Displaying publications 1 - 20 of 91 in total

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  1. Komahan K, Reidpath DD
    Am. J. Epidemiol., 2014 Aug 1;180(3):325-9.
    PMID: 24944286 DOI: 10.1093/aje/kwu129
    Correct identification of ethnicity is central to many epidemiologic analyses. Unfortunately, ethnicity data are often missing. Successful classification typically relies on large databases (n > 500,000 names) of known name-ethnicity associations. We propose an alternative naïve Bayesian strategy that uses substrings of full names. Name and ethnicity data for Malays, Indians, and Chinese were provided by a health and demographic surveillance site operating in Malaysia from 2011-2013. The data comprised a training data set (n = 10,104) and a test data set (n = 9,992). Names were spliced into contiguous 3-letter substrings, and these were used as the basis for the Bayesian analysis. Performance was evaluated on both data sets using Cohen's κ and measures of sensitivity and specificity. There was little difference between the classification performance in the training and test data (κ = 0.93 and 0.94, respectively). For the test data, the sensitivity values for the Malay, Indian, and Chinese names were 0.997, 0.855, and 0.932, respectively, and the specificity values were 0.907, 0.998, and 0.997, respectively. A naïve Bayesian strategy for the classification of ethnicity is promising. It performs at least as well as more sophisticated approaches. The possible application to smaller data sets is particularly appealing. Further research examining other substring lengths and other ethnic groups is warranted.
  2. Reidpath DD, Ahmadi K
    PMID: 25126103 DOI: 10.1186/1742-7622-11-9
    Measures of household socio-economic position (SEP) are widely used in health research. There exist a number of approaches to their measurement, with Principal Components Analysis (PCA) applied to a basket of household assets being one of the most common. PCA, however, carries a number of assumptions about the distribution of the data which may be untenable, and alternative, non-parametric, approaches may be preferred. Mokken scale analysis is a non-parametric, item response theory approach to scale development which appears never to have been applied to household asset data. A Mokken scale can be used to rank order items (measures of wealth) as well as households. Using data on household asset ownership from a national sample of 4,154 consenting households in the World Health Survey from Vietnam, 2003, we construct two measures of household SEP. Seventeen items asking about assets, and utility and infrastructure use were used. Mokken Scaling and PCA were applied to the data. A single item measure of total household expenditure is used as a point of contrast.
  3. Masood M, Reidpath DD
    Curr Med Res Opin, 2014 May;30(5):857-63.
    PMID: 24328497 DOI: 10.1185/03007995.2013.875466
    BACKGROUND: The aim of this paper was to review the types of approaches currently utilized in the analysis of multi-country survey data, specifically focusing on design and modeling issues with a focus on analyses of significant multi-country surveys published in 2010.
    METHODS: A systematic search strategy was used to identify the 10 multi-country surveys and the articles published from them in 2010. The surveys were selected to reflect diverse topics and foci; and provide an insight into analytic approaches across research themes. The search identified 159 articles appropriate for full text review and data extraction.
    RESULTS: The analyses adopted in the multi-country surveys can be broadly classified as: univariate/bivariate analyses, and multivariate/multivariable analyses. Multivariate/multivariable analyses may be further divided into design- and model-based analyses. Of the 159 articles reviewed, 129 articles used model-based analysis, 30 articles used design-based analyses. Similar patterns could be seen in all the individual surveys.
    CONCLUSION: While there is general agreement among survey statisticians that complex surveys are most appropriately analyzed using design-based analyses, most researchers continued to use the more common model-based approaches. Recent developments in design-based multi-level analysis may be one approach to include all the survey design characteristics. This is a relatively new area, however, and there remains statistical, as well as applied analytic research required. An important limitation of this study relates to the selection of the surveys used and the choice of year for the analysis, i.e., year 2010 only. There is, however, no strong reason to believe that analytic strategies have changed radically in the past few years, and 2010 provides a credible snapshot of current practice.
  4. Masood M, Reidpath DD
    BMJ Open, 2016 Jan 07;6(1):e008173.
    PMID: 26743697 DOI: 10.1136/bmjopen-2015-008173
    OBJECTIVES: Measuring the intraclass correlation coefficient (ICC) and design effect (DE) may help to modify the public health interventions for body mass index (BMI), physical activity and diet according to geographic targeting of interventions in different countries. The purpose of this study was to quantify the level of clustering and DE in BMI, physical activity and diet in 56 low-income, middle-income and high-income countries.
    DESIGN: Cross-sectional study design.
    SETTING: Multicountry national survey data.
    METHODS: The World Health Survey (WHS), 2003, data were used to examine clustering in BMI, physical activity in metabolic equivalent of task (MET) and diet in fruits and vegetables intake (FVI) from low-income, middle-income and high-income countries. Multistage sampling in the WHS used geographical clusters as primary sampling units (PSU). These PSUs were used as a clustering or grouping variable in this analysis. Multilevel intercept only regression models were used to calculate the ICC and DE for each country.
    RESULTS: The median ICC (0.039) and median DE (1.82) for BMI were low; however, FVI had a higher median ICC (0.189) and median DE (4.16). For MET, the median ICC was 0.141 and median DE was 4.59. In some countries, however, the ICC and DE for BMI were large. For instance, South Africa had the highest ICC (0.39) and DE (11.9) for BMI, whereas Uruguay had the highest ICC (0.434) for MET and Ethiopia had the highest ICC (0.471) for FVI.
    CONCLUSIONS: This study shows that across a wide range of countries, there was low area level clustering for BMI, whereas MET and FVI showed high area level clustering. These results suggested that the country level clustering effect should be considered in developing preventive approaches for BMI, as well as improving physical activity and healthy diets for each country.
    KEYWORDS: Body Mass Index (BMI); Intraclass correlation coefficient (ICC); Physical activity (METs)
    Study name: World Health Survey (Malaysia is a study site)
  5. Masood M, Reidpath DD
    PLoS ONE, 2017;12(6):e0178928.
    PMID: 28662041 DOI: 10.1371/journal.pone.0178928
    BACKGROUND: This study explores the relationship between BMI and national-wealth and the cross-level interaction effect of national-wealth and individual household-wealth using multilevel analysis.
    METHODS: Data from the World Health Survey conducted in 2002-2004, across 70 low-, middle- and high-income countries was used. Participants aged 18 years and over were selected using multistage, stratified cluster sampling. BMI was used as outcome variable. The potential determinants of individual-level BMI were participants' sex, age, marital-status, education, occupation, household-wealth and location(rural/urban) at the individual-level. The country-level factors used were average national income (GNI-PPP) and income inequality (Gini-index). A two-level random-intercepts and fixed-slopes model structure with individuals nested within countries was fitted, treating BMI as a continuous outcome.
    RESULTS: The weighted mean BMI and standard-error of the 206,266 people from 70-countries was 23.90 (4.84). All the low-income countries were below the 25.0 mean BMI level and most of the high-income countries were above. All wealthier quintiles of household-wealth had higher scores in BMI than lowest quintile. Each USD10000 increase in GNI-PPP was associated with a 0.4 unit increase in BMI. The Gini-index was not associated with BMI. All these variables explained 28.1% of country-level, 4.9% of individual-level and 7.7% of total variance in BMI. The cross-level interaction effect between GNI-PPP and household-wealth was significant. BMI increased as the GNI-PPP increased in first four quintiles of household-wealth. However, the BMI of the wealthiest people decreased as the GNI-PPP increased.
    CONCLUSION: Both individual-level and country-level factors made an independent contribution to the BMI of the people. Household-wealth and national-income had significant interaction effects.
    Study name: World Health Survey (Malaysia is a study site)
  6. Soyiri IN, Reidpath DD
    Int J Gen Med, 2012;5:693-705.
    PMID: 22973117 DOI: 10.2147/IJGM.S34647
    Asthma is a global public health problem and the most common chronic disease among children. The factors associated with the condition are diverse, and environmental factors appear to be the leading cause of asthma exacerbation and its worsening disease burden. However, it remains unknown how changes in the environment affect asthma over time, and how temporal or environmental factors predict asthma events. The methodologies for forecasting asthma and other similar chronic conditions are not comprehensively documented anywhere to account for semistructured noncausal forecasting approaches. This paper highlights and discusses practical issues associated with asthma and the environment, and suggests possible approaches for developing decision-making tools in the form of semistructured black-box models, which is relatively new for asthma. Two statistical methods which can potentially be used in predictive modeling and health forecasting for both anticipated and peak events are suggested. Importantly, this paper attempts to bridge the areas of epidemiology, environmental medicine and exposure risks, and health services provision. The ideas discussed herein will support the development and implementation of early warning systems for chronic respiratory conditions in large populations, and ultimately lead to better decision-making tools for improving health service delivery.
  7. Soyiri IN, Reidpath DD
    PLoS ONE, 2012;7(10):e47823.
    PMID: 23118897 DOI: 10.1371/journal.pone.0047823
    The concept of forecasting asthma using humans as animal sentinels is uncommon. This study explores the plausibility of predicting future asthma daily admissions using retrospective data in London (2005-2006). Negative binomial regressions were used in modeling; allowing the non-contiguous autoregressive components. Selected lags were based on partial autocorrelation function (PACF) plot with a maximum lag of 7 days. The model was contrasted with naïve historical and seasonal models. All models were cross validated. Mean daily asthma admission in 2005 was 27.9 and in 2006 it was 28.9. The lags 1, 2, 3, 6 and 7 were independently associated with daily asthma admissions based on their PACF plots. The lag model prediction of peak admissions were often slightly out of synchronization with the actual data, but the days of greater admissions were better matched than the days of lower admissions. A further investigation across various populations is necessary.
  8. Soyiri IN, Reidpath DD
    Environ Health Prev Med, 2013 Jan;18(1):1-9.
    PMID: 22949173 DOI: 10.1007/s12199-012-0294-6
    Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.
  9. Soyiri IN, Reidpath DD
    Int J Gen Med, 2012;5:381-9.
    PMID: 22615533 DOI: 10.2147/IJGM.S31079
    Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation.
  10. Soyiri IN, Reidpath DD
    PLoS ONE, 2013;8(10):e78215.
    PMID: 24147122 DOI: 10.1371/journal.pone.0078215
    Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.
  11. Soyiri IN, Reidpath DD, Sarran C
    Int J Biometeorol, 2013 Jul;57(4):569-78.
    PMID: 22886344 DOI: 10.1007/s00484-012-0584-0
    Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.
  12. Reidpath DD, Allotey P, Pokhrel S
    Health Res Policy Syst, 2011 Jan 06;9:1.
    PMID: 21210997 DOI: 10.1186/1478-4505-9-1
    BACKGROUND: There are strong arguments for social science and interdisciplinary research in the neglected tropical diseases. These diseases represent a rich and dynamic interplay between vector, host, and pathogen which occurs within social, physical and biological contexts. The overwhelming sense, however, is that neglected tropical diseases research is a biomedical endeavour largely excluding the social sciences. The purpose of this review is to provide a baseline for discussing the quantum and nature of the science that is being conducted, and the extent to which the social sciences are a part of that.

    METHODS: A bibliographic analysis was conducted of neglected tropical diseases related research papers published over the past 10 years in biomedical and social sciences. The analysis had textual and bibliometric facets, and focussed on chikungunya, dengue, visceral leishmaniasis, and onchocerciasis.

    RESULTS: There is substantial variation in the number of publications associated with each disease. The proportion of the research that is social science based appears remarkably consistent (<4%). A textual analysis, however, reveals a degree of misclassification by the abstracting service where a surprising proportion of the "social sciences" research was pure clinical research. Much of the social sciences research also tends to be "hand maiden" research focused on the implementation of biomedical solutions.

    CONCLUSION: There is little evidence that scientists pay any attention to the complex social, cultural, biological, and environmental dynamic involved in human pathogenesis. There is little investigator driven social science and a poor presence of interdisciplinary science. The research needs more sophisticated funders and priority setters who are not beguiled by uncritical biomedical promises.

  13. Allotey P, Reidpath DD, Pokhrel S
    Health Res Policy Syst, 2010 Oct 21;8:32.
    PMID: 20961461 DOI: 10.1186/1478-4505-8-32
    Centuries of scientific advances and developments in biomedical sciences have brought us a long way to understanding and managing disease processes, by reducing them to simplified cause-effect models. For most of the infectious diseases known today, we have the methods and technology to identify the causative agent, understand the mechanism by which pathology is induced and develop the treatment (drugs, vaccines, medical or surgical procedures) to cure, manage or control.Disease, however, occurs within a context of lives fraught with complexity. For any given infectious disease, who gets it, when, why, the duration, the severity, the outcome, the sequelae, are bound by a complex interplay of factors related as much to the individual as it is to the physical, social, cultural, political and economic environments. Furthermore each of these factors is in a dynamic state of change, evolving over time as they interact with each other. Simple solutions to infectious diseases are therefore rarely sustainable solutions. Sustainability would require the development of interdisciplinary sciences that allow us to acknowledge, understand and address these complexities as they occur, rather than rely solely on a form of science based on reducing the management of disease to simple paradigms.In this review we examine the current global health responses to the 'neglected' tropical diseases, which have been prioritised on the basis of an acknowledgment of the complexity of the poverty-disease cycle. However research and interventions for neglected tropical diseases, largely neglect the social and ecological contextual, factors that make these diseases persist in the target populations, continuing instead to focus on the simple biomedical interventions. We highlight the gaps in the approaches and explore the potential of enhanced interdisciplinary work in the development of long term solutions to disease control.
  14. Aborigo RA, Allotey P, Reidpath DD
    Soc Sci Med, 2015 May;133:59-66.
    PMID: 25841096 DOI: 10.1016/j.socscimed.2015.03.046
    Traditional medical systems in low income countries remain the first line service of choice, particularly for rural communities. Although the role of traditional birth attendants (TBAs) is recognised in many primary health care systems in low income countries, other types of traditional practitioners have had less traction. We explored the role played by traditional healers in northern Ghana in managing pregnancy-related complications and examined their relevance to current initiatives to reduce maternal morbidity and mortality. A grounded theory qualitative approach was employed. Twenty focus group discussions were conducted with TBAs and 19 in-depth interviews with traditional healers with expertise in managing obstetric complications. Traditional healers are extensively consulted to manage obstetric complications within their communities. Their clientele includes families who for either reasons of access or traditional beliefs, will not use modern health care providers, or those who shop across multiple health systems. The traditional practitioners claim expertise in a range of complications that are related to witchcraft and other culturally defined syndromes; conditions for which modern health care providers are believed to lack expertise. Most healers expressed a willingness to work with the formal health services because they had unique knowledge, skills and the trust of the community. However this would require a stronger acknowledgement and integration within safe motherhood programs.
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