Correspondence/Rebuttal pubs.acs.org/est
Response to Comment on Randomized Intervention Study of Solar Disinfection of Drinking Water in the Prevention of Dysentery in Kenyan Children Aged Under 5 Years
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least-squares regression, which estimates means based on minimizing the squared distance between each observation and its predicted value, quantile regression estimates quantiles by minimizing the absolute difference between each observation and its predicted value. While the CONSORT recommendations are ideal for controlled trials with fixed periods of observation, they translate poorly into the conditions of a longitudinal study in which the outcome is a regression slope (which is what height for age or weight for age are) rather than a simple measurement. Given the use of fractional polynomial regression to estimate the curvilinear association between age and anthropometric measures, the regression slope estimate will necessarily be a polynomial: a combination of two or more terms. Furthermore, the variable amount of data available on each participant would mean that unadjusted medians would have to be interpreted in the light of (a) the age distribution of the children, (b) the number of days of data available, and (c) in the case of those in the SODIS group, the length of time since starting the trial. We believe that there is no simple solution to presenting a simple univariate summary of data like this, and indeed, it was a problem that exercised us considerably in the presentation of the manuscript. Likewise, while we presented the average effect of one year of SODIS on growth parameters, we were not suggesting that this effect was the same at all ages. Rather, the limitations of the amount of data made it beyond the scope of the study to estimate the differential effects at different ages. We were not suggesting that a year of SODIS added 0.8 cm to the height of a child, regardless of age. Consequently, the table presented by Dr Arnold and his colleagues is somewhat misleading. We ourselves had insufficient data on children aged under 12 months at recruitment to examine for a differential effect of SODIS on height. A reanalysis of the data comparing the effects of SODIS in 96 such children with the effects in the 560 older children did not reveal a significant difference (Wald posthoc test P = 0.991) but the power of the study in such a subgroup analysis is low. We do not believe that the use of the WHO growth standards would have been informative. All such norms are useful where no local norms are available. Nonetheless we preferred instead to use our extensive data to construct norms for our study population. Knowing the adverse conditions under which many of the study population lived, we felt that this could have had a significant impact on growth, in the absence of any intervention. Electoral and postelectoral violence in Kenya are also likely to have had an effect on food security. Regression models allowing flexible estimation of quantiles of interest offer a more externally valid control in such circumstances, we believe. Systematic departures from WHO
r Arnold and colleagues make some interesting reflections, and raise a number of important issues upon which we too would like to comment. Dr Arnold and his colleagues have misinterpreted our description of the data collection in a way that neither we nor the reviewers anticipated by suggesting “...that field staff who promoted SODIS in the treatment group may have also assessed health outcomes, but this point remains unclear.” To prevent any further confusion, we would like to make it explicit that field staff on their regular visits reminded participants in the SODIS group to use SODIS, and answered any queries they might have had. The primary function of these regular visits was to collect the diarrheal diaries, which had been completed previously by the carer each day, and to hand out fresh, blank diaries. While courtesy bias is a significant (but unquantified) problem in nonblinded trials such as this one, we are unaware of any research to suggest that the problem is reduced by using different staff to conduct training and to collect data. We have previously discussed elsewhere the difficulties associated with assessing compliance with solar disinfection.1 Observed compliance, even when unannounced, is unreliable, since households within a community usually know in a matter of minutes when a field worker has appeared, and bottles can quickly appear on roofs. Assessing compliance only in the SODIS group leaves open the serious risk of confounding, since those most likely to comply with the SODIS protocol are most likely to be those who are most health conscious. For this reason, we previously adopted a compliance indicator based on data protocol compliance, which could be applied to both intervention and control groups,1 and demonstrated that compliance, thus measured, predicted whether SODIS would significantly reduce dysentery or not.2 However, as we explained in the present paper, we could not apply this compliance metric in the Kenyan study data because postelection civic unrest and internal displacement of many of the study participants during the 2007−2008 disturbances that followed, resulted in factors other than compliance determining the completeness of the data. We believe, however, that Dr Arnold and his colleagues have raised a valuable point, which is the need for a valid measure of trial compliance that can be applied to both control and intervention groups, something significantly lacking in the literature to date. We provided little detail on the regression methods used to model the anthropometric indices as they are well-known. Fractional polynomial regression is almost three decades old, and the interested reader is referred to Royston’s original account.3 Fractional polynomial models model curvilinear relationships using an efficient series of orthogonal transformations of the independent variable (age, in this case). The method can be applied to most types of regression model. Quantile regression, likewise, is well-established and, indeed, owes part of its methodology to Laplace (1749−1827). Unlike © 2012 American Chemical Society
Published: February 2, 2012 3033
dx.doi.org/10.1021/es3003958 | Environ. Sci. Technol. 2012, 46, 3033−3034
Environmental Science & Technology
Correspondence/Rebuttal
norms in both the controls and intervention children would simply have made the effects of SODIS more problematic to estimate.
Martella du Preez† Ronan M. Conroy‡ Kevin G. McGuigan§,* †
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Natural Resources and the Environment, CSIR, PO Box 395, Pretoria, South Africa ‡ Division of Population Health Sciences, Royal College of Surgeons in Ireland, 123 St Stephens Green, Dublin 2, Ireland § Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, 123 St Stephens Green, Dublin 2, Ireland
AUTHOR INFORMATION
Corresponding Author
*Phone: +353 1 4022207; e-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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REFERENCES
(1) McGuigan, K. G.; Samaiyar, P.; du Preez, M.; Conroy, R. M. A high compliance randomised controlled field trial of solar disinfection (SODIS) of drinking water and its impact on childhood diarrhoea in rural Cambodia. Environ. Sci. Technol. 2011, 45 (18), 7862−7867. (2) du Preez, M.; McGuigan, K. G.; Conroy, R. M. Solar disinfection of drinking water (SODIS) in the prevention of dysentery in South African children aged under 5 years: the role of participant motivation. Environ. Sci. Technol. 2010, 44 (22), 8744−8749. (3) Royston, P. Regression models using fractional polynomials of continuous covariates. Parsimonious parametric modelling. Appl. Stat. 1994, 43, 49−467.
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