Prevalence of Plasmodium falciparum and Salmonella typhi Infection and Coinfection and Their Association With Fever in Northern Tanzania

Background: Plasmodium falciparum and Salmonella typhi are major causes of fever in the tropics. Although these infections are caused by different organisms and are transmitted via different mechanisms, they have similar epidemiologic and clinical features. This study aimed to determine the prevalence of S. typhi and P. falciparum infections and their associations with fever at 2 sites in Northern Tanzania.   Methods: This was a community-based, cross-sectional study, conducted from February to June 2016, involving 128 randomly selected individuals, aged between 1 and 70 years. Sixty-three (49.2%) participants were recruited from Bondo Ward, Tanga Region, and 65 (50.8%) were recruited from Magugu Ward, Manyara Region. Blood samples were collected by venepuncture into sterile microtubes. Detection of pathogen DNA was achieved via a multiplex real-time polymerase chain reaction assay. Data analysis was done using Stata, version 14. Prevalence data were presented as numbers and percentages, and chi-square analysis was used to assess associations. P values of .05 or less were considered statistically significant.   Results: Of 128 participants, 31 (24.2%) and 17 (13.3%) tested positive for P. falciparum and S. typhi infection, respectively. Of the 63 participants from Bondo, 31 (49.2%) had P. falciparum parasitaemia. None of the participants from Magugu tested positive for Plasmodium parasitaemia. S. typhi bacteraemia was detected in 11 (17.5%) of 63 and 6 (9.2%) of 65 participants in Bondo and Magugu, respectively. P. falciparum–S. typhi coinfection was only detected in Bondo (n=6, 9.5%). Age was the only variable that showed a significant association with both P. falciparum and S. typhi infection; falling within the 5- to 9-year or 10- to 15-year age groups was associated with both infections (Χ2=2.1; P=.045). Among the 30 patients with Plasmodium parasitaemia, 7 (23.3%) had fever, whereas 2 (12.5%) of 16 patients infected by S. typhi had fever. P. falciparum infection (Χ2=12.4, P<.001) and P. falciparum–S. typhi coinfection (X2=5.5, P=.019) were significantly associated with fever, while S. typhi infection alone was not.   Conclusion: S. typhi and P. falciparum were considerably prevalent in the area. One-third of the P. falciparum–S. typhi coinfected individuals in Bondo had fever. P. falciparum infection was an important contributor to febrile illness in Bondo. In the presence of coinfections with P. falciparum and S. typhi, the use of malaria rapid diagnostic tests should be emphasised to reduce irrational use of medications

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Toxoplasma gondii seroprevalence among pregnant women attending antenatal clinic in Northern Tanzania

Abstract Background Acute Toxoplasma gondii infection during pregnancy represents a risk for congenital disease, especially among women without previous exposure to infection. There is, however, a paucity of information about the epidemiology of T. gondii infection in pregnant women in Tanzania. This study aimed to determine the seroprevalence of T. gondii infection and associated demographic, clinical, and behavioral risk factors in pregnant women attending ante-natal clinic (ANC) at Kilimanjaro Christian Medical Center (KCMC), a referral medical center in Northern Tanzania. Methods A hospital-based cross-sectional study was carried out from 1 February to 30 April 2017. Data on maternal demographic characteristics, obstetric history, knowledge, and practices related to T. gondii infection were collected from 254 pregnant women attending antenatal care at KCMC. A sample of 4 mL of blood was collected from each participant and sera prepared from each sample. Serum samples were tested for the presence of specific T. gondii IgG and IgM antibodies by indirect Enzyme-Linked Immunosorbent Assay (ELISA). DNA was extracted from whole blood for polymerase chain reaction (PCR) testing, targeting the DNA sequence coding for the Internal Transcribed Spacer 1 (ITS1). Results The overall T. gondii seroprevalence, including both IgM- and IgG-positive individuals, was 44.5%. Of the 254 tested women, 102 and 23 were seropositive for T. gondii-specific IgG and IgM antibodies respectively and 113 individuals had antibodies of either or both classes. All IgM-positive samples were also tested by PCR, and all were negative. The majority (90%) of the women surveyed had never heard about toxoplasmosis. Consumption of raw vegetables [aOR = 0. 344; 95% CI 0.151–0.784; p = 0.011] and having regular contact with soil [aOR = 0.482; 95% CI 0.268–0.8681; p = 0.015] were both associated with T. gondii antibody status. Inverse relationships with probability of T. gondii exposure were observed, such that these practices were associated with reduced probability of antibody detection. Conclusion Based on serology results, we report widespread exposure to T. gondii infection among pregnant women attending ANC in KCMC. The complex interaction of risk factors for T. gondii infection needs to be studied in larger longitudinal studies

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to- biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g., RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Aboveground Biomass Density Models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) Lidar Mission

NASAs Global Ecosystem Dynamics Investigation (GEDI) is collecting space-borne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDIs footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDIs waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we select the best input predictor variables, data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favors combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) does not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and that the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Abstracts of Tanzania Health Summit 2020

This book contains the abstracts of the papers/posters presented at the Tanzania Health Summit 2020 (THS-2020) Organized by the Ministry of Health Community Development, Gender, Elderly and Children (MoHCDGEC); President Office Regional Administration and Local Government (PORALG); Ministry of Health, Social Welfare, Elderly, Gender, and Children Zanzibar; Association of Private Health Facilities in Tanzania (APHFTA); National Muslim Council of Tanzania (BAKWATA); Christian Social Services Commission (CSSC); & Tindwa Medical and Health Services (TMHS) held on 25–26 November 2020. The Tanzania Health Summit is the annual largest healthcare platform in Tanzania that attracts more than 1000 participants, national and international experts, from policymakers, health researchers, public health professionals, health insurers, medical doctors, nurses, pharmacists, private health investors, supply chain experts, and the civil society. During the three-day summit, stakeholders and decision-makers from every field in healthcare work together to find solutions to the country’s and regional health challenges and set the agenda for a healthier future. Summit Title: Tanzania Health SummitSummit Acronym: THS-2020Summit Date: 25–26 November 2020Summit Location: St. Gasper Hotel and Conference Centre in Dodoma, TanzaniaSummit Organizers: Ministry of Health Community Development, Gender, Elderly and Children (MoHCDGEC); President Office Regional Administration and Local Government (PORALG); Ministry of Health, Social Welfare, Elderly, Gender and Children Zanzibar; Association of Private Health Facilities in Tanzania (APHFTA); National Muslim Council of Tanzania (BAKWATA); Christian Social Services Commission (CSSC); & Tindwa Medical and Health Services (TMHS)