Ambient and indoor temperature affects thermal comfort and human health. In a changing climate with a predicted change in temperature extremes, understanding indoor temperatures, both hot and cold, of different housing types is important. This study aimed to assess the hourly, daily and monthly variation in indoor temperatures in different housing types, namely formal houses, informal houses, flats, government-built low-cost houses and old, apartheid era low-cost housing, in five impoverished urban communities in Johannesburg, South Africa. During the cross-sectional survey of the Health, Environment and Development study data loggers were installed in 100 homes (20 per suburb) from February to May 2014. Indoor temperature and relative humidity were recorded on an hourly basis. Ambient outdoor temperatures were obtained from the nearest weather station. Indoor and outdoor temperature and relative humidity levels were compared; and an inter-comparison between the different housing types were also made. Apparent temperature was calculated to assess indoor thermal comfort. Data from 59 retrieved loggers showed a significant difference in monthly mean indoor temperature between the five different housing types (p < 0.0001). Low cost government-built houses and informal settlement houses had the greatest variation in temperature and experienced temperatures between 4 and 5 °C warmer than outdoor temperatures. Housing types occupied by poor communities experienced indoor temperature fluctuations often greater than that observed for ambient temperatures. Families living in government-built low-cost and informally-constructed homes are the most at risk for indoor temperature extremes. These types of housing should be prioritised for interventions aimed at assisting families to cope with extreme temperatures, gaining optimal thermal comfort and preventing temperature-related health effects.

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.

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’.

Background: Shelter and safe housing is a basic human need that brings about a sense of ownership, self-sufficiency, 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).

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.

Measuring national progress towards the Sustainable Development Goals (SDGs) enables the identification of gaps which need to be filled to end poverty, protect the planet and improve lives. Progress is typically calculated using indicators stemming from published methodologies. South Africa tracks progress towards the SDGs at a national scale, but aggregated data may mask progress, or lack thereof, at local levels. To assess the progress towards achievement of the SDGs in four low-income, rural villages (Giyani) in South Africa and to relate the findings to national SDG indicators. Using data from a cross-sectional environmental health study, the global indicator framework for the SDGs was applied to calculate indicators for Giyani. Local progress towards SDG achievement was compared with national progress, to contextualize and supplement national scale tracking. Village scores were mostly in line with country scores for those indices which were computable, given the available data. Low data availability prevented a complete local progress assessment. Higher levels of poverty prevail in the study villages compared to South Africa as a whole (17.7% compared to 7.4%), high unemployment (49.0% compared to 27.3%) and lack of access to information via the Internet (only 4.2% compared to 61.8%) were indicators in the villages identified as falling far short of the South African averages. Understanding progress towards the SDGs at a local scale is important when trying to unpack national progress. It shines a light upon issues that are not picked up by national composite assessments yet require most urgent attention. Gaps in data required to measure progress towards targets represents a serious stumbling block, preventing the creation of a true reflection of local and national scale progress.