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Dawn Tladi,1 Lucky Mokgatlhe,2 Theo Nell,3 Sheila Shaibu,4 Ronel Mitchell,5 Comfort Mokgothu,1 Tebogo Gabonthone,1 Omphile Hubona1 1Department of Sport Science, University of Botswana, Gaborone, Botswana; 2Department of Statistics, University of Botswana, Gaborone, Botswana; 3Centre for Cardio-metabolic Research in Africa (CARMA), Integrated Metabolic Research Group, Department of Physiological Sciences, Faculty of Natural Sciences, University of Stellenbosch, Stellenbosch, 7600, South Africa; 4School of Nursing and Midwifery, The Aga Khan University, Nairobi, Kenya; 5Gaborone Biokinetics, Gaborone, BotswanaCorrespondence: Dawn TladiDepartment of Sport Science, Private Bag 00702, Gaborone, BotswanaTel +267 71896028Fax +267 3185096Email tladidm@ub.ac.bwPurpose: The metabolic syndrome (MetS) is on the rise in Sub-Saharan Africa, attributed to increased and uncontrollable urbanization accompanied by its lifestyle changes. Non-communicable diseases, such as hypertension, diabetes, and obesity, which are components of the (MetS) are also on the increase in Botswana. To date, no study has determined the prevalence of the MetS in the apparently healthy Batswana adults. The objective of the study was to determine the prevalence of the MetS among the 25– 65-year-old Batswana residing in urban and neighboring semi-urban areas of Gaborone.Participants and Methods: A cross-sectional study was used to collect data from N=794 participants, n=383 men and n=411 women, residing in Gaborone and two surrounding semi-urban areas. Data collected included demographic, anthropometric measurements, blood pressure (BP), blood glucose, triglycerides, high-density lipoprotein cholesterol (HDL-C) and total cholesterol.Results: A high prevalence of 26.8% was reported, with women mostly afflicted (35.0% vs 18.0%). The MetS risk factors found to be common in women were low HDL-C at (50% vs 48.7%) compared to men, while proportions with elevated BP (50.3% vs 39.4%) were prominent in men.

English: The measure of time to event (failure) for units on longitudinal clinical visits cannot always be ascertained exactly. Instead only time intervals within which the event occurred may be recorded. That being the case, each unit's failure will be described by a single interval resulting in grouped interval data over the sample. Yet, due to non-compliance to visits by some units, failure will be described by endpoints within which the event has occurred. These endpoints may encompass several intervals, hence overlapping intervals across units. Furthermore, some units may not realize the event of interest within the preset duration of study, hence are censored. Finally, several events of interest can be investigated on a single unit resulting in several failure times that inevitably are dependent. All these prescribe an interval-censored survival data with multiple-failure times. Three models of analysing interval-censored survival data with two failure times were applied to four sets of data. For the distribution free methods, Cox's hazard with either a log-log transform or logit transform on the baseline conditional survival probabilities was used to derive the likelihood. The Independence assumption model (lW) work under the assumption that the lifetimes are independent and any dependence exists through the use of common covariates. The second model that do not necessarily assume independence, computes the joint failure probabilities for two lifetimes by Bayes' rule of conditioning on the interval of failure for one lifetime, hence Conditional Bivariate model (CB). The use of Clayton and Farley-Morgenstern bivariate Copulas (CC) with inbuilt dependence parameter was the other model. For parametric models the IW and CC methods were applied to the data sets on the assumption that the marginal distribution of the lifetimes is Weibull. The traditional classical estimation method of Newton-Raphson was used to find optimum parameter estimates and their variances s, Afrikaans: Tyd tot 'n gebeurtenis (faling) van eenhede op 'n regiment van longitudinale kliniese besoeke kan nie altyd presies bepaal word nie. Gewoonlik kan net 'n interval waarin 'n gebeurtenis plaasgevind het bepaal word. In so 'n geval word elke eenheid se faling beskryf deur 'n enkele interval wat lei tot gegroepeerde data oor die hele steekproef. Verder, as gevolg van nie-nakornings van besoeke deur sommige eenhede, kan die falings slegs beskryf word deur die eindpunte van die tydperk waarin die gebeurtenis plaasgevind het. Hierdie eindpunte mag verskeie intervalle insluit, en kryons dus oorvleuende tydperke oor eenhede. Verder mag die gebeurtenis van belang by sekere eenhede nie plaasvind binne die voorafbepaalde tydperk van studie nie, en is dus gesensoreerd. Laastens, verskeie gebeurtenisse van belang kan ondersoek word op In enkele eenheid. Die resultaat is dan meervoudige falingstye wat uiteraard dan afhanklik is. Bogenoemde situasie word dan beskryf as interval-gesensoreerde oorlewingsdata met meervoudige falingstye. Drie modelle vir die analise van interval-gesensoreerde oorlewingsdata met twee' falingstye is toegepas op vier data stele. Vir verdelingsvrye metodes is Cox se gevaarfunksie met of 'n log-log transformasie of 'n logit transformasie op die basislyn voorwaardelike oorlewingswaarskynlikhede gebruik om die aanneemlikheidsfunksie af te lei. Die Onafhanklikheidsaanname model (IW) neem aan dat die leeftye inherent onafhanklik is en dat afhanklikheid slegs ingebring word deur gemeenskaplike koveranderlikes. Die tweede model aanvaar nie onafhanklikheid nie, maar bereken die gesamentlike falingswaarskynlikhede deur die voorwaardelike waarskynlikheid vir die interval van een leeftyd gegee die ander leeftyd se interval, te bereken. Dit is die Voorwaardelike tweeveranderlike model (CB). Die Clayton en Farley-Morgenstern tweeveranderlike Copulas (CC) met ingeboude afhanklikheidsparameters is die derde model. Vir parametriese modelle is die lW en CC metod