Dynamics of the double burden of malnutrition in Guatemala: a secondary data analysis of the demographic and health surveys from 1998-2015

OBJECTIVE

We estimated the prevalence and time trends of the double burden of malnutrition (DBM) in Guatemala and explored its occurrence based on socio-demographic factors.

STUDY DESIGN

This was a secondary data analysis using information from four Demographic and Health Surveys covering the period 1998-2015.

METHODS

The unit of analysis was the household within which information was gathered from women 18-49 years and their children, 6-59 months. The main outcome was the prevalence of any DBM in the household (co-existence of undernutrition and overnutrition in a woman, her children or both). We estimated the prevalence of any DBM by survey and analysed time trends. Stepwise logistic regression was used to explore the occurrence of DBM and socio-demographic factors.

RESULTS

We analysed 39,749 households across all surveys. The prevalence of any DBM was 25.3% (95%CI: 22.1-28.7) in 1998-99, 23.8% (22.0-25.8) in 2002, 25.9% (24.3-27.5) in 2008-09 and 24.2% (22.9-25.5) in 2014-15, with no significant change over time (P = 0.782). Characteristics associated with lower odds of any DBM were rural residence, female-headed household, wealth and women's secondary education. Higher odds were seen for households with electricity, women >25y, indigenous and with >2 children.

CONCLUSION

Our findings revealed that a quarter of Guatemala's households suffer from DBM, which has remained unchanged for 17 years. Interventions should prioritise urban areas, households of lower socio-economic status and those less educated. To increase awareness of policymakers of this pressing public health concern, further research on DBM could be strengthened by prospective study designs, integrating all household members and expanding the types of malnutrition.

Associations of pre-existing comorbidities and COVID-19 in-hospital mortality: an unCoVer analyses

Background

Accumulated evidence on risk factors for adverse COVID-19 outcomes revealed that old age and male sex are main associates, next to pre-existing comorbidities, as analysed from scattered single cohorts of hospitalised COVID-19 patients of accessible electronic medical records. Hence, evidence from federated analyses is called for to provide a more comprehensive and robust analyses of risk factors.

Methods

Using the unCoVer network, i.e., a research platform of 29 partners for the expert use of patient data as routinely gathered in real-world healthcare settings, present analyses restricted to available data of four hospitals from Spain, Slovakia, Romania and Bosnia and Herzegovina covering 8,287 hospitalised COVID-19 patients. In-hospital death after COVID-19 diagnosis was examined in relation to common pre-existing comorbidities using virtual pooling of logistic regression models adjusted for age and sex.

Results

Patients were on average 60.1 (± 20.9) years, 50.7% were male, almost half (43.3%) had at least one pre-existing comorbidity (17.4% having obesity, 21.9% hypertension, 18.0% diabetes and 13.7% cardiovascular diseases (CVD)), and 12.6% died during hospitalisation. Patients with comorbidities had a higher risk of mortality that was increasing with the number of comorbidities: from a virtual pooled odds ratio of 1.16 (95%CI: 0.96, 1.40) for one vs none to 1.30 (1.04, 1.64) and 2.14 (1.64, 2.79) for two and three or more comorbidities, respectively. Of the comorbidities, highest risk was seen for CVD (1.68; 1.40, 2.01), followed by hypertension (1.40; 1.19, 1.64) and diabetes (1.27; 1.07, 1.50), and the lowest for obesity (1.13; 0.94, 1.37).

Conclusions

By federated analyses, this study confirmed that the number of comorbidities was a strong risk factor for in-hospital death after COVID-19, in particular CVD. The unCoVer platform pursues using scattered data sources by innovative computational resources and integrated information for enhanced impact.

Key messages

Comparing health insurance and survey data in estimating prevalence of chronic diseases

Background

Population prevalence of chronic conditions can be estimated from national health surveys and from administrative data sources such as insurance records. This study evaluated the agreement between the Belgian Health Interview Survey (BHIS) and the Belgian compulsory health insurance data (BCHI) in ascertaining chronic hypertension, hypercholesterolemia and diabetes in Belgium.

Methods

The most recent cycle of BHIS (2018) provided the self-reported prevalence of diabetes, hypertension, and hypercholesterolemia among a representative sample of Belgian adults. For BCHI, the chronic conditions were attributed for every individual in the BHIS reviewing the medication prescription records identified using the ATC/DDD system. These two data sources were linked through unique identifiers by STATBEL. Disease prevalence, measures of agreement, and measures of concordance were estimated. Logistic regression was performed to determine the factors affecting agreement between BHIS and BCHI’s disease classifications.

Results

Data linkage was done for 9,753 individuals aged 15 years and older. From the sample, BHIS and BCHI respectively identified 5.9% and 5.6% diabetes cases, 18% and 24% of hypertension cases, and 18% and 16% of hypercholesterolemia cases. The kappa coefficient between BCHI and self-reported diabetes, hypertension, and hypercholesterolemia was 0.79, 0.59, and 0.49, respectively. Gender, age, and subjective health significantly affected the agreement in chronic condition classification between BHIS and BCHI.

Conclusions

Data on reimbursed drugs is a potential alternative method in the surveillance of chronic diabetes. This procedure could be used in estimating disease prevalence but further validation is needed to evaluate its applicability and bias in other chronic conditions.

Key messages

• BCHI is a possible alternative data source for the surveillance of diabetes in the population.

• BCHI overestimated hypertension and underestimated hypercholesterolemia prevalence.

Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis

Background

Since the onset of the pandemic, the unCoVer network has been identifying real-world data from EMR of hospitalised patients with COVID-19 across countries. These heterogeneous data are integrated into a multi-user data repository operated through Opal/DataSHIELD, an interoperable open-source server application, providing privacy-preserving access to individual-level information for federated data analyses.

Methods

unCoVer’s federated data platform provided access to EMR collected between 02/2020 - 04/2022 from 6 hospitals in Bosnia and Herzegovina (1), Romania (2), Spain (2), and Turkey (1) for a total of 14,236 patients. Demographics, and co-morbidities at admission, length of hospital stay and intensive care (ICU) needs, are presented according to the patients’ status at discharge.

Results

A total of 11,248 (79.0%) of all patients reviewed recovered from COVID-19 after an average 11.5 (SD 10.8) days hospitalised, with only 4.09% of patients needing ICU. A smaller proportion of patients were transferred (5.93%), and 2143 (15.1%) were considered in-hospital deaths after an average 11.6 (SD 10.5) days in the hospital where most (81.2%) needed ICU. Recovered patients had a mean age of 57.7 (SD 16.3) years old, and gender neutral (51.2% men), in contrast to deceased patients that were 74.2 (SD 12.4) years old (59.7% men). Current smoking was infrequent for both recovered or deceased patients (3.27%, and 2.83%, respectively). Cardiometabolic conditions were less commonly reported among later recovered patients in comparison with deceased patients: obesity (10.7% vs 12.1%), diabetes (15.9% vs 27.4%), hypertension (23.2% vs 42.7%), and CVD (9.33% vs 44.9%). Chronic pulmonary disease was also more frequent among deceased patients (10.3% vs 18.1%).

Conclusions

Characteristics of hospitalised COVID-19 patients differ according to outcomes at discharge with more in-hospital death reported among older, chronic patients across 6 hospitals in 4 countries.

Key messages

• Federated analyses provide unique opportunities for robust results by privacy-preserving accessing individual-level data from heterogeneous data sources.

• The unCoVer network aims to demonstrate the usability of the infrastructure to address research questions related to the COVID-19 while extending the concept to other clinical areas.

Inequalities in non-communicable diseases across the European Union: current state and trends from 2000 to 2019

Background

Non-communicable diseases (NCDs) remain the leading cause of disease burden in the European Union (EU). However, this burden varies across Member States, driven by the socioeconomic and demographic structure of the populations, as well as health care and social support systems in each country. This geographical gradient in the burden of NCDs represents health inequalities that may have expanded as a result of population ageing, migration, and economic crisis, and historic backgrounds.

Methods

Using data from the GBD 2019 study, we quantify the inequality gap between EU countries, characterize the geographical gradient of total and individual NCDs, and analyze the trends over the last 20 years.

Results

A considerable gap exists in the rates of NCD-related DALYs between the country with the lowest burden (Slovenia in 2019) and the highest (Bulgaria in 2019), with a constant (p-trend > 0.05) ratio of 1.6 (95%CI, 1.57; 1.64) since the year 2000. The largest inequality was observed for the burden of stroke between the lowest (France in 2019) and the highest (Bulgaria in 2019) with a ratio of 7.47 (6.78; 8.24) Increasing steadily (p-trend < 0.001) since 2000. Using the relative index of inequality to characterize the gradient of inequalities across the EU, a decreasing trend (p < 0.001) can be observed for NCD-related DALYs rates from 1.56 (1.44; 1.7) in 2000 to in 1.42 (1.33; 1.52) 2019.

Conclusions

Despite overall improvements in health, spatial disparities related to NCDs in the EU persist. Our estimates provide a baseline to inform future equitable health policies.

Different protein composition of low-calorie diet differently impacts adipokine profile irrespective of weight loss in overweight and obese women

BACKGROUND AND AIMS

High-protein (HP) diets have shown benefits in cardiometabolic markers such as insulin or triglycerides but the responsible mechanisms are not known. We aimed to assess the effect of three energy-restricted diets with different protein contents (20%, 27%, and 35%; ∼80% coming from animal source) on plasma adipokine concentration and its association with changes in cardiometabolic markers.

METHODS

Seventy-six women (BMI 32.8 ± 2.93) were randomized to one of three calorie-reduced diets, with protein, 20%, 27%, or 35%; carbohydrates, 50%, 43%, or 35%; and fat, 30%, for 3 months. Plasma adipokine (leptin, resistin, adiponectin, and retinol-binding protein 4; RBP4) levels were assessed.

RESULTS

After 3 months, leptin concentration decreased in all groups without differences among them, while resistin levels remained unchanged. Adiponectin concentration heterogeneously changed in all groups (P for trend = 0.165) and resistin concentration did not significantly change. RPB4 significantly decreased by -17.5% (-31.7, -3.22) in 35%-protein diet (P for trend = 0.024 among diets). Triglycerides improved in women following the 35%-protein diet regardless of weight loss; RBP4 variation significantly influenced triglyceride concentration change by 24.9% and 25.9% when comparing 27%- and 35%- with 20%-protein diet, respectively.

CONCLUSIONS

A 35%-protein diet induced a decrease in RBP4 regardless of weight loss, which was directly associated with triglyceride concentration improvement. These findings suggest that HP diets improve the cardiometabolic profile, at least in part, through changes in adipokine secretion. Whether this beneficial effect of HP diet is due to improvements in hepatic or adipose tissue functionality should be elucidated.

CLINICAL TRIAL REGISTRATION

The clinical trial has been registered in ClinicalTrials.gov (Identifier: NCT02160496).

Seasonality of food groups and total energy intake: a systematic review and meta-analysis

BACKGROUND/OBJECTIVES

The aim of this systematic review and meta-analysis was to assess the effect of season on food intake from selected food groups and on energy intake in adults.

SUBJECTS/METHODS

The search process was based on selecting publications listed in the following: Medline, Scopus, Web of Science, Embase and Agris. Food frequency questionnaires, 24-h dietary recalls and food records as methods for assessment of dietary intake were used to assess changes in the consumption of 11 food groups and of energy intake across seasons. A meta-analysis was performed.

RESULTS

Twenty-six studies were included. Articles were divided into those reporting data on four seasons (winter, spring, summer and autumn) or on two seasons (pre-and post-harvest). Four of the studies could be utilized for meta-analysis describing changes in food consumption across four season scheme: from winter to spring fruits decreased, whereas vegetables, eggs and alcoholic beverages increased; from spring to summer vegetable consumption further increased and cereals decreased; from summer to autumn fruits and cereals increased and vegetables, meat, eggs and alcoholic beverages decreased; from autumn to winter cereals decreased. A significant association was also found between energy intake and season, for 13 studies reporting energy intake across four seasons (favors winter) and for eight studies across pre- and post-harvest seasons (favors post-harvest).

CONCLUSIONS

The winter or the post-harvest season is associated with increased energy intake. The intake of fruits, vegetables, eggs, meat, cereals and alcoholic beverages is following a seasonal consumption pattern and at least for these foods season is determinant of intake.

Development and validation of a questionnaire to evaluate lifestyle-related behaviors in elementary school children

Background

The SI! Program promotes cardiovascular health through a multilevel school-based intervention on four lifestyle-related components: diet, physical activity, understanding the body and heart, and management of emotions. We report here the development and validation of the KAH (knowledge, attitudes and habits)-questionnaire adapted for elementary school children (6–7 years old) as a tool for the forthcoming evaluation of the SI! Program, where the KAH scoring will be the primary outcome. The efficacy of such an intervention will be based on the improvements in children’s KAH towards a healthy lifestyle.

Methods

The questionnaire validation process started with a pool of items proposed by the pedagogical team who developed the SI! Program for elementary school. The questionnaire was finalized by decreasing the number of items from 155 to 48 using expert panels and statistical tests on the responses from 384 children (ages 6–7). A team of specialized psychologists administered the questionnaire at schools providing standard directions for the final administration. The internal consistency was assessed using Cronbach’s α coefficients. Reliability was measured through the split-half method, and problematic items were detected applying the item response theory. Analysis of variance and Tukey’s test of additivity were used for multiple comparisons.

Results

The final KAH-questionnaire for elementary school children should be administered to children individually by trained staff. The 48 items-questionnaire is divided evenly between the 4 components of the intervention, with an overall Cronbach’s α = 0.791 (α = 0.526 for diet, α = 0.537 for physical activity, α = 0.523 for human body and heart, and α = 0.537 for management of emotions).

Conclusions

The KAH-questionnaire is a reliable instrument to assess the efficacy of the SI! Program on instilling healthy lifestyle-related behaviors in elementary school children.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-2248-6) contains supplementary material, which is available to authorized users.

Variation in fasting and non-fasting serum enterolactone concentrations in women of the Malmö Diet and Cancer cohort

OBJECTIVES

The aim of this study was to examine the variation of enterolactone from fasting and non-fasting blood of middle-aged healthy women eating a normal diet to determine the usefulness of a single sample in epidemiological studies.

SUBJECTS AND METHODS

Twenty-six women born between 1940 and 1950 were recruited within the Malmö Diet and Cancer cohort. Three non-fasting and two overnight fasting samples were collected from each individual during a 5-week period. Twenty-one participated in all measurements. Enterolactone concentrations were analyzed by time-resolved fluoroimmunoassay.

RESULTS

The within-subject and between-subject variations (coefficient of variations, CV) were estimated to 59 and 89% respectively for fasting samples and 71 and 67% for non-fasting samples. The intraclass correlation coefficients (ICC) were estimated to 0.66 (95% confidence interval (CI) 0.35-0.84) for fasting and 0.48 (95% CI, 0.22-0.72) for non-fasting samples.

CONCLUSIONS

Although the estimated ICC for blood samples was moderate, it indicates that enterolactone levels of both fasting and non-fasting blood samples should be useful in future projects within the Malmö Diet and Cancer cohort.