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Arch Public Health
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Archives of Public Health
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BioMed Central London

1359
10.1186/s13690-024-01359-5
Research
The impact of an integrative healthcare system on longevity in a nonagenarian population in Northern Mexico: an observational study
Hughes-García Melissa 1
Ojeda-Salazar Daniela Abigail 23
Rivera-Cavazos Andrea 23
Garza-Silva Arnulfo 23
Cepeda-Medina Andrea Belinda 23
Fernández-Chau Iván Francisco 23
Morales-Rodriguez Devany Paola 23
Sanz-Sánchez Miguel Ángel 3
González-Cantú Arnulfo 23
Romero-Ibarguengoitia Maria Elena elenaromero83@gmail.com
mromeroi@novaservicios.com.mx

23
1 Geriatrics Department, Hospital Clinica Nova de Monterrey, San Nicolás de los Garza, Nuevo Leon, Mexico
2 Research Department, Hospital Clinica Nova de Monterrey, San Nicolás de los Garza, Nuevo Leon, Mexico
3 https://ror.org/02arnxw97 grid.440451.0 0000 0004 1766 8816 División de Ciencias de la Salud, Universidad de Monterrey, San Pedro Garza Garcia, Nuevo Leon, Mexico
9 9 2024
9 9 2024
2024
82 1505 1 2024
11 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Despite the growth in the older population, there is a noticeable research gap regarding integrative health systems for older people and their impact on longevity in nonagenarians. This study aimed to evaluate the effect of an integrative health system consisting of medical services, recreational facilities, and housing on longevity in a population of nonagenarians in Northern Mexico.

Methods

This was a cross-sectional, retrospective, descriptive-analytical study in which we measured and analyzed medical history such as number of hospitalizations, visits to geriatric consultation, hypertension, history of chronic pain, polypharmacy, dementia, rheumatic disease, diabetes mellitus, insomnia, depression, ischemic cardiomyopathy, among others. We also measured social engagement and number of caregivers. A logistic regression was performed to evaluate the predictors of mortality in this population.

Results

We included one hundred and ninety-five nonagenarians with a mean (SD) age of 94 (4.2) years and of which 112 (55.7%) were female. The findings from logistic regression analysis indicated that a higher frequency of hospitalizations was associated with an elevated mortality risk (OR = 1.272, p = 0.049). Conversely, increased visits to geriatric consultation services as primary care were linked to a reduced mortality risk (OR = 0.953, p = 0.002). Additionally, social engagement displayed a protective effect (OR = 0.336, p = 0.05).

Conclusions

This study highlighted the role of systemic health approaches in extending life through insights into nonagenarian patients’ involvement in primary care, as measured by consultation frequency, and participation in social activities, mitigating mortality risks. Meanwhile, it emphasized the potential consequences of higher hospitalization rates on increased mortality risk.

Keywords

Nonagenarian
Health system
Longevity
Mortality
Social factors
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmc Text box 1. Contributions to the literature	
• There are limited examples of integrative healthcare systems for elderly patients in Latin American countries and how these may provide distinct benefits in relation to longevity.	
• Active participation in primary care and social engagement plays a pivotal role in reducing mortality in nonagenarian patients, thus promoting longevity.	
• Efforts should be made to adapt existing healthcare systems, to provide elderly patients with holistic care and foster their long-term survival.	

Background

Challenges of aging and the impact of health systems

Across numerous countries, the global populace has witnessed a notable surge in the older demographic. According to the United Nations, older individuals are now outpacing other age groups in terms of population growth [1]. Projections indicate that by the year 2050, the number of individuals that are 80 years or older will triple from 143 million to 426 million. This significant rise in life expectancy can be attributed to various demographic and social factors including fertility rate and improvement in survival [2]. Important determinants of longevity are genetics, lifestyle choices, and socioeconomic status and social engagement [3–7]. As a result, the population will encounter challenges associated with aging, including an increased susceptibility to chronic diseases and various cognitive and physiological changes [8, 9].

The accessibility of efficient healthcare services plays a crucial role in influencing life expectancy [10, 11]. In 2020, the World Health Organization (WHO) declared “The Decade of Healthy Ageing 2021–2030,” focusing on four key action areas to promote healthy aging. One of these areas is providing access to long-term care for older adults in need [12] . Regular visits to primary healthcare providers enable early detection of issues like malnutrition and facilitate the administration of vaccines, thereby reducing the likelihood of developing certain diseases later in life [10]. Supporting this idea, different countries have developed healthcare systems to improve quality of life. For instance, the Care and Dependency Support System in Costa Rica, established in 2021, exemplifies a range of services including home care, residential care, and telecare, all aimed at enhancing the well-being of individuals; however, this program does not include services besides medical related care such as recreational or housing [13]. Another example is the National System of Care in Uruguay, initiated in 2016 , that focuses on promoting the development of autonomy in dependent individuals, provide medical services, and assistance [14].

Mexico’s healthcare landscape and the older adults

Mexico has both public and private healthcare systems, which provide different services and respond to the needs of different populations, including older individuals. The Seguro Popular program was introduced in 2004 as part of the Instituto Mexicano del Seguro Social (IMSS). It provided hospital and pharmaceutical services for people with no access to private healthcare, and it contributed to a rise in life expectancy of about one-half year [15]. Nonetheless, in 2020 it ceased to exist and was replaced by the INSABI (Instituto de Salud para el Bienestar) [16], which by 2022 transferred its functions to an older program, IMSS Bienestar, with limitations such as its restricted outreach across Mexico, cuts in medical personnel, and lack of formal public health policies regarding the care of older individuals [17]. Conversely, the IMSS also has a program called GeriatrIMSS that focuses on the medical and nutritional care of older adults aged 60 years or more. This program provides medical care through family practitioners and refers patients to a geriatrician when necessary. However, it lacks an integrative approach to patient care [18]. Another component of Mexico’s public healthcare system is the Instituto de Seguridad y Servicios Sociales para Trabajadores del Estado (ISSSTE), which was founded in 1959 and addresses the healthcare needs of workers of the state and their families [19]. This institution provides programs focused on gerontological care such as social, cultural, and recreational activities and economic benefits, though in contrast with the program GeriatrIMSS they lack clinical primary care services for the elderly population [20]. Within the private healthcare sector of Northern Mexico, Hospital Clinica Nova is a health system which was founded by the industrial group Alfa in 1977 and it provides multiple health-related services to its target population, including pharmacy, hospital services, health education, nutrition, psychological care, and social work. This model is later explained in more detail.

Integrative healthcare systems and longevity

Healthcare systems have historically managed patients’ diverse needs in a fractured and uncoordinated fashion, which has sparked an interest in exploring integrative approaches that can respond to all the needs of each patient. The definition of an integrative health model is not standardized and varies throughout the literature, with studies categorizing models as such when they encompass a multidisciplinary team (e.g., medical doctors, nurses, social workers, psychologists, occupational or physical therapists, etc.), such as those mentioned in a review by Marino et al. [21]. Examples include the DGIP (Dutch Geriatric Intervention Program) in the Netherlands, which contributed to participants’ well-being and functionality, and the PACE (Program of All-Inclusive Care for the Elderly) model in the US, which interestingly showed an increased survival rate among its participants compared to controls, hinting at the impact of holistic care on longevity. Other studies, however, ascribe the attributes ‘integrative’ or ‘integrated’ to designate broader interventions beyond conventional or direct care, such as the Mitsugi hospital complex in Japan which is integrated into the community’s public administration and includes nursing home and rehabilitation facilities, home visits, and seasonal events [22]. However, the scientific literature on the relationship between lifespan and healthcare systems remains lacking, especially in Latin America. Further studies are needed to address the question of whether integrative models are a significant factor in extending life expectancy, particularly if elements other than the strictly clinical are encompassed.

Hospital Clinica Nova’s model

Hospital Clinica Nova is a unique integrative healthcare model in Northern Mexico that has been providing high-quality medical services to the workers of a leading steel manufacturing company in the country for the past 40 years, since its founding in 1977. It operates as a private healthcare systemthat attends workers from the steel company and their families, bringing continuous medical care from youth to old age, which is rare in the context of Mexico’s healthcare landscape where most patients are treated discontinuously and by multiple doctors. Hospital Clinica Nova’s model has three main components: its health system, its recreational facilities, and its housing and savings benefits.

Hospital Clinica Nova’s health system

Depending on the patients’ age group, primary care is provided by pediatricians, internal medicine specialists, or geriatricians,  who conduct preventative consults and a tight follow-up. This system also includes specialized medicine (i.e., cardiology, neurology, etc.) when appropriate, as well as health education, nutrition, psychology, and social work. Patients have wider access to medications, vaccines, and clinical studies due to the fact that they do not have to pay for them directly, since these resources are paid for by the steel manufacturing company through payment reversals from the IMSS and private funding, which mitigates the barriers to healthcare commonly imposed by economic status. Hospital Clinica Nova also has a higher physician/population ratio in respect to Mexico, with 5 physicians available per 1,000 patients compared to 2.4 per 1,000 patients nationwide [23], and it has a markedly reduced waiting period for scheduling consults (only 3 days) compared to Mexico’s public healthcare system, which ranges from 10.8 weeks for the IMSS to 12.2 weeks for the ISSSTE [24]. This integration of different health components is expected to have inherent benefits in respect to lifespan, since a more coordinated delivery of care in its multiple facets could facilitate adherence and thus improve overall health.

Hospital Clinica Nova’s recreational facilities

Concerning the hospital’s recreational facilities, these provide a space for families to perform cultural, sports, and community activities, thus fostering their holistic development and networking. This is theorized to play a role in promoting our population’s healthy aging, as providing these amenities encourages social engagement.

Hospital Clinica Nova’s housing and savings benefits

Finally, as the third component of Hospital Clinica Nova’s model, the steel industry provides its workers’ families with housing options in the areas adjacent to the factories, which means most of them live in the same neighborhood or nearby, and it also bestows upon them a savings bank for their future. As Hospital Clinica Nova is located in the zone surrounding the factories, this translates to a community of patients who are geographically close to the hospital and have similar social status, which mitigates the limitations for healthcare access imposed by difficulties with transportation, especially for older individuals. Overall life expectancy in Mexico was 68.8 in 2021 [25], whereas San Nicolás de los Garza, Nuevo León, where our health system operates, had a life expectancy of 73.2 during that time according to the last statistics [26]. Therefore, we hypothesize that our health system could have an effect on longevity in this specific region.

In short, Hospital Clinica Nova fulfills both previously discussed definitions for ‘integrative health models’, as it incorporates a multifaceted approach of care and provides ancillary services and facilities. Its model emphasizes continued, interdisciplinary preventative medicine and social partaking while buffering social determinants of health such as financial status and physical distance, both of which normally constitute extrinsic barriers to healthcare access. For a clearer understanding of Hospital Clinica Nova’s integrative health model, see Fig. 1.

Fig. 1 Hospital Clinica Nova’s integrative healthcare model. Hospital Clinica Nova’s integrative healthcare model encompasses prevention-focused and personalized medical attention, spaces for social and physical activities, and financial-household benefits provided by the steel industry

To analyze how our model has impacted our population and fostered their health consistently through old age, we decided to study our longest-lived patients, which are a group of nonagenarians that have been exposed to Hospital Clinica Nova’s system for the last 40 years. Thus, the aim of this study was to evaluate the impact of our health system on longevity by describing the demographic, clinical, and social characteristics of our medical institution’s nonagenarian population and identifying their predictors of mortality risk. Specifically, we wanted to evaluate how certain components of our healthcare model have a protective effect against mortality and thus promote survival through old age, such as primary care visits to geriatricians and internal medicine specialists, hospitalization, outpatient visits, social engagement promoted through our recreational facilities, among other variables. The findings of this study are expected to provide a greater understanding of aging in a geriatric model which emphasizes prevention and social engagement.

Methods

This cross-sectional, retrospective, observational study was conducted at a private healthcare facility located in Northern Mexico (San Nicolás de los Garza, Nuevo León) called Hospital Clinica Nova. The study included all individuals of 90 to 99 years old who had undergone prior assessment at the hospital during outpatient consultations or hospitalizations from 2017 to 2022 and excluded those who had empty clinical records or presented data inconsistencies. Approval for the study was obtained from the local Institutional Review Board (23112022-CN-GER-CI). It adhered to the Strengthening the reporting of observational studies in epidemiology (STROBE) guidelines [27] and the Declaration of Helsinki, which delineate ethical principles governing medical research involving human subjects. Considering the retrospective nature of the study, the requirement for a consent form was unnecessary.

Data collection

We conducted data extraction from medical records, specifically targeting all individuals between 90 and 99 years who were beneficiaries of the aforementioned healthcare facility. The study encompassed medical records from 2017 to 2022. Patients were divided into two groups depending on their mortality status. The study included and evaluated the following variables in both groups: age (years), gender (male/female), marital status (single, married, widowed, divorced), body mass index (BMI), medical history: including history of consuming tobacco and alcohol, and comorbidities analyzed as dichotomous variables based on the factor’s presence such as type 2 diabetes mellitus, hypertension, dyslipidemia, chronic pain (lumbar, joint or other), dementia (alzheimer, vascular, other), rheumatic disease (osteoarthritis or other), insomnia, depression, ischemic cardiomyopathy, peripheral vascular disease, cancer (basocellular skin cancer, protatic or other), chronic kidney disease, falls, heart failure, COVID-19, chronic obstructive pulmonary disease (COPD), stroke, peptic ulcer, hepatic disease, and hemiplegia. Polypharmacy was characterized by the concurrent usage of five or more medications as recognized by the World Health Organization. Other variables that were analyzed were previous visits to the clinic (including number of visits to internal medicine, geriatrics, emergencies, or hospitalizations; analyzed in a numerical manner), and social status [measured by socioeconomic status, the number of caregivers, engagement in social activities, and family visits (if the patient is visited by their family); analyzed in a categorical manner]. The point of mortality status was assessed once patients reached 90 years of age and until 99 years, to ensure they were all nonagenarians. The rationale of the variables described above was to show all the available clinical and social data extracted from medical records and to understand its differences in deceased and living nonagenarian patients. All these clinical and social variables that reflect our integrative health system were initially computed into our regression model. To ensure the completeness and accuracy of the data extracted from clinical records, it was imperative to meticulously evaluate each record individually. Our focus was on assessing the consistency of variables over the years of follow-up from 2017 to 2022. Records that displayed inconsistencies or were empty were omitted from the research analysis.

Statistical analysis

The normality of numerical variables was assessed through Kolmogorov-Smirnov and Shapiro-Wilk tests. Descriptive statistics included mean, standard deviation (SD), median, interquartile range (IQR), frequencies, and percentages. Chi-square test was used to determine statistical differences between the deceased and alive groups for categorical variables; while unpaired t-tests (ensuring homogeneity of variances for t-tests through the Levene test) and Mann-Whitney tests were used for continuous variables depending on the distribution and normality of the data.

A binary logistic regression model was employed to assess the relationship between mortality (dependent variable) and a set of healthcare factors such as demographic data, comorbidities, number of hospitalizations and visits to the geriatrics clinic, as well as social factors. Y (mortality) was the binary outcome variable (i.e., Y = 1 if death of the patient occurs, Y = 0 otherwise). The probability that Y = 1 was denoted as P(Y = 1) = p. The logit function is the natural logarithm of the odds \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\frac{p}{1-p}$$\end{document}: logit(p) = log\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\left(\frac{p}{1-p}\right)$$\end{document} = β0 + β1 × 1 + β2 × 2 + … + βkXk. Assumptions for logistic regression such as a binary dependent variable, independence of observations, absence of collinearity, linearity of independent variables and log odds, and minimum sample size were considered. All variables that were clinically relevant to predict mortality were entered simultaneously by enter method and those with multicollinearity (VIF > 10) or not statistically significant were pruned to form a final model with the best Nagelkerke’s r-square. Goodness of fit was assessed using the Hosmer-Lemeshow test. We specifically adjusted for potential confounders based on literature and expert opinion to ensure the robustness of our findings against biased estimates. Missing Completely at Random values were analyzed through complete case analysis. Data was analyzed with SPSS 25 (SPSS Inc. Chicago, IL, USA). An alpha level of 0.05 was defined as the threshold for statistical significance; therefore, a p-value less than 0.05 was considered significant.

Results

A total of 201 participants with medical records between 2017 and 2022 was collected, and, after selection criteria were applied, there were a total of 195 patients. The median (IQR) age was 94 (4.2). Females constituted the majority, accounting for 112 (55.7%) participants. At the time of data analysis, 121 (62.0%) participants were deceased. Detailed demographic characteristics including marital status and BMI of the nonagenarian patients are provided in Table 1.

Table 1 Demographic data in nonagenarians determined by mortality

Variable	Total Sample
(n = 195)	Alive
(n = 74)	Deceased
(n = 121)	p-value	
Age (years)ª	94 (4.2)	95.1 (3)	93 (4)	< 0.001d	
Womenb	112 (55,7%)	43 (58.1%)	64 (52.9%)	0.478e	
Marital status

(n = 165)b

	Married	86 (52.1%)	37 (61.7%)	49 (46.7%)	0.177e	
Widowed	70 (34,8%)	22(36.7%)	48(45.7%)	
Single	8 (4.8%)	1 (1.7%)	7 (6.7%)	
Divorced	1 (0.6%)	0 (0%)	1 (1.0%)	
BMIc

(n = 55)

	23,59(5,9)ª	25.27 (6.4)	24.12 (4.3)	0.431f	
a Data presented in median (IQR)

b Data presented in frequency (percentage)

c Data presented in mean (SD)

d p-value calculated through Mann-Whitney U

e p-value calculated through chi-squared test

f p-value calculated through unpaired t-test

Comorbidities in nonagenarians

Regarding comorbidities in the medical records, hypertension (n = 136, 70.1%) was the most prevalent comorbidity among nonagenarians, followed by chronic pain which was significantly more prevalent in living subjects than deceased [n = 46(62.2) vs. n = 53 (43)], p = 0.009. COPD was significantly higher in the deceased group [18(14.9%) vs. 2(2.7%)], p = 0.007. History of dementia was on the borderline of significance, being greater in deceased patients than in living patients at the moment of the study [61(50.4%) vs. 27(36.5%)], p = 0.058. Dislypidemia was more prevalent in the alive group [20(27%) vs. 18(14.9%), p = 0.038]. Moreover, the history of falls was more common in living patients, with 19 cases (25.7%) compared to just 8 cases (6.6%) in the deceased group (p < 0.001). Comorbidities and their subclassification divided by deceased and living groups can be found in Table 2.

Table 2 Clinical data in nonagenarians

Variable	Total Sample
(n = 195)	Alive
(n = 74)	Deceased
(n = 121)	p-value ª	
Hypertension

n = 194

	136 (70.1)	55 (74.3)	81 (67.5)	0.313	
Chronic Pain	History of Chronic Pain	98 (50.3)	46 (62.2)	52 (43)	0.009	
Lumbar	52 (53.1)	25 (54.3)	27 (51.9)	0.862	
Joint	35 (35.7)	15 (32.6)	20 (38.5)	
Other types of pain b	29 (62.9)	6 (12.9)	4 (9.6)	
Polypharmacy	165 (84.6)	67 (90.5)	98 (81)	0.073	
Dementia	History of dementia	88 (45.1)	27 (36.5)	61 (50.4)	0.058	
Vascular	42 (47.7)	8 (29.6)	34 (55.7)	< 0.001	
Alzheimer	18 (20.5)	13 (48.1)	5 (8.2)	
Other types of dementia c	28 (31.8)	5 (22.3)	22 (36.1)	
Rheumatic disease	History of Rheumatic disease	64 (32.8)	25 (33.8)	39 (32.2)	0.823	
Degenerative Osteoarthritis	55 (85.9)	22 (88)	33 (84.6)	0.805	
Other rheumatic diseases d	9 (14)	3 (12)	6 (15.5)	
Type 2 Diabetes Mellitus	60 (30.8)	25 (33.8)	35 (28.9)	0.476	
Insomnia	47 (24.1)	22 (29.7)	25 (20.7)	0.151	
Depression	46 (23.6)	19 (25.7)	27 (22.3)	0.592	
Ischemic Cardiomyopathy	44 (22.6)	15 (20.3)	29 (24)	0.549	
Peripheral Vascular disease	37 (19%)	16 (21.6)	21(17.4)	0.461	
Cancer	History of Cancer	39 (20)	14 (18.9)	25 (20.7)	0.768	
Basocellular (Skin)	11 (26.8)	5 (33.3)	6 (23.1)	0.523	
Prostate	8 (19.5)	2 (13.3)	7 (28)	
Other types of cancer e	22 (56.4)	7 (50)	13 (52)	
Dyslipidemia	28 (19.5)	20 (27)	18 (14.9)	0.038	
Chronic Kidney Disease	37 (19)	14 (18.9)	23 (19)	0.988	
Falls	27 (13.8)	19 (25.7)	8 (6.6)	< 0.001	
Heart Failure	24 (12.3)	10 (13.5)	14 (11.6)	0.689	
COVID-19	22 (11.3)	8 (10.8)	14 (11.6)	0.871	
Chronic Obstructive Pulmonary Disease	20 (10.3)	2 (2.7)	18 (14.9)	0.007	
Stroke	19 (9.7)	5 (6.8)	14 (11.6)	0.271	
Absence of Comorbidities	7 (3.6)	2 (2.7)	5 (4.1)	0.711	
Peptic Ulcer	3 (1.5)	1 (1.4)	2 (1.7)	1.000	
Hepatic Disease	2 (1.0)	1 (1.4)	1 (0.8)	1.000	
Hemiplegia	1 (0.5)	0 (0)	1 (0.8)	1.000	
Smoking and Alcoholism	1(0.5)	1 (1.4)	0 (0)	-	
The data is presented in frequency (percentage)

a p-value analyzed through chi-squared test

b Other types of pain involved are cervical and muscular pain but also any type of neuralgia

c Other types of dementia included Parkinson’s disease and senile dementia

d Other types of rheumatic diseases include rheumatoid arthritis, gout and polymyalgia

e Other types of cancer include colon cancer, spinocellular skin cancer, tongue cancer, breast cancer, non-specified skin cancer, lung cancer, kidney and bladder cancer

Healthcare service use in nonagenarians

In terms of visits to the clinic during 2017–2022, it’s worth noting that hospitalization was significantly more frequent among deceased patients [97(80.2%) vs. 44(59.5%), p = 0.002], and a statistically higher median (IQR) of hospitalizations was found in the same group [2 (2) vs. 1 (2),p = 0.002]. The median (IQR) of outpatient visits was significantly higher in living patients [76 (56) vs. 44(71),p < 0.001]. Also, the number of visits to the internal medicine department [20.5 (14) vs. 17 (16), p = 0.036] and geriatric department [23 (15) vs. 8 (21), p < 0.001] was higher in the living group. Emergency department visits were not different between the studied groups. The rest of the information regarding clinical visits is shown in Table 3.

Table 3 Clinical visits in nonagenarians during 2017–2022

Variable (n = 127)	Total Sample
(n = 195)	Alive
(n = 74)	Deceased
(n = 121)	p-valuea	
Hospitalization History	141 (72.3)	44 (59.5%)	97 (80.2%)	0.002	
Outpatients Visits	59(69)	76 (56)	44 (71)	< 0.001	
Internal Medicine Visits	19(15)	20.5 (14)	17 (16)	0.036	
Geriatric Visits	15(23)	23 (15)	8 (21)	< 0.001	
Number of hospitalizations in the last year	2(3)	1 (2)	2 (2)	0.002	
Emergency Department Visits	8(9)	8 (8)	8 (9)	0.865	
The data is presented in median (IQR)

a p value analyzed through Mann-Whitney U

Social status of nonagenarians

A total of 127 subjects had information in relation to social status. All subjects had the same socioeconomic status (middle class). The majority of nonagenarians had a caregiver (n = 126, 99.2%), and 111 (55.2%) received visits. Ninety-two nonagenarians (45.8%) engaged in social activities (e.g. social gatherings, cultural events, or recreational activities indoors and outdoors), and there was a significantly higher proportion of socially engaged patients in the living vs. deceased group [32(86.5%) vs. 60(66.7%), p = 0.023]. The social status of nonagenarians is illustrated in Table 4.

Table 4 Social data in nonagenarians

Variable (n = 127)	Frequency (%)	Alive
(n = 37)	Deceased
(n = 90)	p-valuea	
Nonagenarians with Caregivers	126(99.2)	36 (97.3)	90 (100)	0.117	
Number of Caregivers	0	1 (2.7)	1 (2.7)	0 (0)	0.289	
1	13 (10.2)	4 (10.8)	9 (10)	
2	113 (89)	32 (86.5)	81 (90)	
Receives Visits	111 (87.4)	34 (91.9)	77 (85.6)	0.328	
Social Activityb	92 (45.8)	32 (86.5)	60 (66.7)	0.023	
The data is presented in frequency (percentage)

a p value analyzed through chi-squared test

bSocial activity: Social gatherings, cultural events, recreational activities both indoors and outdoors

Mortality model of healthcare and social factors in nonagenarians

A logistic regression model was developed to assess mortality risk factors in nonagenarians, incorporating significant variables derived from demographics, comorbidities, clinical visits and social data. The model was developed with 127 subjects that had complete demographic, clinic and social information. All variables were included in the initial model through the enter method and were eliminated if they were not statistically significant or if they were multicollinear. We pruned the model to find the one with the best Nagelkerke’s r-square. The final model revealed that a higher number of hospitalizations was associated with an elevated mortality risk (OR = 1.272, p = 0.049, 95% CIs = 1.001–1.617). In contrast, a greater frequency of visits to the geriatric consultation service was identified as a protective factor against mortality (OR = 0.953, p = 0.002, 95% CIs = 0.924–0.982). Additionally, social activity demonstrated a protective effect (OR = 0.336, p = 0.05, 95% CIs = 0.111–1.024) Nagelgerke’s r-square was 0.197. The formula for the logistic regression model is as follows:

Y is the binary outcome variable (i.e., Y = 1 if death of the patient occurs, Y = 0 otherwise). The probability that Y = 1 is denoted as P(Y = 1) = p. The logit function is the natural logarithm of the odds \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\frac{p}{1-p}$$\end{document}:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\operatorname{logit}(p)=\log \left(\frac{p}{1-p}\right)=\beta_0+\beta_1 X_1+\beta_2 X_2+\beta_3 X_3$$\end{document}

where β0 (intercept) = 2.027, β1 = −1.089, X1 is Social activity, β2 = 0.049, X2 is Number of geriatric visits, β3 = 0.241, and X3 is Number of hospitalizations.

A detailed presentation of the logistic regression model, elucidating mortality risk factors related to social, clinical, and hospitalization aspects in nonagenarians, is provided in Table 5.

Table 5 Logistic regression model to determine social, clinical and hospitalization risk factors for mortality in nonagenarians

Variable	β	Standard error	p-value	OR	95% C.I.	
Intercept	2.027	0.634	< 0.01	7.589		
Social Activity	-1.089	0.568	0.05	0.336	0.111–1.024	
Number of Geriatric visits	-0.049	0.016	< 0.01	0.953	0.924–0.982	
Number of Hospitalizations	0.241	0.122	0.05	1.272	1.001–1.617	
Nagelkerke’s R2 = 0.197

Discussion

The study describes the effect of forty years of exposure in an integrative healthcare system on longevity in a nonagenarian population. Attendance to primary care consultations through a geriatric clinic and engagement in social activities decrease mortality, while an increased number of hospitalizations increases mortality. Additionally, there was a high incidence of polypharmacy, and the most prevalent comorbidity among nonagenarians was arterial hypertension. COPD and dementia had a higher prevalence in deceased patients, while chronic pain, dyslipidemia, and falls were more common in living patients.

Our study showed that the majority of our population was female. This is frequently found in other studies [28–31]. Newman et al. suggest multiple explanations for this phenomenon, such as men’s use of fewer preventive health services than women and men’s higher risk of major causes of death including cardiovascular disease, violent deaths (i.e. accidents, homicide), and lung cancer. Overall, the gender gap in longevity is thought to be a complex interplay of environmental and biological factors [32].

There was a significant proportion of nonagenarian’s patients included in our study that had a documented pattern of polypharmacy. This result is consistent with other studies in which a direct correlation between higher age and prevalence of polypharmacy was observed [33]. Factors such as female gender, older age, and prevalence of chronic comorbidities are associated with higher pharmaceutical use [34], thus explaining the higher incidence of polypharmacy in this study which mainly consists of females. Also, most guidelines for chronic diseases recommend the use of combined drug therapy, therefore accounting for a higher prevalence of polypharmacy in nonagenarians [35]. It is worth noting that access to exclusively public healthcare systems has been associated with a decrease in polypharmacy, whereas in private healthcare systems such as ours, subjects have access to more medications [34].

We found a high prevalence of hypertension among our population, which keeps in line with prior research [36–40]. This is expected given that mechanisms such as arterial stiffening, endothelial dysfunction, and cardiac remodeling tend to intensify with age, increasing the risk for comorbidities like dementia, physical disability, and falls/fractures [41, 42]. In different studies regarding nonagenarians, COPD was among one of the most frequent primary diagnoses and causes of hospitalization, secondary to its higher prevalence with increasing age [43]. COPD alone is associated with a 3-fold risk of death in patients 80 years old or above and, since it commonly accompanies other chronic conditions, it leads to increased hospital readmission, care complexities, and mortality [44]. The latter could explain its significantly higher prevalence in our deceased patient group. We also found a borderline significant prevalence of dementia in deceased patients. These results are consistent with a variety of papers in which cognitive impairment and dementia are identified as significant predictors of mortality [45, 46]. Dementia is related to multiple endangering conditions including frailty, trauma, and other diseases like stroke and pneumonia, thus shortening the life expectancy of those afflicted [47–49]. The risk of dementia continues to rise among the oldest old due to a combination of factors, including the effects of aging (the time-dependent accumulation of changes and damage responsible for functional decline) and various elements that contribute to cognitive decline at different stages of life, often exacerbated by comorbidities such as hypertension [38, 50, 51].

Numerous studies have explored the correlation between chronic pain and advancing age, particularly within the older adult population, yet they have not definitively established a significant link with mortality [52, 53]. In our investigation, we observed a notable prevalence of pain, with consistent findings of higher pain levels in living participants compared to the deceased. A study conducted by Shega et al., which examined the connection between persistent pain and 5-year mortality, revealed an intriguing pattern. It indicated that women experiencing persistent pain exhibited a reduced risk of mortality when contrasted with both men with and without pain, as well as women without pain [54]. While this decline in mortality might be attributed to a more frequent utilization of the healthcare system with a consistent follow-up by patients with chronic pain, the precise role of gender in this phenomenon remains not fully understood [55, 56].

A higher prevalence of dyslipidemia was observed in living patients compared to deceased ones, an unexpected but congruent discovery that fits with the findings of several previous studies on predictors of mortality in older adults, particularly nonagenarians [57–60]. Several hypotheses have been formulated for this with varying degrees of evidence, including the possibility that low serum lipid levels reflect frailty (due for example to cachexia or malnutrition) or subclinical disease, the presence of a survival bias in which patients more susceptible to the atherosclerotic effects of cholesterol die before reaching old age, or the role of cholesterol in maintaining membrane integrity in aging cells and its less harmful effects on calcified vessels [57–60]. In contrast, a recent study by Pancani et al. on a cohort of 433 Italian nonagenarians found no statistically significant difference in mortality between patients with and without dyslipidemia [31].

There was a higher prevalence of falls in living patients, which could strike as paradoxical considering the high mortality and morbidity associated with falls in the older people (e.g. death, moderate-severe injury, fear of falling, loss of independence) [61]. We found no literature upon which to construct a coherent explanation for this. One plausible interpretation is that deceased patients had fewer opportunities to experience falls due to the simple fact of their limited time of exposure. As these patients passed away, they were no longer at risk of falling. It’s important to note that our data collection was confined to the period from 2017 to 2022, and the incidence of previous falls is unknown, potentially influencing these results. Finally, the interpretation of this finding is restricted by the present study’s lack of information regarding patients’ functional status, a variable that has been found to influence falls and could have further elucidated surviving patients’ apparent higher incidence [40].

Furthermore, our investigation revealed that frequent hospitalizations are associated with an elevated mortality risk. This aligns with existing studies in which hospital readmissions, extended hospital stays, and specific comorbidities are associated with an increase in patients’ mortality risk [62, 63]. However, it’s important to acknowledge a limitation in our study. While we possess data on the frequency of hospital admissions within the time frame from 2017 to 2022, we lack information regarding the specific causes of these admissions. Future research could explore this aspect, as understanding the root causes of hospitalizations would provide valuable insights into this complex relationship with mortality.

Our study demonstrates that an increase in primary care consultations in the geriatrics department was associated with a decreased risk in mortality. While the evidence in regard to this topic within the older population is rather limited, some studies state that patients with more primary care visits have lower cardiovascular and cancer mortality rates [62]. Additionally, research suggests that increased primary care visits in the preceding years correlate with shorter hospital stays and reduced end-of-life costs [63, 64]. Furthermore, another study emphasizes that enhanced primary care access may not only lower costs but also enhance the quality of care at the end of life [65].

Multiple studies have consistently found that heightened social interactions and engagement, coupled with an expanded social network, are linked to decreased mortality rates among older individuals [6, 7]. A study in Beijing concluded that social support networks composed by neighbors, friends, or family play a significant role in promoting healthy aging, acting directly or indirectly by promoting healthy-practices or self-efficacy [66]. The present study revealed a significant connection between social activity and its protective effects, which corresponds with the increasing consensus established by the aforementioned research. Therefore, this finding propels the evidence supporting a substantial correlation between social activities and a marked decrease in mortality risk.

We consider this study of value since it reflects how a unique system with an integrative approach (including private healthcare, recreational spaces, and housing) can impact longevity. We describe the clinical and social characteristics and the mortality predictors of people benefiting from this comprehensive system, showing that adequate healthcare and social partaking can increase lifespan outcomes; see Fig. 2.

Fig. 2 Factors impacting mortality in nonagenarians. The figure shows how Hospital Clinica Nova’s integrative healthcare model impacts mortality in nonagenarians

This article contributes to the literature on how different healthcare systems can result in improved health outcomes in the elderly, specifically those pertaining to survival and lifespan. Papers regarding this particular subject are scarce and their conclusions differ. A previous systematic review of twelve studies that evaluated integrated healthcare systems for older people, five of which focused on their influence on mortality, reported that there was no significant effect on this outcome [67]. Meanwhile, another systematic review of an integrative health system in a Taiwan population of more than 800,000 subjects demonstrated that its implementation provided benefits in terms of survival [68]. Our results keep in line with the latter study, since they showed that stricter adherence derives into decreased mortality risk. Furthermore, many integrative healthcare models described in the studies on this matter are concerned only with the clinical angle, albeit with an interdisciplinary team, such as those featured in Marino et al. [21]. Health systems that include recreational areas for sports or social activities and housing are lacking in the literature, with a few exceptions such as a community-based integrated care system in Mitsugi, Japan, where residents’ attendance to medical check-ups increased as a result [22].

This study was primarily limited by its retrospective design, which required the extraction of data from medical records leading to a risk of incomplete reporting data, and its cross-sectional design also limits its potential for establishing cause-effect relationships. Additionally, our efforts were hindered by the absence of functional scales for the population under study, which is an important factor in further comprehending the mortality risk. Despite encountering these challenges, we collected extensive data from the subjects, meticulously cross-checking electronic medical records with manual checks, thus reducing the risk of incomplete or inaccurate data acquisition. Further studies may be necessary to explore the impact of this integrative healthcare model in other age groups.

Conclusion

This study contributes to the limited available research on how 40 years of exposure to a robust health system that integrates clinical, recreational, and housing domains can impact longevity in a nonagenarian population. These findings highlight that the active participation of these patients in primary care and social activities plays a pivotal role in reducing their mortality, while increasing hospitalizations positively correlate with mortality risk.

Acknowledgements

Not applicable.

Author contributions

M.H.G contributed to the conceptualization of this study, investigation, and Methodology. D.A.O.S contributed to the conceptualization of this study, literature review, statistical analysis, drafting and writing final version of the manuscript. A.R.C contributed to the investigation, methodology, and statistical analysis. A.G.S contributed to constructing the database and performing its statistical analysis. A.B.C.M. contributed to the literature review, drafting, manuscript writing, Methodology, and investigation. I.F.F.C Contributed to the literature review, writing the manuscript, drafting and writing final manuscript, methodology and investigationD. P.M.R contributed to statistical analysis, methodology, and investigation. M.A.S.S contributed to the methodology and investigation. A.G.C contributed to the methodology and investigation. M.E.R.I contributed to original idea, conceptualization, investigation, methdology, project manager, statistical analysis performance, drafting and writing final manuscript. All authors read and approved the final manuscript.

Funding

This article received no funding.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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