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P058 Associations between sleep parameters, non-communicable diseases, HIV status and medications in older, rural south africans
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  1. Francesc Xavier Gomez-Olive1,
  2. Julia K Rohr2,
  3. Laura C Roden3,
  4. Dale E Rae4 and
  5. Malcolm von Schantz5
  1. 1MRC/WITS Rural Public Health and Health Transitions Research Unit, School of Public Health, University of the Witwatersrandd, Johannesburg, South Africa
  2. 2Harvard Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, Massachusetts, USA, Cambridge, USA
  3. 3Department of Molecular and Cell Biology, Faculty of Science, University of Cape Town, Cape Town, South Africa, CAPE TOWN, SOUTH AFRICA
  4. 4Division of Exercise Science and Sports Medicine, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, Cape Town, South Africa
  5. 5Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK

Abstract

Introduction Sleep interacts with both infectious diseases and NCDs, but is a frequently neglected aspect of health and well-being. In South Africa, both NCD and HIV are highly prevalent, making the study of the association of sleep disorders with this double epidemic an imperative.

Methods We investigated sleep habits and their interactions with HIV and NCDs in 5059 individuals 40 years and above from the study ‘Health and Aging in Africa: a Longitudinal Study of an INDEPTH community’ in Agincourt, South Africa. We collected sociodemographic data, anthropometric measurements, and blood pressure, and tested for glucose, haemoglobin and HIV. Major individual components of the Pittsburgh Sleep Quality index (PSQI) questionnaire were analysed, in addition to other questions on health and well-being. Sex comparisons were carried out using an independent t-test, a Mann-Whitney U test or a Fisher’s Exact test. The association of demographic variables and sleep parameters were measured using logistic regression.

Results Self-reported sleep duration was 8.2±1.6h. Insufficient sleep associated with age, education, unemployment, and obesity. Restless sleep associated with age, education, unemployment, and being female and single table 1. Obesity associated with self-reported bedtime, wake time, insufficient sleep, snoring/gasping/periods of stopping breathing during sleep. Hypertension associated with shorter sleep duration, poor sleep quality, restless sleep, and sleep apnoea symptoms. Diabetes associated with bedtime, restless sleep, and snoring table 2. HIV+ individuals not on anti-retroviral therapy (ART) reported more nocturnal awakenings than those on ART (p=0.029) and HIV- individuals (p=0.024).

View this table:
Abstract P058 Table 1

Logistic regression models for association between demographic and sleep variables. Each of the six sleep parameters (dependent variables was evaluated in a separate model with the demographic variables serving as independent variables

View this table:
Abstract P058 Table 2

Logistic regression models for associations between sleep parameters and health conditions. Each of the six health conditions (dependent variables) was evaluated in separate model for each off the 11 sleep variables (independent variables), adjusted for BMI category and demographic characteristics (age group, sex, education group, employment status, marital status, wealth index quintile). The obesity outcome was not adjusted for BMI.

Discussion These data provide a basis for further studies of the relationship between sleep and other risk factors. It also provides valuable data about sleep habits in Africa, with potential for future analysis of how it is affected by urbanisation and industrialisation. Finally, it offers a unique opportunity for a population-based comparison of the effects of treated and untreated HIV infection, increasingly unavailable elsewhere.

http://dx.doi.org/10.1136/bmjresp-2019-bssconf.58

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