Malaria transmission rates estimated from serological data.

A mathematical model was used to estimate malaria transmission rates based on serological data. The model is minimally stochastic and assumes an age-dependent force of infection for malaria. The transmission rates estimated were applied to a simple compartmental model in order to mimic the malaria transmission. The model has shown a good retrieving capacity for serological and parasite prevalence data.     

Methods for estimating remission rates from cross-sectional survey data: application and validation using data from a national migraine study

Knowledge about remission rates can affect treatment decisions and facilitate etiologic discoveries. However, little is known about remission of many chronic episodic disorders, including migraine. This is partly due to the fact that medical records do not fully capture the history of these conditions, since patients might stop seeking care once they no longer have symptoms. For these disorders, remission rates would typically be obtained from prospective observational studies. Prospective studies of remission for chronic episodic conditions are rarely conducted, however, and suffer from many analytical challenges, such as outcome-dependent dropout. Here the authors propose an alternative approach that is appropriate for use with cross-sectional survey data in which reported age of onset was recorded. The authors estimated migraine remission rates using data from a 2004 national survey. They took a Bayesian approach and modeled sex- and age-specific remission rates as a function of incidence and prevalence. The authors found that remission rates were an increasing function of age and were similar for men and women. Follow-up survey data from migraine cases (2005) were used to validate the methods. The remission curves estimated from the validation data were very similar to the ones from the cross-sectional data.     

Method for mapping population-based case-control studies: an application using generalized additive models

Background  

Mapping spatial distributions of disease occurrence and risk can serve as a useful tool for identifying exposures of public health concern. Disease registry data are often mapped by town or county of diagnosis and contain limited data on covariates. These maps often possess poor spatial resolution, the potential for spatial confounding, and the inability to consider latency. Population-based case-control studies can provide detailed information on residential history and covariates.     

Meta-analysis of survey data: application to health services research

Traditionally, meta-analysis methods have been developed and used to combine data from several independent clinical trials as well as observational studies, but have not been as widely used in survey research. This paper describes the steps in conducting such a meta-analysis of surveys, to obtain a single summary estimate from a combination of individual-level and summary data. The methods are applied in the context of a project aimed at obtaining an estimate of the prevalence of use of electronic health records.     

Introduction and snapshot review: Relating infectious disease transmission models to data

Disease transmission models are becoming increasingly important both to public health policy makers and to scientists across many disciplines. We review some of the key aspects of how and why such models are related to data from infectious disease outbreaks, and identify a number of future challenges in the field. Copyright © 2010 John Wiley & Sons, Ltd.     

Imputation of confidential data sets with spatial locations using disease mapping models

Data that include fine geographic information, such as census tract or street block identifiers, can be difficult to release as public use files. Fine geography provides information that ill‐intentioned data users can use to identify individuals. We propose to release data with simulated geographies, so as to enable spatial analyses while reducing disclosure risks. We fit disease mapping models that predict areal‐level counts from attributes in the file and sample new locations based on the estimated models. We illustrate this approach using data on causes of death in North Carolina, including evaluations of the disclosure risks and analytic validity that can result from releasing synthetic geographies. Copyright © 2014 John Wiley & Sons, Ltd.     

User fee exemptions and equity in access to caesarean sections: an analysis of patient survey data in Mali

ABSTRACT: INTRODUCTION: Little rigorous evidence exists on how health service utilization varies across socioeconomic groups after a user fee exemption policy has been implemented, and the evidence that does exist is mixed. In this paper, we estimate the distribution of caesarean section deliveries across socioeconomic groups following Mali's implementation of a fee exemption policy for caesareans in 2005. METHODS: We conducted a patient survey in 2010 to collect socioeconomic data from 2,477 women who had caesareans in a representative sample of 25 facilities across all regions of Mali. We used these data along with data from the most recent Demographic and Health Survey to construct a wealth index and classify women into population-based wealth groupings. We compared the wealth distribution of women delivering via caesarean section to that of a nationally representative sample of women giving birth. RESULTS: We found that wealthier women make up a disproportionate share of those having free caesareans, five years after implementation of the fee exemption policy. Women in the richest two quintiles accounted for 58 percent of all caesareans, while women in the poorest two quintiles accounted for 27 percent of all caesareans. Fewer women in the poorest two-fifths of the population are receiving caesareans than what we would expect given their share in the population of women giving birth. CONCLUSIONS: While fee exemptions remove important financial barriers to accessing priority maternal health services, they are insufficient to ensure equal access among wealth groups.     

Modeling the process leading to abortion: an application to French survey data

In this study, we model women's recourse to induced abortion as resulting from a process that starts with sexual intercourse and contraceptive use (or nonuse), continues with the occurrence of an unintended pregnancy, and ends with the woman's decision to terminate the pregnancy and her access to abortion services. Our model includes two often-neglected proximate determinants of abortion: sexual practices and access to abortion services. We relate three sociodemographic characteristics--women's educational level, their relationship status, and their age--step by step to the stages of the abortion process. We apply our framework using data from the COCON survey, a national survey on reproductive health conducted in France in 2000. Our model shows that sociodemographic variables may have opposite impacts as the abortion process unfolds. For example, women's educational level can be positively linked to the probability of practicing contraception but negatively linked to the propensity to carry the unintended pregnancy to term. This conceptual framework brings together knowledge that is currently dispersed in the literature and helps to identify the source of abortion-rate differentials.     

Fitting marginalized two‐part models to semicontinuous survey data arising from complex samples

ObjectiveTo accurately model semicontinuous data from complex surveys, we extend marginalized two‐part models to a design‐based inferential framework and provide guidance on incorporating complex sample designs.Data Sources2014 Medical Expenditure Panel Survey (MEPS).Study DesignWe describe the use of pseudo‐Maximum Likelihood Estimation and Jackknife Repeated Replication for estimating model parameters and sampling variance, respectively. We illustrate our approach using MEPS, modeling total healthcare expenditures in 2014 as a function of respondents’ age and family income. We provide SAS and R code for implementing the extension, assessing model‐fit indices, and evaluating the need to incorporate complex sampling features.Data Extraction MethodsData obtained from www.meps.ahrq.gov.Principle FindingsA 100 percentage‐point increase in family income as a percent of the federal poverty level was associated with a 5%‐6% increase in healthcare spending. People over 65 had an increase of 4‐5 times compared to those younger. Accounting for complex sampling in the models led to different parameter estimates and wider confidence intervals than the unweighted models. Ignoring complex sampling could lead to inaccurate finite population inference.ConclusionResearchers should account for complex sampling features when analyzing semicontinuous data from surveys.     

Bayesian non-response models for categorical data from small areas: an application to BMD and age

We provide a Bayesian analysis of data categorized into two levels of age (younger than 50 years, at least 50 years) and three levels of bone mineral density (normal, osteopenia, osteoporosis) for white females at least 20 years old in the third National Health and Nutrition Examination Survey. For the sample, the age of each individual is known, but some individuals did not have their BMD measured. We use two types of models: In the ignorable non-response models the propensity to respond does not depend on BMD and age of an individual, while in the non-ignorable non-response models it does. These are the baseline models which are used to derive all models for testing. Our non-ignorable non-response models are 'close' to the ignorable non-response models, thereby reducing the effects of the assumptions about non-respondents that cannot be tested in non-response models. We have data from 35 counties, small areas, and therefore our models are hierarchical, a feature that allows a 'borrowing of strength' across the counties, and they provide a substantial reduction in variation. The non-ignorable non-response models are generalizations of the ignorable non-response models, and therefore, the non-ignorable non-response models allow broader inference. The joint posterior density of the parameters for each model is complex, and therefore, we fit each model using Markov chain Monte Carlo methods to obtain samples which are used to make inference about BMD and age. For each county we can estimate the proportion of individuals in each BMD and age cell of the categorical table, and we can assess the relation between BMD and age using the Bayes factor. A sensitivity analysis shows that there are differences (typically small) in inference that permits different levels of association between BMD and age. A simulation study shows that there is not much difference between the baseline ignorable and non-ignorable non-response models.     

Malaria vaccines: immunogen selection and epitope mapping

In recent years major efforts have been made to characterize parasite antigens thought to be suitable candidates for malaria vaccines. Many of the relevant plasmodial antigens have been found to contain extensive areas of short amino acid sequences organized in tandem repeats. These are usually strongly antigenic, forming linear epitopes seen by antibodies of the infected host. Several such epitopes have been identified and subunit vaccines are being designed in which synthetic peptides or gene constructs serve as immunogens. However, as an efficient malaria vaccine should give rise to anamnestic T-dependent antibody responses following reinfection after vaccination as well as to antibody independent cell-mediated immunity, efforts are now also being made to identify T-cell epitopes on the vaccine candidate antigens. In this paper the current Plasmodium falciparum sporozoite vaccines and the merozoite antigen Pf155/RESA, a possible candidate for a P. falciparum blood stage vaccine, serve as examples to illustrate recent advances made in this area as well as some of the problems remaining to be resolved.     

Bayesian disease mapping using product partition models

Our objective is to develop a model to estimate the relative risk of disease in each area, Ai, i=1, ... , n, of a region and to identify areas of unusually high or low risk. We use a product partition model (PPM) in which we assume that the true relative risks can be partitioned into a number of components or sets of areas where the relative risks are equal. The PPM allows the data to weight those partitions likely to hold and inference about particular parameters may be made by first conditioning on the partition and then averaging over all partitions. We develop Markov chain Monte Carlo (MCMC) techniques to approximate the posterior distributions of the partitions and the parameters. We first test the method in a simulation study and then apply it to data for two separate groups of different types of cancer in the Mid-Western Health Board region in Ireland. The results are compared with those obtained using the standardized mortality ratio method, an empirical Bayes method, a spatial scan method and a nonparametric Bayesian method.     

A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data

We propose a transmission model to estimate the main characteristics of influenza transmission in households. The model details the risks of infection in the household and in the community at the individual scale. Heterogeneity among subjects is investigated considering both individual susceptibility and infectiousness. The model was applied to a data set consisting of the follow-up of influenza symptoms in 334 households during 15 days after an index case visited a general practitioner with virologically confirmed influenza.Estimating the parameters of the transmission model was challenging because a large part of the infectious process was not observed: only the dates when new cases were detected were observed. For each case, the data were augmented with the unobserved dates of the start and the end of the infectious period. The transmission model was included in a 3-levels hierarchical structure: (i) the observation level ensured that the augmented data were consistent with the observed data, (ii) the transmission level described the underlying epidemic process, (iii) the prior level specified the distribution of the parameters. From a Bayesian perspective, the joint posterior distribution of model parameters and augmented data was explored by Markov chain Monte Carlo (MCMC) sampling.The mean duration of influenza infectious period was estimated at 3.8 days (95 per cent credible interval, 95 per cent CI [3.1,4.6]) with a standard deviation of 2.0 days (95 per cent CI [1.1,2.8]). The instantaneous risk of influenza transmission between an infective and a susceptible within a household was found to decrease with the size of the household, and established at 0.32 person day(-1) (95 per cent CI [0.26,0.39]); the instantaneous risk of infection from the community was 0.0056 day(-1) (95 per cent CI [0.0029,0.0087]). Focusing on the differences in transmission between children (less than 15 years old) and adults, we estimated that the former were more likely to transmit than adults (posterior probability larger than 99 per cent), but that the mean duration of the infectious period was similar in children (3.6 days, 95 per cent CI [2.3,5.2]) and adults (3.9 days, 95 per cent CI [3.2,4.9]). The posterior probability that children had a larger community risk was 76 per cent and the posterior probability that they were more susceptible than adults was 79 per cent.     

Adjusting for confounding by neighborhood using generalized linear mixed models and complex survey data

When investigating health disparities, it can be of interest to explore whether adjustment for socioeconomic factors at the neighborhood level can account for, or even reverse, an unadjusted difference. Recently, we proposed new methods to adjust the effect of an individual‐level covariate for confounding by unmeasured neighborhood‐level covariates using complex survey data and a generalization of conditional likelihood methods. Generalized linear mixed models (GLMMs) are a popular alternative to conditional likelihood methods in many circumstances. Therefore, in the present article, we propose and investigate a new adaptation of GLMMs for complex survey data that achieves the same goal of adjusting for confounding by unmeasured neighborhood‐level covariates. With the new GLMM approach, one must correctly model the expectation of the unmeasured neighborhood‐level effect as a function of the individual‐level covariates. We demonstrate using simulations that even if that model is correct, census data on the individual‐level covariates are sometimes required for consistent estimation of the effect of the individual‐level covariate. We apply the new methods to investigate disparities in recency of dental cleaning, treated as an ordinal outcome, using data from the 2008 Florida Behavioral Risk Factor Surveillance System (BRFSS) survey. We operationalize neighborhood as zip code and merge the BRFSS data with census data on ZIP Code Tabulated Areas to incorporate census data on the individual‐level covariates. We compare the new results to our previous analysis, which used conditional likelihood methods. We find that the results are qualitatively similar. Copyright © 2012 John Wiley & Sons, Ltd.     

Mixture models for cancer survival analysis: application to population-based data with covariates

The interest in estimating the probability of cure has been increasing in cancer survival analysis as the curability of many cancer diseases is becoming a reality. Mixture survival models provide a way of modelling time to death when cure is possible, simultaneously estimating death hazard of fatal cases and the proportion of cured case. In this paper we propose an application of a parametric mixture model to relative survival rates of colon cancer patients from the Finnish population-based cancer registry, and including major survival determinants as explicative covariates. Disentangling survival into two different components greatly facilitates the analysis and the interpretation of the role of prognostic factors on survival patterns. For example, age plays a different role in determining, from one side, the probability of cure, and, from the other side, the life expectancy of fatal cases. The results support the hypothesis that observed survival trends are really due to a real prognostic gain for more recently diagnosed patients.     

Confidentiality concerns with mapping survey data in reproductive health research

The increasing availability of georeferenced datasets creates new opportunities to perform spatial analysis of social science and public health survey data, but also raises ethical issues regarding the potential for unintended violation of the confidentiality of respondents. This article examines these ethical challenges by reflecting on the experience of a study mapping the facilities that provide abortion-related services in Cambodia. The technique of masking is examined as a potential method for preventing reidentification of respondents in georeferenced surveys. Broader solutions are offered for ways to balance the potentially conflicting goals of spatial analysis and protection of confidentiality.     

Cartography of trachoma in Mali: results of a national survey

Prior to a campaign to eliminate blinding trachoma, a survey of the prevalence of the disease was conducted in the seven administrative regions of Mali between March 1996 and June 1997. In each region (with the exception of Bamako District) a random sample of thirty clusters was taken from the general population, in accordance with the principle of probability proportionate to the size of the communities. All children under 10 years of age and all women over 14 years were examined. The simplified coding proposed by WHO was used for data gathering. A total of 15,310 children and 11,530 women were examined. The prevalence of active trachoma, follicular (TF) or intense (TI), was estimated to be 34.9% among children under 10 years of age (95% CI: 32.3-37.6). The prevalence of TI showing the intensity of trachoma was 4.2% (95% CI: 3.5-5.0) among the same children. The prevalence increased up to the age of 3 years, when it reached 49.2%. The prevalence of TF/TI was 35.7% among boys and 34.3% among girls. The prevalence of entropion trichiasis among women over 14 years of age was 2.5% (95% CI: 2.1-2.9), and 1% had central corneal opacity (95% CI: 0.8-1.3). These prevalences increased with age, such that 10% of women over 70 years of age had trichiasis. By region of the country, the prevalence ranged from 23.1% of active trachoma among children in Ségou, to 46.2% in Gao. The prevalence of entropion trichiasis was 0.65% in Gao region and 3.9% in Koulikoro region. This survey allows the trachoma treatment needs of Mali to be quantified. We estimate that 1.09 million children under 10 years are carriers of active trachoma and require local or general antibiotic treatment. If all the under-10-year-olds from all villages where TF/TI exceeded 20% were to be treated, a total of 2.436 million children would be involved. A total of 85,000 adults should have surgery to correct trichiasis and avoid the onset of blindness.