Predictors Of Multimorbidity Research Articles

The association between primary ovarian insufficiency and increased multimorbidity in a large prospective cohort (Canadian Longitudinal Study on Aging)

ObjectiveTo describe the prevalence of multimorbidity among individuals with premature ovarian insufficiency (POI) and early menopause, in comparison to average age of menopause. DesignProspective cohort SubjectsThis prospective cohort encompassed female postmenopausal individuals from the Canadian Longitudinal Study on Aging (CLSA). The CLSA collected cross-sectional data from 50,000 community-dwelling Canadians aged 45 to 85 between 2010 and 2015. ExposureThe primary exposure was primary ovarian insufficiency (defined by onset of menopause younger than 40 years). Comparators included average age of menopause (age 46 to 55 years), early menopause (40-45 years), late onset menopause (56-65 years), and those who underwent a hysterectomy. Main outcome measuresThe primary outcome was multimorbidity, which was defined as two or more chronic conditions. The secondary outcome was severe multimorbidity (defined as three or more chronic conditions) as well as frequencies of specific chronic conditions among a comprehensive list of 15 individual conditions. We assessed the association between multimorbidity and age at menopause using logistic regression and odds ratios, with confidence intervals set at 95%. Odds ratios were adjusted for known predictors of multimorbidity, including age, menopause hormone therapy (MHT), education, ethnicity, self-reported loneliness, living alone, BMI, smoking habits, nutritional risk, social participation, and physical activity. ResultsA total of 12,339 postmenopausal participants were included, of which 374 (3.0%) experienced POI and 1396 (11.3%) experienced early menopause. The prevalence of multimorbidity was 64.8% and 51.1% among those with POI and early menopause respectively. In contrast, only 43.9% of individuals with average age of menopause (age 46 to 55 years) had multimorbidity. The OR for multimorbidity in the POI population was 2.5 (95% CI 2.0-3.1) in comparison to those who underwent the average age of menopause. This relationship was maintained after adjustment for confounders (aOR 2.0, 95% CI: 1.5-2.5). The prevalence of severe multimorbidity was also double in the POI group in comparison in the average age group (39.2% versus 21.1%). There was significantly increased risk of ischemic heart disease (aOR 2.8, 95% CI: 1.7-4.7), gastric ulcers (aOR 1.6, 95% CI: 1.1-2.3) and osteoporosis (aOR 1.6, 95% CI: 1.2-2.1) in the POI group. ConclusionsIndividuals with POI and early menopause experience increased multimorbidity in comparison to those undergoing menopause at an average age. This trend persists even after adjusting for significant multimorbidity risk factors.

Determinants of multimorbidity among elderly population in maharashtra, India: Logistic regression analysis.

Population aging is an emerging global trend. Because of decreasing fertility rates and improved healthcare, the lifespan of elderly population increased. Consequently, proportion of elderly population is increasing at an alarming rate. This is accompanied by an increased recognition of the occurrence of multimorbidity and associated mortality risks. So, the purpose of this study was to determine the prevalence and predictors of multimorbidity among elderly population in Maharashtra with its variation among socio-demographic spectrum, functional health and health behaviors. Sample of elderly population aged > 60 years were selected to examine multimorbidity and its associated risk factors. Statistical methods such as Chi-square test were used to show the association between multimorbidity and other covariates. Binary logistic regression analysis was used to understand the effects of predictor variables on multimorbidity. Receiver Operating Characteristic (ROC) Curve Analysis was carried out to improve the performance of the classification model by using a modified cut-off probability value. Z scores were calculated to compare model performance in training data and test data. The prevalence of multimorbidity in Maharashtra in training data and test data was found to be 32.8% and 32.9%. Residence, living arrangement, MPCE Quintile, marital status, work status, education, tobacco consumption, physical activity, Instrumental Activities of Daily Living (IADL), Activities of Daily Living (ADL) and self-rated health of elderly population were important determinants that exert a significant adverse effect on multimorbidity. Prediction percentages indicate that appropriate actions should be undertaken to ensure good quality of life for all the elderly in Maharashtra.

Prevalence and predictors of multimorbidity in older adults, a community-based cross-sectional study.

Multimorbidity is a condition that refers to the coexistence of two or more chronic diseases or long-term medical conditions in an individual. It has become one of the key issues in the public health sector, particularly among older adults. So, this study aimed to estimate the prevalence of multimorbidity and its associated factors among older adults. The present study was a community-based cross-sectional study carried out on the elderly population (≥60 years) in a rural area of the study district, Gujarat. The estimated sample size was 384, and the final sample size was taken as 420, considering the multistage sampling procedure for fair distribution. The eligibility criteria included elderly people ≥60 years old, who were permanent residents (living for ≥6 months) of the study district and willing to participate in the study. Elderly people who were not willing to participate in the study for their reasons were excluded. A multistage sampling procedure was used to choose the study participants. The data collection tools included a pretested, predesigned, semi-structured questionnaire. The questionnaire consisted of two parts: sociodemographic details and morbidity details. Categorical variables were described by frequency and percentage, and numerical variables were described by mean ± SD. Logistic regression analyses were applied to test the relationship of categorized independent and dependent variables, and all tests were two-tailed with statistical significance set at the probability value (P < 0.05). Out of the 420 study participants, 52.1% were male and 47.9% were female, with a mean age of 70.70 years (SD ± 8.18 years). The prevalence of multimorbidity was 50%, with males having a mean number of morbidities of 1.38 ± 1.06 and females having a mean number of morbidities of 1.44 ± 0.99. As the age increased, the strength of association of multimorbidity also increased. Literacy level, marital status, occupation (business, farmer, housewife), and family history were statistically associated with multimorbidity in the study participants in both bivariate and multivariate analyses (P < 0.05). The study provides valuable insights into the health status of the elderly population in the rural area and can be used to inform healthcare policies and interventions aimed at improving the health outcomes of this population.

Adiposity indicators exhibit depot- and sex-specific associations with multimorbidity onset: A cohort study of the UK Biobank.

This study investigated the depot- and sex-specific associations of adiposity indicators with incident multimorbidity and comorbidity pairs. We selected 382 678 adults without multimorbidity (≥2 chronic diseases) at baseline from the UK Biobank. General obesity, abdominal obesity and body fat percentage indices were measured. Cox proportional hazard regression analyses of general obesity indices revealed that for every one-unit increase in body mass index, the risk of incident multimorbidity increased by 5.2% (95% confidence interval 5.0%-5.4%). A dose-response relationship was observed between general obesity degrees and incident multimorbidity. The analysis of abdominal obesity indices showed that for every 0.1 increment in waist-to-height ratio and waist-to-hip ratio, the risk of incident multimorbidity increased by 42.0% (37.9%-46.2%) and 27.9% (25.7%-30.0%), respectively. Central obesity, as defined by waist circumference, contributed to a 23.2% increased risk of incident multimorbidity. Hip circumference and hip-to-height ratio had protective effects on multimorbidity onset. Consistent findings were observed for males and females. Body fat percentage elevated 3% (0.2%-5.9%) and 5.3% (1.1%-9.7%) risks of incident multimorbidity in all adults and females, respectively. Arm fat percentages elevated 5.3% (0.8%-9.9%) and 19.4% (11.0%-28.5%) risks of incident multimorbidity in all adults and males, respectively. The general obesity indices, waist circumference, waist-to-height ratio, waist-to-hip ratio and central obesity increased the onset of comorbidity pairs, whereas hip circumference and hip-to-height ratio decreased the onset of comorbidity pairs. These adiposity indicators mainly affect diabetes mellitus-related comorbidity onset in males and hypertensive-related comorbidity onset in females. Adiposity indicators are predictors of multimorbidity and comorbidity pairs and represent a promising approach for intervention.

Predictors of Multimorbidity (Defined as Diabetes + Hypertension) Amongst Males Aged 15-54 in India: An Urban/Rural Split Analysis.

Objectives: This study aimed to determine which sociodemographic and lifestyle factors may act as predictors of multimorbidity (defined as diabetes + hypertension) amongst men aged 15-54 within urban and rural areas of India. Methods: Data from the latest 2019-2021 India NFHS-5 survey were utilized. Presumed cases of multimorbidity were defined as men who had DM + HTN. A total of 22,411 men in urban areas and 66,768 rural men were analyzed using mixed-effect multi-level binary logistic regression models. Results: Various predictors were found to have a statistically significant association to multimorbidity. Urban areas: Age, region of residence, wealth, religion, occupation, and BMI. Rural areas: Age, education, region of residence, wealth, occupation, caste, BMI, alcohol consumption, media exposure, and tobacco consumption. Conclusion: Departing from the broad operational definitions often studied within literature, this study provided insight into one of the most prevalent specific multimorbidities across India. The urban/rural split analyses revealed substantial differences in high-risk characteristics across both areas, which have commonly been overlooked. These findings may better inform policymakers and assist in effectively reducing multimorbidity-related burden through area-specific preventative programs.

Multimorbidity in the working-age population of Serbia: Results from the 2019 National Health Survey

Background/Aim. Population aging and the increase in the prevalence of chronic diseases led to a rise in the number of people who live with more than one disease. The aim of the study was to determine the prevalence and predictors of multimorbidity in the working-age population (WAP) of Serbia. Methods. The study is part of ?The 2019 Serbian National Health Survey?, a cross-sectional study conducted on a representative stratified two-stage sample. For this paper, a representative data sample for 9,473 persons of the WAP (aged 15-64 years) was used. Multimorbidity was defined as the co-occurrence of two or more of 13 chronic conditions. Data on chronic conditions were self-reported, and data on body mass and body height were measured. Multivariable logistic regression was used to assess predictors of multimorbidity. Results. Multimorbidity prevalence among WAP was 12.0%, and it was significantly higher among women (13.3%) than in men (10.6%). The predictors of multimorbidity were: female gender, increasing age, lower level of education, lower income, unemployment, retirement, widowhood, and divorce. Being overweight and obese were associated with higher odds of multimorbidity in both men and women. Conclusion. Multimorbidity is an important public health problem amongst WAP in Serbia due to its high prevalence, especially among vulnerable groups, and its inequality in frequency among different socioeconomic groups.

Patterns and Predictors of Multimorbidity in the Azar Cohort.

The co-existence of chronic diseases (CDs), a condition defined as multimorbidity (MM), is becoming a major public health issue. Therefore, we aimed to determine the patterns and predictors of MM in the Azar Cohort. We evaluated the prevalence of MM in 15,006 (35-70-year old) subjects of the Azar Cohort Study. MM was defined as the co-existence of two or more CDs. Data on the subjects' socioeconomic status, demographics, sleeping habits, and physical activity were collected using questionnaires. The overall prevalence of MM was 28.1%. The most prevalent CDs, in decreasing order, were obesity, hypertension, depression, and diabetes. Obesity, depression, and diabetes were the most co-occurring CDs. The MM risk increased significantly with age, illiteracy, and in females. Also, the subjects within the lowest tertile of physical activity level (OR=1.89; 95% CI: 1.75-2.05) showed higher MM risk than those with the highest level of physical activity. Findings regarding current smoking status indicated that being an ex-smoker or smoker of other types of tobacco significantly increased the risk of MM. The reduction of MM is possible by promoting public health from an early age among people of various socioeconomic conditions. It is vital to offer the necessary health support to the aging population of Iran.

Patterns and predictors of non-communicable disease multimorbidity among older adults in India: evidence from longitudinal ageing study in India (LASI), 2017-2018.

Escalating non-communicable disease multimorbidity rates among older adults is an emerging public health concern in India, but the literature sparsely addresses the epidemiology of multimorbidity. We explore levels, patterns, combinations and predictors of multimorbidity among older adults using information on 59,764 individuals, aged 45years and older, from the first wave of Longitudinal Ageing Study in India (LASI), 2017-2018. We computed multimorbidity score for sixteen non-communicable diseases to identify frequently occurring morbidity patterns (dyads and triads) and assess the relationship between multimorbidity and selected background characteristics. Near third of the older adult population is affected by multimorbidity, with hypertension, gastrointestinal disorders, musculoskeletal disorders, diabetes and skin diseases being the most common. Policymakers should seek strategies to increase early detection and prevention of chronic diseases, delay the age at onset of disease for those who are not affected and improve management for those affected with multiple disease conditions.

Gender inequalities in prevalence, pattern and predictors of multimorbidity among geriatric population in rural West Bengal.

Multimorbidity is considered as a priority for global health research. It is defined as the coexistence of two or more chronic health conditions in an individual. It is increasingly being recognized as a major concern for primary care physicians due to its huge impact on individual, family, and societal level. The aim of this study was to find the quantum of gender-related inequalities and determinants of multimorbidity among the elderly people in a rural part of West Bengal. This is a cross-sectional study carried out among 246 elderly people (60 years and above) in a community setting from Oct 2017 to Sep 2018. Logistic regression analysis was done to find out the predictors of multimorbidity. Data were analyzed using the SPSS software (version 16.0. Chicago, SPSS Inc.). Approximately 82% of the study subjects were suffering from multi-morbidity with a significant difference between males (80.9%) and females (88.5%). In binary logistics, people of both the genders aged >70 years, who had less than primary level education, had more than three children, whose source of income was from their children (sons/daughters), were dependent on others for daily routine were at high risk of being multimorbid; whereas depression was a significant predictor of multimorbidity in females and not in males. In multivariable analysis, age remained the only significant predictor for both the gender and for females; depression remained significant after adjusting with significant variables in binary logistics. Morbidity screening at each visit, individual as well as family counseling and lifestyle modifications help to cope with the rising burden of multimorbidity at the primary care level. More insight into the epidemiology of multi-morbidity is necessary to take steps to prevent it, lower its burden and align health-care services as per needs.

The impact of the epidemiology of multimorbidity on health policy across settings and countries

Abstract Issue/Problem The epidemiology of multiple health conditions (also known as multimorbidity) is increasingly being studied across many settings and countries around the world. More specifically, prevalence and predictors of multimorbidity are being explored among clinical and population-based samples from high-, middle- and low-income countries in rural and urban settings. While the prevalence of multimorbidity tends to increase among ageing populations, a growing proportion of adults under the age of 65 years are also living with multimorbidity, indicating a need for effective interventions. Description of Problem There is a need to identify consistent and distinct epidemiological patterns of multimorbidity across different settings and countries. More specifically, the prevalence and consequence of multimorbidity may have different implications in high-, middle- and low-income countries, as well as between rural and urban settings. This understanding can be informed by extracting information from relevant publications and conducting a survey of multimorbidity researchers on the impact of this research on subsequent health policy priorities and decisions in their specific setting or country. Intended Results The responses and insights provided by multimorbidity researchers from the completion of the survey (consisting of both open- and closed-ended questions) will be presented, including how the epidemiology of multimorbidity has been conducted to date and how this research may or may not be informing policy priorities and decisions in their respective countries. As well, the focus of interventions to prevent multimorbidity occurrence will be explored across settings. Lessons Overall, this presentation will aim to summarize key patterns in both epidemiology and policy responses to multimorbidity occurrence and implications across settings and countries. This work will also aim to create a more formal community of multimorbidity researchers around the world.

Prevalence and predictors of multimorbidity among immigrant Asian Indian women residing in Sydney Australia: A cross-sectional study

Abstract Background: There has been a rise in multimorbidity as people age and technology advances which is challenging for health systems. Multimorbidity prevalence varies globally due to various biological and social risk factors which can be accentuated or mitigated for populations in migration. This study investigated the prevalence and predictors of multimorbidity amongst a group of migrant Asian Indian women living in Australia. Methods: A cross-sectional descriptive study design using convenience sampling investigated the multimorbidity risk factors among first generation migrant Asian Indian women in Australia. This study was part of a larger study titled “Measuring Acculturation and Psychological Health of Senior Indian Women Living in Australia” that was conducted in Sydney, Australia. Data were collected using validated instruments as well as investigator developed questions. Women completed questionnaire surveys either by themselves or through the assistance of bilingual coordinators as English was not their first language. Results: 26% of the participants had one chronic condition and 74% had multimorbidities. The prevalence of individual conditions included cardiovascular disease 67.0%, osteoarthritis 57.6%, depression 37.4%, diabetes 31.5%, chronic respiratory conditions 10.8%, cancer 4.9% and nephrological problems 1.47%. In the unadjusted model, factors such as increasing age, education level, employment status, living arrangements, low physical activity, and elements of acculturative stress were significantly associated with multimorbidity. Multi-variable analysis identified the acculturative stress factor of threat to ethnic identity as a predictor of multimorbidity. Conclusion: Identifying the key determinants of multimorbidity in older adults from a migrant community with pre-existing risk factors can assist with the development of culturally appropriate strategies to identify people at risk of health conditions and to mitigate the health effects of acculturative stress.

Prevalence, Patterns, and Predictors of Multimorbidity in Adults With Acquired Brain Injury at Admission to Staged Community-Based Rehabilitation

ObjectivesTo describe the prevalence, patterns, and predictors of multimorbidity in adults with an acquired brain injury (ABI) on presentation to a community-based neurorehabilitation service. DesignRetrospective cohort study using routinely collected admissions and clinical data. SettingCommunity-based neurorehabilitation. ParticipantsIndividuals (N=263) with non-traumatic brain injury (NTBI; n=187 [71.1%]) versus traumatic brain injury (TBI; n=76 [28.9%]). InterventionsNot applicable. Main Outcome MeasuresComorbidity was defined as the co-occurrence of at least one chronic condition in conjunction with a primary diagnosis of ABI. Multimorbidity was defined as the co-occurrence of 2 or more chronic conditions across 2 or more body systems, in conjunction with a primary diagnosis of ABI. ResultsComorbidity was present in 72.2% of participants overall, whereas multimorbidity was present in 35.4% of the cohort. The prevalence of comorbidity (76% vs 63%; P=.036) and multimorbidity (40% vs 24%; P=.012) was higher in NTBI compared with participants with TBI. Participants with NTBI had a higher prevalence of physical health multimorbidities, including cardiovascular (44% vs 6%; P<.001) and endocrine (34% vs 10%; P=.002) disease, whereas participants with TBI had a higher prevalence of mental health conditions (79% vs 48%; P<.001). Depression (36.3%) and hypertension (25.8%) were the most common diagnoses. Increasing age was the only significant predictor of multimorbidity. ConclusionsMost participants experienced multimorbidity. Effective management of multimorbidity should be included as part of individual rehabilitation for ABI and planning of resource allocation and service delivery. The results of this study can help guide the provision of treatment and services for individuals with ABI in community-based rehabilitation. Our study highlights access to mental health, cardiovascular, endocrine, and neurology services as essential components of rehabilitation for ABI.

Predictors of multimorbidity among the Kurdish population living in the Northwest of Iran

BackgroundThere is limited information about the predictors of multimorbidity (MM) among ethnic minority older adults in Iran. This study aimed to examine the impact of sociodemographic and lifestyle factors on the prevalence of MM, among older Kurdish people living in the Northwest of Iran.MethodsThe current study was conducted in Bukan city located in West Azarbaijan province in the Northwest of Iran. The data for this study was based on the enrolment phase of a longitudinal study on ageing among the Kurdish population aged 50–94 yearswhich was designed according to the elderly component of the PERSIAN Cohort in Iran. Stratified random sampling was used to select people aged 50 + from eight health centres in Bukan from Oct 2017 to Dec 2018. Those who accepted the invitation and completed the baseline questionnaire were included in this study (N = 1493; Response rate 75%). A range of different factors,including sociodemographic and lifestyle factors as well as self-reported chronic conditions, was obtained by trained interviewers. MM was defined as “coexistence of two or more chronic conditions in the same person”. We included a list of 36 diseases/conditions in this analysis. Descriptive analysis and logistic regression were performed to compare the prevalence of MM according to different factors.ResultsOver a third of the participants (36.6%) had ≥2chronic conditionsand 15.7% had≥3 chronic conditions. Hypertension, diabetes, musculoskeletal conditions, fatty liver, and heart diseases were common chronic conditions. In a fully adjusted logistic regression model, older age (ORadj = 1.92, 95% CI: 1.48–2.48), being female (ORadj = 1.49, 95%CI: 1.14–1.94), living without aspouse (ORadj = 1.81, 95%CI: 1.34–2.44), and inadequate consumption of fruit and vegetables (ORadj = 1.33, 95%CI: 1.06–1.67) were associated with the higher prevalence of MM.ConclusionThis study found that the prevalence of MM is relatively high among older Kurdishadults. Sociodemographic differences in the prevalence of MM might be of interest tothe health care system,and the prevalence of common chronic conditions in this study may highlight the need forlifestyle modifications in this community.

Magnitude and determinants of multimorbidity and health care utilization among patients attending public versus private primary care: a cross-sectional study from Odisha, India

BackgroundMultimorbidity in primary care is a challenge not only for developing countries but also for low and medium income countries (LMIC). Health services in LMIC countries are being provided by both public and private health care providers. However, a critical knowledge gap exists on understanding the true extent of multimorbidity in both types of primary care settings.MethodsWe undertook a study to identify multimorbidity prevalence and healthcare utilization among both public and private primary care attendees in Odisha state of India. A total of 1649 patients attending 40 primary care facilities were interviewed using a structured multimorbidity assessment questionnaire collecting information on 22 chronic diseases, medication use, number of hospitalization and number of outpatient visits.ResultThe overall prevalence of multimorbidity was 28.3% and nearly one third of patients of public facilities and one fourth from private facilities had multimorbidity. Leading diseases among patients visiting public facilities included acid peptic diseases, arthritis and chronic back pain. No significant difference in reporting of hypertension and diabetes across the facilities was seen. Besides age, predictors of multimorbidity among patients attending public facilities were, females [AOR: 1.6; 95% CI 1.1–1.3] and non-aboriginal groups [AOR: 1.6; 95%CI 1.1–2.3] whereas, in private females [AOR: 1.6; 95%CI 1.1–2.4], better socioeconomic conditions [AOR 1.4; 95% CI 1.0–2.1] and higher educational status [primary school completed [AOR 2.6; 95%CI 1.6–4.2] and secondary schooling and above [AOR 2.0; 95%CI 1.1–3.6] with reference to no education were seen to be the determinants of multimorbidity. Increased number of hospital visits to public facilities were higher among lower educational status patients [IRR: 1.57; 95% CI 1.13–2.18] whereas, among private patients, the mean number of hospital visits was 1.70 times more in higher educational status [IRR: 1.70; 95%CI 1.01–3.69]. The mean number of medicines taken per day was higher among patients attending private hospitals.ConclusionOur findings suggest that, multimorbidity is being more reported in public primary care facilities. The pattern and health care utilization in both types of settings are different. A comprehensive care approach must be designed for private care providers.

Incidence and predictors of multimorbidity among a multiethnic population in Malaysia: a community-based longitudinal study

Multimorbidity in older adults needs to be assessed as it is a risk factor for disability, cognitive decline, and mortality. A community-based longitudinal study was performed to determine the incidence and to identify possible predictors of multimorbidity among multiethnic older adults population in Malaysia. Comprehensive interview-based questionnaires were administered among 729 participants aged 60years and above. Data were analyzed from the baseline data of older adults participating in the Towards Useful Aging (TUA) study (2014-2016) who were not affected by multimorbidity (349 without any chronic diseases and 380 with one disease). Multimorbidity was considered present in an individual reporting two or more chronic diseases. After 1½ years of follow-up, 18.8% of participants who were initially free of any diseases and 40.9% of those with one disease at baseline, developed multimorbidity. The incidence rates were 13.7 per 100 person-years and 34.2 per 100 person-years, respectively. Female gender, smoking, and irregular preparing of food (lifestyle) were predictors for incidence of multimorbidity, especially in those without any disease, while Body Mass Index (BMI) 22-27kg/m2 and inadequate daily intake of iron were identified as predictors of multimorbidity among participants who already have one disease. The incidence rates of multimorbidity among Malaysian older adults were between the ranges of 14-34 per 100 person-years at a 1½-year follow-up. Gender, smoking, BMI 22-27kg/m2, inadequate daily intake of iron and lack of engagement in leisure or lifestyle physical activities were possible predictors in the development of multimorbidity. There is a need to formulate effective preventive management strategies to decelerate multimorbidity among older adults.

Association between body mass index and prevalence of multimorbidity: a cross-sectional study

ObjectivesTo explore the prevalence of multimorbidity in Serbia according to sex and body mass index (BMI) categories, and to examine the association between BMI and multimorbidity. In addition, this study examined the relationships between the main demographic and socio-economic characteristics of the population (age, settlement, education) and multimorbidity. Study designSecondary analysis of data from the 2013 Serbian National Health Survey. MethodsThis study analyzed data from 13,103 participants aged ≥20 years with BMI data. The associations between BMI, age, education and multimorbidity were analyzed by multivariate logistic regression. ResultsThe overall prevalence of multimorbidity was higher than the overall prevalence of a single disease (26.9% vs 20.7%). The proportion of participants who reported two or more chronic diseases increased with each BMI category in both sexes, reaching the highest values in obese category III. Odds ratios (ORs) for the prevalence in all morbidity groups increased gradually with BMI category, and the highest OR values were found in obese category III. Males of obese category III were seven times more likely to have multimorbidity [OR 7.2, 95% confidence interval (CI) 4.2–12.6] than males of normal weight, whereas females of obese category III were nine and a half times more likely to have multimorbidity (OR 9.5, 95% CI 4.0–22.4) than females of normal weight. In the multivariable analysis, age (both sexes), low and middle level of education (males), and rural settlement and low level of education (females) were found to be predictors of multimorbidity. ConclusionsThis study found positive associations between obesity and multimorbidity and between overweight and multimorbidity. Recognizing these associations is of great importance from both clinical and public health perspectives because this could lead to an integrated approach for patients.

Chronic diseases among the elderly in a rural Vietnam: prevalence, associated socio-demographic factors and healthcare expenditures

BackgroundGlobally, the population of elderly persons is increasing as well as the prevalence of chronic diseases. This change is causing increased healthcare costs to health care systems threatening to push many households into poverty. Low and middle income countries are projected to experience the greatest impact from this change. This study aims to describe the prevalence of common chronic diseases (CCDs) among the elderly in Vietnam, the associated socio-demographic factors and healthcare expenditures.MethodsThis is a cross-sectional study in the FilaBavi demographic surveillance site in Vietnam. 2873 persons over 60 years were randomly sampled. Prevalence of CCDs was reported from study subjects who previously were informed by physicians. Healthcare expenditures were determined from recall of expenses during the last hospital visit. Binomial logistic regression was done to determine the socio demographic predictors of having a CCD or multiple CCDs. Mean healthcare expenditures for the elderly with CCDs and those without CCDs were summarised and compared.ResultsForty two percent of the elderly were found to have at least one CCD. Joint problems were the most common CCD at 35 %, followed by hypertension at 15 % and chronic bronchitis at 11 %. Being female (OR = 1.51, 95 % CI = 1.03–2.21, p-value = 0.036), higher education (OR = 2.54, 95 % CI = 1.13–5.74, p-value = 0.025) and having advanced age (OR = 1.92, 95 % CI = 1.22–3.00, p-value = 0.005), were associated with common chronic diseases in the elderly. Outpatient healthcare expenditures were found to be significantly higher for the elderly with CCDs than those without CCDs.ConclusionsHigher education and being female are important key predictors of having a CCD, while wealth quintile is a predictor of multimorbidity, in the elderly. Healthcare expenditures for outpatient health services are higher for elderly persons with CCDs and these costs should be targeted when planning for financial protection.

Incidence and predictors of multimorbidity in the elderly: a population-based longitudinal study.

BackgroundWe aimed to calculate 3-year incidence of multimorbidity, defined as the development of two or more chronic diseases in a population of older people free from multimorbidity at baseline. Secondly, we aimed to identify predictors of incident multimorbidity amongst life-style related indicators, medical conditions and biomarkers.MethodsData were gathered from 418 participants in the first follow up of the Kungsholmen Project (Stockholm, Sweden, 1991–1993, 78+ years old) who were not affected by multimorbidity (149 had none disease and 269 one disease), including a social interview, a neuropsychological battery and a medical examination.ResultsAfter 3 years, 33.6% of participants who were without disease and 66.4% of those with one disease at baseline, developed multimorbidity: the incidence rate was 12.6 per 100 person-years (95% CI: 9.2–16.7) and 32.9 per 100 person-years (95% CI: 28.1–38.3), respectively. After adjustments, worse cognitive function (OR, 95% CI, for 1 point lower Mini-Mental State Examination: 1.22, 1.00–1.48) was associated with increased risk of multimorbidity among subjects with no disease at baseline. Higher age was the only predictor of multimorbidity in persons with one disease at baseline.ConclusionsMultimorbidity has a high incidence at old age. Mental health-related symptoms are likely predictors of multimorbidity, suggesting a strong impact of mental disorders on the health of older people.

Multimorbidity prevalence in the general population: the role of obesity in chronic disease clustering

BackgroundThe role of obesity in the prevalence and clustering of multimorbidity, the occurrence of two or more chronic conditions, is understudied. We estimated the prevalence of multimorbidity by obesity status, and the interaction of obesity with other predictors of multimorbidity.MethodsData from adult respondents (18 years and over) to the Health Quality Council of Alberta 2012 Patient Experience Survey were analyzed. Multivariable regression models were fitted to test for associations.ResultsThe survey sample included 4803 respondents; 55.8% were female and the mean age was 47.8 years (SD, 17.1). The majority (62.0%) of respondents reported having at least one chronic condition. The prevalence of multimorbidity, including obesity, was 36.0% (95% CI, 34.8 – 37.3). The prevalence of obesity alone was 28.1% (95% CI 26.6 – 29.5). Having obesity was associated with more than double the odds of multimorbidity (odds ratio = 2.2, 95% CI 1.9 – 2.7) compared to non-obese.ConclusionsThe prevalence of multimorbidity in the general population is high, but even higher in obese than non-obese persons. These findings may be relevant for surveillance, prevention and management strategies for multimorbidity.