A simulation model for diarrhoea and other common recurrent infections: a tool for exploring epidemiological methods

Estimating Disease Duration in Cross-sectional Surveys

BACKGROUND

In some common episodic conditions, such as diarrhea, respiratory infections, or fever, episode duration can reflect disease severity. The mean episode duration in a population can be estimated if both the incidence and prevalence of the condition are known. In this article, we discuss how an estimator of the average episode duration may be obtained based on prevalence alone if data are collected for two consecutive units of time (usually days) in the same person.

METHODS

We derive a maximum likelihood estimator of episode duration, explore its behavior through a simulation study, and illustrate its use through a real example.

RESULTS

We show that for two consecutive days, the estimator of the mean episode duration in a population equals one plus twice the ratio of the number of subjects with the condition on both days to the number of subjects with only 1 day ill. The estimator can be extended to account for 3 or 4 consecutive days. The estimator assumes nonoverlapping episodes and a time-constant incidence rate and is more precise for shorter than for longer average episode durations.

CONCLUSION

The proposed method allows estimating the mean duration of disease episodes in cross-sectional studies and is applicable to large demographic and health surveys in low-income settings that routinely collect data on diarrhea and respiratory illness. The method may further be used for the calculation of the duration of infectiousness if test results are available for two consecutive days, such as paired throat swabs for influenza.

Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases

Longitudinal healthcare claims databases are frequently used for studying the comparative safety and effectiveness of medications, but results from these studies may be biased due to residual confounding. It is unclear whether methods for confounding adjustment that have been shown to perform well in small, simple nonrandomized studies are applicable to the large, complex pharmacoepidemiologic studies created from secondary healthcare data. Ordinary simulation approaches for evaluating the performance of statistical methods do not capture important features of healthcare claims. A statistical framework for creating replicated simulation datasets from an empirical cohort study in electronic healthcare claims data is developed and validated. The approach relies on resampling from the observed covariate and exposure data without modification in all simulated datasets to preserve the associations among these variables. Repeated outcomes are simulated using a true treatment effect of the investigator's choice and the baseline hazard function estimated from the empirical data. As an example, this framework is applied to a study of high versus low-intensity statin use and cardiovascular outcomes. Simulated data is based on real data drawn from Medicare Parts A and B linked with a prescription drug insurance claims database maintained by Caremark. Properties of the data simulated using this framework are compared with the empirical data on which the simulations were based. In addition, the simulated datasets are used to compare variable selection strategies for confounder adjustmentvia the propensity score, including high-dimensional approaches that could not be evaluated with ordinary simulation methods. The simulated datasets are found to closely resemble the observed complex data structure but have the advantage of an investigator-specified exposure effect.

Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases

Longitudinal healthcare claims databases are frequently used for studying the comparative safety and effectiveness of medications, but results from these studies may be biased due to residual confounding. It is unclear whether methods for confounding adjustment that have been shown to perform well in small, simple nonrandomized studies are applicable to the large, complex pharmacoepidemiologic studies created from secondary healthcare data. Ordinary simulation approaches for evaluating the performance of statistical methods do not capture important features of healthcare claims. A statistical framework for creating replicated simulation datasets from an empirical cohort study in electronic healthcare claims data is developed and validated. The approach relies on resampling from the observed covariate and exposure data without modification in all simulated datasets to preserve the associations among these variables. Repeated outcomes are simulated using a true treatment effect of the investigator's choice and the baseline hazard function estimated from the empirical data. As an example, this framework is applied to a study of high versus low-intensity statin use and cardiovascular outcomes. Simulated data is based on real data drawn from Medicare Parts A and B linked with a prescription drug insurance claims database maintained by Caremark. Properties of the data simulated using this framework are compared with the empirical data on which the simulations were based. In addition, the simulated datasets are used to compare variable selection strategies for confounder adjustmentvia the propensity score, including high-dimensional approaches that could not be evaluated with ordinary simulation methods. The simulated datasets are found to closely resemble the observed complex data structure but have the advantage of an investigator-specified exposure effect.

Effect of household-based drinking water chlorination on diarrhoea among children under five in Orissa, India: a double-blind randomised placebo-controlled trial

BACKGROUND

Boiling, disinfecting, and filtering water within the home can improve the microbiological quality of drinking water among the hundreds of millions of people who rely on unsafe water supplies. However, the impact of these interventions on diarrhoea is unclear. Most studies using open trial designs have reported a protective effect on diarrhoea while blinded studies of household water treatment in low-income settings have found no such effect. However, none of those studies were powered to detect an impact among children under five and participants were followed-up over short periods of time. The aim of this study was to measure the effect of in-home water disinfection on diarrhoea among children under five.

METHODS AND FINDINGS

We conducted a double-blind randomised controlled trial between November 2010 and December 2011. The study included 2,163 households and 2,986 children under five in rural and urban communities of Orissa, India. The intervention consisted of an intensive promotion campaign and free distribution of sodium dichloroisocyanurate (NaDCC) tablets during bi-monthly households visits. An independent evaluation team visited households monthly for one year to collect health data and water samples. The primary outcome was the longitudinal prevalence of diarrhoea (3-day point prevalence) among children aged under five. Weight-for-age was also measured at each visit to assess its potential as a proxy marker for diarrhoea. Adherence was monitored each month through caregiver's reports and the presence of residual free chlorine in the child's drinking water at the time of visit. On 20% of the total household visits, children's drinking water was assayed for thermotolerant coliforms (TTC), an indicator of faecal contamination. The primary analysis was on an intention-to-treat basis. Binomial regression with a log link function and robust standard errors was used to compare prevalence of diarrhoea between arms. We used generalised estimating equations to account for clustering at the household level. The impact of the intervention on weight-for-age z scores (WAZ) was analysed using random effect linear regression. Over the follow-up period, 84,391 child-days of observations were recorded, representing 88% of total possible child-days of observation. The longitudinal prevalence of diarrhoea among intervention children was 1.69% compared to 1.74% among controls. After adjusting for clustering within household, the prevalence ratio of the intervention to control was 0.95 (95% CI 0.79-1.13). The mean WAZ was similar among children of the intervention and control groups (-1.586 versus -1.589, respectively). Among intervention households, 51% reported their child's drinking water to be treated with the tablets at the time of visit, though only 32% of water samples tested positive for residual chlorine. Faecal contamination of drinking water was lower among intervention households than controls (geometric mean TTC count of 50 [95% CI 44-57] per 100 ml compared to 122 [95% CI 107-139] per 100 ml among controls [p<0.001] [n = 4,546]).

CONCLUSIONS

Our study was designed to overcome the shortcomings of previous double-blinded trials of household water treatment in low-income settings. The sample size was larger, the follow-up period longer, both urban and rural populations were included, and adherence and water quality were monitored extensively over time. These results provide no evidence that the intervention was protective against diarrhoea. Low compliance and modest reduction in water contamination may have contributed to the lack of effect. However, our findings are consistent with other blinded studies of similar interventions and raise additional questions about the actual health impact of household water treatment under these conditions.

TRIAL REGISTRATION

ClinicalTrials.govNCT01202383 Please see later in the article for the Editors' Summary.

Epidemiological methods in diarrhoea studies--an update

BACKGROUND

Diarrhoea remains a leading cause of morbidity and mortality but is difficult to measure in epidemiological studies. Challenges include the diagnosis based on self-reported symptoms, the logistical burden of intensive surveillance and the variability of diarrhoea in space, time and person.

METHODS

We review current practices in sampling procedures to measure diarrhoea, and provide guidance for diarrhoea measurement across a range of study goals. Using 14 available data sets, we estimated typical design effects for clustering at household and village/ neighbourhood level, and measured the impact of adjusting for baseline variables on the precision of intervention effect estimates.

RESULTS

Incidence is the preferred outcome measure in aetiological studies, health services research and vaccine trials. Repeated prevalence measurements (longitudinal prevalence) are appropriate in high-mortality settings where malnutrition is common, although many repeat measures are rarely useful. Period prevalence is an inadequate outcome if an intervention affects illness duration. Adjusting point estimates for age or diarrhoea at baseline in randomized trials has little effect on the precision of estimates. Design effects in trials randomized at household level are usually <2 (range 1.0–3.2). Design effects for larger clusters (e.g. villages or neighbourhoods) vary greatly among different settings and study designs (range 0.1–25.8).

CONCLUSIONS

Using appropriate sampling strategies and outcome measures can improve the efficiency, validity and comparability of diarrhoea studies. Allocating large clusters in cluster randomized trials is compromized by unpredictable design effects and should be carried out only if the research question requires it.

Design and validation of a data simulation model for longitudinal healthcare data

Evaluating performance characteristics of analytic methods developed to identify treatment effects in longitudinal healthcare data has been hindered by lack of an objective benchmark to measure performance. Relationships between drugs and subsequent treatment effects are not precisely quantified in real-world data, and simulated data offer potential to augment method development by providing data with known, measurable characteristics. However, the use of simulated data has been limited due to its inability to adequately reflect the complexities inherent in real-world databases that are necessary for effective method development. The goal of this study was to develop and evaluate a model for simulating longitudinal healthcare data that adequately captures these complexities. An empiric design was chosen that utilizes the characteristics of a real healthcare database as simulation input. This model demonstrates the potential for simulated data with known characteristics to adequately reflect complex relationships among diseases and treatments as recorded in healthcare databases.

Estimating the effect of recurrent infectious diseases on nutritional status: sampling frequency, sample-size, and bias

There is an ongoing interest in studying the effect of common recurrent infections and conditions, such as diarrhoea, respiratory infections, and fever, on the nutritional status of children at risk of malnutrition. Epidemiological studies exploring this association need to measure infections with sufficient accuracy to minimize bias in the effect estimates. A versatile model of common recurrent infections was used for exploring how many repeated measurements of disease are required to maximize the power and logistical efficiency of studies investigating the effect of infectious diseases on malnutrition without compromising the validity of the estimates. Depending on the prevalence and distribution of disease within a population, 15-30 repeat measurements per child over one year should be sufficient to provide unbiased estimates of the association between infections and nutritional status. Less-frequent measurements lead to a bias in the effect size towards zero, especially if disease is rare. In contrast, recall error can lead to exaggerated effect sizes. Recall periods of three days or shorter may be preferable compared to longer recall periods. The results showed that accurate estimation of the association between recurrent infections and nutritional status required closer follow-up of study participants than studies using recurrent infections as an outcome measure. The findings of the study provide guidance for choosing an appropriate sampling strategy to explore this association.

Sampling strategies to measure the prevalence of common recurrent infections in longitudinal studies

Background

Measuring recurrent infections such as diarrhoea or respiratory infections in epidemiological studies is a methodological challenge. Problems in measuring the incidence of recurrent infections include the episode definition, recall error, and the logistics of close follow up. Longitudinal prevalence (LP), the proportion-of-time-ill estimated by repeated prevalence measurements, is an alternative measure to incidence of recurrent infections. In contrast to incidence which usually requires continuous sampling, LP can be measured at intervals. This study explored how many more participants are needed for infrequent sampling to achieve the same study power as frequent sampling.

Methods

We developed a set of four empirical simulation models representing low and high risk settings with short or long episode durations. The model was used to evaluate different sampling strategies with different assumptions on recall period and recall error.

Results

The model identified three major factors that influence sampling strategies: (1) the clustering of episodes in individuals; (2) the duration of episodes; (3) the positive correlation between an individual's disease incidence and episode duration. Intermittent sampling (e.g. 12 times per year) often requires only a slightly larger sample size compared to continuous sampling, especially in cluster-randomized trials. The collection of period prevalence data can lead to highly biased effect estimates if the exposure variable is associated with episode duration. To maximize study power, recall periods of 3 to 7 days may be preferable over shorter periods, even if this leads to inaccuracy in the prevalence estimates.

Conclusion

Choosing the optimal approach to measure recurrent infections in epidemiological studies depends on the setting, the study objectives, study design and budget constraints. Sampling at intervals can contribute to making epidemiological studies and trials more efficient, valid and cost-effective.