Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.
Background Childhood obesity poses a global health challenge. In recent years, there has been an increase in interventions that begin in pregnancy, putting the concept of early programming and early risk factors into practice. The present study aims to update the findings regarding interventions in the first 1000 days of life. Methods A systematic review based on the PRISMA guidelines was carried out in PubMed, WoS, Scopus and CINAHL to obtain the articles to be analysed. We included those studies published between 2016 and 2021. Human interventions that started within the first 1000 days of life and acted on at least one programming factor were included. Once selected, coding and quantitative content analysis was carried out to obtain a profile of the interventions during the first 1000 days. Results From all screened articles, 51 unique interventions, which met the selection criteria, were included. The majority of interventions (81%) took place in high-income areas. Almost all (86%) were targeted at the general population. The majority (54%) started in the second trimester of pregnancy. A clear majority (61%) ended at the time of birth. 44% of the interventions included all pregnant women. Only 48% of these interventions were focused on improving the nutritional status of the offspring in the short term. Most interventions collected the baby's weight at birth (68%). Conclusions It can be concluded that current interventions are not covering as many aspects as they should. Future research should be conducted more frequently in developing countries and target disadvantaged groups. These interventions should include all pregnant women, regardless of their nutritional status, aiming to cover as many programming factors as possible and extending through the first 1000 days of life, with body mass index or skinfolds as measures of effectiveness during this period.
Background Multimorbidity (MM) is associated with lower quality of life, greater disability, and higher use of health services. It is a complex problem that is difficult to capture due to the broad spectrum of concurrent chronic diseases involved. There is a need to identify and characterize patterns of chronic conditions in the local context of specific population groups. The DEMMOCAD project aims to respond to this knowledge gap by detecting patterns of MM and their inequalities in the province of Cadiz (Spain). Methods A cross-sectional study was carried out by means of telephone interviews with people over 50 years of age. The final sample was 1592 individuals with MM. A latent class analysis was carried out to identify patterns of MM from 31 chronic conditions. First, the appropriate number of classes was established, considering model fit indices, class size, and clinical interpretability. Subsequently, covariates were introduced into the model using the three-step approach, a technique that minimizes biases in the multinomial regression model. Results Preliminary analyses of the goodness-of-fit indices of the model derived five MM patterns (entropy = 0.727): (C1) mild MM; (C2) cardiovascular; (C3) musculoskeletal; (C4) musculoskeletal plus mental; and (C5) complex MM. Compared with class C1, persons in class C5 were significantly older and less educated, class C4 had a lower income, class C3 was smokers and disabled, and class C2 was characteristic among older males. All four classes also showed lower scores on mental and physical dimensions of the SF12 scale compared to class C1. Conclusions In addition to providing an adjusted characterization of the population of the area analyzed, these initial findings highlight the existence of social inequalities in multimorbidity at the local level that should be addressed by implementing policies targeting the most vulnerable groups in Cadiz (low socioeconomic status groups, people with disabilities, and the elderly). Key messages
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