Application of Semi-Markov Process For Model Incremental Change in HIV Staging with Cost Effect

In the recent past, both non-parametric and parametric approaches have consistently been used to model cost effectiveness in a variety of health applications. This study applies the semi-Markov model while presenting the sojourn time with well-defined probability distributions. We employed the Weibull distribution to model the hazard function for each of the defined transition paths. We defined three distinct states of the semi-Markov process using the quantity of HIV virus in the blood of an HIV-infected person i.e., viral load (VL) copies in a milliliter (copies/mL). The three states were defined; VL < 200 copies/mL, 200 copies/mL < VL < 1,000 copies/mL, VL > 1,000 copies/mL and an absorbing state which is naturally death. We also developed a cumulative cost function, purposely to determine the average estimated cost per patient in each of the defined states. Incremental Cost Effectiveness Ratio (ICER) was utilized in the analysis of cost-effectiveness while comparing two program strategies i.e., Patients under the differentiated care model (DCM) and those who are not considered to be in any model of differentiated care during their respective ongoing clinical follow up. Results show the mean cost of the patients for each state 1, 2, and 3 was $765,$ 829, and $1,395 respectively. More so, the computed ICER ratio was$ 484/life-year-saved. In conclusion, the cost of keeping patients in state 1 (on DCM) was relatively cheaper and more efficient compared to the other states

Phylogenetic analysis of Bunyamwera and Ngari viruses (family Bunyaviridae, genus Orthobunyavirus) isolated in Kenya

Orthobunyaviruses, tri-segmented, negative-sense RNA viruses, have long been associated with mild to severe human disease in Africa, but not haemorrhagic fever. However, during a Rift Valley fever outbreak in East Africa in 1997–1998, Ngari virus was isolated from two patients and antibody detected in several others with haemorrhagic fever. The isolates were used to identify Ngari virus as a natural Orthobunyavirus reassortant. Despite their potential to reassort and cause severe human disease, characterization of orthobunyaviruses is hampered by paucity of genetic sequences. Our objective was to obtain complete gene sequences of two Bunyamwera virus and three Ngari virus isolates from recent surveys in Kenya and to determine their phylogenetic positioning within the Bunyamwera serogroup. Newly sequenced Kenyan Bunyamwera virus isolates clustered closest to a Bunyamwera virus isolate from the same locality and a Central African Republic isolate indicating that similar strains may be circulating regionally. Recent Kenyan Ngari isolates were closest to the Ngari isolates associated with the 1997–1998 haemorrhagic fever outbreak. We observed a temporal/geographical relationship among Ngari isolates in all three gene segments suggesting a geographical/temporal association with genetic diversity. These sequences in addition to earlier sequences can be used for future analyses of this neglected but potentially deadly group of viruses.Scholarship to Collins Odhiambo by the Swedish International Development Cooperation Agency (SIDA) through the African Regional Postgraduate Programme in Insect Science (ARPPIS).http://journals.cambridge.org/action/displayJournal?jid=HYGhb2016Medical Virolog

Prevalence of reproductive tract infections and the predictive value of girls’ symptom-based reporting: findings from a cross-sectional survey in rural western Kenya

Objectives Reproductive tract infections (RTIs), including sexually acquired, among adolescent girls is a public health concern, but few studies have measured prevalence in low-middle-income countries. The objective of this study was to examine prevalence in rural schoolgirls in Kenya against their reported symptoms. Methods In 2013, a survey was conducted in 542 adolescent schoolgirls aged 14–17 years who were enrolled in a menstrual feasibility study. Vaginal self-swabbing was conducted after girls were interviewed face-to-face by trained nurses on symptoms. The prevalence of girls with symptoms and laboratory-confirmed infections, and the sensitivity, specificity, positive and negative predictive values of symptoms compared with laboratory results, were calculated. Results Of 515 girls agreeing to self-swab, 510 answered symptom questions. A quarter (24%) reported one or more symptoms; most commonly vaginal discharge (11%), pain (9%) or itching (4%). Laboratory tests confirmed 28% of girls had one or more RTI. Prevalence rose with age; among girls aged 16–17 years, 33% had infections. Bacterial vaginosis was the most common (18%), followed by Candida albicans (9%), Chlamydia trachomatis (3%), Trichomonas vaginalis (3%) and Neisseria gonorrhoeae (1%). Reported symptoms had a low sensitivity and positive predictive value. Three-quarters of girls with bacterial vaginosis and C. albicans, and 50% with T. vaginalis were asymptomatic. Conclusions There is a high prevalence of adolescent schoolgirls with RTI in rural Kenya. Public efforts are required to identify and treat infections among girls to reduce longer-term sequelae but poor reliability of symptom reporting minimises utility of symptom-based diagnosis in this population. Trial registration number: ISRCTN17486946

Experience from a pilot point-of-care CD4 enumeration programme in Kenya

No abtract available

Improving performance of hurdle models using rare-event weighted logistic regression: an application to maternal mortality data

In this paper, performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. This research aimed to develop and assess the performance of the Poisson and Negative Binomial (NB) Hurdle Rare Event Weighted Logistic Regression (REWLR) models, applied to simulated data with various degrees of zero inflation and to Nairobi county’s maternal mortality data. The study data on maternal mortality were pulled from JPHES. The data contain the number of maternal deaths, which is the outcome variable, and other obstetric and demographic factors recorded in MNCH facilities in Nairobi between October 2021 and January 2022. The models were also fit and evaluated based on simulated data with varying degrees of zero inflation. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best performing model for zero inflated count data due to rare events

Improving performance of hurdle models using rare-event weighted logistic regression: an application to maternal mortality data

In this paper, performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. This research aimed to develop and assess the performance of the Poisson and Negative Binomial (NB) Hurdle Rare Event Weighted Logistic Regression (REWLR) models, applied to simulated data with various degrees of zero inflation and to Nairobi county's maternal mortality data. The study data on maternal mortality were pulled from JPHES. The data contain the number of maternal deaths, which is the outcome variable, and other obstetric and demographic factors recorded in MNCH facilities in Nairobi between October 2021 and January 2022. The models were also fit and evaluated based on simulated data with varying degrees of zero inflation. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best performing model for zero inflated count data due to rare events

R code from Improving performance of hurdle models using rare-event weighted logistic regression: an application to maternal mortality data

In this paper, performance of hurdle models in rare events data is improved by modifying their binary component. Rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. This research aimed to develop and assess the performance of the Poisson and Negative Binomial (NB) Hurdle Rare Event Weighted Logistic Regression (REWLR) models, applied to simulated data with various degrees of zero inflation and to Nairobi county’s maternal mortality data. The study data on maternal mortality were pulled from JPHES. The data contain the number of maternal deaths, which is the outcome variable, and other obstetric and demographic factors recorded in MNCH facilities in Nairobi between October 2021 and January 2022. The models were also fit and evaluated based on simulated data with varying degrees of zero inflation. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best performing model for zero inflated count data due to rare events

Maternal Mortality Data from Improving performance of hurdle models using rare-event weighted logistic regression: an application to maternal mortality data

In this paper, performance of hurdle models in rare events data is improved by modifying their binary component. Rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. This research aimed to develop and assess the performance of the Poisson and Negative Binomial (NB) Hurdle Rare Event Weighted Logistic Regression (REWLR) models, applied to simulated data with various degrees of zero inflation and to Nairobi county’s maternal mortality data. The study data on maternal mortality were pulled from JPHES. The data contain the number of maternal deaths, which is the outcome variable, and other obstetric and demographic factors recorded in MNCH facilities in Nairobi between October 2021 and January 2022. The models were also fit and evaluated based on simulated data with varying degrees of zero inflation. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best performing model for zero inflated count data due to rare events