic diseases. Education can empower individuals with health knowledge and promote healthier lifestyle choices, thereby reducing the risk of disease. Promoting educational opportunities, especially in underserved communities, can positively impact public health.
Income sources[24], particularly reliance on salary and pension, are linked to higher rates of chronic diseases, possibly due to the stress associated with financial responsibilities or the nature of past occupations, as reflected in Tables 1 and 3.Living with a partner is associated with a higher incidence of chronic diseases[25], which might be related to shared lifestyle and environmental factors[26].
Social security status, particularly among Urban Residents, shows a higher prevalence of multiple chronic diseases. This could indicate differences in the type of healthcare accessed or the health profiles of individuals in different insurance categories[27], as shown in Table 3. The social security situation significantly contributes to chronic disease risk, with "New Rural" and "Urban Resident" participants exhibiting substantially higher risks compared to "Urban Employee" individuals. This suggests that disparities in healthcare access and socioeconomic conditions in different social security categories may contribute to varying disease risks. Addressing these disparities through targeted interventions and policy measures is crucial for mitigating the burden of chronic diseases.
The relationship between various diseases and BI scores.
The findings from the data set provide insightful observations into the relationship between various diseases and biometric scores in Table 2. Diseases such as hypertension[28], diabetes, and cerebral infarction[15] have shown a significant impact on BI scores. This could be indicative of the physiological changes these diseases bring about, which are effectively captured by biometric measurements. The notable significance in conditions like stroke,[29] lung disease[30], and eye conditions[31] further underscores the potential of BI scores as reliable indicators of these specific health issues.
On the other hand, diseases such as heart disease, high cholesterol, and stomach disease did not demonstrate a significant association with BI score changes. This could suggest that the BI scores used in the study may not be sensitive or specific enough to detect changes brought about by these conditions. Alternatively, it could indicate that these diseases do not significantly alter the BI measures evaluated.
Disease risk and self-care and dependence levels
Across various diseases such as hypertension, diabetes, heart disease, and mobility issues, a recurring trend emerges. Individuals who exhibit self-sufficient or mildly dependent tend to face a heightened risk of these diseases when compared to their counterparts with moderately dependent. Conversely, those with severely dependent often experience an even higher risk of these diseases.
When comparing disease risks between individuals with self-sufficient or mildly dependent and those with moderately dependent, it becomes evident that those in the moderately dependent category show significantly elevated risks[32] for heart diseases, hypertension, and diabetes. In contrast, individuals with severely dependent surprisingly exhibit significantly reduced risks for these diseases. However, when it comes to Mobility Issues, both moderate and severely dependent lead to a substantial increase in disease risk compared to those with mild dependence. These findings underscore the intricate and multifaceted relationship between disease risk and the level of dependence among elderly individuals.
This study's regional focus, while providing detailed insights into the elderly population in a specific area of China, limits the broader applicability of the findings. Future research should incorporate a more diverse geographical scope to enhance misrepresentations. The utilization of established health assessment scales requires further validation in the unique context of the pandemic, especially considering the varying sensitivities to pandemic-related stressors among the elderly. Focusing predominantly on individuals aged 80 and above, the study may not fully represent the diverse experiences of the entire elderly demographic. Additionally, the long-term health impacts of COVID-19, in the context of China's stringent response measures, remain uncertain, necessitating ongoing research.
On the strength side, the study's in-depth regional approach provides valuable data for understanding the immediate pandemic impacts on the elderly. The integration of both psychological and physical health assessments offers a comprehensive view of the multifaceted effects of the pandemic. The methodological rigor, marked by the professional training of the survey team and adherence to established standards, ensures the reliability and validity of the findings.