The global shift toward an ageing society is accelerating, with the proportion of individuals aged 60 and above expected to reach 22% by 2050. According to the World Health Organization (WHO), healthy ageing is contingent upon preserving functional capacity, a process deeply influenced by an individual’s surrounding social and physical environment. ^[1,2]

Population ageing is becoming a major public health challenge, particularly in low and middle income countries where the growth of the elderly population is occurring alongside an increasing burden of chronic diseases. This demographic transition poses unique challenges in India, where the WHO identifies an urgent requirement for integrated, community centered health systems because older adults commonly live with multiple longterm conditions that require continuous care, regular followup, and family support. ^[3-5]

Morbidity in older adults is not limited to a single disease; it often includes a broad mix of non-communicable, sensory, musculoskeletal, and respiratory conditions that affect day to day functioning and health care needs. Understanding the morbidity profile of the geriatric population is therefore essential for planning preventive, promotive, and curative services tailored to this age group. ^[4,6]

The burden of morbidity may also differ substantially between urban and rural settings. Rural older adults often face barriers related to health care access, delayed diagnosis, limited specialist services, and lifelong exposure to occupational and environmental risks. Urban older adults may have better access to diagnostic and treatment facilities, but they are also exposed to different lifestyle and epidemiological patterns. Comparing these two settings is necessary to identify inequalities in disease burden and to guide area specific geriatric health interventions. ^[3,7,8]

Evidence from Indian studies shows that chronic morbidity is common among the elderly in both community and facility settings, with musculoskeletal problems, visual impairment, hypertension, and diabetes frequently reported. Studies from urban slums, rural communities, and comparative rural urban settings have consistently documented a high burden of illness and important differences by place of residence. ^[9-13]

This issue is particularly relevant in Uttarakhand, where migration, changing family structures, and unequal distribution of health resources shape the experience of ageing. Haldwani block provides an important setting for such an assessment because it includes both urban and rural populations within the same broader geographic region. Community based evidence from this setting can help clarify the magnitude and pattern of morbidity among older adults and support more responsive public health planning. ^[4,14]

Although several studies from India have documented selected chronic illnesses among the elderly, locally relevant data on the overall morbidity profile and rural urban differences remain limited in this region. A clearer understanding of these patterns is important for strengthening geriatric services, prioritizing screening strategies, and improving chronic disease management at the primary care level. ^[4,7,8,15]

This study was conducted with aim to assess the morbidity profile among the geriatric population in Haldwani block and compare it between urban and rural areas and to identify factors associated with them.

A community based cross sectional study was conducted in the urban and rural field practice areas of the department of community medicine of a medical college in Nainital district of Uttarakhand. The study was carried out over a period of 12 months from November 2024 to October 2025 in the areas under urban health training center and the rural health training center. The study population comprised elderly individuals aged 60 years and above who were residing in the selected urban and rural field practice areas. Only permanent residents who had been living in the area for at least five years and who provided written informed consent were included. Elderly relatives or visitors who were temporarily staying in the household at the time of the survey and elderly having severe cognitive impairment were excluded from the study. The study was conducted under the parent study with sample size of 610 participants was estimated based on prevalence of good quality of life (60.5) observed by Soren SK et al.[15] and after applying a design effect of 1.5 and allowing for 10% non-response, with equal allocation to urban and rural strata. Accordingly, 305 participants each were enrolled from urban and rural areas. Stratified random sampling was used, with stratification at the level of area. Within each stratum, households were selected by systematic random sampling from the list of eligible households available with field staff; every 10th eligible elderly was included. When more than one eligible participant was present in a household, one was selected by lottery method. Data were collected through house to house visits using a predesigned and pretested semi-structured interview schedule. Information was obtained by one to one interview. The questionnaire was validated by the faculties of the department of community medicine of the medical college. The Content Validity Index (CVI) for the questionnaire was 0.83. The questionnaire was pilot tested on 65 participants in the similar study setting. The questionnaire captured socio-demographic characteristics, including age, sex, education, occupation, marital status, family type, living arrangement, socioeconomic status, pension status, and financial dependence. Morbidity assessment included chronic conditions such as diabetes mellitus, hypertension, coronary heart disease, chronic obstructive pulmonary disease, asthma, tuberculosis, cervical spondylitis, arthritis, lower backache, thyroid disorder, cancer, diminution of vision, and diminution of hearing. The principal outcome variable was the presence of morbidity, defined as the presence of one or more chronic conditions. Multimorbidity was considered as the presence of two or more chronic conditions. Information on these conditions was obtained through participant self reporting, which was verified against available medical records (prescriptions, hospital discharge summaries and recent laboratory reports) presented by the participants during the home visit. The participants were categorized into three socio-economic classes according to Modified BG Prasad Scale according to CPI IW for November 2024. The upper class as Class I; upper middle and middle class as Class II; lower middle and lower class as Class III.[16] Ethical considerations Ethical clearance was obtained from the institutional ethics committee of the medical college; vide letter number 806/GMC/IEC/2024. The parent study was registered with the Clinical Trials Registry–India under CTRI/2024/12/077944. Written informed consent was obtained from all participants at the time of interview. Statistical analysis Data were entered, cleaned, and coded in Microsoft Excel before analysis. For the thesis, statistical analysis was originally performed in Jamovi version 2.6.26, with descriptive measures such as mean, standard deviation, median, interquartile range, and percentages. For the morbidity focused analysis, descriptive statistics were generated, followed by comparison of proportions using the chi square test. Multivariable binary logistic regression was used to identify factors independently associated with any chronic illness and with multimorbidity. Adjusted odds ratios with 95% confidence intervals were reported, and a p value of less than 0.05 was considered statistically significant.

This community based study analyzed 610 elderly individuals aged ≥60 years, equally distributed between rural (n=305) and urban (n=305) field practice areas of Haldwani block. Age distribution showed almost similar patterns across areas, with 57.2% aged 60-69 years (rural: 59.3%, urban: 55.1%), 29.2% aged 70-79 years (rural: 26.6%, urban: 31.8%), and 13.6% aged ≥80 years (rural: 14.1%, urban: 13.1%).

Table 1. Distribution of sociodemographic characteristics by area of residence (Rural vs. Urban) among geriatric population in Haldwani block (N=610)

Overall females particpants were for than males (males: 48.7%, females: 51.3%), with nearly equal proportions in both areas. Educational attainment revealed, illiteracy was more prevalent in rural areas (58.5% vs. 41.5%), while graduates/postgraduates were predominantly urban (59.8% vs. 40.2%). Family structure also varied across the areas: nuclear families were more common in urban areas (54.5% vs. 45.5%), whereas joint (52.4% vs. 47.6%) and three generation households (52.7% vs. 47.3%) predominated rurally.

Socioeconomic status per Modified BG Prasad classification 2024 showed Class II (lower middle) dominance (72.9% overall), with balanced rural urban distribution across classes. Most participants lived with children (91.5%), followed by spouse only (8.2%), only a few lived alone (0.3%).

Out of all participants enrolled in the study, more than half 354 (58.0%) were found to have at least one chronic illness, indicating some form of chronic morbidity. Among those with illness, slightly above half of proportion had more than one chronic condition (53.7% of the ill, 31.1% of the total) classified as having multimorbidity.

Diminution of vision emerged as the most prevalent condition in the study, affecting 181 participants (29.7%), followed closely by hypertension (29.5%, n=180) and diabetes (22.8%, n=139). Lower backache was also common, reported in 19.7% of the population (n=120). Conditions such as diminution of hearing (12.5%), arthritis (10.8%), coronary heart disease (6.2%) and COPD (6.1%) were less frequent. Very few had conditions like thyroid disorder (2.1%), asthma (2.6%), cervical spondylitis (1.8%), tuberculosis (1.0%), and cancer (0.5%).

Figure 1. Heatmap of chronic morbidity prevalence by specific conditions across rural urban strata among geriatric population in Haldwani block.

The heatmap visually depicts condition specific morbidity patterns, revealing musculoskeletal disorders as the predominant burden (visual impairment: 38-45%, arthritis/lower backache: 32-40% across strata), followed by cardio-metabolic conditions (hypertension: 25-35%, diabetes: 15-22%). Rural areas consistently showed around two fold higher prevalence across most conditions versus urban, with highest disparities for COPD/asthma (rural: 18-22% vs. urban: 8-12%) and hearing impairment (rural: 25% vs. urban: 15%).

Color intensity gradients highlight multimorbidity hotspots, rural oldest elderly exhibited co-occurrence of either visual, musculoskeletal or respiratory conditions at twice to thrice urban percentage, while urban patterns concentrated on isolated cardio metabolic diseases.

Table 2. Bivariate associations of sociodemographic characteristics with presence of any chronic morbidity among geriatric population in Haldwani block (N=610).

Note: # Fischer Exact Test.

Chronic morbidity affected 354 participants (58.0%; 95% CI: 54.1-61.9), revealing evident rural urban disparities. Rural elderly exhibited significantly higher morbidity prevalence (71.1%) compared to urban counterparts (44.9%; χ²=43.1, p<0.001), confirming residence as a major determinant of health outcomes.

Age showed a clear gradient: prevalence increased from 55.0% (60-69 years) to 55.1% (70-79 years) and 77.1% (≥80 years; χ²=14.4, p<0.001), underscoring vulnerability among the oldest elderly. Lower educated participants had higher frequency of chronic morbidity, with illiterate participants showing 72.3% prevalence versus 48.4% among high school completers (χ²=20.6, p<0.001), likely reflecting cumulative effects of limited health literacy and preventive care access.

Sex differences were non significant (females: 60.0%, males: 55.9%; χ²=1.09, p=0.970), as were occupation (χ²=6.78, p=0.134), socioeconomic status (χ²=1.35, p=0.507), and family type (χ²=4.48, p=0.107).

Table 3. Multivariable logistic regression analysis of factors independently associated with presence of chronic morbidity among geriatric population in Haldwani block (N=610).

Multivariable analysis confirmed rural residence as the strongest morbidity predictor (aOR=3.29, 95% CI: 2.33-4.65, p<0.001), with urban elderly showing 70% lower odds after adjustment for confounders. This protective urban effect (aOR=0.304, 95% CI: 0.215-0.429) likely reflects better healthcare access, diagnostic capabilities, and lifestyle factors despite similar socio demographic profiles.

Advanced age ≥80 years independently tripled morbidity odds (aOR=3.90, 95% CI: 2.01-7.58, p<0.001) versus 60-69 years, (70-79 years: aOR=1.22, 95% CI: 0.83-1.80, p=0.312). Some factors weren’t significance post adjustment: sex (male aOR=1.04, p=0.707), family type (joint: aOR=1.45, p=0.213; three generation: aOR=0.95, p=0.743), marital status (widowed: aOR=0.55, p=0.078), and socioeconomic class (Class I: aOR=1.58, p=0.241; Class II: aOR=1.32, p=0.451).

For multimorbidity, urban residence again showed a strong protective association (aOR 0.24, 95% CI 0.16-0.35, p<0.001). Advancing age remained a strong independent predictor, with an 11% increase in the odds of multimorbidity per additional year of age (aOR 1.11, 95% CI 1.08-1.14, p<0.001).

Health seeking behaviour showed a significant rural urban difference. Among rural elderly, government facilities were the most commonly used source of care (56.3%), followed by private doctors (23.8%), whereas among urban elderly, private doctors were preferred by most participants (61.2%), followed by government facilities (33.5%). The observed difference in care seeking pattern was statistically significant (χ² = 78.34, df = 2, p < 0.001).

Our study in Haldwani block reveals a high morbidity burden among elderly, with more than half of them affected by at least one chronic condition and one third experiencing multimorbidity. Visual impairment, hypertension, diabetes, and lower backache were the most prevalent conditions. These findings are consistent with Usha P. et al., who reported in a similar Uttarakhand based study that visual impairment (35.6%) and hypertension (24.7%) were the leading chronic issues. Similarly, Jan R. et al. observed that musculoskeletal (38.5%) and eye related disorders (34.2%) were the primary contributors to the morbidity profile of rural elderly in North India.[4,6]

Clear rural urban disparity was observed, where rural elderly exhibited three fold higher adjusted odds of chronic morbidity, highlights a clear health inequality. While Pengpid and Peltzer and the LASI study found that urban populations often show higher cardiometabolic multimorbidity due to sedentary lifestyles and dietary transitions, our results show a rural excess when broader morbidity including sensory and musculoskeletal conditions is considered. This aligns with Verma V. et al. and Nagar S. et al., who found that rural elderly had higher prevalence rates for arthritis and respiratory complaints, attributing this to cumulative occupational strain and restricted access to diagnostic facilities, which leads to prolonged, untreated illness.[3,7,8,17]

Advanced age (≥80 years) emerged as the strongest independent predictor of morbidity, a finding strongly supported by Soren SK. et al., who identified age related physiological decline as the primary driver of multimorbidity (OR 2.14). This is further reinforced by Joshi K. et al. and the LASI study, which similarly report that the prevalence of multimorbidity rises sharply after age 70. The lack of independent association for sex or socioeconomic status in our multivariable model suggests that structural barriers, specifically those related to geography and age related vulnerability, are the primary drivers in our population. This is corroborated by Medhi GK. et al., who noted that regardless of socioeconomic background, the sheer duration of morbidity in the oldest elderly often dictates their health status. [15,17-19]

Our findings align with those reported by Bartwal J. et al. in the same rural field area a decade earlier, similarly identified ocular, cardiovascular, and musculoskeletal disorders as predominant geriatric morbidities and similar distribution of health seeking behaviour. While their study reported a higher overall prevalence (88.6%), our results confirm the persistence of these conditions and the significant age related increase in morbidity. The shift toward higher observed rates of hypertension and diabetes in the present study likely reflects evolving epidemiological patterns, lifestyle changes, and improved diagnostic reach in the region. This comparison highlights that while the high burden of chronic illness in Haldwani is enduring, the morbidity profile is shifting, necessitating ongoing, adaptive primary care strategies for geriatric health.[20]

The results emphasize the need for a tailored geriatric care strategy. While urban areas require a focus on metabolic risk reduction, the higher rural burden necessitates strengthened mobile health outreach, regular eye and hearing examinations, and robust primary care level follow up to address chronic morbidity before it results in severe functional impairment. By integrating these services, health systems can mitigate the unequal burden of aging across diverse geographic settings. Sponsorship and Funding Nil Conflict of Interests Nil

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