Determining classes of food items for health requirements and nutrition guidelines using Gaussian mixture models

DATA AVAILABILITY STATEMENT : The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.INTRODUCTION : The identification of classes of nutritionally similar food items is important for creating food exchange lists to meet health requirements and for informing nutrition guidelines and campaigns. Cluster analysis methods can assign food items into classes based on the similarity in their nutrient contents. Finite mixture models use probabilistic classification with the advantage of taking into account the uncertainty of class thresholds. METHODS : This paper uses univariate Gaussian mixture models to determine the probabilistic classification of food items in the South African Food Composition Database (SAFCDB) based on nutrient content. RESULTS : Classifying food items by animal protein, fatty acid, available carbohydrate, total fibre, sodium, iron, vitamin A, thiamin and riboflavin contents produced data-driven classes with differing means and estimates of variability and could be clearly ranked on a low to high nutrient contents scale. Classifying food items by their sodium content resulted in five classes with the class means ranging from 1.57 to 706.27 mg per 100 g. Four classes were identified based on available carbohydrate content with the highest carbohydrate class having a mean content of 59.15 g per 100 g. Food items clustered into two classes when examining their fatty acid content. Foods with a high iron content had a mean of 1.46 mg per 100 g and was one of three classes identified for iron. Classes containing nutrientrich food items that exhibited extreme nutrient values were also identified for several vitamins and minerals. DISCUSSION : The overlap between classes was evident and supports the use of probabilistic classification methods. Food items in each of the identified classes were comparable to allowed food lists developed for therapeutic diets. This datadriven ranking of nutritionally similar classes could be considered for diet planning for medical conditions and individuals with dietary restrictions.The South African Medical Research Council.http://frontiersin.org/Nutritionam2024StatisticsSDG-02:Zero HungerSDG-03:Good heatlh and well-bein

Statistical methods for the analysis of food composition databases

DATA AVAILABILITY STATEMENT : No new data were created or analysed in this study. Data sharing is not applicable to this article.Evidence-based knowledge of the relationship between foods and nutrients is needed to inform dietary-based guidelines and policy. Proper and tailored statistical methods to analyse food composition databases (FCDBs) could assist in this regard. This review aims to collate the existing literature that used any statistical method to analyse FCDBs, to identify key trends and research gaps. The search strategy yielded 4238 references from electronic databases of which 24 fulfilled our inclusion criteria. Information on the objectives, statistical methods, and results was extracted. Statistical methods were mostly applied to group similar food items (37.5%). Other aims and objectives included determining associations between the nutrient content and known food characteristics (25.0%), determining nutrient co-occurrence (20.8%), evaluating nutrient changes over time (16.7%), and addressing the accuracy and completeness of databases (16.7%). Standard statistical tests (33.3%) were the most utilised followed by clustering (29.1%), other methods (16.7%), regression methods (12.5%), and dimension reduction techniques (8.3%). Nutrient data has unique characteristics such as correlated components, natural groupings, and a compositional nature. Statistical methods used for analysis need to account for this data structure. Our summary of the literature provides a reference for researchers looking to expand into this area.The South African Medical Research Council.https://www.mdpi.com/journal/nutrientsam2023Statistic

N-Acetyltransferase 2 Genotypes among Zulu-Speaking South Africans and Isoniazid and N-Acetyl-Isoniazid Pharmacokinetics during Antituberculosis Treatment.

The distribution of N-acetyltransferase 2 gene (NAT2) polymorphisms varies considerably among different ethnic groups. Information on NAT2 single-nucleotide polymorphisms in the South African population is limited. We investigated NAT2 polymorphisms and their effect on isoniazid pharmacokinetics (PK) in Zulu black HIV-infected South Africans in Durban, South Africa. HIV-infected participants with culture-confirmed pulmonary tuberculosis (TB) were enrolled from two unrelated studies. Participants with culture-confirmed pulmonary TB were genotyped for the NAT2 polymorphisms 282C>T, 341T>C, 481C>T, 857G>A, 590G>A, and 803A>G using Life Technologies prevalidated TaqMan assays (Life Technologies, Paisley, UK). Participants underwent sampling for determination of plasma isoniazid and N-acetyl-isoniazid concentrations. Among the 120 patients, 63/120 (52.5%) were slow metabolizers (NAT2*5/*5), 43/120 (35.8%) had an intermediate metabolism genotype (NAT2*5/12), and 12/120 (11.7%) had a rapid metabolism genotype (NAT2*4/*11, NAT2*11/12, and NAT2*12/12). The NAT2 alleles evaluated in this study were *4, *5C, *5D, *5E, *5J, *5K, *5KA, *5T, *11A, *12A/12C, and *12M. NAT2*5 was the most frequent allele (70.4%), followed by NAT2*12 (27.9%). Fifty-eight of 60 participants in study 1 had PK results. The median area under the concentration-time curve from 0 to infinity (AUC0-∞) was 5.53 (interquartile range [IQR], 3.63 to 9.12 μg h/ml), and the maximum concentration (Cmax) was 1.47 μg/ml (IQR, 1.14 to 1.89 μg/ml). Thirty-four of 40 participants in study 2 had both PK results and NAT2 genotyping results. The median AUC0-∞ was 10.76 μg·h/ml (IQR, 8.24 to 28.96 μg·h/ml), and the Cmax was 3.14 μg/ml (IQR, 2.39 to 4.34 μg/ml). Individual polymorphisms were not equally distributed, with some being represented in small numbers. The genotype did not correlate with the phenotype, with those with a rapid acetylator genotype showing higher AUC0-∞ values than those with a slow acetylator genotype, but the difference was not significant (P = 0.43). There was a high prevalence of slow acetylator genotypes, followed by intermediate and then rapid acetylator genotypes. The poor concordance between genotype and phenotype suggests that other factors or genetic loci influence isoniazid metabolism, and these warrant further investigation in this population

Dwelling characteristics influence indoor temperature and may pose health threats in LMICs

BACKGROUND: Shelter and safe housing is a basic human need that brings about a sense of ownership, selfsufficiency, and citizenship. Millions of people around the world live in inadequate dwellings in unhealthy areas, such as urban slums. These dwellings may experience indoor temperatures that impact inhabitants’ health. Indoor dwelling temperatures vary depending on many factors including geographic location, such as inland versus coastal. In an era of climate change, understanding how dwelling characteristics influence indoor temperature is important, especially in low- and middle-income countries, to protect health. OBJECTIVE: To assess indoor temperature in low-cost dwellings located in a coastal setting in relation to dwelling characteristics. METHODS: Indoor temperature and relative humidity loggers were installed from 1 June 2017 to 15 May 2018 in 50 dwellings in two settlements in a coastal town on the east coast of South Africa. Ambient outdoor temperature data were obtained from the national weather service, indoor temperature data were converted into apparent temperature, and heat index calculations were made to consider possible heat-health risks. A household questionnaire and dwelling observation assessment were administered. A mixed-effects linear regression model was constructed to consider the impact of dwelling characteristics on indoor apparent temperature. FINDINGS: Among 17 dwellings with all data sets, indoor temperatures were consistently higher than, and well correlated (r = 0.92) with outdoor temperatures. Average differences in indoor and outdoor temperatures were about 4°C, with statistically significant differences in percentage difference of indoor/outdoor between seasons (p < 0.001). Heat indices for indoor temperatures were exceeded mostly in summer, thereby posing possible health risks. Dwellings with cement floors were statistically significantly cooler than any other floor type across all seasons. CONCLUSIONS: Low-cost dwellings experienced temperatures indoors higher than outdoor temperatures in part due to floor type. These results help inform interventions that consider housing and human health (n = 289).The South African Medical Research Council (SAMRC), the Nelson Mandela University (NMU) and the National Research Foundation (South Africa).https://www.annalsofglobalhealth.orgpm2020Geography, Geoinformatics and Meteorolog

Indoor temperatures in patient waiting rooms in eight rural primary health care centers in Northern South Africa and the related potential risks to human health and wellbeing

Increased temperatures affect human health and vulnerable groups including infants, children, the elderly and people with pre-existing diseases. In the southern African region climate models predict increases in ambient temperature twice that of the global average temperature increase. Poor ventilation and lack of air conditioning in primary health care clinics, where duration of waiting time may be as long as several hours, pose a possible threat to patients seeking primary health care. Drawing on information measured by temperature loggers installed in eight clinics in Giyani, Limpopo Province of South Africa, we were able to determine indoor temperatures of waiting rooms in eight rural primary health care facilities. Mean monthly temperature measurements inside the clinics were warmer during the summer months of December, January and February, and cooler during the autumn months of March, April and May. The highest mean monthly temperature of 31.4 2.7 C was recorded in one clinic during February 2016. Maximum daily indoor clinic temperatures exceeded 38 C in some clinics. Indoor temperatures were compared to ambient (outdoor) temperatures and the mean difference between the two showed clinic waiting room temperatures were higher by 2–4 C on average. Apparent temperature (AT) incorporating relative humidity readings made in the clinics showed ‘realfeel’ temperatures were >4 C higher than measured indoor temperature, suggesting a feeling of ‘stuffiness’ and discomfort may have been experienced in the waiting room areas. During typical clinic operational hours of 8h00 to 16h00, mean ATs fell into temperature ranges associated with heat–health impact warning categories of ‘caution’ and ‘extreme caution’.Supplementary material: Figure S1: Indoor clinic temperatures, Figure S2: Mean indoor temperature experienced at each time point during each month for clinic 1, as an illustration of daily variation in indoor temperatures measurements, Figure S3: Indoor clinic apparent temperature, Figure S4: Differences between indoor clinic ambient apparent temperature and ambient temperature, Figure S5: Mean apparent temperature during clinic open hours of 8h00 to 16h00 compared to mean apparent temperature during all hours of the day, Table S1: Indoor clinic temperature and humidity measurements, Table S2: Ambient (outdoor) mean, minimum and maximum temperature and relative humidity measurements made at the Thohoyandou airport by month, Table S3: Monthly averages were compared for each clinic and the ambient (outdoor) temperature measurements and tested for statistically significant differences, Table S4: Mean apparent temperature (AT) per month for each clinic, with standard deviation and 1st and 99th percentiles.A South African Medical Research Council Flagship Grant, as well as funds from National Treasury under its Economic Competitiveness and Support Package, and a National Research Foundation Y-Rated Researchers grant.http://www.mdpi.com/journal/ijerpham2017Geography, Geoinformatics and Meteorolog

Classroom temperature and learner absenteeism in public primary schools in the Eastern Cape, South Africa

Children spend a significant proportion of their time at school and in school buildings. A healthy learning environment that supports children should be thermally conducive for learning and working. Here, we aimed to study the relations between indoor classroom temperatures and learner absenteeism as a proxy for children’s health and well-being. This one-year prospective study that spanned two calendar years (from June 2017 to May 2018) entailed measurement of indoor classroom temperature and relative humidity, calculated as apparent temperature (Tapp) and collection of daily absenteeism records for each classroom in schools in and around King Williams Town, Eastern Cape province, South Africa. Classroom characteristics were collected using a standardized observation checklist. Mean indoor classroom temperature ranged from 11 to 30 C, while mean outdoor temperature ranged from 6 C to 31 C during the sample period. Indoor classroom temperatures typically exceeded outdoor temperatures by 5 C for 90% of the study period. While multiple factors may influence absenteeism, we found absenteeism was highest at low indoor classroom Tapp (i.e., below 15 C). Absenteeism decreased as indoor Tapp increased to about 25 C before showing another increase in absenteeism. Classroom characteristics differed among schools. Analyses of indoor classroom temperature and absenteeism in relation to classroom characteristics showed few statistically significant relations—although not exceptionally strong ones—likely because of the multiple factors that influence absenteeism. However, given the possible relationship between indoor temperature and absenteeism, there is a learning imperative to consider thermal comfort as a fundamental element of school planning and design. Furthermore, additional research on factors besides temperature that affect learner absenteeism is needed, especially in rural areas.The South African Medical Research Councilhttps://www.mdpi.com/journal/ijerpham2022Geography, Geoinformatics and Meteorolog

Antibiotic and antifungal use in paediatric departments at three academic hospitals in South Africa

OBJECTIVES : South Africa implemented a National Strategic Framework to optimise antimicrobial stewardship in 2014; however, there is limited data on how this has affected prescribing, especially to children treated in academic centres. METHODS : We conducted a point prevalence survey using the World Health Organization (WHO) methodology to evaluate antibiotic and antifungal prescribing practices in paediatric departments at three academic hospitals in South Africa. RESULTS : We recorded 1946 antimicrobial prescriptions in 1191 children, with 55.2% and 39.2% of the antibiotics classified as WHO AWaRe Access and Watch drugs, respectively. There were significant differences in prescription of Reserve antibiotics and antifungals between institutions. Receipt of WHO Watch and Reserve antibiotics was independently associated with infancy (<12 months) and adolescents (13-17 years) (adjusted relative risk [aRR]: 2.09-9.95); prolonged hospitalisation (aRR: 3.29-30.08); rapidly or ultimately fatal illness (aRR: 1.94 to 5.52); and blood transfusion (aRR: 3.28-5.70). Antifungal prescribing was associated with treatment of hospital-associated infection (aRR: 2.90), medical prophylaxis (aRR: 3.30), and treatment in intensive care units (aRR: 2.15-2.27). CONCLUSIONS : Guidance on optimisation of infection prevention and control practice and strengthening of antimicrobial stewardship would impact positively on the care of sick children in our setting.UNICEF.http://www.elsevier.com/locate/ijregihj2024Paediatrics and Child HealthSDG-03:Good heatlh and well-bein

Healthcare-associated infections drive antimicrobial prescribing in pediatric departments at three academic hospitals in South Africa

BACKGROUND : The prevalence of antimicrobial prescriptions for healthcare-associated infections (HAI) in South Africa is largely unknown. This study aimed to estimate the point prevalence of pediatric antibiotic and antifungal usage in 3 South African academic hospitals. METHODS : This cross-sectional study included hospitalized neonates and children (0-15 years). We used the World Health Organization methodology for antimicrobial point prevalence studies, with weekly surveys to achieve a sample size of ~400 at each site. RESULTS : Overall, 1,946 antimicrobials were prescribed to 1,191 patients. At least 1 antimicrobial was prescribed for 22.9% [95% confidence interval (CI): 15.5-32.5%] of patients. The prevalence of antimicrobial prescribing for HAI was 45.6%. In the multivariable analysis, relative to children 6-12 years, neonates [adjusted relative risk (aRR): 1.64; 95% CI: 1.06-2.53], infants (aRR: 1.57; 95% CI: 1.12-2.21) and adolescents (aRR: 2.18; 95% CI: 1.45-3.29) had significantly increased risk of prescriptions for HAI. Being preterm (aRR: 1.33; 95% CI: 1.04-1.70) and underweight (aRR: 1.25; 95% CI: 1.01-1.54) was predictive of antimicrobial usage for HAI. Having an indwelling device, surgery since admission, blood transfusions and classification as rapidly fatal on McCabe score also increased the risk of prescriptions for HAI. CONCLUSIONS : The high prevalence of antimicrobial prescribing for HAI to treat children with recognized risk factors in academic hospitals in South Africa is concerning. Concerted efforts need to be made to strengthen hospital-level infection prevention and control measures, with a critical review of antimicrobial usage through functional antibiotic stewardship programs to preserve the available antimicrobial armamentarium at the hospital level.UNICEF and in part, supported by a grant awarded by the Carnegie Corporation of New York.https://journals.lww.com/pidj/pages/default.aspxhj2024Paediatrics and Child HealthSDG-03:Good heatlh and well-bein

Identifying Nutrient Patterns in South African Foods to Support National Nutrition Guidelines and Policies

Food composition databases (FCDBs) provide the nutritional content of foods and are essential for developing nutrition guidance and effective intervention programs to improve nutrition of a population. In public and nutritional health research studies, FCDBs are used in the estimation of nutrient intake profiles at the population levels. However, such studies investigating nutrient co-occurrence and profile patterns within the African context are very rare. This study aimed to identify nutrient co-occurrence patterns within the South African FCDB (SAFCDB). A principal component analysis (PCA) was applied to 28 nutrients and 971 foods in the South African FCDB to determine compositionally similar food items. A second principal component analysis was applied to the food items for validation. Eight nutrient patterns (NPs) explaining 73.4% of the nutrient variation among foods were identified: (1) high magnesium and manganese; (2) high copper and vitamin B12; (3) high animal protein, niacin, and vitamin B6; (4) high fatty acids and vitamin E; (5) high calcium, phosphorous and sodium; (6) low moisture and high available carbohydrate; (7) high cholesterol and vitamin D; and (8) low zinc and high vitamin C. Similar food patterns (FPs) were identified from a PCA on food items, yielding subgroups such as dark-green, leafy vegetables and, orange-coloured fruit and vegetables. One food pattern was associated with high sodium levels and contained bread, processed meat and seafood, canned vegetables, and sauces. The data-driven nutrient and food patterns found in this study were consistent with and support the South African food-based dietary guidelines and the national salt regulations

Differential associations of cardio-metabolic diseases by population group, gender and adiposity in South Africa.

AIMS:To describe the distribution and examine the associations of diabetes, hypertension and hypercholesterolaemia across and within population groups, gender and body mass index (BMI) categories. METHODS:This national cross-sectional study was conducted in 2013 among ≥18-year-old black African, coloured, white and Indian adults self-selected for screening. Data collection included self-reported behavioural risk factors and clinical measurements comprising blood pressure, anthropometry and point-of-care random blood glucose and cholesterol assessments. RESULTS:Among the 7711 participants, 2488 men and 5223 women, the prevalence of diabetes and hypertension increased by BMI category across population groups. Compared with white men and women, black African men (odds ratio: 2.66, 95% confidence interval: 1.70-4.16) and women (2.10, 1.49-2.96), coloured men (2.28, 1.44-3.60) and women (2.15, 1.52-3.05) and Indian men (4.38, 2.65-7.26) and women (3.64, 2.50-5.32) were significantly more likely to have diabetes. The odds for hypertension were significantly higher only in coloured men compared with white men (1.37, 1.02-1.83), while it was significantly higher in black African, coloured and Indian women compared with white women. The odds for hypercholesterolaemia were significantly lower in black African men (0.64, 0.49-0.84) and women (0.52, 0.43-0.62) compared with white men and women, and significantly higher in Indian men (1.47, 1.05-2.08) compared with white men. Black African women compared with their male counterparts were less likely to have diabetes (0.64, 0.46-0.89). Black African (0.66, 0.54-.082), coloured (0.65, 0.50-0.84) and white (0.69, 0.53-0.88) women were significantly less likely to have hypertension compared with their male counterparts. The odds for hypercholesterolaemia were higher in coloured (1.44, 1.16-1.80) and white (1.47, 1.18-1.84) women compared with their counterparts. CONCLUSIONS:The cardio-metabolic diseases of diabetes, hypertension and hypercholesterolaemia were differentially associated with population groups and gender in South Africa. The insights obtained highlight the need for multi-disciplinary targeted management approaches in high-risk populations