Clinical-pathological presentation, treatment and outcomes of ovarian cancer cases at moi teaching and referral hospital (mtrh), eldoret

Introduction: Ovarian cancer is the third most frequent cause of death amongst gynecological cancers both locally and globally. It presents with vague nonspecific symptoms and is histologically heterogeneous. Ovarian cancer management is primarily surgical followed by adjuvant chemotherapy depending on the histological type and the surgical stage. Objectives: To determine the clinical-pathological presentation, treatment and outcomes of ovarian cancer patients at Moi Teaching and Referral Hospital (MTRH), Eldoret. Methods: This was a retrospective chart review of ovarian cancer patients managed between January 2010 and August 2017 at MTRH. Data were analyzed using STATA version 15. Survival trends were generated using Kaplan Meier method. Results: A total of 124 medical charts of patients with ovarian cancer were retrieved, 29 had incomplete data and were excluded, and 95 were evaluable and included in this review. Over half, (63%) presented in stage 3 and 4 though there was no significant association between histology and stage of disease [X2(6) =4.72, p=0.58]. The median age at diagnosis was 47 years with 55-80 years being the modal age group (36%). Majority (57%) were married and 83.9% were unemployed. Only 66% had documented histopathology, with Epithelial Ovarian Cancer (EOC) being most common (70%), [serous (50%) and mucinous (11.4%)]. Sex cord stromal tumors 11%. Germ cell tumors amounted to 11% (dygerminomas 50%and Yolk sac tumors (25%) Bivariate analysis revealed significant association only between histology and parity [X2 (6) = 28.8, p\u3c0.001]. Those reviewed contributed a total of 138.2 person-years to the study and 11(12%) died, giving a diseasespecific mortality rate of 79.6 per 1,000 person years (95% CI: 44.1-143.8). Mortality was highest among those with epithelial histology 109 (95% CI: 48.8-241.9) per 1,000 person years and those who had neoadjuvant chemotherapy then surgery as a treatment option, 373.1 (95% CI: 93.3-1491.8) per 1,000 person years. Those who underwent upfront surgery followed by adjuvant chemotherapy and sex cord stromal cancer had higher survival probability. Conclusion: Ovarian cancer at MTRH is diagnosed at advanced stages III and IV of disease and has a lower median age at presentation. EOC is the commonest histological type and serous subtype is the most lethal. Mortality was highest among those with EOC and those who underwent neoadjuvant chemotherapy. Granulosa cell tumor is the only sex cord stromal type reported in our setting and it exhibited a higher survival probability. Germ cell tumors were mainly found in nulliparous women

Spatiotemporal patterns of successful TB treatment outcomes among HIV co-infected patients in Kenya

Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, KenyaConvergence of the Tuberculosis (TB) and HIV epidemics threatens the management of TB treatment. These has been evidenced by various studies describing how HIV cc-infection propagates unsuccessful TB treatment outcomes. Information on the spatiotemporal patterns of successful TB treatment outcomes remain less understood despite the multi-organizational TB treatment efforts. This study uses case notification data to evaluate the spatiotemporal patterns of successful TB treatment outcomes for HIV co-infected patients in Kenya. This study used the case notification data from the Kenya National TB control program to investigate successful TB treatment outcomes in forty-seven counties in the period 2012 - 2017. The population of study was HIV co-infected cases with known TB treatment outcome. Achi-squre test was performed to determine the association between treatment outcomes and risk factors; TB- type, age, gender, ART therapy and patient type. The study also assessed the geographic patterns and temporal trends by mapping the TB treatment success rate in each county for the six-year period. Using the Integrated Nested Laplace Approach (INLA), the TB treatment success of HIV co-infected patients was modeled. The spatial parameters assumed the BesagYork-Mollie (BYM) specification. The temporally structured effect was represented through a neighboring structure and the temporally unstructured effects using a Gaussian exchangeable prior. Among the 172233 HIV co-infected cases included in the analysis, 135973 (78.9%) achieved successful TB treatment outcomes. Female cases registered higher treatment success rates (80.1%) compared to the male cases (77.8%). The cases on Anti-Retroviral Therapy (ART) recorded a success rate of 79.9% against 69.1% for their counterpart not on ART. The spatial trend depicted increased treatment success in some parts of the country with a relatively high level of associated certainty, characterized by a spatial relative success above 1 and posterior probabilities above 0.8. The temporal trend of treatment success showed an increase in the treatment success of TB in HIV coinfected cases. Overall, the success rate was still below 85% particularly for Homabay, Siaya, Kisumu, Migori and Busia counties in western Kenya. The successful TB treatment outcomes for HIV coinfected cases in Kenya were slightly below the 85% standard threshold set by the World Health Organization. Our study showed that even though co-infected cases have an increased risk of unsuccessful treatment outcomes, enhanced treatment monitoring improved the treatment outcome in most counties for the six-year period.Department of Mathematical Sciences, Pan African University Institute of Basic Sciences Technology and Innovation, Nairobi, Kenya Epidemiology and Biostatistics Division, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa. School of Mathematics, Statistics Computer Science, University of Kwa-Zulu Natal, Pietermaritzburg, South Africa

Epidemiological profile and clinico-pathological features of pediatric gynecological cancers at Moi Teaching & Referral Hospital, Kenya

Background: The main pediatric (0–18 years) gynecologic cancers include stromal carcinomas (juvenile granulosa cell tumors and Sertoli-Leydig cell tumors), genital rhabdomyosarcomas and ovarian germ cell. Outcomes depend on time of diagnosis, stage, tumor type and treatment which can have long-term effects on the reproductive career of these patients. This study seeks to analyze the trends in clinical-pathologic presentation, treatment and outcomes in the cases seen at our facility. This is the first paper identifying these cancers published from sub-Saharan Africa. Method: Retrospective review of clinico-pathologic profiles and treatment outcomes of pediatric gynecologic oncology patients managed at MTRH between 2010 and 2020. Data was abstracted from gynecologic oncology database and medical charts. Results: Records of 40 patients were analyzed. Most, (92.5%, 37/40) of the patients were between 10 and 18 years. Ovarian germ cell tumors were the leading histological diagnosis in 72.5% (29/40) of the patients; with dysgerminomas being the commonest subtype seen in 12 of the 37 patients (32.4%). The patients received platinum-based chemotherapy in 70% of cases (28/40). There were 14 deaths among the 40 patients (35%) Conclusion: Surgery remains the main stay of treatment and fertility-sparing surgery with or without adjuvant platinum-based chemotherapy are the standard of care with excellent prognosis following early detection and treatment initiation. LMICs face several challenges in access to quality care and that affects survival of these patients. Due to its commonality, ovarian germ cell cancers warrant a high index of suspicion amongst primary care providers attending to adnexal masses in this age group

Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the “First 90”

Background: The UNAIDS 90-90-90 Fast-Track targets provide a framework for assessing coverage of HIV testing services (HTS) and awareness of HIV status – the “first 90.” In Kenya, the bulk of HIV testing targets are aligned to the five highest HIV-burden counties. However, we do not know if most of the new HIV diagnoses are in these five highest-burden counties or elsewhere. Methods: We analyzed facility-level HTS data in Kenya from 1 October 2015 to 30 September 2016 to assess the spatial distribution of newly diagnosed HIV-positives. We used the Moran's Index (Moran's I) to assess global and local spatial auto-correlation of newly diagnosed HIV-positive tests and Kulldorff spatial scan statistics to detect hotspots of newly diagnosed HIV-positive tests. For aggregated data, we used Kruskal-Wallis equality-of-populations non-parametric rank test to compare absolute numbers across classes. Results: Out of 4,021 HTS sites, 3,969 (98.7%) had geocodes available. Most facilities (3,034, 76.4%), were not spatially autocorrelated for the number of newly diagnosed HIV-positives. For the rest, clustering occurred as follows; 438 (11.0%) were HH, 66 (1.7%) HL, 275 (6.9%) LH, and 156 (3.9%) LL. Of the HH sites, 301 (68.7%) were in high HIV-burden counties. Over half of 123 clusters with a significantly high number of newly diagnosed HIV-infected persons, 73(59.3%) were not in the five highest HIV-burden counties. Clusters with a high number of newly diagnosed persons had twice the number of positives per 1,000,000 tests than clusters with lower numbers (29,856 vs. 14,172). Conclusions: Although high HIV-burden counties contain clusters of sites with a high number of newly diagnosed HIV-infected persons, we detected many such clusters in low-burden counties as well. To expand HTS where most needed and reach the “first 90” targets, geospatial analyses and mapping make it easier to identify and describe localized epidemic patterns in a spatially dispersed epidemic like Kenya's, and consequently, reorient and prioritize HTS strategies.publishedVersio

Tobacco use and mass media utilization in sub-Saharan Africa.

Media utilization has been identified as an important determinant of tobacco use. We examined the association between self-reported tobacco use and frequency of mass media utilization by women and men in nine low-to middle-income sub-Saharan African countries.Data for the study came from Demographic and Health Surveys conducted in Burkina Faso, Ethiopia, Liberia, Lesotho, Malawi, Swaziland, Uganda, Zambia and Zimbabwe over the period 2006-2011. Each survey population was a cross-sectional sample of women aged 15-49 years and men aged 15-59 years, with information on tobacco use and media access being obtained by face-to-face interviews. An index of media utilization was constructed based on responses to questions on the frequency of reading newspapers, frequency of watching television and frequency of listening to the radio. Demographic and socioeconomic variables were considered as potentially confounding covariates. Logistic regression models with country and cluster specific random effects were estimated for the pooled data.The risk of cigarette smoking increased with greater utilization to mass media. The use of smokeless tobacco and tobacco use in general declined with greater utilization to mass media. The risk of tobacco use was 5% lower in women with high media utilization compared to those with low media utilization [Adjusted Odds Ratio (AOR) = 0.95, 95% confidence interval (CI):0.82-1.00]. Men with a high media utilization were 21% less likely to use tobacco compared to those with low media utilization [AOR = 0.79, 95%CI = 0.73-0.85]. In the male sample, tobacco use also declined with the increased frequency of reading newspapers (or magazines), listening to radio and watching television.Mass media campaigns, conducted in the context of comprehensive tobacco control programmes, can reduce the prevalence of tobacco smoking in sub-Saharan Africa. The reach, intensity, duration and type of messages are important aspects of the campaigns but need to also address all forms of tobacco use

Principal component factor analysis of media utilization.

<p>Principal component factor analysis of media utilization.</p

Prevalence rates of tobacco use, cigarette smoking and smokeless tobacco use by selected covariates.

<p>Prevalence rates of tobacco use, cigarette smoking and smokeless tobacco use by selected covariates.</p

Logistic regression analysis results for the Male population.

<p>* p<0.05</p><p>** p<0.01</p><p>*** p<0.0.</p><p>Logistic regression analysis results for the Male population.</p

Logistic regression analysis results for the Female population.

<p>* p<0.05</p><p>** p<0.01</p><p>*** p<0.001.</p><p>Logistic regression analysis results for the Female population.</p

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya.

The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012-2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts