Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

Estimating under-five mortality in space and time in a developing world context

Accurate estimates of the under-five mortality rate in a developing world context are a key barometer of the health of a nation. This paper describes a new model to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is wishing to estimate under-five mortality rate across regions and years and to investigate the association between the under-five mortality rate and spatially varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980-2014 using data from the Demographic and Health Surveys, which use stratified cluster sampling. We use a binomial likelihood with fixed effects for the urban/rural strata and random effects for the clustering to account for the complex survey design. Smoothing is carried out using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in Kenya in the under-five mortality rate in the period 1980-2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. In exploratory work, we examine whether a variety of spatial covariate surfaces can explain the variability in under-five mortality rate. Temperature, precipitation, a measure of malaria infection prevalence, and a measure of nearness to cities were candidates for inclusion in the covariate model, but the interplay between space, time, and covariates is complex.

Geogenomic Segregation and Temporal Trends of Human Pathogenic Escherichia coli O157:H7, Washington, USA, 2005-20141

The often-noted and persistent increased incidence of Escherichia coli O157:H7 infections in rural areas is not well understood. We used a cohort of E. coli O157:H7 cases reported in Washington, USA, during 2005–2014, along with phylogenomic characterization of the infecting isolates, to identify geographic segregation of and temporal trends in specific phylogenetic lineages of E. coli O157:H7. Kernel estimation and generalized additive models demonstrated that pathogen lineages were spatially segregated during the period of analysis and identified a focus of segregation spanning multiple, predominantly rural, counties for each of the main clinical lineages, Ib, IIa, and IIb. These results suggest the existence of local reservoirs from which humans are infected. We also noted a secular increase in the proportion of lineage IIa and IIb isolates. Spatial segregation by phylogenetic lineage offers the potential to identify local reservoirs and intervene to prevent continued transmission.

Responding to Mycobacterium chimaera heater-cooler unit contamination: international and national intersectoral collaboration coordinated in the state of Queensland, Australia

BACKGROUND

The index case of Mycobacterium chimaera infection in a patient following open cardiac surgery in the state of Queensland, Australia prompted a centralized coordinated response to mitigate the risk.

AIM

To describe the public health response to M. chimaera contamination of heater-cooler units (HCUs) and patient infection.

METHODS

A public health sector strategy was developed with national and international consultation to respond to the threat of HCUs contaminated with M. chimaera. Data linkage of non-tuberculous mycobacterium notifications and selected procedures was undertaken where potential use of HCUs was identified through hospitalization records. Water sampling and testing protocols were standardized. Public disclosure and patient notification were undertaken.

FINDINGS

A single case of disseminated M. chimaera infection in a patient has been diagnosed to date in Queensland, Australia. Ten of 12 (83%) LivaNova Stockert 3T HCUs from five hospitals tested positive for M. chimaera. In total, 5650 patients were notified by post of their potential risk of exposure. Use of the telehealth call centre was modest. M. chimaera was also found in extracorporeal membrane oxygenation heater units produced by two other device manufacturers, four of which tested positive prior to commissioning for use.

CONCLUSIONS

Rapid international collaboration optimized the Queensland Health response to potential M. chimaera exposure during cardiac surgery. State-wide collaboration ensured a transparent, consistent approach to contacting patients and informing the public of the potential risk. A framework for ongoing risk management, clinical awareness and laboratory diagnosis was established. No further cases of M. chimaera infection have been identified in Queensland.

Associations between social capital and depression: A study of adult twins

Social capital is associated with depression independently of individual-level risk factors. We used a sample of 1586 same-sex twin pairs to test the association between seven measures of social capital and two related measures of neighborhood characteristics with depressive symptoms accounting for uncontrolled selection factors (i.e., genetics and shared environment). All measures of cognitive social capital and neighborhood characteristics were associated with less depressive symptoms in between-twin analysis. However, only measures of cognitive social capital were significantly associated with less depressive symptoms within-pairs. These results demonstrate that cognitive social capital is associated with depressive symptoms free of confounding from genetic and environmental factors shared within twins.

Hyak mortality monitoring system: innovative sampling and estimation methods - proof of concept by simulation

Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low- to middle-income countries varies from partial coverage to essentially nothing at all. Consequently the state of the art for public health information in low- to middle-income countries is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space - data amalgamation. Data sources amenable to this approach include sample surveys, sample registration systems, health and demographic surveillance systems, administrative records, census records, health facility records and others. We propose a new statistical framework for gathering health and population data - Hyak - that leverages the benefits of sampling and longitudinal, prospective surveillance to create a cheap, accurate, sustainable monitoring platform. Hyak has three fundamental components: Data amalgamation: A sampling and surveillance component that organizes two or more data collection systems to work together: (1) data from HDSS with frequent, intense, linked, prospective follow-up and (2) data from sample surveys conducted in large areas surrounding the Health and Demographic Surveillance System (HDSS) sites using informed sampling so as to capture as many events as possible;Cause of death: Verbal autopsy to characterize the distribution of deaths by cause at the population level; andSocioeconomic status (SES): Measurement of SES in order to characterize poverty and wealth. We conduct a simulation study of the informed sampling component of Hyak based on the Agincourt HDSS site in South Africa. Compared with traditional cluster sampling, Hyak's informed sampling captures more deaths, and when combined with an estimation model that includes spatial smoothing, produces estimates of both mortality counts and mortality rates that have lower variance and small bias.

Associations between neighbourhood characteristics and depression: a twin study

BACKGROUND

Depression is an important contributor to the global burden of disease. Besides several known individual-level factors that contribute to depression, there is a growing recognition that neighbourhood environment can also profoundly affect mental health. This study assessed associations between three neighbourhood constructs-socioeconomic deprivation, residential instability and income inequality-and depression among adult twin pairs. The twin design is used to examine the association between neighbourhood constructs and depression, controlling for selection factors (ie, genetic and shared environmental factors) that have confounded purported associations.

METHODS

We used multilevel random-intercept Poisson regression among 3738 same-sex twin pairs from a community-based twin registry to examine the association between neighbourhood constructs and depression. The within-pair association controls for confounding by genetic and environmental factors shared between twins within a pair, and is the main parameter of interest. Models were adjusted for individual-level income, education and marital status, and further by neighbourhood-level population density.

RESULTS

When twins were analysed as individuals (phenotypic model), all neighbourhood constructs were significantly associated with depression. However, only neighbourhood socioeconomic deprivation showed a significant within-pair association with depression. A 10-unit within-pair difference in neighbourhood socioeconomic deprivation was associated with 6% greater depressive symptoms (1.06, 95% CI 1.01 to 1.11); the association did not substantially change in adjusted models.

CONCLUSION

This study provides new evidence linking neighbourhood socioeconomic deprivation with greater depression. Future studies should employ longitudinal designs to better test social causation versus social selection.

Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogeneous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic models with only minimal modifications to the model dynamics and/or emission distribution. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school.

Air pollution exposure is associated with MRSA acquisition in young U.S. children with cystic fibrosis

Background

The role of air pollution in increasing susceptibility to respiratory tract infections in the cystic fibrosis (CF) population has not been well described. We recently demonstrated that chronic PM_2.5 exposure is associated with an increased risk of initial Pseudomonas aeruginosa acquisition in young children with CF. The purpose of this study was to determine whether PM_2.5 exposure is a risk factor for acquisition of other respiratory pathogens in young children with CF.

Methods

We conducted a retrospective study of initial acquisition of methicillin susceptible and methicillin resistant Staphylococcus aureus (MSSA and MRSA), Stenotrophomonas maltophilia and Achromobacter xylosoxidans in U.S. children <6 years of age with CF using the CF Foundation Patient Registry, 2003–2009. Multivariable Weibull regression with interval-censored outcomes was used to evaluate the association of PM_2.5 concentration in the year prior to birth and risk of acquisition of each organism.

Results

During follow-up 63%, 17%, 24%, and 5% of children acquired MSSA, MRSA, S. maltophilia, and A. xylosoxidans, respectively. A 10 μg/m³ increase in PM_2.5 exposure was associated with a 68% increased risk of MRSA acquisition (Hazard Ratio: 1.68; 95% Confidence Interval: 1.24, 2.27). PM_2.5 was not associated with acquisition of other respiratory pathogens.

Conclusions

Fine particulate matter is an independent risk factor for initial MRSA acquisition in young children with CF. These results support the increasing evidence that air pollution contributes to pulmonary morbidities in the CF community.

Seasonality of acquisition of respiratory bacterial pathogens in young children with cystic fibrosis

Background

Seasonal variations are often observed for respiratory tract infections; however, limited information is available regarding seasonal patterns of acquisition of common cystic fibrosis (CF)-related respiratory pathogens. We previously reported differential seasonal acquisition of Pseudomonas aeruginosa in young children with CF and no such variation for methicillin-susceptible Staphylococcus aureus acquisition. The purpose of this study was to describe and compare the seasonal incidence of acquisition of other respiratory bacterial pathogens in young children with CF.

Methods

We conducted a retrospective study to describe and compare the seasonal incidence of methicillin-resistant Staphylococcus aureus (MRSA), Stenotrophomonas maltophilia, Achromobacter xylosoxidans, and Haemophilus influenzae acquisition in young CF patients residing in the U.S. using the Cystic Fibrosis Foundation National Patient Registry, 2003-2009. Log-linear overdispersed Poisson regression was used to evaluate seasonal acquisition of each of these pathogens.

Results

A total of 4552 children met inclusion criteria. During follow-up 910 (20%), 1161 (26%), 228 (5%), and 2148 (47%) children acquired MRSA, S. maltophilia, A. xylosoxidans and H. influenzae, respectively. Compared to winter season, MRSA was less frequently acquired in spring (Incidence Rate Ratio [IRR]: 0.79; 95% Confidence Interval [CI]: 0.65, 0.96) and summer (IRR: 0.69; 95% CI: 0.57, 0.84) seasons. Similarly, a lower rate of A. xylosoxidans acquisition was observed in spring (IRR: 0.59; 95% CI: 0.39, 0.89). For H. influenzae, summer (IRR: 0.88; 95% CI: 0.78, 0.99) and autumn (IRR: 0.78; 95% CI: 0.69, 0.88) seasons were associated with lower acquisition rates compared to winter. No seasonal variation was observed for S. maltophilia acquisition.

Conclusion

Acquisition of CF-related respiratory pathogens displays seasonal variation in young children with CF, with the highest rate of acquisition for most pathogens occurring in the winter. Investigation of factors underlying these observed associations may contribute to our understanding of the aetiology of these infections and guide future infection control strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2511-9) contains supplementary material, which is available to authorized users.

Time series modeling of pathogen-specific disease probabilities with subsampled data

Many diseases arise due to exposure to one of multiple possible pathogens. We consider the situation in which disease counts are available over time from a study region, along with a measure of clinical disease severity, for example, mild or severe. In addition, we suppose a subset of the cases are lab tested in order to determine the pathogen responsible for disease. In such a context, we focus interest on modeling the probabilities of disease incidence given pathogen type. The time course of these probabilities is of great interest as is the association with time-varying covariates such as meteorological variables. In this set up, a natural Bayesian approach would be based on imputation of the unsampled pathogen information using Markov Chain Monte Carlo but this is computationally challenging. We describe a practical approach to inference that is easy to implement. We use an empirical Bayes procedure in a first step to estimate summary statistics. We then treat these summary statistics as the observed data and develop a Bayesian generalized additive model. We analyze data on hand, foot, and mouth disease (HFMD) in China in which there are two pathogens of primary interest, enterovirus 71 (EV71) and Coxackie A16 (CA16). We find that both EV71 and CA16 are associated with temperature, relative humidity, and wind speed, with reasonably similar functional forms for both pathogens. The important issue of confounding by time is modeled using a penalized B-spline model with a random effects representation. The level of smoothing is addressed by a careful choice of the prior on the tuning variance.

Temporal Trends in Geographic and Sociodemographic Disparities in Colorectal Cancer Among Medicare Patients, 1973-2010

PURPOSE

Colorectal cancer (CRC) incidence and mortality in the United States have steadily declined since the 1980s, but racial and socioeconomic disparities remain. The influence of geographic factors is poorly understood and may be affected by evolving insurance coverage and screening test uptake. We characterized temporal trends in the association between geographic and sociodemographic factors and CRC outcomes.

METHODS

We used the 1973-2010 SEER-Medicare files to identify patients aged ≥65 years with and without CRC. Beneficiary residential ZIP codes were used to extract local-level data. We constructed multivariable logistic regression models for CRC incidence and mortality using geographic and sociodemographic variables in 4 time periods: (1) 1973-1997; (2) 1998-2001; (3) 2002-2006; and (4) 2007-2010.

FINDINGS

We analyzed 1,093,758 records, including 336,321 CRC cases. Compared to urban residence, small rural residence was strongly associated with increased CRC incidence (OR 1.50, 95% CI: 1.43-1.57) and mortality (OR 1.35, 95% CI: 1.26-1.45) in 1973-1997, but the associations diminished by 2007-2010 (OR 1.09, 95% CI: 1.04-1.15 for incidence; OR 1.10, 95% CI: 1.01-1.20 for mortality). The disparity between blacks and whites increased over time for both incidence (OR 1.09, 95% CI: 1.05-1.13 in 1973-1997 vs OR 1.32, 95% CI: 1.27-1.37 in 2007-2010) and mortality (OR 1.22, 95% CI: 1.16-1.28 in 1973-1997 vs OR 1.34, 95% CI: 1.26-1.42 in 2007-2010). High socioeconomic status was associated with greater incidence and mortality in 1973-1997, but it became protective after 1998.

CONCLUSIONS

Although disparities persist among Medicare beneficiaries, the relationship between geographic and sociodemographic factors and CRC incidence and mortality has evolved over time.

PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH

Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on environmental predictors. To do this, we estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system is treated as a continuous-time hidden Markov model, where the hidden Markov states are the numbers of people who are susceptible, infected, or recovered at each time point, and the observed states are the numbers of cholera cases reported. We use a Bayesian framework to fit this hidden SIRS model, implementing particle Markov chain Monte Carlo methods to sample from the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulation and data from Mathbaria, Bangladesh. Parameter estimates are used to make short-term predictions that capture the formation and decline of epidemic peaks. We demonstrate that our model can successfully predict an increase in the number of infected individuals in the population weeks before the observed number of cholera cases increases, which could allow for early notification of an epidemic and timely allocation of resources.

Using Small-Area Estimation to Calculate the Prevalence of Smoking by Subcounty Geographic Areas in King County, Washington, Behavioral Risk Factor Surveillance System, 2009-2013

Introduction

King County, Washington, fares well overall in many health indicators. However, county-level data mask disparities among subcounty areas. For disparity-focused assessment, a demand exists for examining health data at subcounty levels such as census tracts and King County health reporting areas (HRAs).

Methods

We added a “nearest intersection” question to the Behavioral Risk Factor Surveillance System (BRFSS) and geocoded the data for subcounty geographic areas, including census tracts. To overcome small sample size at the census tract level, we used hierarchical Bayesian models to obtain smoothed estimates in cigarette smoking rates at the census tract and HRA levels. We also used multiple imputation to adjust for missing values in census tracts.

Results

Direct estimation of adult smoking rates at the census tract level ranged from 0% to 56% with a median of 10%. The 90% confidence interval (CI) half-width for census tract with nonzero rates ranged from 1 percentage point to 37 percentage points with a median of 13 percentage points. The smoothed-multiple–imputation rates ranged from 5% to 28% with a median of 12%. The 90% CI half-width ranged from 4 percentage points to 13 percentage points with a median of 8 percentage points.

Conclusion

The nearest intersection question in the BRFSS provided geocoded data at subcounty levels. The Bayesian model provided estimation with improved precision at the census tract and HRA levels. Multiple imputation can be used to account for missing geographic data. Small-area estimation, which has been used for King County public health programs, has increasingly become a useful tool to meet the demand of presenting data at more granular levels.

A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound

Cluster detection is an important public health endeavor, and in this article, we describe and apply a recently developed Bayesian method. Commonly used approaches are based on so-called scan statistics and suffer from a number of difficulties, which include how to choose a level of significance and how to deal with the possibility of multiple clusters. The basis of our model is to partition the study region into a set of areas that are either "null" or "non-null," the latter corresponding to clusters (excess risk) or anticlusters (reduced risk). We demonstrate the Bayesian method and compare with a popular existing approach, using data on breast, brain, lung, prostate, and colorectal cancer, in the Puget Sound region of Washington State.

Excavating Neandertal and Denisovan DNA from the genomes of Melanesian individuals

Although Neandertal sequences that persist in the genomes of modern humans have been identified in Eurasians, comparable studies in people whose ancestors hybridized with both Neandertals and Denisovans are lacking. We developed an approach to identify DNA inherited from multiple archaic hominin ancestors and applied it to whole-genome sequences from 1523 geographically diverse individuals, including 35 previously unknown Island Melanesian genomes. In aggregate, we recovered 1.34 gigabases and 303 megabases of the Neandertal and Denisovan genome, respectively. We use these maps of archaic sequences to show that Neandertal admixture occurred multiple times in different non-African populations, characterize genomic regions that are significantly depleted of archaic sequences, and identify signatures of adaptive introgression.

Restricted Covariance Priors with Applications in Spatial Statistics

We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance- covariance matrix. Specifically, we introduce a new distribution called the truncated G-Wishart distribution that has support over precision matrices that lead to positive associations between the random effects of neighboring regions while preserving conditional independence of non-neighboring regions. We describe Markov chain Monte Carlo sampling algorithms for the truncated G-Wishart prior in a disease mapping context and compare our results to Bayesian hierarchical models based on intrinsic autoregression priors. A simulation study illustrates that using the truncated G-Wishart prior improves over the intrinsic autoregressive priors when there are discontinuities in the disease risk surface. The new model is applied to an analysis of cancer incidence data in Washington State.

Space-Time Smoothing of Complex Survey Data: Small Area Estimation for Child Mortality

Many people living in low and middle-income countries are not covered by civil registration and vital statistics systems. Consequently, a wide variety of other types of data including many household sample surveys are used to estimate health and population indicators. In this paper we combine data from sample surveys and demographic surveillance systems to produce small area estimates of child mortality through time. Small area estimates are necessary to understand geographical heterogeneity in health indicators when full-coverage vital statistics are not available. For this endeavor spatio-temporal smoothing is beneficial to alleviate problems of data sparsity. The use of conventional hierarchical models requires careful thought since the survey weights may need to be considered to alleviate bias due to non-random sampling and non-response. The application that motivated this work is estimation of child mortality rates in five-year time intervals in regions of Tanzania. Data come from Demographic and Health Surveys conducted over the period 1991-2010 and two demographic surveillance system sites. We derive a variance estimator of under five years child mortality that accounts for the complex survey weighting. For our application, the hierarchical models we consider include random effects for area, time and survey and we compare models using a variety of measures including the conditional predictive ordinate (CPO). The method we propose is implemented via the fast and accurate integrated nested Laplace approximation (INLA).