Applications of GIS and Spatial Statistics for Malaria Research in Rural Zambia: Evaluation of Risk Factors and Risk Mapping in Nchelenge District and Elimination Strategies in Macha

Objective: The goals of this dissertation project were to understand malaria transmission dynamics in two different settings in Zambia. The specific aims in Nchelenge District, an uncontrolled transmission setting, were to describe the individual-, household- and environmental-level risk factors for malaria (Paper 1); and to generate and validate seasonal malaria risk maps (Paper 2). The specific aims in Macha District, a low transmission setting, were to describe factors associated with sustained bednet use (Paper 3), and determine the efficiency of reactive case detection and focal drug administration in treating infections missed by passive surveillance (Paper 4). Methods: Both sites are part of the International Center for Excellence in Malaria Research (ICEMR) for southern Africa. Satellite images are used to generate sampling frames, and households randomly selected for enrollment. Questionnaires, blood samples, mosquitoes and GPS coordinates are collected. Multilevel models with random effects were built for the odds of RDT positivity in Nchelenge District (Paper 1). Logistic regression and prediction models were used to create seasonal malaria risk maps and validated using RMSE in Nchelenge District (Paper 2). A multi-level longitudinal model with random intercepts was generated to determine factors associated with bednet use in Macha District (Paper 3). A simulation model was constructed to predict the distribution of RDT and PCR cases of malaria, to determine the efficiency of reactive case detection and focal drug administration in Macha District (Paper 4). Results: Age, report of symptoms, and proximity to certain ecological features increased risk of malaria infection, and varied by season (Paper 1). Risk maps accurately predicted household malaria risk; prediction was best in the rainy season and for smaller households (<4 members) (Paper 2). Several factors including presence of nuisance mosquitoes and distance to healthcare facilities affected reported bednet use (Paper 3). Reactive case detection identified and treated RDT positive cases that cluster around index households; focal drug administration would treat PCR positive RDT negative cases missed otherwise (Paper 4). Conclusions: In high transmission settings, spatial targeting of high-risk areas and populations is necessary to reduce malaria transmission; risk maps and school-based interventions may be suggested. In a low transmission setting, sustained use of personal protective measures and implementation of active case detection strategies to treat every remaining case is necessary for elimination

Building resilience in communities most vulnerable to environmental stressors

Evidence suggests that the frequency and intensity of environmental hazards such as floods, cyclones, and droughts may be increasing, leading to high volatility in many parts of the world. The impact of these events falls unequally on the most vulnerable individuals, households, and communities. To fully understand and address the needs of vulnerable communities, the Population Council is building on its existing research, deep global research expertise, and proven approaches in reaching and working with vulnerable populations to examine how humans interact with their environments and explore how to test and develop successful strategies for building resilience. This report presents an overview of the areas where the Council can leverage research to develop, test, and scale up evidence-based programs and interventions to strengthen resilience in the communities where we work

Evidence to inform an integrated social and behavior change strategy in the Sahel

Breakthrough RESEARCH completed this literature review, which summarized the evidence, or lack thereof, in the Sahel, to establish a rationale for the planned RISE II SBC evaluation as described in the study protocol. The literature review provides an overview of the health and development challenges in the region, along with priority health behaviors and their determinants followed by a description of SBC programmatic approaches that have addressed behavioral determinants and health outcomes in the region

Neonatal and perinatal mortality in the urban continuum:A geospatial analysis of the household survey, satellite imagery and travel time data in Tanzania

Introduction Neonatal mortality might be higher in urban areas. This paper aims to minimize challenges related to misclassification of neonatal deaths and stillbirths, and oversimplification of the variation in urban environments to accurately estimate the direction and strength of the association between urban residence and neonatal/perinatal mortality in Tanzania. Methods The Tanzania Demographic and Health Survey (DHS) 2015-16 was used to assess birth outcomes for 8,915 pregnancies among 6,156 women of reproductive age, by urban or rural categorization in the DHS and based on satellite imagery. The coordinates of 527 DHS clusters were spatially overlaid with the 2015 Global Human Settlement Layer, showing the degree of urbanisation based on built environment and population density. A three-category urbanicity measure (core urban, semi-urban, and rural) was defined and compared to the binary DHS measure. Travel time to the nearest hospital was modelled using least-cost path algorithm for each cluster. Bivariate and multi-level multivariable logistic regression models were constructed to explore associations between urbanicity and neonatal/perinatal deaths. Results Both perinatal and neonatal mortality rates were highest in core urban and lowest in rural clusters. Bivariate models showed higher odds of neonatal death (OR=1.85; 95% CI: 1.12, 3.08) and perinatal death (OR=1.60; 95% CI 1.12, 2.30) in core urban compared to rural clusters. In multivariable models, these associations had the same direction and size, but were no longer statistically significant. Travel time to nearest hospital was not associated with neonatal or perinatal mortality. Conclusion Addressing the higher rates of neonatal and perinatal mortality in densely populated urban areas is critical for Tanzania to meet national and global reduction targets. Urban populations are diverse, and certain neighbourhoods or sub-groups may be disproportionately affected by poor birth outcomes. Research must sample within and across urban areas to differentiate, understand and minimize risks specific to urban settings. Key questions What is already known? - Urban advantage in health outcomes has been questioned, both for adult and child mortality - An analysis of neonatal mortality using Demographic and Health Survey data in Tanzania in 2015-16 showed double risk in urban compared to rural areas - This phenomenon might be occurring in other sub-Saharan African countries What are the new findings? - Categorisation of locations as urban or rural on the 2015-16 Demographic and Health Survey in Tanzania is both simplistic and inaccurate - Risks of neonatal and perinatal mortality are highest in core, densely populated urban areas in mainland Tanzania, and lowest in rural areas - Travel time to nearest public hospital was not associated with neonatal or perinatal mortality in mainland Tanzania What do the new findings imply? - Extent of urbanicity as an exposure follows a spectrum and needs to be measured and understood as such - Explanatory models specific to neonatal and perinatal mortality in core urban areas are urgently needed to guide actions toward reducing existing high rate - Known risk factors such as anaemia and young maternal age continue to play a role in neonatal and perinatal mortality and must be urgently addressed

COVID-19-related knowledge, attitudes, and practices among adolescents and young people in Bihar and Uttar Pradesh, India: Study description

To control the spread of COVID-19 in India and to aid the efforts of the Ministry of Health and Family Welfare (MOHFW), the Population Council and other non-governmental organizations are conducting research to assess residents’ ability to follow sanitation and social distancing precautions under a countrywide lockdown. The Population Council COVID-19 study team is implementing rapid phone-based surveys to collect information on knowledge, attitudes and practices, as well as needs, among 2,041 young people (ages 19-23 years) and/or an adult household member, sampled from an existing prospective cohort study with a total sample size of 20,574 in Bihar (n=10,433) and Uttar Pradesh (n=10,141). Baseline was conducted from April 3-22; subsequent iterations of the survey are planned to be conducted on a monthly basis. Baseline findings on awareness of COVID-19 symptoms, perceived risk, awareness of and ability to carry out preventive behaviors, misconceptions, and fears will inform the development of government and other stakeholders’ interventions and/or strategies. We are committed to openly sharing the latest versions of the study description, questionnaires, deidentified or aggregated datasets, and preliminary results. Data and findings can also be shared with partners working in COVID-19 response

The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements.

BACKGROUND: Many low- and middle-income countries have implemented control measures against coronavirus disease 2019 (COVID-19). However, it is not clear to what extent these measures explain the low numbers of recorded COVID-19 cases and deaths in Africa. One of the main aims of control measures is to reduce respiratory pathogen transmission through direct contact with others. In this study, we collect contact data from residents of informal settlements around Nairobi, Kenya, to assess if control measures have changed contact patterns, and estimate the impact of changes on the basic reproduction number (R0). METHODS: We conducted a social contact survey with 213 residents of five informal settlements around Nairobi in early May 2020, 4 weeks after the Kenyan government introduced enhanced physical distancing measures and a curfew between 7 pm and 5 am. Respondents were asked to report all direct physical and non-physical contacts made the previous day, alongside a questionnaire asking about the social and economic impact of COVID-19 and control measures. We examined contact patterns by demographic factors, including socioeconomic status. We described the impact of COVID-19 and control measures on income and food security. We compared contact patterns during control measures to patterns from non-pandemic periods to estimate the change in R0. RESULTS: We estimate that control measures reduced physical contacts by 62% and non-physical contacts by either 63% or 67%, depending on the pre-COVID-19 comparison matrix used. Masks were worn by at least one person in 92% of contacts. Respondents in the poorest socioeconomic quintile reported 1.5 times more contacts than those in the richest. Eighty-six percent of respondents reported a total or partial loss of income due to COVID-19, and 74% reported eating less or skipping meals due to having too little money for food. CONCLUSION: COVID-19 control measures have had a large impact on direct contacts and therefore transmission, but have also caused considerable economic and food insecurity. Reductions in R0 are consistent with the comparatively low epidemic growth in Kenya and other sub-Saharan African countries that implemented similar, early control measures. However, negative and inequitable impacts on economic and food security may mean control measures are not sustainable in the longer term

Experiences among adults and adolescents during the COVID-19 pandemic from four locations across Kenya—Study description

To control the spread of coronavirus, the COVID-19 National Emergency Response Committee (NERC) in Kenya, chaired by the Ministry of Health (MOH), has implemented prevention and mitigation measures. To inform the Government of Kenya’s shorter- and longer-term response strategies, the Population Council COVID-19 study team utilizes rapid phone-based surveys to collect information on knowledge, attitudes, practices and needs among a longitudinal cohort of heads of household sampled from existing prospective cohort studies. The first was carried out across five Nairobi urban informal settlements; the baseline survey (n=2,009) was conducted March 30–31 with subsequent follow-up surveys conducted April 13–14 (n=1,764), May 10-11 (n=1,750), and June 13-16 (n=1,529) (to be carried out one per subsequent quarter dependent on funding). Adolescents in the Nairobi cohort (n=1,022) were also interviewed in the June round of data collection. The survey was expanded to communities with existing prospective cohort studies in Wajir County (adults n=1,322 and adolescents n=1,234), Kilifi County (adults n=1,288 and adolescents n=1,178), and Kisumu County (adults n=858 and adolescents n=973), adapted for rural settings with the first round conducted between July–August 2020, the second between February–March 2021, and the third between June–August 2021

Spatio-temporal patterns of pre-eclampsia and eclampsia in relation to drinking water salinity at the district level in Bangladesh from 2016 to 2018

This analysis examines whether salinity in drinking water is associated with pre-eclampsia and eclampsia (PE/E), a leading cause of maternal morbidity and mortality. Bangladesh’s national health information system data were extracted at the district level (n = 64) to assess PE/E rates, and these were overlaid with three environmental measures approximating drinking water salinity, remotely sensed low-elevation coastal zone (LECZ), monthly rainfall data, and electrical conductivity of groundwater (i.e., water salinity). Results from a negative binomial fixed effects model suggest PE/E rates are higher with less rainfall (dry season), lower population density, and that district level rates of PE/E increase with higher groundwater salinity and in the high risk LECZ category closest to the coast. Results suggest that drinking water salinity may be associated with PE/E and that using national health surveillance data can improve understanding of this association. This approach can potentially be leveraged in the future to inform targeted interventions to high risk regions and times