Improving the measurement of live weight and body condition score in sheep : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science, Massey University, Turitea, Palmerston North, New Zealand

Liveweight (LW) and body condition score (BCS) are important performance indicators in sheep management, providing a basis for decision making. Therefore, accurate measurement of these traits is imperative. The overall aims of this thesis were to 1) Determine the factors affecting the rate of LW loss of fasting sheep, 2) derive equations to predict LW and LW change over a short time period (1 to 8 hours), 3) evaluate the factors affecting the relationship between ewe LW and BCS, and 4) derive equations for predicting ewe BCS. In the LW studies, lambs were offered three herbage availability levels (Low, Medium and High) in autumn or winter. Similarly, mixed-aged ewes at different physiological states were offered two herbage levels (Low or High). These studies were conducted in two stages: A) calibration stage and B) validation stage. Equations to predict without delay LW were developed at the calibration stage and validated on data collected from independent ewes from different farms. The rate of ewe LW loss was influenced by herbage type and availability, and season. Further, in pregnant ewes, liveweight loss was influenced by the stage of pregnancy, but not pregnancy-rank. Applying correction equations improved the prediction accuracy of without delay LW estimates up to 55% and 69% in ewe lambs and mixed aged ewes compared with using the delayed weights, respectively. For the BCS studies, LW and BCS data of ewes were collected at regular times of the annual production cycle until they were six years of age. Using a ewe’s LW and BCS records to predict their current BCS using a linear model gave moderately accurate estimates. A different dataset, which included foetal- and fleece weight-adjusted LW and height at withers was then used. It was found that equations combining LW, LW change and previous BCS explained more variability in current BCS and were more accurate than LW-alone based models but the addition of adjusted LW and height at withers gave no further benefit to the BCS prediction models. Applying machine learning classification algorithms such as extreme gradient boosted trees and Random forest on a 3-point BCS scale achieved very good BCS prediction accuracies (> 85%). These combined findings provide useful prediction equations that could be incorporated into weighing systems, which along with EID would improve sheep production by aiding management decision making

Developing Guidelines for Functional Substitutes for Ugandan Marriage Practices

Problem Since the introduction of Christianity in Uganda there has been a tension between customary and Christian marriage practices, each struggling to exist at the expense of the other. The crux of the struggle is the validity and sufficiency of customary marriage vis-a-vis Christian marriage rites. Christian youth of marriageable age have been caught in the crossfire, and marital stability has been one of the casualties of the struggle. This study suggests bridging the gap between the two practices. Method Biblical teaching on marriage is established through exegesis of texts such as Gen 2:20-24, Matt 19:4-6, and 1 Cor 7. The nature and significance of Ganda customary marriage are ascertained from various ethnologies. A process of critical contextualization is suggested, by which customary marriage is evaluated in the light of biblical teaching, leading to the creation of appropriate functional substitutes. Conclusion The aim of the critical contextualization approach is to develop a marriage practice that is biblical and authentically African, one that responds to indigenous needs and concerns. This approach will in the long run enhance marital stability as well as people\u27s commitment to Christian teaching

Delays by people living with HIV/AIDS in accessing antiretroviral therapy

Objective: To understand, by qualitative enquiry, the underlying reasons and narratives for patients that delay in accessing antiretroviral treatment.Design: A qualitative design was used, where patients were interviewed using the free attitude interview technique, after being selected based on a screening question: “How long did it take you to present at a clinic or hospital for treatment after receiving your human immunodeficiency virus-positive result?”Setting and subjects: Eight patients from the human immunodeficiency virus clinic at Potchefstroom were interviewed.Outcome measures: The interviews were transcribed verbatim and organised into themes.Results: The following themes were identified: stigma and discrimination, ignorance and lack of perceived risk of infection, denial and healthcare system constraints. These are discussed and quotations from the interviewed patients included.Conclusion: This qualitative study has contributed to an understanding of why patients delay in accessing highly active antiretroviral therapy. Some of the reasons supplied by patients have been documented globally. Others are poignantly coloured by personal stories. By understanding patients’ perspectives and feelings, emphasis can be placed on the reduction of stigma, denial, practical clinic constraints and appropriate types of health education.Keywords: qualitative research, delays, access, antiretroviral drugs, ARV

Interventions and assessment tools addressing key concepts people need to know to appraise claims about treatment effects: a systematic mapping review

Overview of all included studies. (XLSX 133 kb

Adverse drug reaction reporting among health care workers at Mulago National Referral and Teaching hospital in Uganda

Background: Adverse Drug Reactions (ADRs) are an important contributor to patient morbidity and hospitalisation in Uganda. Under-reporting of ADRs may increase medicine-induced morbidity and mortality among patients. This study determined the extent of ADR reporting, and associated factors, among healthcare workers in Uganda.Methods: A quantitative, cross-sectional, study was conducted. Pretested, semi-structured questionnaires were administered to 289 randomly sampled healthcare workers over a three-month period in Mulago National Referral Hospital, Uganda. The primary outcome was the proportion of healthcare workers who had ever reported an ADR. Data was double-entered in Epidata version 3.0, cleaned and exported to STATA version 10.1 for analysis.Results: The overall response rate was 77.2% (n=223). The majority of the respondents were females (139, 62.3%). The median age of all respondents was 32.6 years (min-23; max-65). Only about 16.6% (n=37) of healthcare workers had ever reported an ADR. Very few (n= 84, 37.7%) healthcare workers knew the tools used in ADR reporting. Less than a quarter (n=41, 18.4%) of the healthcare workers knew where to report ADRs. Lack of training was reported as the major (56.5%, 126) deterrent to reporting ADRs by healthcare workers.Conclusion: Adverse drug reactions are under-reported in Uganda, and healthcare workers have insufficient knowledge of existing pharmacovigilance systems, including ADR reporting systems. To address these challenges, there is need to sensitize and train healthcare workers in patient-centred aspects of medicine surveillance, so as to provide appropriate care while optimising patient safety.Keywords: Adverse drug reaction, ADR, adverse effects, reporting, healthcare worker, pharmacovigilance, Ugand

A predictive model for early detection of diabetes mellitus using machine learning

A Project Report Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and TechnologyDiabetes is a chronic, metabolic disease characterized by elevated levels of blood glucose or blood sugar that over time can bring severe damage to vital organs including the heart, blood vessels, eyes, kidneys and nerves. Diabetes is therefore one of the major priorities in medical science research. Type 2 diabetes is common in adults, either because of inadequate insulin production, or when the body’s cells fail to respond properly to the produced insulin. For all the diabetes cases, it’s found out that 90% are Type 2 diabetes. Of the 422 million people with diabetes worldwide, 336 million people are found in developing countries, and 1.6 million people die of diabetes each year according to statistics by the World Health Organization. Around 19.8 million adults in Africa have Type 2 diabetes but approximately 75% are unaware of their condition (undiagnosed). Most people are undiagnosed because many people lack knowledge of symptoms for diabetes, and others are not diagnosed due to lack of testing kits more especially in rural areas. African governments have scaled up purchasing and distribution of diagnostic kits but the majority of the population has not been reached. Researchers have been developing predictive models for Type 2 diabetes, but African populations are not widely included in their datasets. The developed models may therefore not accurately identify at-risk populations in the African context. The main emphasis of this research was to come up with a machine learning prediction model to find out Ugandans likely to be suffering from Type 2 diabetes (output classes: high risk or low risk), based on input symptoms. Random Forest, Support Vector Machine, Naïve Bayes, and AdaBoost classifiers were trained on anonymised, real patient data with twelve features including age, gender (male or female), systolic blood pressure, residence (town or village), diastolic blood pressure, family Member with diabetes, alcohol intake, smoker, hypertensive, obesity, physically inactive and body mass index. This research’s experimental results after the comparison of the Accuracy Score and Confusion Matrix for all the above algorithms, the Random Forest classifier emerged the premier with the accuracy score of 85.4%, thus the experimental results shown that performance of Random Forest classifier as being significant superior compared to all other the machine learning algorithms

Delay to access antiretroviral therapy in people living with HIV/AIDS in Potchefstroom.

Background: The government of South Africa rolled out free anti-retroviral treatment in 2004 but many people living with HIV still present late for treatment while others choose to die rather than accessing this free treatment. This qualitative study was done at Potchefstroom Provincial Hospital Wellness Clinic to establish why many people living with HIV in Potchefstroom present late for treatment. Aim: To establish why people living with HIV/AIDS in Potchefstroom delay in accessing antiretroviral therapy. Objectives: 1.To conduct interviews with selected patients, in order to understand why they delayed accessing antiretroviral treatment. 2. To assess the demographics of patients who delayed in accessing antiretroviral treatment. Methods: This is a qualitative study carried out at the Wellness clinic of the Potchefstroom provincial hospital in the North West province of South Africa. Eight adult participants (3 females and 5 males) were interviewed individually in English, in one-on-one free attitude interviews and the conversations were audio-taped by the researcher. All the respondents were from the nearby black township of Ikageng. The respondents were selected after meeting the inclusion criteria of the study. The researcher asked the respondents to freely describe the reasons why they started antiretroviral treatment late. In addition, a semi-structured open ended questionnaire was also used by the researcher to prompt responses. Interviews went on until saturation point was reached. The audio-taped interviews were transcribed verbatim and the responses were analyzed. Responses from the different participants which bore similarity were assigned a similar color code. By using the cut and paste method, all responses bearing the same color code were pasted on one page resulting into the themes. Results: Four broad themes emerged as the reasons responsible for the late accessing of ART by the respondents. These themes were: 1. Stigma and discrimination, 2. Ignorance and lack of perceived risk of infection, 3. Denial, and 4. Health care system constraints. Conclusions: This small study though not exhaustive by any means, has highlighted some of the reasons why people living with HIV present late for help. The study was done using participants from only one peri-urban population. It is therefore not easy to generalize the results to the whole of South Africa, a country with a lot of social and economical diversities among its people. A bigger study over a wider geographical area might reveal different findings

The effects of exhaust gas recirculation (EGR) on the performance of diesel engine.

Masters Degree (Mechanical Engineering). University of KwaZulu-Natal. Durban, 2018.The aim of this work was to study the effects of EGR on the performance of a diesel engine using waste plastic pyrolysis oil (WPPO) and conventional diesel (CD). WPPO was developed through the pyrolysis extraction method. The blends were made up of WPPO and conventional diesel mixed in the ratios of WPPO10, WPPO20, WPPO30, WPPO40 and WPPO100. The EGR % flow rate chosen was 0 % to 30 % graduated in intervals of 5 %. The lower blend ratios of WPPO10 and WPPO20 showed lower values of brake specific fuel consumption (BSFC) compared to conventional diesel values and the high blend ratio of WPPO100. The brake thermal energy (BTE) showed increased values for lower blend ratios of WPPO10 and WPPO20 of 8.35 % and 8.15 % respectively with application of an EGR % flow rate of 15 % compared to high blend ratios of WPPO30, WPPO40 and WPPO100. The application of EGR % flow rate was observed to cause no significant change in the engine brake power (BP) for all the test fuels used. The application of EGR % flow rate in increasing rates reduced exhaust gas temperature (EGT), with conventional diesel reporting 440 ⁰C at 5 % EGR flow rate and 340 ⁰C being the lowest at 30 % EGR flow rate. The application of EGR % flow rate reduced the amount of hydrocarbon emissions emitted by the applied test fuels across the board. At EGR flows rate of 5 %, 10 %, 15 %, 20 %, 25 % and 30 %, conventional diesel had 43 ppm, 57 ppm, 70 ppm, 82 ppm and 85 ppm respectively. As the blend ratio increased with increased EGR % flow rate there was an increased rate of NOX emissions. At 20 % EGR flow rate, blends WPPO10, WPPO2O, WPPO30, WPPO40 and WPPO100 had 591ppm, 645 ppm, 750 ppm, 778 ppm and 851 ppm respectively compared to at the 10 % EGR flow rate where their values were 830 ppm, 971 ppm, 1031 ppm, 1151 ppm and 1116 ppm respectively. There was a significant continuous and marginal increase in the percentage of carbon emissions by volume as the load increased across all the test fuels irrespective of the EGR % flow rate. At 80 % engine load the value for WPPOB100 was 2.0 % up from 1.65 % by volume at part engine load, while the value of conventional diesel was 4.1 % at 80 % engine load compared to 2.95 % by volume at part engine load. The application of EGR % flow rate increased the carbon dioxide emission exponentially by almost doubling the values. At 10 % EGR flow rate the value of conventional diesel was 3.85 % compared to WPPOB100 at 6.25 %, WPPOB10 at 4.75 %, WPPOB20 at 4.25 %, WPPOB30 at 3.95 %, and WPPOB40 at 6.65 %

Outcome of paediatric supracondylar humeral fractures treated by surgery at the University Teaching Hospital of Kigali, Rwanda

Background: Supracondylar humeral fractures are common elbow injuries in children. When these fractures are managed improperly, patients end up having complications that interfere with daily activities. Objective: This study was conducted to evaluate the functional outcome of supracondylar humeral fractures treated with surgery at the University Teaching Hospital of Kigali, Rwanda. Methodology: This was a combined retrospective and cross-sectional observational study. The sample size was 108. Theatre logs and OPD files were used to obtain patients' clinical information. Functional outcome was evaluated using QuickDASH score. Results: Children having 6 to 10 years of age were predominant (54.6%). Male to female ratio was 1.9:1. The left elbow was most affected (60.2%). Seventy five patients (69.4%) were within normal range, 27 (25.0%) patients had mild disability, 5 (4.7%) patients had moderate disability, and 1 (0.9%) patient had severe disability. There were no patients with very severe disability. The most common complication was elbow stiffness (59.3%), followed by gunstock deformity (27.1%). The only determinant of functional outcome found was the time (within 4 days or beyond) from injury to management (OR: 0.993, 95% CI: {0.987-1.000}, p-value: 0.048). Conclusion: For operable supracondylar humeral fractures in children, operation within 4 days and good follow up of patients in postoperative period are crucial for good functional outcome