Prioritization of zoonotic diseases of public health significance in Nigeria using the one-health approach.

Nigeria, with a population of over 190 million people, is rated among the 10 countries with the highest burden of infectious and zoonotic diseases globally. In Nigeria, there exist a sub-optimal surveillance system to monitor and track priority zoonoses. We therefore conducted a prioritization of zoonotic diseases for the first time in Nigeria to guide prevention and control efforts. Towards this, a two-day in-country consultative meeting involving experts from the human, animal, and environmental health backgrounds prioritized zoonotic diseases using a modified semi-quantitative One Health Zoonotic Disease Prioritization tool in July 2017. Overall, 36 of 52 previously selected zoonoses were identified for prioritization. Five selection criteria were used to arrive at the relative importance of prioritized diseases based on their weighted score. Overall, this zoonotic disease prioritization process marks the first major step of bringing together experts from the human-animal-environment health spectrum in Nigeria. Importantly, the country ranked rabies, avian influenza, Ebola Virus Disease, swine influenza and anthrax as the first five priority zoonoses in Nigeria. Finally, this One Health approach to prioritizing important zoonoses is a step that will help to guide future tracking and monitoring of diseases of grave public health importance in Nigeria.

Trend of malaria cases in Kaduna State using routine surveillance data, 2011-2015.

INTRODUCTION: in 2015, 212 million malaria cases and 429,000 malaria deaths were estimated globally. Kaduna State, located in northern Nigeria had a malaria prevalence rate of 36.7% among children less than 5 years old which was higher than the national average of 27%. We assessed the trend of malaria cases in Kaduna State from 2011 to 2015, to analyse trend of malaria in Kaduna as well as describe malaria in time, place and person. METHODS: we conducted secondary data analysis of Kaduna State malaria data between January 2011 and December 2015. Data were extracted from the Integrated Disease Surveillance and Response (IDSR) 003 form. Data of uncomplicated malaria defined as "any person with fever or history of fever within 24 hours; without signs of severe disease (vital organ dysfunction)" was analysed. In IDSR, a case of malaria is based on presumed diagnosis. Frequencies and proportions were calculated. We also conducted trend analysis of incidence of malaria. RESULTS: in the period under study, 1,031,603 malaria cases were recorded with 238 deaths (CFR = 0.23 per 1,000). There was a downward trend with a slope of -3287.2. The data showed higher seasonal variation for quarters 2 (1430.96) and 3 (Q2 = 6,460.23) compared to Quarters 1 (6,857.19) and 4 (-1,034.01). Overall, the age group 12 -59 months had the highest number of incident cases 225, 537 (20.3%). Malaria death was highest in children 1 to 11 months (26.5%) and least, in children 0 -28 days (2.5%). CFR was also highest in children 1 to 11 months (0.45 per 1,000). The highest incidence of malaria cases was in Jaba Local Government Area (47.7%) and the least, in Lere (2.4%). CONCLUSION: there was a decreased incidence of malaria from 2011 to 2015. Malaria was most common in the second and third quarters of each year. Age group 12-59 months was most affected. Kaduna State Malaria Programme should sustain the programs it is implementing and focus more on the under-five years age group.

Rural-urban differentials in the rates and factors associated with post-neonatal mortality in Nigeria: Evidence from the 2013 national household survey.

BACKGROUND: The burden of post-neonatal mortality remains considerably high in Nigeria. This study examines the rural-urban differences in post-neonatal mortality rates (PNMR) and associated factors in Nigeria. METHODS: Dataset from the 2013 Nigeria demographic and health survey, disaggregated by rural-urban residence, was analyzed. PNMR was reported using frequency tabulation, whereas, factors associated were first evaluated using Chi-Square test and further examined using multivariable logistic regression analysis. RESULTS: A total of 30384 singleton livebirths (20449 in rural and 9935 in urban residences) in the five years preceding the survey was included in this study. PNMR in rural and urban residences were 34 (95%CI: 31 - 38) and 22 (95%CI: 18 - 26) deaths per 1000 live births (P<0.001), respectively. In rural residence, living in the South-West region reduced the odds of post-neonatal mortality by 63% (Adjusted OR [AOR]: 0.372, 95%CI: 0.187 - 0.732)). In urban residence, poor wealth index (AOR: 1.660, 95%CI: 1.024 - 2.689), living in the South-East region (AOR: 2.902, 95%CI: 1.470 - 5.726), and home delivery (AOR: 1.539, 95%CI: 1.016 - 2.330) increased the odds of post-neonatal mortality. Regardless of residence, the use of solid cooking-fuels (Rural: AOR: 2.394, 95%CI: 1.211 - 4.734; Urban: AOR: 1.912, 95%CI: 1.206 - 3.030), birth interval < 24 months (Rural: AOR: 1.880, 95%CI: 1.557 - 2.270; Urban: AOR: 1.630, 95%CI: 1.042 - 2.550) and lack of breastfeeding (Rural: AOR: 2.547, 95%CI: 2.089 - 3.105; Urban: AOR: 2.152, 95%CI: 1.496 - 3.096) increased the odds of post-neonatal mortality. CONCLUSION: PNMR and associated factors differ in rural and urban Nigeria. Post-neonates in urban areas had better survival chances. Intervention efforts would need to prioritize findings in this study. This article is protected by copyright. All rights reserved.