Mapping local variation in household overcrowding across Africa from 2000 to 2018: a modelling study

Spatial variation in housing construction material in low- and middle-income countries: A Bayesian spatial prediction model of a key infectious diseases risk factor and social determinant of health

Housing infrastructure and quality is a major determinant of infectious disease risk and other health outcomes in regions where vector borne, waterborne and neglected tropical diseases are endemic. It is important to quantify the geographical distribution of improvements to dwelling components to identify and target resources towards populations at risk. This study aimed to model the sub-national spatial variation in housing materials using covariates with quasi-global coverage and use the resulting estimates to map predicted coverage across the world's low- and middle-income countries. Data on materials used in dwelling construction were sourced from nationally representative household surveys conducted since 2005. Materials used for construction of flooring, walls, and roofs were reclassified as improved or unimproved. Households lacking location information were georeferenced using a novel methodology. Environmental and demographic spatial covariates were extracted at those locations for use as model predictors. Integrated nested Laplace approximation models were fitted to obtain, and map predicted probabilities for each dwelling component. The dataset compiled included information from households in 283,000 clusters from 350 surveys. Low coverage of improved housing was predicted across the Sahel and southern Sahara regions of Africa, much of inland Amazonia, and areas of the Tibetan plateau. Coverage of improved roofs and walls was high in the Central Asia, East Asia and Pacific and Latin America and the Caribbean regions. Improvements in all three components, but most notably floors, was low in Sub-Saharan Africa. The strongest determinants of dwelling component quality related to urbanization and economic development, suggesting that programs should focus on supply-side interventions, providing resources for housing improvements directly to the populations that need them. These findings are made available to researchers as files that can be imported into a GIS for integration into relevant analyses to derive improved estimates of preventable health burdens attributed to housing.

The epidemiology and impact of persistent Campylobacter infections on childhood growth among children 0-24 months of age in resource-limited settings

Background

Campylobacter is the leading cause of bacterial gastroenteritis worldwide. It is generally associated with an acute gastrointestinal infection causing a self-limiting diarrheal episode. However, there is evidence that persistent/recurrent carriage of Campylobacter also occurs. In hyperendemic settings the epidemiology and consequences of persistent Campylobacter enteric infections is poorly studied.

Methods

Risk factors for and growth consequences of persistent Campylobacter infections detected by polymerase chain reaction (qPCR) were evaluated with data from the MAL-ED birth cohort study in children 0-24 months of age between November 2009 and February 2012. A persistent Campylobacter infection was defined as three or more consecutive Campylobacter positive monthly stools.

Findings

Across all study sites, 45.5% (781/1715) of children experienced at least one persistent Campylobacter episode. The average cumulative duration of days in which children with persistent Campylobacter were positive for Campylobacter spp. was 150 days (inter-quartile range: 28-236 days). Children who experienced a persistent Campylobacter episode had an attained 24-month length-for-age (LAZ) score that was 0.23 (95% (CI): -0.31, -0.15) less than children without a persistent Campylobacter episode. Among children who had at least one episode of Campylobacter over a 3-month or 9-month window, persistent episodes were not significantly associated with poorer 3-month weight gain (-28.7 g, 95% CI: -63.4 g, 6.0 g) but were associated with poorer 9-month linear growth (-0.134 cm 95% CI: -0.246, -0.022) compared to children with an episode that resolved within 31 days.

Interpretation

Persistent/recurrent Campylobacter infection is common among children and has a measurable negative impact on linear growth in early childhood.

Funding

Funding for this study was provided by the Bill and Melinda Gates Foundation (OPP1066146 and OPP1152146), the National Institutes of Health United States (R01AI158576 and R21AI163801 to MNK and CTP; K43TW012298 to FS; K01AI168493 to JMC; GOL was supported by K01AI145080. This research was also supported in part by USDA-ARS CRIS project 2030-42000-055-00D. The funders had no role in study design, study implementation, data analysis, or interpretation of the results.

Epidemiological insights into neonatal deaths: The role of cooking fuel pollution in Colombia

PURPOSE

Household air pollution is one of the leading causes of death and disease globally. Emerging evidence of elevated risk of neonatal death has been reported in Africa and South Asia. However, evidence on the extent of the problem in Latin America is limited despite the persistent use of highly polluting cooking fuels. We assessed whether the use of high-polluting household cooking fuels increases the risk of neonatal death compared to low-polluting fuels in Colombia.

METHODS

We used cross-sectional data from the 2005-2015 Colombian Demographic Health Survey and performed a survey-featured multivariate logistic regression. We selected adjustment covariates based on a causal diagram, addressed missing data through multiple imputation, and conducted several sensitivity analysis, such as propensity score matching.

RESULT

We found evidence suggesting an increased risk of neonatal death in households using high-polluting fuels (OR: 1.48; 95% CI: 0.91, 2.39). The sensitivity analyses were consistent with the main analysis.

CONCLUSION

We observed increased odds of neonatal death associated with using high-polluting household cooking fuels compared to low-polluting fuels, although this association was not statistically significant. This study contributes evidence to a region where the issue is not yet a priority and should be included in national-level discussions and interventions that impact cooking fuel use patterns.

Mapping TB incidence across districts in Uganda to inform health program activities

BACKGROUND

Identifying spatial variation in TB burden can help national TB programs effectively allocate resources to reach and treat all people with TB. However, data limitations pose challenges for subnational TB burden estimation.

METHODS

We developed a small-area modeling approach using geo-positioned prevalence survey data, case notifications, and geospatial covariates to simultaneously estimate spatial variation in TB incidence and case notification completeness across districts in Uganda from 2016-2019. TB incidence was estimated using 1) cluster-level data from the national 2014-2015 TB prevalence survey transformed to incidence, and 2) case notifications adjusted for geospatial covariates of health system access. The case notification completeness surface was fit jointly using observed case notifications and estimated incidence.

RESULTS

Estimated pulmonary TB incidence among adults varied >10-fold across Ugandan districts in 2019. Case detection increased nationwide from 2016 to 2019, and the number of districts with case detection rates >70% quadrupled. District-level estimates of TB incidence were five times more precise than a model using TB prevalence survey data alone.

CONCLUSION

A joint spatial modeling approach provides useful insights for TB program operation, outlining areas where TB incidence estimates are highest and health programs should concentrate their efforts. This approach can be applied in many countries with high TB burden.

The Planetary Child Health & Enterics Observatory (Plan-EO): A protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

BACKGROUND

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

METHODS

The Planetary Child Health & Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

DISCUSSION

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making and disseminating rigorously obtained, generalizable disease burden estimates. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available for download to the research and stakeholder communities. These can then be used as inputs to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

STUDY REGISTRATION

PROSPERO protocol #CRD42023384709.

The Planetary Child Health & Enterics Observatory (Plan-EO): a protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

Background

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

Methods

The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

Discussion

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making rigorously obtained, generalizable disease burden estimates freely available and accessible to the research and stakeholder communities. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available to the research and stakeholder communities both within the webpage itself and for download. These inputs can then be used to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

Study registration

PROSPERO protocol #CRD42023384709.

The Planetary Child Health & Enterics Observatory (Plan-EO): a protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs

Background

Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments.

Methods

The Planetary Child Health and Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants.

Discussion

As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making rigorously obtained, generalizable disease burden estimates freely available and accessible to the research and stakeholder communities. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available to the research and stakeholder communities both within the webpage itself and for download. These inputs can then be used to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection.

Study registration

PROSPERO protocol #CRD42023384709.