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dc.contributor.authorHaregu, TN
dc.contributor.authorWekesah, FM
dc.contributor.authorMohamed, SF
dc.contributor.authorMutua, MK
dc.contributor.authorAsiki, G
dc.contributor.authorKyobutungi, C
dc.date.accessioned2020-12-09T23:51:39Z
dc.date.available2020-12-09T23:51:39Z
dc.date.issued2018-11-07
dc.identifierpii: 10.1186/s12889-018-6056-7
dc.identifier.citationHaregu, T. N., Wekesah, F. M., Mohamed, S. F., Mutua, M. K., Asiki, G. & Kyobutungi, C. (2018). Patterns of non-communicable disease and injury risk factors in Kenyan adult population: a cluster analysis. BMC PUBLIC HEALTH, 18 (Suppl 3), https://doi.org/10.1186/s12889-018-6056-7.
dc.identifier.issn1471-2458
dc.identifier.urihttp://hdl.handle.net/11343/253345
dc.description.abstractBACKGROUND: Non-communicable diseases and unintentional injuries are emerging public health problems in sub-Saharan Africa. These threats have multiple risk factors with complex interactions. Though some studies have explored the magnitude and distribution of those risk factors in many populations in Kenya, an exploration of segmentation of population at a national level by risk profile, which is crucial for a differentiated approach, is currently lacking. The aim of this study was to examine patterns of non-communicable disease and injury risk through the identification of clusters and investigation of correlates of those clusters among Kenyan adult population. METHODS: We used data from the 2015 STEPs survey of non-communicable disease risk factors conducted among 4484 adults aged between 18 and 69 years in Kenya. A total of 12 risk factors for NCDs and 9 factors for injury were used as clustering variables. A K-medians Cluster Analysis was applied. We used matching as the measure of the similarity/dissimilarity among the clustering variables. While clusters were described using the risk factors, the predictors of the clustering were investigated using multinomial logistic regression. RESULTS: We have identified five clusters for NCDs and four clusters for injury based on the risk profile of the population. The NCD risk clusters were labelled as cluster hypertensives, harmful users, the hopefuls, the obese, and the fat lovers. The injury risk clusters were labelled as helmet users, jaywalkers, the defiant and the compliant. Among the possible predictors of clustering, age, gender, education and wealth index came out as strong predictors of the cluster variables. CONCLUSION: This cluster analysis has identified important clusters of adult Kenyan population for non-communicable disease and injury risk profiles. Risk reduction interventions could consider these clusters as potential target in the development and segmentation of a differentiated approach.
dc.languageEnglish
dc.publisherBMC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titlePatterns of non-communicable disease and injury risk factors in Kenyan adult population: a cluster analysis
dc.typeJournal Article
dc.identifier.doi10.1186/s12889-018-6056-7
melbourne.affiliation.departmentMelbourne School of Population and Global Health
melbourne.source.titleBMC Public Health
melbourne.source.volume18
melbourne.source.issueSuppl 3
dc.rights.licenseCC BY
melbourne.elementsid1355510
melbourne.contributor.authorHaregu, Tilahun
dc.identifier.eissn1471-2458
melbourne.accessrightsOpen Access


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