A Comparative Study on the Quality of Life of Leprosy Patients in Kilifi and Kwale Counties in Kenya

Background:Kenya at present is in the post-elimination phase of leprosy having achieved a prevalence of <1 case per 10,000 persons in 1989. In 2019 Kenya notified 163 leprosy patients, highest being in Kilifi and Kwale counties. About a quarter (26%) of the notified leprosy patients had grade 2 disability at the time of diagnosis, this being the most severe form of disability that may indicate a late diagnosis. This study aimed at assessing the quality of life of leprosy patients to guide policies and programs intended to enhance the health and well-being of leprosy patients.Materials and Methods:This was a case-control study conducted in Kilifi and Kwale Counties. For every leprosy index patient enrolled, two controls were identified within the same village to match the case. Descriptive statistics were used to summarise demographic and clinical variables. The World Health Organisation (WHOQOL-BREF) tool was used to measure the quality of life. The tool derived four (4) domains of physical health, psychological status, social relationship, and environmental profile. These were transformed into a scale between 0 to 100 for analysis. The F-test was used to compare mean scores in the four domains between cases and controls. The quality of life among the index cases against their controls was further analysed using conditional logistic regression models.Results: A total of 98 leprosy patients and 167 controls were evaluated for quality of life. On the perception of quality of life, leprosy patients had significantly lower mean transformed scores of 39 (SD 25) versus 49 (SD 25) p= <0.0001 compared to controls. Similarly, index cases had lower health satisfaction scores of 42 (SD 26) compared to controls scoring 61(SD 27) p=<0.001. Overall leprosy patients had statistically significant poorer scores on physical health, psychological health, social relationships and environmental QoL domains. Differences were most remarkable in the psychological domain, with a mean transformed score of 53 (SD 20) versus 68 (SD 16) p= < 0.0001 for controls. The overall quality of life model revealed that leprosy patients who were found to have either diabetes or hypertension enjoyed a better overall quality of life with OR of 10.98 and 1.22 respectively with a p-value <0.00001. Patients with tuberculosis and HIV presented the poorest quality of life with ORs of 0.49 and 0.14 respectively.Conclusion: The quality of life of the leprosy patients was significantly lower than that of the community controls in all the domains. Governments and communities need to prioritize rehabilitation measures such as the provision of artificial limbs, cataract surgery, and social protection disbursements to help leprosy victims improve their quality of life

Mortality during treatment for tuberculosis; a review of surveillance data in a rural county in Kenya.

BackgroundGlobally in 2016, 1.7 million people died of Tuberculosis (TB). This study aimed to estimate all-cause mortality rate, identify features associated with mortality and describe trend in mortality rate from treatment initiation.MethodA 5-year (2012-2016) retrospective analysis of electronic TB surveillance data from Kilifi County, Kenya. The outcome was all-cause mortality within 180 days after starting TB treatment. The risk factors examined were demographic and clinical features at the time of starting anti-TB treatment. We performed survival analysis with time at risk defined from day of starting TB treatment to time of death, lost-to-follow-up or completing treatment. To account for 'lost-to-follow-up' we used competing risk analysis method to examine risk factors for all-cause mortality.Results10,717 patients receiving TB treatment, median (IQR) age 33 (24-45) years were analyzed; 3,163 (30%) were HIV infected. Overall, 585 (5.5%) patients died; mortality rate of 12.2 (95% CI 11.3-13.3) deaths per 100 person-years (PY). Mortality rate increased from 7.8 (95% CI 6.4-9.5) in 2012 to 17.7 (95% CI 14.9-21.1) in 2016 per 100PY (PtrendConclusionsWe found most deaths occurred within three months and an increasing mortality rate during the time under review among patients on TB treatment. Our results therefore warrant further investigation to explore host, disease or health system factors that may explain this trend

Tuberculosis Treatment Adherence among Patients Taking Anti-TB Drugs in Kilifi County, Kenya

Introduction: TB treatment adherence is proven to be the single factor associated with TB treatment success. Poor treatment adherence increases the spread of new TB cases and the likelihood of developing MDRTB. Thus, this study aimed to determine factors influencing TB treatment adherence in Malindi Subcounty, Kilifi County Kenya.Materials and Methods: The study adopted a cross-sectional study design. A Structured standardized questionnaire from the Morisky medication adherence scale and Focus group discussion were used to collect data. Purposive sampling was used to select 8-high burden facilities, we used descriptive statistics and multiple logistic regression to analyse the data.Results: Two hundred and thirty-five (235) patients were sampled. TB treatment adherence level was 75% in Malindi sub-county. Living with family (OR =3.01; CI: 1.45-6.25, P=0.003), basic knowledge on TB (OR; 4.078, CI: 2.039-8.154, P=0.001), perceived severity (OR=2.186, CI: 1.088-4.393, P=0.028) and perceived susceptibility (OR=0.477, CI: 0.303-0.752, P=0.001), patient satisfaction (OR; 1.824, CI: 1.257-2.647), P=0.002) and enrolment of TB patients to support groups (OR; 0.353, CI: 0.438-1.538), P=0.031) were factors associated with TB treatment adherence.Conclusion and Recommendation: Factors like family support, basic knowledge of TB and patient support increase TB treatment adherence.We recommend community advocacy on TB, policies on integration of TB services and enrolment of TB patients to support groups to increase TB treatment adherence

Burden of HIV and treatment outcomes among TB patients in rural Kenya: a 9-year longitudinal study

Abstract Background Although tuberculosis (TB) patients coinfected with HIV are at risk of poor treatment outcomes, there is paucity of data on changing trends of TB/HIV co-infection and their treatment outcomes. This study aims to estimate the burden of TB/HIV co-infection over time, describe the treatment available to TB/HIV patients and estimate the effect of TB/HIV co-infection on TB treatment outcomes. Methods This was a retrospective data analyses from TB surveillance in two counties in Kenya (Nyeri and Kilifi): 2012‒2020. All TB patients aged ≥ 18 years were included. The main exposure was HIV status categorised as infected, negative or unknown status. World Health Organization TB treatment outcomes were explored; cured, treatment complete, failed treatment, defaulted/lost-to-follow-up, died and transferred out. Time at risk was from date of starting TB treatment to six months later/date of the event and Cox proportion with shared frailties models were used to estimate effects of TB/HIV co-infection on TB treatment outcomes. Results The study includes 27,285 patients, median (IQR) 37 (29‒49) years old and 64% male. 23,986 (88%) were new TB cases and 91% were started on 2RHZE/4RH anti-TB regimen. Overall, 7879 (29%, 95% 28‒30%) were HIV infected. The proportion of HIV infected patient was 32% in 2012 and declined to 24% in 2020 (trend P-value = 0.01). Uptake of ARTs (95%) and cotrimoxazole prophylaxis (99%) was high. Overall, 84% patients completed six months TB treatment, 2084 (7.6%) died, 4.3% LTFU, 0.9% treatment failure and 2.8% transferred out. HIV status was associated with lower odds of completing TB treatment: infected Vs negative (aOR 0.56 (95%CI 0.52‒0.61) and unknown vs negative (aOR 0.57 (95%CI 0.44‒0.73). Both HIV infected and unknown status were associated with higher hazard of death: (aHR 2.40 (95%CI 2.18‒2.63) and 1.93 (95%CI 1.44‒2.56)) respectively and defaulting treatment/LTFU: aHR 1.16 (95%CI 1.01‒1.32) and 1.55 (95%CI 1.02‒2.35)) respectively. HIV status had no effect on hazard of transferring out and treatment failure. Conclusion The overall burden of TB/HIV coinfection was within previous pooled estimate. Our findings support the need for systematic HIV testing as those with unknown status had similar TB treatment outcomes as the HIV infected