PLoS Medicine最新文献

Background: People with severe mental illness (SMI) have higher rates of a range of physical health conditions, yet little is known regarding the clustering of physical health conditions in this population. We aimed to investigate the prevalence and clustering of chronic physical health conditions in people with SMI, compared to people without SMI.

Methods and findings: We performed a cohort-nested accumulated prevalence study, using primary care data from the Clinical Practice Research Datalink (CPRD), which holds details of 39 million patients in the United Kingdom. We identified 68,783 adults with a primary care diagnosis of SMI (schizophrenia, bipolar disorder, or other psychoses) from 2000 to 2018, matched up to 1:4 to 274,684 patients without an SMI diagnosis, on age, sex, primary care practice, and year of registration at the practice. Patients had a median of 28.85 (IQR: 19.10 to 41.37) years of primary care observations. Patients with SMI had higher prevalence of smoking (27.65% versus 46.08%), obesity (24.91% versus 38.09%), alcohol misuse (3.66% versus 13.47%), and drug misuse (2.08% versus 12.84%) than comparators. We defined 24 physical health conditions derived from the Elixhauser and Charlson comorbidity indices and used logistic regression to investigate individual conditions and multimorbidity. We controlled for age, sex, region, and ethnicity and then additionally for health risk factors: smoking status, alcohol misuse, drug misuse, and body mass index (BMI). We defined multimorbidity clusters using multiple correspondence analysis (MCA) and K-means cluster analysis and described them based on the observed/expected ratio. Patients with SMI had higher odds of 19 of 24 conditions and a higher prevalence of multimorbidity (odds ratio (OR): 1.84; 95% confidence interval [CI]: 1.80 to 1.88, p < 0.001) compared to those without SMI, particularly in younger age groups (males aged 30 to 39: OR: 2.49; 95% CI: 2.27 to 2.73; p < 0.001; females aged 18 to 30: OR: 2.69; 95% CI: 2.36 to 3.07; p < 0.001). Adjusting for health risk factors reduced the OR of all conditions. We identified 7 multimorbidity clusters in those with SMI and 7 in those without SMI. A total of 4 clusters were common to those with and without SMI; while 1, heart disease, appeared as one cluster in those with SMI and 3 distinct clusters in comparators; and 2 small clusters were unique to the SMI cohort. Limitations to this study include missing data, which may have led to residual confounding, and an inability to investigate the temporal associations between SMI and physical health conditions.

Conclusions: In this study, we observed that physical health conditions cluster similarly in people with and without SMI, although patients with SMI had higher burden of multimorbidity, particularly in younger age groups. While interventions aimed at the general population may also be appropriate for those with SMI, t

Background: Different nutrient profiles (NPs) have been developed in Latin America to assess the nutritional quality of packaged food products. Recently, the Mexican NP was developed as part of the new warning label regulation implemented in 2020, considering 5 warning octagons (calories, sugar, sodium, saturated fats, and trans fats) and 2 warning rectangles (caffeine and non-nutritive sweeteners). The objective of this cross-sectional study was to evaluate the Mexican NP and other NPs proposed or used in Latin America against the Pan American Health Organization (PAHO) model.

Methods and findings: Nutrition content data of 38,872 packaged food products available in the Mexican market were collected in 2016 and 2017. The evaluation of the Mexican NP, including its 3 implementation phases of increasing stringency (2020, 2023, and 2025), was conducted by comparing the percentage of products classified as "healthy" (without warnings) or "less healthy" (with 1 or more warnings), as well as the number and type of warnings assigned to food products, against the PAHO NP. Using the calibration method, we compared the classifications produced by the PAHO model against those produced by the NP models of Ecuador, Chile (3 phases), Peru (2 phases), Uruguay, and Brazil. Kappa coefficients and Pearson correlations were estimated, and proportion tests were performed. We found that the 3 implementation phases of the Mexican NP had near to perfect agreement in the classification of healthy foods (Mexico NP models: 19.1% to 23.8%; PAHO model: 19.7%) and a strong correlation (>91.9%) with the PAHO model. Other NPs with high agreement with the PAHO model were the Ecuador (89.8%), Uruguay (82.5%), Chile Phase 3 (82.3%), and Peru Phase 2 (84.2%) NPs. In contrast, the Peru Phase 1, Brazil, and Chile Phase 1 NP models had the highest percentage of foods classified as healthy (49.2%, 47.1%, and 46.5%, respectively) and the lowest agreement with the PAHO model (69.9%, 69.3%, and 73%, respectively). Study limitations include that warnings considered by the Mexican NP models were evaluated as if all the warnings were octagon seals, while 2 out of the 7 were rectangular warnings (caffeine and non-nutritive sweeteners), and that our data are limited by the quality of the information reported in the list of ingredients and the nutrition facts table of the products.

Conclusions: The 3 implementation phases of the Mexican NP were useful to identify healthy food products. In contrast, the Peru Phase 1, Brazil, and Chile Phase 1 NP models may have limited usefulness for the classification of foods according to the content of ingredients of concern. The results of this study may inform countries seeking to adapt and evaluate existing NP models for use in population-specific applications.

Background: Disease and disability from alcohol use disproportionately impact people in low- and middle-income countries (LMICs). While varied interventions have been shown to reduce alcohol use in high-income countries, their efficacy in LMICs has not been assessed. This systematic review describes current published literature on patient-level alcohol interventions in LMICs and specifically describes clinical trials evaluating interventions to reduce alcohol use in LMICs.

Methods and findings: In accordance with PRISMA, we performed a systematic review using an electronic search strategy from January 1, 1995 to December 1, 2020. Title, abstract, as well as full-text screening and extraction were performed in duplicate. A meta-summary was performed on randomized controlled trials (RCTs) that evaluated alcohol-related outcomes. We searched the following electronic databases: PubMed, EMBASE, Scopus, Web of Science, Cochrane, WHO Global Health Library, and PsycINFO. Articles that evaluated patient-level interventions targeting alcohol use and alcohol-related harm in LMICs were eligible for inclusion. No studies were excluded based on language. After screening 5,036 articles, 117 articles fit our inclusion criteria, 75 of which were RCTs. Of these RCTs, 93% were performed in 13 middle-income countries, while 7% were from 2 low-income countries. These RCTs evaluated brief interventions (24, defined as any intervention ranging from advice to counseling, lasting less than 1 hour per session up to 4 sessions), psychotherapy or counseling (15, defined as an interaction with a counselor longer than a brief intervention or that included a psychotherapeutic component), health promotion and education (20, defined as an intervention encouraged individuals' agency of taking care of their health), or biologic treatments (19, defined as interventions where the biological function of alcohol use disorder (AUD) as the main nexus of intervention) with 3 mixing categories of intervention types. Due to high heterogeneity of intervention types, outcome measures, and follow-up times, we did not conduct meta-analysis to compare and contrast studies, but created a meta-summary of all 75 RCT studies. The most commonly evaluated intervention with the most consistent positive effect was a brief intervention; similarly, motivational interviewing (MI) techniques were most commonly utilized among the diverse array of interventions evaluated.

Conclusions: Our review demonstrated numerous patient-level interventions that have the potential to be effective in LMICs, but further research to standardize interventions, populations, and outcome measures is necessary to accurately assess their effectiveness. Brief interventions and MI techniques were the most commonly evaluated and had the most consistent positive effect on alcohol-related outcomes.

Trial registration: Protocol Registry: PROSPERO C

Background: India launched the National Rural Health Mission (NRHM) in 2005 to strengthen its primary healthcare system in high-focus and northeast-focus states. One of the NRHM objectives was to reduce child undernutrition in India.

Methods and findings: We used data from 1992, 1998, 2005, and 2015 National Family Health Survey (NFHS) of India to evaluate trends in child undernutrition prevalence before and after NRHM and across different categories of focus states. Stunting, Wasting, and Comprehensive Index of Anthropometric Failure (CIAF) were assessed using the World Health Organization (WHO) growth curves to assess chronic, acute, and overall undernutrition. The study included 187,452 children aged 3 years or under. Survey-weighted and confounder-adjusted average annualized reduction rates (AARRs) and predicted probability ratios were used to assess trends and socioeconomic disparities for child undernutrition, respectively. Nationwide, the prevalence of all types of undernutrition decreased from 1992 to 2015. However, the trends varied before and after NRHM implementation and differentially by focus states. After NRHM, acute undernutrition declined more rapidly among high-focus states (AARR 1.0%) but increased in normal-focus states (AARR -1.9% per year; p-value for the difference <0.001). In contrast, the prevalence of chronic undernutrition declined more rapidly (AARR 1.6%) in the normal-focus states in comparison to high-focus states (0.3%; p-value for the difference = 0.01). Income and caste-based disparities in acute undernutrition decreased but did not disappear after the implementation of the NRHM. However, similar disparities in prevalence of chronic undernutrition appear to be exacerbated after the implementation of the NRHM. Major limitations of this study include the observational and cross-sectional design, which preclude our ability to draw causal inferences.

Conclusions: Our results suggests that NRHM implementation might be associated with improvement in wasting (acute) rather than stunting (chronic) forms of undernutrition. Strategies to combat undernutrition equitably, especially in high-focus states, are needed.