5 research outputs found
Supplemental Material - Poisoning patterns and factors associated with treatment outcomes among patients: A case study of Kiambu county hospitals, Kenya
No full textSupplemental Material for Poisoning patterns and factors associated with treatment outcomes among patients: A case study of Kiambu county hospitals, Kenya by James Maina Githinji, Michael Mungoma, Kinara Fossa, Jesse Ngugi, Samwel Ondiek, Prabhjot Juttla, Alfred Owino Odongo, Moses Ndiritu and Magoma Mwancha-Kwasa in Toxicology Research and Application</p
Prevalence and determinants of self-reported hypertension in urban poor settlements of Johannesburg
No full textA research report submitted to the School of Public Health, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Epidemiology and Biostatistics. November 2017.Background: Hypertension is the leading risk factor for cardiovascular disease in Africa. Cardiovascular disease is rated as the number one cause of death in Africa. Previously, hypertension was known to predominantly affect the affluent population but recently the condition has been emerging even among the poorer population, rendering it a greater burden. In South Africa its prevalence level has significantly escalated, particularly in urban areas, with higher incidence among the poor. The prevalence of self-reported hypertension and its risk factors is not well documented in the urban impoverished settlements. Understanding determinants and the prevalence of self-reported hypertension in these areas will help develop improved awareness, prevention and control strategies. This study aimed to determine the prevalence and determinants of self-reported hypertension in five urban impoverished sites in Johannesburg, South Africa.
Methods: Secondary data analysis was done on data from the HEAD study which involved a sample of households from five urban poor areas. Prevalence levels of self-reported hypertension were estimated within the study areas. Summary measures of the data were computed and presented in a descriptive table. Distribution of the potential risk factors by prevalence of self-reported hypertension was also done. Lastly, binary logistic regression was used to model the unadjusted and adjusted association between the identified risk factors and self-reported hypertension.
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Results: The prevalence of self-reported hypertension among households in the five urban impoverished sites was 20 percent (n=107). The independent predictors of hypertension were study area (Riverlea, Hillbrow), race, age, gender (0.25-0.49 and ≥0.75), work (0.5-0.74, and ≥0.75), monthly income (ZAR 1000-2000, 2001-5000, and >5000), presence of another non-communicable disease and socioeconomic status (middle). Results from the adjusted model showed that race, sex, age and presence of at least one other non-communicable disease are were significantly associated with self-reported hypertension
Conclusion: The study’s findings strengthen the case that age, sex, race, and co-morbid non-communicable diseases are associated with self-reported hypertension. Interventions that target the urban poor population and that focus on increasing awareness and context specific risk reduction are recommended. Further, the association with these factors should be confirmed by carrying out a more robust population-based study to inform policyLG201
Poisoning patterns and factors associated with treatment outcomes among patients: A case study of Kiambu county hospitals, Kenya
No full textBackgroundRising poisoning incidences worldwide, primarily in developing countries, remain ambiguous due to paucity of data and poison centres. This study evaluates patterns and factors causing poor outcomes in Kiambu County, Kenya.MethodsA records-based retrospective cross-sectional study of poisoning cases who presented to nine facilities between June 2015 and July 2020 was conducted. The data collected was analysed through descriptive, bivariate, and multivariate logistic regression using STATA version 13
Trends in cervical cancer screening uptake and cytology outcomes at Kiambu Level 5 Hospital, Kenya
No full textIntroduction: Cervical cancer remains a major public health concern in Kenya, yet facility-level data describing Pap smear screening uptake and cytology outcomes within routine health services are limited. This study described trends in Pap smear screening uptake and patient cytology outcomes at Kiambu Level 5 Hospital (KL5H) between 2014 and 2020.
Methods: We conducted a facility-based retrospective study utilising data extracted from the cervical cancer screening and treatment daily activities register (MOH 412) and the Pap smear laboratory register. Descriptive statistical analysis was performed using Stata version 12.
Results: A total of 3,457 women underwent Pap smear screening within the study period. Among these, 33.0% (1,147/3,457) were aged 40–49 years, 34.3% (1,192/3,457) were HIV positive, and 62.2% (2,160/3,457) were index screening tests. The Pap smear results indicated that the majority (94.6%) were negative for intraepithelial lesions or malignancy, while 5.4% exhibited premalignant Pap smears. Only 0.06% of the participants had malignant Pap smear results. Annual Pap smear screening increased from 370 women in 2014 to a peak of 978 in 2019, before declining to 551 in 2020.
Conclusion: Most women screened had normal cytology results, with a small proportion showing premalignant or malignant lesions. Pap smear screening uptake at KL5H increased following clinic establishment, with fluctuations over time and a decline observed in 2020. These findings underscore the importance of sustaining facility-based cervical cancer screening services and support the need for targeted efforts to maintain and enhance screening coverage in the county
Modelling the seasonal and spatial variation of malaria transmission in relation to mortality in Africa
No full textAbout three billion people worldwide are estimated to be at risk of malaria transmission. In developing countries, malaria is believed to be a major cause of morbidity and mortality, mostly in children under five years. It is among the indirect causes of maternal mortality and infants’ deaths due to low-birth-weights. Malaria brings huge economic burden due to number of days lost during sickness and deaths, sustaining a vicious cycle of disease and poverty in sub Saharan Africa (SSA) and high attribute of disability-adjusted life years.
A number of malaria control interventions to reduce intensity of transmission have been successfully implemented in the regions of SSA, however, elimination of malaria is still a dream in many developing countries today. Failures in global eradication are related to resistance in insecticides and anti-malarial drugs, and health systems related factors. The Roll Back Malaria (RBM) partnership reinforced new strategies to combat malaria with long-term goal of eradicating the disease globally. This was facilitated by increasing funding for malaria research, improve multi disciplinary initiatives and make malaria among the main agenda of all international health and development forums. The reduction in mortality, especially in children has been reported recently and is associated with achievements in intervention strategies, improvements in malaria diagnosis and treatment. However, poor natural acquisition of malaria immunity in children as a consequence of weak or no exposure is a major epidemiological concern and brings a fear of higher mortality rates or shifting of age of death to older children. Understanding and quantify links between transmission, intervention, immunity and mortality is key for sustainable progress towards malaria control targets.
A comprehensive analysis of information on malaria transmission, vital events, drivers of transmission and mortality-related risk factors is required to achieve that. Lack of vital registration systems in developing countries hinders availability of appropriate data to conduct such analysis. Establishment of Demographic Surveillance Systems (DSS) in many developing countries aims to fill these information gaps. One of the initiatives integrated within DSSs is the Malaria Transmission Intensity and Mortality Burden across Africa (MTIMBA) project. The project compiled a database of mosquito collections at selected sites in Africa over a large number of locations, using standardized methodologies for a period of three years. The entomological parameters were linked with routinely monitored vital events within the DSS. The MTIMBA database is the most comprehensive entomological database ever collected in Africa which allows studying spatial-temporal variation in malaria transmission in relation to mortality.
Malaria is an environmental disease hence transmission varies with climate as it modifies population, survival, distribution and infectivity of malaria vectors. Quantification of association between climate and transmission is important to allow prediction of risk even in areas that field data cannot be easily obtained. Development in geographical information systems (GIS) and availability of remote sensing (RS) data facilitates availability of environment and climate data at high space and time resolutions allowing accurate estimation of outcome-factor relationship.
However, DSS data are large, sparse, zero-inflated and are characterized by seasonal patterns, spatial and temporal correlations. Standard models assume independence between observations, an assumption which do not hold for correlated data, hence utilizing these models might result into biased estimates. Geostatistical modeling of large, sparse and zero inflated space-time data is computational challenging specifically in the estimation of the spatial processes. The spatial correlation is accounted by introducing location-specific random effect parameters which are assumed to arise from a spatial process quantified by a multivariate normal distribution. The models are highly parameterized and their fit is computationally intensive. Bayesian computational algorithms such as Markov Chain Monte Carlo (MCMC) can be used to fit these models. Estimation of the spatial process requires inversion of the covariance matrix at each simulation point. The dimension of the matrix increases exponentially with number of locations and the inversion becomes infeasible when the size is too large. Recent techniques overcome this problem by approximating the spatial process from a subset of locations. These methods have been applied on Gaussian outcomes observed over a grid. Extension and formulation of rigorous methods to efficient model MTIMBA data are needed to allow precise prediction of malaria transmission at locations with mortality data to enhance studying the association. Lastly, seasonality in climatic conditions which introduces seasonal patterns in transmission and mortality data, should be accounted for when modelling such data.
The objectives of this thesis were to i) develop Bayesian geostatistical models to analyze very large and sparse geostatistical and temporal non-Gaussian data with seasonal patterns and ii) apply these models to (a) estimate space-time heterogeneity in malaria transmission (b) assess mortality variations between different ages during the first year of life while adjusting for seasonality and (c) determine the relation between transmission intensity and risk of mortality in children and adult population after taking into account control interventions. This work used an extract of MTIMBA data from the Rufiji DSS (RDSS) collected between October 2001 and September 2004.
Evaluation of approaches to capture seasonal pattern is discussed in Chapter 2 and applied to estimate mortality peaks at different stages of infant life. In Chapter 3, models approximating the spatial process from a subset of locations were developed to assess effect of climate, seasonal and spatial pattern of sporozoite rate (SR) of An. funestus and An. gambiae in RDSS. A rigorous approach to analyze malaria transmission data using Entomology Inoculation Rate (EIR) data, which is the product of mosquito density and SR, is discussed in Chapter 4. Zero-inflated models were used to account for over-dispersion and zero-inflation in the data. High resolution EIR estimates were produced for the RDSS. Exposure surfaces obtained in Chapter 4, were aligned with mortality events to assess the relationship between all-cause mortality and malaria transmission. Geostatistical Bernoulli discrete-time regression models adjusted for age and ITN possession were used for that analysis. The results of these analyses are presented in Chapters 5 and 6. The EIR was incorporated in the model as a covariate with measure of uncertainty.
This work is a building block on the insight and understanding of association between malaria transmission and all-cause mortality. The strength of results of this work relies on EIR estimates predicted at high spatial (household level) and temporal resolution by employing rigorous geostatistical models fitted on large entomological data. The better exposure estimates obtained are able to more accurately estimate the mortality-transmission relation
