Background: Treatment non-adherence poses significant risks to health outcomes and impedes the health system’s efficiency, hence curtailing progress towards the end Tuberculosis (TB) strategy under SDG 3.3. Despite i...Background: Treatment non-adherence poses significant risks to health outcomes and impedes the health system’s efficiency, hence curtailing progress towards the end Tuberculosis (TB) strategy under SDG 3.3. Despite interventions to address TB treatment non-adherence, Kenya still reports high TB treatment non-adherence rates of 35% and consequently poor treatment outcome rates. Health Care Workers (HCWs) play a critical role in linking the population to health services, yet little is known of their influence on patients’ TB treatment non-adherence in Kenya. Objective: To analyze HCW-related factors associated with TB treatment non-adherence among patients in Kisumu East Sub-County. Methods: Health facility-based analytical cross-sectional mixed-method study. A Semi-structured questionnaire on treatment adherence and patients’ perceptions of HCWs during the clinic visit was administered to 102 consenting adult (out of a total census of 107 adults) drug-susceptible TB patients. 12 purposively selected HCWs by rank from 6 health facilities participated in Key Informant Interview sessions. Medication adherence was measured using the Morisky Medication Adherence Scale and then expressed as a dichotomous variable. Quantitative analysis utilized STATA version 15.1 while qualitative deductive thematic analysis was done using NVIVO version 14. Results: TB treatment non-adherence rate of 26% (CI: 18% - 36%) was recorded. Overall, patients who felt supported in dealing with the illness were 8 times more likely to adhere to treatment compared to those who were not (aOR = 7.947, 95% CI: 2.214 - 28.527, p = 0.001). Key HCW related factors influencing adherence to treatment included: friendliness (cOR = 4.31, 95% CI: 1.514 - 12.284, p = 0.006), respect (cOR = 6.679, 95% CI: 2.239 - 19.923, p = 0.001) and non-discriminatory service (cOR = 0.1478, 95% CI: 0.047 - 0.464, p = 0.001), communication [adequacy of consultation time (cOR = 6.563, 95% CI: 2.467 - 17.458, p = 0.001) and patients’ involvement in their health decisions (cOR = 3.02 95% CI: 1.061 - 8.592, p = 0.038)] and education and counselling (cOR = 4.371, 95% CI: 1.725 - 11.075, p = 0.002). Conclusion: The study results underline importance of patient-centered consultation for TB patients and targeted education and counselling for improved treatment adherence.展开更多
Apparel processing is an essential industry in providing clothing needs for the population. The Export Processing Zone (EPZ) in Kenya employs many employees. Garment processing releases respirable dust particles, thus...Apparel processing is an essential industry in providing clothing needs for the population. The Export Processing Zone (EPZ) in Kenya employs many employees. Garment processing releases respirable dust particles, thus exposing workers to risks to the respiratory system. The study determined the respirable dust health concerns among workers in Apparel Processing Companies (APCs) in EPZ in Machakos County, Kenya. A cross-sectional descriptive design was employed where four companies were studied. Three hundred and sixty-seven participants were selected through systematic random sampling. Data was collected using questionnaires and Interview guides. The study established that workers were exposed to respirable dust PM<sub>2.5</sub> ranging from 40.89 ± 24.0 μg·m<sup>−3</sup> to 87.49 ± 45.2 μg·m<sup>−3</sup> with a mean of 65.61 ± 31.5 μg·m<sup>−3</sup>. While PM<sub>2.5</sub> ranged from 63.59 ± 21.2 μg·m<sup>−3</sup> to 313.41 ± 468.0 μg·m<sup>−3</sup>. With a mean of 104.02 ± 26.0 μg·m<sup>−3</sup>. Workers complained of different respirable dust-related diseases. The most prevalent conditions were sneezing and coughing (86.4%), chest pains (41.1%), blocked chests (36.8%), and allergic reactions to dust (18.3%). The APC should develop an OSH management system that includes;a dust management policy, dust monitoring, Risk Assessments, Engineering controls installations, medical examination, Training on dust management, PPE provision, and use enforcement.展开更多
Limited data is available on the coronavirus disease 2019(COVID-19),critical illness rate,and in-hospital mortality in the African setting.This study investigates determinants of critical illness and in-hospital morta...Limited data is available on the coronavirus disease 2019(COVID-19),critical illness rate,and in-hospital mortality in the African setting.This study investigates determinants of critical illness and in-hospital mortality among COVID-19 patients in Kenya.We conducted a retrospective cohort study at Kenyatta National Hospital(KNH)in Kenya.Multivariate logistic regression and Cox proportional hazard regression were employed to determine predictor factors for intensive care unit(ICU)admission and in-hospital mortality,respectively.In addition,the Kaplan-Meier model was used to compare the survival times using log-rank tests.As a result,346(19.3%)COVID-19 patients were admitted to ICU,and 271(15.1%)died.The majority of those admitted to the hospital were male,1,137(63.4%)and asymptomatic,1,357(75.7%).The most prevalent clinical features were shortness of breath,fever,and dry cough.In addition,older age,male,health status,patient on oxygen(O2),oxygen saturation levels(SPO2),headache,dry cough,comorbidities,obesity,cardiovascular diseases(CVDs),diabetes,chronic lung disease(CLD),and malignancy/cancer can predicate the risk of ICU admission,with an area under the receiver operating characteristic curve(AUC-ROC)of 0.90(95%confidence interval[CI]:0.88–0.92).Survival analysis indicated 271(15.1%)patients died and identified older age,male,headache,shortness of breath,health status,patient on oxygen,SPO2,headache,comorbidity,CVDs,diabetes,CLD,malignancy/cancer,and smoking as risk factors for mortality(AUC-ROC:0.90,95%CI:0.89–0.91).This is the first attempt to explore predictors for ICU admission and hospital mortality among COVID-19 patients in Kenya.展开更多
文摘Background: Treatment non-adherence poses significant risks to health outcomes and impedes the health system’s efficiency, hence curtailing progress towards the end Tuberculosis (TB) strategy under SDG 3.3. Despite interventions to address TB treatment non-adherence, Kenya still reports high TB treatment non-adherence rates of 35% and consequently poor treatment outcome rates. Health Care Workers (HCWs) play a critical role in linking the population to health services, yet little is known of their influence on patients’ TB treatment non-adherence in Kenya. Objective: To analyze HCW-related factors associated with TB treatment non-adherence among patients in Kisumu East Sub-County. Methods: Health facility-based analytical cross-sectional mixed-method study. A Semi-structured questionnaire on treatment adherence and patients’ perceptions of HCWs during the clinic visit was administered to 102 consenting adult (out of a total census of 107 adults) drug-susceptible TB patients. 12 purposively selected HCWs by rank from 6 health facilities participated in Key Informant Interview sessions. Medication adherence was measured using the Morisky Medication Adherence Scale and then expressed as a dichotomous variable. Quantitative analysis utilized STATA version 15.1 while qualitative deductive thematic analysis was done using NVIVO version 14. Results: TB treatment non-adherence rate of 26% (CI: 18% - 36%) was recorded. Overall, patients who felt supported in dealing with the illness were 8 times more likely to adhere to treatment compared to those who were not (aOR = 7.947, 95% CI: 2.214 - 28.527, p = 0.001). Key HCW related factors influencing adherence to treatment included: friendliness (cOR = 4.31, 95% CI: 1.514 - 12.284, p = 0.006), respect (cOR = 6.679, 95% CI: 2.239 - 19.923, p = 0.001) and non-discriminatory service (cOR = 0.1478, 95% CI: 0.047 - 0.464, p = 0.001), communication [adequacy of consultation time (cOR = 6.563, 95% CI: 2.467 - 17.458, p = 0.001) and patients’ involvement in their health decisions (cOR = 3.02 95% CI: 1.061 - 8.592, p = 0.038)] and education and counselling (cOR = 4.371, 95% CI: 1.725 - 11.075, p = 0.002). Conclusion: The study results underline importance of patient-centered consultation for TB patients and targeted education and counselling for improved treatment adherence.
文摘Apparel processing is an essential industry in providing clothing needs for the population. The Export Processing Zone (EPZ) in Kenya employs many employees. Garment processing releases respirable dust particles, thus exposing workers to risks to the respiratory system. The study determined the respirable dust health concerns among workers in Apparel Processing Companies (APCs) in EPZ in Machakos County, Kenya. A cross-sectional descriptive design was employed where four companies were studied. Three hundred and sixty-seven participants were selected through systematic random sampling. Data was collected using questionnaires and Interview guides. The study established that workers were exposed to respirable dust PM<sub>2.5</sub> ranging from 40.89 ± 24.0 μg·m<sup>−3</sup> to 87.49 ± 45.2 μg·m<sup>−3</sup> with a mean of 65.61 ± 31.5 μg·m<sup>−3</sup>. While PM<sub>2.5</sub> ranged from 63.59 ± 21.2 μg·m<sup>−3</sup> to 313.41 ± 468.0 μg·m<sup>−3</sup>. With a mean of 104.02 ± 26.0 μg·m<sup>−3</sup>. Workers complained of different respirable dust-related diseases. The most prevalent conditions were sneezing and coughing (86.4%), chest pains (41.1%), blocked chests (36.8%), and allergic reactions to dust (18.3%). The APC should develop an OSH management system that includes;a dust management policy, dust monitoring, Risk Assessments, Engineering controls installations, medical examination, Training on dust management, PPE provision, and use enforcement.
文摘Limited data is available on the coronavirus disease 2019(COVID-19),critical illness rate,and in-hospital mortality in the African setting.This study investigates determinants of critical illness and in-hospital mortality among COVID-19 patients in Kenya.We conducted a retrospective cohort study at Kenyatta National Hospital(KNH)in Kenya.Multivariate logistic regression and Cox proportional hazard regression were employed to determine predictor factors for intensive care unit(ICU)admission and in-hospital mortality,respectively.In addition,the Kaplan-Meier model was used to compare the survival times using log-rank tests.As a result,346(19.3%)COVID-19 patients were admitted to ICU,and 271(15.1%)died.The majority of those admitted to the hospital were male,1,137(63.4%)and asymptomatic,1,357(75.7%).The most prevalent clinical features were shortness of breath,fever,and dry cough.In addition,older age,male,health status,patient on oxygen(O2),oxygen saturation levels(SPO2),headache,dry cough,comorbidities,obesity,cardiovascular diseases(CVDs),diabetes,chronic lung disease(CLD),and malignancy/cancer can predicate the risk of ICU admission,with an area under the receiver operating characteristic curve(AUC-ROC)of 0.90(95%confidence interval[CI]:0.88–0.92).Survival analysis indicated 271(15.1%)patients died and identified older age,male,headache,shortness of breath,health status,patient on oxygen,SPO2,headache,comorbidity,CVDs,diabetes,CLD,malignancy/cancer,and smoking as risk factors for mortality(AUC-ROC:0.90,95%CI:0.89–0.91).This is the first attempt to explore predictors for ICU admission and hospital mortality among COVID-19 patients in Kenya.
