Epidemiology of measles cases, vaccine effectiveness, and performance towards measles elimination in The Gambia.

INTRODUCTION: In 2011, member states of the World Health Organization (WHO) Africa Regional Office (AFRO) resolved to eliminate Measles by 2020. Our study aims to assess The Gambia's progress towards the set AFRO measles elimination target and highlight surveillance and immunisation gaps to better inform future measles prevention strategies. MATERIAL AND METHODS: A retrospective review of measles surveillance data for the period 2011-2019, was extracted from The Gambia case-based measles surveillance database. WHO-UNICEF national coverage estimates were used for estimating national level MCV coverage. Measles post campaign coverage survey coverage estimates were used to estimate national measles campaign coverage. RESULTS: One hundred and twenty-five of the 863 reported suspected cases were laboratory confirmed as measles cases. More than half (53.6%) of the confirmed cases have unknown vaccination status, 24% of cases were vaccinated, 52.8% of cases occurred among males, and 72.8% cases were among urban residents. The incidence of measles cases per million population was lowest (0) in 2011-2012 and highest in 2015 and 2016 (31 and 23 respectively). The indicator for surveillance sensitivity was met in all years except in 2016 and 2019. Children aged 5-9 years (Incidence Rate Ratio-IRR = 0.6) and residents of Central River region (IRR = 0.21) had lower measles risk whilst unvaccinated (Adjusted IRR = 5.95) and those with unknown vaccination status (IRR 2.21) had higher measles risk. Vaccine effectiveness was 89.5%. CONCLUSION: The Gambia's quest to attain measles elimination status by 2020 has registered significant success but it is unlikely that all target indicators will be met. Vaccination has been very effective in preventing cases. There is variation in measles risk by health region, and it will be important to take it into account when designing prevention and control strategies. The quality of case investigations should be improved to enhance the quality of surveillance for decision making.

Comparison of thiopentone with lidocaine spray vs propofol for laryngeal mask airway insertion at tikur anbessa specialized hospital. A prospective cohort study.

BACKGROUND: Insertion of laryngeal mask airway (LMA) requires an adequate depth of anesthesia. Optimal insertion conditions and hemodynamic stability during LMA insertion are mainly influenced by the choice of the intravenous induction agent. Propofol was recommended as a standard induction agent for LMA insertion. Due to unavailability and cost for treatment Propofol is not easily availed, thus this study aimed at assessing the effect of thiopentone with lidocaine spray compared to Propofol on hemodynamic change and LMA insertion on the patient undergoing elective surgery. METHODS: Eighty-four participants were followed in a prospective cohort study based on the induction type of either thiopentone-lidocaine group (TL) or Propofol (P). Hemodynamic variables, LMA insertion condition, apneic time, and cost of treatment during the perioperative time were recorded. Data were checked for normality using the Shapiro-Wilk test. Numeric data were analyzed unpaired student's t-test or Manny Whitney test. Categorical data were analyzed by the chi-square test. A p-value ≤ 0.05 was considered a statistically significant difference. RESULT: The comparison of data showed that a significant reduction in mean arterial blood pressure (MAP) in the Propofol group during the first 10 min. The MAP at first minute after LMA insertion was 78.4 ± 5.5 in the Propofol group compared to 81.8 ± 5.6 in thiopentone-lidocaine group p < 0.001. the mean MAP at 5th and 10th minutes after LMA insertion is also significantly lower in the Propofol group compared to the thiopentone-lidocaine group, p < 0.05. There were no statistically significant differences regarding the heart rate change and insertion conditions between the two groups. Mean apneic time was 138 ± 45.8 s in the Propofol group and 85 ± 13.8 s in thiopentone-lidocaine group p < 0.001. Thiopentone-lidocaine group had a lower treatment cost compared to the Propofol group. CONCLUSION: Thiopentone with 10% topical Lignocaine is an alternative for the insertion of LMA to Propofol, with better hemodynamic stability and cost-effectiveness.

An epidemiological analysis of Acute Flaccid Paralysis (AFP) surveillance in Kenya, 2016 to 2018.

BACKGROUND: The poliovirus has been targeted for eradication since 1988. Kenya reported its last case of indigenous Wild Poliovirus (WPV) in 1984 but suffered from an outbreak of circulating Vaccine-derived Poliovirus type 2 (cVDPV2) in 2018. We aimed to describe Kenya's polio surveillance performance 2016-2018 using WHO recommended polio surveillance standards. METHODS: Retrospective secondary data analysis was conducted using Kenyan AFP surveillance case-based database from 2016 to 2018. Analyses were carried out using Epi-Info statistical software (version 7) and mapping was done using Quantum Geographic Information System (GIS) (version 3.4.1). RESULTS: Kenya reported 1706 cases of AFP from 2016 to 2018. None of the cases were confirmed as poliomyelitis. However, 23 (1.35%) were classified as polio compatible. Children under 5 years accounted for 1085 (63.6%) cases, 937 (55.0%) cases were boys, and 1503 (88.1%) cases had received three or more doses of Oral Polio Vaccine (OPV). AFP detection rate substantially increased over the years; however, the prolonged health workers strike in 2017 negatively affected key surveillance activities. The mean Non-Polio (NP-AFP) rate during the study period was 2.87/ 100,000 children under 15 years, and two adequate specimens were collected for 1512 (88.6%) AFP cases. Cumulatively, 31 (66.0%) counties surpassed target for both WHO recommended AFP quality indicators. CONCLUSIONS: The performance of Kenya's AFP surveillance system surpassed the minimum WHO recommended targets for both non-polio AFP rate and stool adequacy during the period studied. In order to strengthen the country's polio free status, health worker's awareness on AFP surveillance and active case search should be strengthened in least performing counties to improve case detection. Similar analyses should be done at the sub-county level to uncover underperformance that might have been hidden by county level analysis.

The role of the Stop Transmission of Polio (STOP) program in developing countries: the experience of Kenya.

BACKGROUND: In 1988, the 41st World Health Assembly (WHA) marked the launch of the Global Polio Eradication Initiative (GPEI) for the eradication of polio. A key component of the GPEI has been the development and deployment of a skilled workforce to implement eradication activities. In 1989, the Stop Transmission of Polio (STOP) was initiated to address skilled human resource gaps and strengthen poliovirus surveillance. This paper describes the role of the STOP 52 team in technical capacity building and health system strengthening in the implementation of polio eradication strategies in Kenya following the outbreak of Circulating Vaccine-derived Poliovirus type 2 (cVDPV2). METHODS: Overview of the STOP program, deployment, and the modality of support are described. Descriptive analysis was conducted using data collected by the STOP 52 team during integrated supportive supervisory visits conducted from July 2018 to September 2019. Analyses were carried out using Epi-Info statistical software (Version 7.0) and maps were developed using Quantum Geographic Information System (Q-GIS) (version 3.12.0). RESULTS: The STOP 52 team supportively supervised 870 health facilities on Expanded Program on Immunization (EPI), and Acute Flaccid Paralysis (AFP) and other Vaccine-Preventable Diseases (VPDs) surveillance in 16 (34.1%) of the 47 counties during the study period. AFP surveillance was conducted in all health facilities supervised leading to the detection and investigation of 11 unreported AFP cases. The STOP 52 team, as part of the outbreak response, provided technical support to five successive rounds of polio Supplementary Immunization Activities (SIAs) conducted during the study period. Moreover, in addressing programmatic data needs, the STOP 52 Data Manager played a valuable role in enhancing the quality and use of data for evidence-based planning and decision-making. The STOP 52 team contributed to the development of operational plans, guidelines and training manuals, and participated in the delivery of various Training of Trainers (TOT) and On-the-Job Training (OJT) on EPI, AFP and other VPDs surveillance including data management. CONCLUSION: The STOP 52 team has contributed to polio eradication efforts in Kenya by enhancing AFP and other VPDs surveillance, supporting polio SIAs, strengthening EPI, use of quality EPI, AFP and other VPDs data, and capacity building of Frontline Health Workers (FLWs). The use of Open Data Kit (ODK) technology during supportive supervision, and AFP and other VPDs surveillance was found to be advantageous. A national STOP program should be modeled to produce a homegrown workforce to ensure the availability of more sustainable technical support for polio eradication efforts in Kenya and possibly other polio-affected countries.

Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms.

BACKGROUND: Skilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled health personnel remains very low. The aim of this study was to identify determinants and develop a predictive model for skilled delivery service use in Ethiopia by applying logistic regression and machine-learning techniques. METHODS: Data from the 2016 Ethiopian Demographic and Health Survey (EDHS) was used for this study. Statistical Package for Social Sciences (SPSS) and Waikato Environment for Knowledge Analysis (WEKA) tools were used for logistic regression and model building respectively. Classification algorithms namely J48, Naïve Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used for model development. The validation of the predictive models was assessed using accuracy, sensitivity, specificity, and area under Receiver Operating Characteristics (ROC) curve. RESULTS: Only 27.7% women received skilled delivery assistance in Ethiopia. First antenatal care (ANC) [AOR = 1.83, 95% CI (1.24-2.69)], birth order [AOR = 0.22, 95% CI (0.11-0.46)], television ownership [AOR = 6.83, 95% CI (2.52-18.52)], contraceptive use [AOR = 1.92, 95% CI (1.26-2.97)], cost needed for healthcare [AOR = 2.17, 95% CI (1.47-3.21)], age at first birth [AOR = 1.96, 95% CI (1.31-2.94)], and age at first sex [AOR = 2.72, 95% CI (1.55-4.76)] were determinants for utilizing skilled delivery services during the childbirth. Predictive models were developed and the J48 model had superior predictive accuracy (98%), sensitivity (96%), specificity (99%) and, the area under ROC (98%). CONCLUSIONS: First ANC and contraceptive uses were among the determinants of utilization of skilled delivery services. A predictive model was developed to forecast the likelihood of a pregnant woman seeking skilled delivery assistance; therefore, the predictive model can help to decide targeted interventions for a pregnant woman to ensure skilled assistance at childbirth. The model developed through the J48 algorithm has better predictive accuracy. Web-based application can be build based on results of this study.

Skilled delivery inequality in Ethiopia: to what extent are the poorest and uneducated mothers benefiting?

BACKGROUND: The fifth Millennium Development Goal (MDG) targeted at improving maternal health. In this regard, Ethiopia has shown substantial progresses in the past two decades. Nonetheless, these impressive gains are unevenly distributed among Ethiopian women with different socio-economic characteristics. This study aimed at investigating levels and trends of skilled delivery service, and wealth and education related inequalities from 2000 to 16. METHODS: Longitudinal data analysis was conducted on Ethiopian Demographic and Health Survey (EDHS) data of 2000, 2005, 2011 and 2016. The outcome variable was skilled delivery, while data on economic status and education level were used as dimensions of inequality. Rate Ratio (RR) and Rate Difference (RD) inequality measures were applied. STATA for windows version 10.1 statistical software was utilized for data analysis and presentation. The strength of association of inequality dimensions with the outcome variable was assessed using a 95% confidence interval. RESULTS: From total deliveries, 5.62%, 6.3%, 10.8% and 28% of them were attended by skilled birth attendant in 2000, 2005, 2011 and 2016 respectively. In the most recent survey (EDHS 2016), proportion of births attended by skilled birth attendance among women who completed secondary and above education was about 5.42 [95% CI (4.53, 6.09)] times more when compared to women with no formal education. Proportion of births attended by skilled birth attendance among women in the richest quintile was about 5.11 [95% CI (3.98, 6.12)] times higher than that of women in the poorest quintile. Moreover, gap of inequality on receiving skilled delivery service has increased substantially from 24.2 (2000) to 53.8 (2016) percentage points between women in the richest and poorest quintiles; and from 44.9 (2000) to 76.0 (2016) percentage points between women who completed secondary and above education and women with no formal education. CONCLUSIONS: Skilled birth attendance remained low and virtually unchanged during the period 2000-2011, but increased substantially in 2016. Gap on wealth and education related inequalities increased linearly during 2000-16. Most pronounced inequalities were observed in women's level of education revealing women with no formal education were the most underserved subgroups. Encouraging women in education and economic development programs should be strengthened as part of the effort to attain Universal Health Coverage (UHC) of Sustainable Development Goals (SDGs) in Ethiopia.

Determinants and development of a web-based child mortality prediction model in resource-limited settings: A data mining approach.

BACKGROUND: Improving child health and reducing child mortality rate are key health priorities in developing countries. This study aimed to identify determinant sand develop, a web-based child mortality prediction model in Ethiopian local language using classification data mining algorithm. METHODS: Decision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques were applied on 11,654 records of Ethiopian demographic and health survey data. Waikato Environment for Knowledge Analysis (WEKA) for windows version 3.6.8 was used to develop optimal models. 8157 (70%) records were randomly allocated to training group for model building while; the remaining 3496 (30%) records were allocated as the test group for model validation. The validation of the model was assessed using accuracy, sensitivity, specificity and area under Receiver Operating Characteristics (ROC) curve. Using Statistical Package for Social Sciences (SPSS) version 20.0; logistic regressions and Odds Ratio (OR) with 95% Confidence Interval (CI) was used to identify determinants of child mortality. RESULTS: The child mortality rate was 72 deaths per 1000 live births. Breast-feeding (AOR= 1.46, (95% CI [1.22. 1.75]), maternal education (AOR= 1.40, 95% CI [1.11, 1.81]), family planning (AOR= 1.21, [1.08, 1.43]), preceding birth interval (AOR= 4.90, [2.94, 8.15]), presence of diarrhea (AOR= 1.54, 95% CI [1.32, 1.66]), father's education (AOR= 1.4, 95% CI [1.04, 1.78]), low birth weight (AOR= 1.2, 95% CI [0.98, 1.51]) and, age of the mother at first birth (AOR= 1.42, [1.01-1.89]) were found to be determinants for child mortality. The J48 model had better performance, accuracy (94.3%), sensitivity (93.8%), specificity (94.3%), Positive Predictive Value (PPV) (92.2%), Negative Predictive Value (NPV) (94.5%) and, the area under ROC (94.8%). Subsequent to developing an optimal prediction model, we relied on this model to develop a web-based application system for child mortality prediction. CONCLUSION: In this study, nearly accurate results were obtained by employing decision tree and rule induction techniques. Determinants are identified and a web-based child mortality prediction model in Ethiopian local language is developed. Thus, the result obtained could support child health intervention programs in Ethiopia where trained human resource for health is limited. Advanced classification algorithms need to be tested to come up with optimal models.