Sun protection has become crucial due to the effects of climate change that has resulted in effects such as extremely high temperatures. Early exposure of children to ultraviolet rays (UVR) makes them vulnerable to developing sun-related diseases later in life. Sun protection through clothing is the most affordable option to use for many people. The study assessed the awareness of sun protection for pre-school children among parents, and awareness of retailers about children’s clothing with sun-protective finishes. This exploratory and descriptive study was conducted to describe the prevailing awareness of sun-protective clothing using a questionnaire that was hand-delivered by children to their parents. A target convenience sample was selected with 20 children in each of four pre-schools from the four administrative regions of Eswatini. A research assistant interviewed managers of purposefully selected retail outlets on whether managers are aware of clothing for children with sun-protective finishes. Results showed that a majority of parents were not aware of the need to protect their children against sun exposure. Those parents who were aware mainly used clothing as a preventative measure against sun exposure. Parents, who viewed sun exposure as a health hazard, were likely to be aware of sun-protective clothing and accessories. Thus, these parents generally selected garments made from light-coloured cotton fabric. Only one retail outlet stocked merchandise with specialised tags for sun-protective clothing. In conclusion, most parents were not aware of the effects of sun exposure and the hazards associated with prolonged exposure to the sun. Only one retail outlet stocked merchandise with sun-protective finishes. The recommendation is to introduce educational programmes in schools and for consumers on protecting children against sun exposure.
Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion - 0.25 (BCI: - 0.46, - 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.
