Democratic reform and health : interpreting causal estimates

In The Lancet Global Health, Hannah Pieters and colleagues (September, 2016)1 analyse the effect of democratic reforms on child mortality across the world. We wish to highlight, however, that even with sophisticated causal inference techniques, such results cannot necessarily be interpreted as causal effects. First, the results are compatible with a number of different theories including that democratic reforms have no effect on health ceteris paribus (ie, holding everything else fixed). Consider the cases of South Africa, Zambia, Mozambique, and Zimbabwe, all notably missing from the analyses but experiencing substantial democratic changes, analysed here using a similar synthetic control analysis (figure).1, 2 No change is observed in South Africa after the end of apartheid in 1994. In Zambia, after reform in 1991, a reduction is observed but not until the price of copper tripled and GDP per capita doubled. In Mozambique, the large fall is likely attributable to the cessation of the civil war in 1993. And in Zimbabwe, democratic restrictions in 1987 did not precipitate an increase in child mortality

Twenty ways to estimate the Log Gaussian Cox Process model with point and aggregated case data: the rts2 package for R

The R package rts2 provides data manipulation and model fitting tools for Log Gaussian Cox Process (LGCP) models. LGCP models are a key method for disease and other types of surveillance, and provide a means of predicting risk across an area of interest based on spatially-referenced and time-stamped case data. However, these models can be difficult to specify and computationally demanding to estimate. For many surveillance scenarios we require results in near real-time using routinely available data to guide and direct policy responses, or due to limited availability of computational resources. There are limited software implementations available for this real-time context with reliable predictions and quantification of uncertainty. The rts2 package provides a range of modern Gaussian process approximations and model fitting methods to fit the LGCP, including estimation of covariance parameters, using both Bayesian and stochastic Maximum Likelihood methods. The package provides a suite of data manipulation tools. We also provide a novel implementation to estimate the LGCP when case data are aggregated to an irregular grid such as census tract areas

Efficient design of geographically-defined clusters with spatial autocorrelation

Clusters form the basis of a number of research study designs including survey and experimental studies. Cluster-based designs can be less costly but also less efficient than individual-based designs due to correlation between individuals within the same cluster. Their design typically relies on \textit{ad hoc} choices of correlation parameters, and is insensitive to variations in cluster design. This article examines how to efficiently design clusters where they are geographically defined by demarcating areas incorporating individuals and households or other units. Using geostatistical models for spatial autocorrelation we generate approximations to within cluster average covariance in order to estimate the effective sample size given particular cluster design parameters. We show how the number of enumerated locations, cluster area, proportion sampled, and sampling method affect the efficiency of the design and consider the optimization problem of choosing the most efficient design subject to budgetary constraints. We also consider how the parameters from these approximations can be interpreted simply in terms of `real-world' quantities and used in design analysis

Generalised Linear Mixed Model Specification, Analysis, Fitting, and Optimal Design in R with the glmmr Packages

We describe the \proglang{R} package \pkg{glmmrBase} and an extension \pkg{glmmrOptim}. \pkg{glmmrBase} provides a flexible approach to specifying, fitting, and analysing generalised linear mixed models. We use an object-orientated class system within \proglang{R} to provide methods for a wide range of covariance and mean functions, including specification of non-linear functions of data and parameters, relevant to multiple applications including cluster randomised trials, cohort studies, spatial and spatio-temporal modelling, and split-plot designs. The class generates relevant matrices and statistics and a wide range of methods including full likelihood estimation of generalised linear mixed models using Markov Chain Monte Carlo Maximum Likelihood, Laplace approximation, power calculation, and access to relevant calculations. The class also includes Hamiltonian Monte Carlo simulation of random effects, sparse matrix methods, and other functionality to support efficient estimation. The \pkg{glmmrOptim} package implements a set of algorithms to identify c-optimal experimental designs where observations are correlated and can be specified using the generalised linear mixed model classes. Several examples and comparisons to existing packages are provided to illustrate use of the packages

Essay 1 : integrating multiple sources of evidence : a Bayesian perspective

Policies and interventions in the health-care system may have a wide range of effects on multiple patient outcomes and operate through many clinical processes. This presents a challenge for their evaluation, especially when the effect on any one patient is small. In this essay, we explore the nature of the health-care system and discuss how the empirical evidence produced within it relates to the underlying processes governing patient outcomes. We argue for an evidence synthesis framework that first models the underlying phenomena common across different health-care settings and then makes inferences regarding these phenomena from data. Bayesian methods are recommended. We provide the examples of electronic prescribing and increased consultant provision at the weekend

Approximate c-Optimal Experimental Designs with Correlated Observations using Combinatorial Optimisation

We review the use of combinatorial optimisation algorithms to identify approximate c-optimal experimental designs when the assumed data generating process is a generalised linear mixed model and there is correlation both between and within experimental conditions. We show how the optimisation problem can be posed as a supermodular function minimisation problem for which algorithms have theoretical guarantees on their solutions. We compare the performance of four variants of these algorithms for a set of example design problems and also against multiplicative methods in the case where experimental conditions are uncorrelated. We show that a local search starting from either a random design or the output of a greedy algorithm provides good performance with the worst outputs having variance $<10\%$ larger than the best output, and frequently better than $<1\%$. We extend the algorithms to robust optimality and Bayesian c-optimality problems

Optimal Study Designs for Cluster Randomised Trials: An Overview of Methods and Results

There are multiple cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at that time point. Identifying the most efficient study design is complex though, owing to the correlation between observations within clusters and over time. In this article, we present a review of statistical and computational methods for identifying optimal cluster randomised trial designs. We also adapt methods from the experimental design literature for experimental designs with correlated observations to the cluster trial context. We identify three broad classes of methods: using exact formulae for the treatment effect estimator variance for specific models to derive algorithms or weights for cluster sequences; generalised methods for estimating weights for experimental units; and, combinatorial optimisation algorithms to select an optimal subset of experimental units. We also discuss methods for rounding weights to whole numbers of clusters and extensions to non-Gaussian models. We present results from multiple cluster trial examples that compare the different methods, including problems involving determining optimal allocation of clusters across a set of cluster sequences, and selecting the optimal number of single observations to make in each cluster-period for both Gaussian and non-Gaussian models, and including exchangeable and exponential decay covariance structures

The effect of health care expenditure on patient outcomes : evidence from English neonatal care

The relationship between health care expenditure and health outcomes has been the subject of recent academic inquiry in order to inform cost-effectiveness thresholds for health technology assessment agencies. Previous studies in public health systems have relied upon data aggregated at the national or regional level; however, there remains debate about whether the supply side effect of changes to expenditure are identifiable using data at this level of aggregation. We use detailed patient data derived from electronic neonatal records across England along with routinely available cost data to estimate the effect of changes to patient expenditure on clinical health outcomes in a well-defined patient population. A panel of 32 neonatal intensive care units for the period 2009–2013 was constructed. Accounting for the potential endogeneity of expenditure a £100 increase in the cost per intensive care cot day (sample average cost: £1,127) is estimated to reduce the risk of mortality of 0.38 percentage points (sample average mortality: 11.0%) in neonatal intensive care. This translates into a cost per life saved in neonatal intensive care of approximately £420,000

Prevalence of plantar ulcer and its risk factors in leprosy:a systematic review and meta-analysis

Background: Plantar ulcers are a leading complication of leprosy that requires frequent visits to hospital and is associated with stigma. The extent of burden of ulcers in leprosy and its risk factors are scant impeding the development of targeted interventions to prevent and promote healing of ulcers. The aim of this review is to generate evidence on the prevalence of plantar ulcer and its risk factors in leprosy. Methods: Databases (Medline, Embase, Web of Science, CINAHL, BVS), conference abstracts and reference lists were searched for eligible studies. Studies were included that reported a point prevalence of plantar ulcer and/or its “risk factors” associated with development of ulcers (either causatively or predictively), including individual level, disease related and bio-mechanical factors. We followed PRISMA guidelines for this review. Random-effects meta-analysis was undertaken to estimate the pooled point prevalence of ulcers. Reported risk factors in included studies were narratively synthesised. This review is registered in PROSPERO: CRD42022316726. Results: Overall, 15 studies (8 for prevalence of ulcer and 7 for risk factors) met the inclusion criteria. The pooled point prevalence of ulcer was 34% (95% CIs: 21%, 46%) and 7% (95% CIs: 4%, 11%) among those with foot anaesthesia and among all people affected by leprosy, respectively. Risk factors for developing ulcers included: unable to feel 10 g of monofilament on sensory testing, pronated/hyper-pronated foot, foot with peak plantar pressure, foot with severe deformities, and those with lower education and the unemployed. Conclusions: The prevalence of plantar ulceration in leprosy is as high as 34% among those with loss of sensation in the feet. However, the incidence and recurrence rates of ulceration are least reported. The inability to feel 10 g of monofilament appears to be a strong predictor of those at risk of developing ulcers. However, there is a paucity of evidence on identifying those at risk of developing plantar ulcers in leprosy. Prospective studies are needed to estimate the incidence of ulcers. Identifying individuals at risk of ulcers will help design targeted interventions to minimize risk factors, prevent ulcers and promote ulcer healing