No abstract

Repeated 24-hour urine collection is considered to be the gold standard for assessing salt intake. This is often impractical in large-population studies, especially in low-middle-income countries. Equations to estimate 24-hour urinary salt excretion from a spot urine sample have been developed, but have not been widely validated in African populations. This study aimed to systematically assess the validity of four existing equations to predict 24-hour urinary sodium excretion (24UNa) from spot urine samples in a nationally representative sample of South Africans. Spot and 24-hour urine samples were collected in a subsample (n = 438) of participants from the World Health Organisation Study on global AGEing and adult health (SAGE) Wave 2 in South Africa in 2015. Measured 24UNa values were compared with predicted 24UNa values from the Kawasaki, Tanaka, INTERSALT and Mage equations using Bland-Altman plots. In this subsample (mean age 52.8 ± 16.4 years; body mass index 30.2 ± 8.2 kg/m 2 ; 76% female; 73% black African; 42% hypertensive), all four equations produced a significantly different population estimate compared with the measured median value of 6.7 g salt/day (IQR 4.4-10.5). Although INTERSALT underestimated salt intake (−3.77 g/d; −1.64 to −7.09), the other equations overestimated by 1.28 g/d (−3.52; 1.97), 6.24 g/d (2.22; 9.45), and 17.18 g/d (8.42; 31.96) for Tanaka, Kawasaki, and Mage, respectively. Bland-Altman curves indicated unacceptably wide levels of agreement. Use of these equations to estimate population level salt intake from spot urine samples in South Africans is not recommended.

ObjectivesTo classify South African adults with chronic health conditions for multimorbidity (MM) risk, and to determine sociodemographic, anthropometric and behavioural factors associated with identified patterns of MM, using data from the WHO’s Study on global AGEing and adult health South Africa Wave 2.DesignNationally representative (for ≥50-year-old adults) cross-sectional study.SettingAdults in South Africa between 2014 and 2015.Participants1967 individuals (men: 623 and women: 1344) aged ≥45 years for whom data on all seven health conditions and socioeconomic, demographic, behavioural, and anthropological information were available.MeasuresMM latent classes.ResultsThe prevalence of MM (coexistence of two or more non-communicable diseases (NCDs)) was 21%. The latent class analysis identified three groups namely: minimal MM risk (83%), concordant (hypertension and diabetes) MM (11%) and discordant (angina, asthma, chronic lung disease, arthritis and depression) MM (6%). Using the minimal MM risk group as the reference, female (relative risk ratio (RRR)=4.57; 95% CI (1.64 to 12.75); p =0.004) and older (RRR=1.08; 95% CI (1.04 to 1.12); p<0.001) participants were more likely to belong to the concordant MM group, while tobacco users (RRR=8.41; 95% CI (1.93 to 36.69); p=0.005) and older (RRR=1.09; 95% CI (1.03 to 1.15); p=0.002) participants had a high likelihood of belonging to the discordant MM group.ConclusionNCDs with similar pathophysiological risk profiles tend to cluster together in older people. Risk factors for MM in South African adults include sex, age and tobacco use.

Recent studies have suggested the common co-occurrence of hypertension and diabetes in South Africa. Given that hypertension and diabetes are known to share common socio-demographic, anthropometric and lifestyle risk factors, the aim of this study was to jointly model the shared and disease-specific geographical variation of hypertension and diabetes. The current analysis used the Study on Global Ageing and Adult Health (SAGE) South Africa Wave 2 (2014/15) data collected from 2761 participants. Of the 2761 adults (median age = 56 years), 641 (23.2%) had high blood pressure on measurement and 338 (12.3%) reported being diagnosed with diabetes. The shared component has distinct spatial patterns with higher values of odds in the eastern districts of Kwa-Zulu Natal and central Gauteng province. The shared component may represent unmeasured health behavior characteristics or the social determinants of health in our population. Our study further showed how a shared component (latent and unmeasured health behavior characteristics or the social determinants of health) is distributed across South Africa among the older adult population. Further research using similar shared joint models may focus on extending these models for multiple diseases with ecological factors and also incorporating sampling weights in the spatial analyses.