Introduction: The rising burden of non-communicable diseases poses a great health system challenge in Kenya. Healthcare workers have a critical role to play in health promotion, in addressing patients’ lifestyle risk factors. However, their own lifestyle habits can influence their attitude and practices towards patient care. Opportunistic counselling of patients by health professionals signifies one of the most cost-effective medical interventions in combatting non-communicable diseases. Objective: To determine the attitudes and practice of health promotion for the prevention and management of non-communicable diseases among healthcare workers In Kakamega county. Methods: A cross-sectional mixed method study was carried out from June to July 2018 at Kakamega county referral hospital. For the quantitative method, one hundred and eighty-five doctors and nurses were recruited through stratified sampling. Data on healthcare workers; socio-demographic characteristics, lifestyle practice, attitude, and practice of health promotion was collected through self- administered questionnaires. Frequency and percentage distributions tables were used to initially describe the study population while, Chi- square test of significance was used to evaluate the association between healthcare workers attitudes and practice of health promotion and their socio-demographic features. Secondly, a total of 12 doctors and nurses were purposively selected based on age, gender and profession of participants for the qualitative method. Data on healthcare workers perspective on health promotion and organizational support factors were collected through in-depth interviews. The recorded interviews were transcribed and data analysis was done using content analysis of thematic areas. A verbatim approach was used to describe study findings. Results: 69.2% of the respondents were females, mean age was 36 years and the median years in profession was 12 years. The respondents exhibited good lifestyle practices with alcohol and tobacco prevalence at 30.8 %and 3.8% respectively. 72% of the respondents demonstrated a positive attitude towards health promotion while, 31% showed good health promotion practices. Less than half of the respondents inquired about a patient’s lifestyle practices during routine visit. The study found that healthcare workers with a positive attitude were four times more likely to have good health promotion practices (OR = 3.54, p<0.001). Lastly findings from the in-depth interviews revealed that staff had no written guidelines on health promotion and that the hospital management had abolished the health promotion unit. Conclusion: The results indicated that a positive health professional attitude is a precursor to good health promotive practices. Recommendation: Efforts to build capacity and support for health promotion in health services should be encouraged. Additionally, health Promotion programs for non- communicable disease should not only target the health of general population but also the health of health care workers. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0720/a.php" alt="Hit counter" /></p>

Objective: The main aim of this study was to establish psychometric properties of the subscales self-realization and health responsibility of the health-promoting lifestyle profile II tool among Kenyan university students Design: The study design was a cross-sectional analytical, that utilized quantitative methods Setting. The study was conducted in Kakamega County, located in Western Kenya. Analysis: Data were analyzed through confirmatory factor analysis, which was conducted using robust maximum likelihood estimation. The factor model was tested for validity and construct validity. Main outcome measures: subscales self-realization and health responsibility of the Health-Promoting Lifestyle Profile II Results: The items for self-realization and health responsibility had a Cronbach's alpha coefficient of 0.72 and 0.80, indicating acceptable reliability. For self-realization, the results of the Chi-square goodness of fit test were significant, χ2(27) = 251.61, p < .001, suggesting that the model did not adequately fit the data. The fit indices showed the RMSEA index was greater than .10, RMSEA = 0.20, 90% CI = [0.18, 0.22], which is indicative of a poor model fit. The CFI was less than .90, CFI = 0.52, suggesting that the model is indicative of a poor model fit. For health responsibility, the results of the Chi-square goodness of fit test were significant, χ2(20) = 272.58, p < .001, suggesting that the model did not adequately fit the data. Fit indices values showed the RMSEA index was greater than .10, RMSEA = 0.25, 90% CI = [0.22, 0.27], which is indicative of a poor model fit. The CFI was less than .90, CFI = 0.75, suggesting that the model is indicative of a poor model fit. Conclusion: In conclusion, within the limitations of this study, the results showed that confirmatory factor analysis could not well fit the items to their latent constructs. This study recommended that in future studies, a shortened version of this tool is subjected to psychometric investigation.

Introduction: Multimorbidity poses a current global health challenge due to its increasing prevalence and burden on individuals and health systems. Evidence suggests that more socially disadvantaged individuals share a disproportionate burden of multimorbidity. The evidence on the relationship between area-level socioeconomic disadvantage and multimorbidity is unclear. Thus, the aim of the current study is to synthesise evidence on the association between area-level socio-economic disadvantage and multimorbidity. Methods: A systematic review was conducted of published literature from inception to January 2020. Search strategy was applied to identify evidence on PubMed (Medline), Ovid (Medline, Embase, Psycinfo) and Web of Science. Studies were selected according to the inclusion and exclusion criteria. Newcastle Ottawa Scale for observational studies was used for quality assessment of included studies. Evidence was synthesised narratively. Results: We identified eight out of 2588 studies identified in the search as per the inclusion and exclusion criteria. Out of the eight studies, five studies confirmed a positive association between area-level socio-economic disadvantage and multimorbidity, two studies presented a negative association, and one study presented no association. Three studies found individuals in deprived areas to have higher multimorbidity than those in affluent areas. Two studies established that individuals in rural areas had higher multimorbidity than their urban counterparts. Two studies found individuals in urban areas to have a higher multimorbidity than those in rural areas. Conclusion: Evidence shows that association between area-level socioeconomic disadvantage and multimorbidity exist. Except for area of residence, clear positive associations were confirmed between area deprivation and multimorbidity. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0798/a.php" alt="Hit counter" /></p>

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