MyMedR

Displaying all 15 publications

Abstract:

Sort:

Fulltext The use of quantile regression to forecast higher than expected respiratory deaths in a daily time series: a study of New York City data 1987-2000

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.
Fulltext Humans as animal sentinels for forecasting asthma events: helping health services become more responsive

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.
Fulltext Evolving forecasting classifications and applications in health forecasting

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.
Fulltext Semistructured black-box prediction: proposed approach for asthma admissions in London

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.
Fulltext An overview of health forecasting

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.
Forecasting asthma-related hospital admissions in London using negative binomial models

Soyiri IN, Reidpath DD, Sarran C

Chron Respir Dis, 2013 May;10(2):85-94.
PMID: 23620439 DOI: 10.1177/1479972313482847

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.
Forecasting peak asthma admissions in London: an application of quantile regression models

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.
Fulltext Complementary feeding and the early origins of obesity risk: a study protocol

Muniandy ND, Allotey PA, Soyiri IN, Reidpath DD

BMJ Open, 2016 11 15;6(11):e011635.
PMID: 27852704 DOI: 10.1136/bmjopen-2016-011635

INTRODUCTION: The rise in the prevalence of childhood obesity worldwide calls for an intervention earlier in the life cycle. Studies show that nutrition during early infancy may contribute to later obesity. Hence, this study is designed to determine if the variation in complementary feeding practices poses a risk for the development of obesity later in life. A mixed methods approach will be used in conducting this study.
METHODS AND ANALYSIS: The target participants are infants born from January to June 2015 in the South East Asia Community Observatory (SEACO) platform. The SEACO is a Health and Demographic Surveillance System (HDSS) that is established in the District of Segamat in the state of Johor, Malaysia. For the quantitative strand, the sociodemographic data, feeding practices, anthropometry measurement and total nutrient intake will be assessed. The assessment will occur around the time complementary feeding is expected to start (7 Months) and again at 12 months. A 24-hour diet recall and a 2-day food diary will be used to assess the food intake. For the qualitative strand, selected mothers will be interviewed to explore their infant feeding practices and factors that influence their practices and food choices in detail.
ETHICS AND DISSEMINATION: Ethical clearance for this study was sought through the Monash University Human Research and Ethics Committee (application number CF14/3850-2014002010). Subsequently, the findings of this study will be disseminated through peer-reviewed journals, national and international conferences.
Fulltext The rural bite in population pyramids: what are the implications for responsiveness of health systems in middle income countries?

Jahan N, Allotey P, Arunachalam D, Yasin S, Soyiri IN, Davey TM, et al.

BMC Public Health, 2014;14 Suppl 2:S8.
PMID: 25081203 DOI: 10.1186/1471-2458-14-S2-S8

Health services can only be responsive if they are designed to service the needs of the population at hand. In many low and middle income countries, the rate of urbanisation can leave the profile of the rural population quite different from the urban population. As a consequence, the kinds of services required for an urban population may be quite different from that required for a rural population. This is examined using data from the South East Asia Community Observatory in rural Malaysia and contrasting it with the national Malaysia population profile.
Does one cigarette make an adolescent smoker, and is it influenced by age and age of smoking initiation? Evidence of association from the U.S. Youth Risk Behavior Surveillance System (2011)

Reidpath DD, Davey TM, Kadirvelu A, Soyiri IN, Allotey P

Prev Med, 2014 Feb;59:37-41.
PMID: 24270054 DOI: 10.1016/j.ypmed.2013.11.011

OBJECTIVES: Evidence that age of smoking initiation represents a risk factor for regular smoking in adolescence is complicated by inconsistencies in the operational definition of smoking initiation and simultaneous inclusion of age as an explanatory variable. The aim of this study was to examine the relationship between age, age of smoking initiation and subsequent regular smoking.
METHODS: A secondary analysis was conducted of the U.S. Youth Risk Behavior Survey 2011. A sex stratified multivariable logistic regression analysis was used to model the likelihood of regular smoking with age and age of smoking initiation as explanatory variables and race/ethnicity as a covariate.
RESULTS: After controlling for race/ethnicity, age and age of smoking initiation were independently associated with regular smoking in males and females. Independent of age, a one year's decrease in the age of smoking initiation was associated with a 1.27 times increase in odds of regular smoking in females (95% CI: 1.192-1.348); and similar associations for males (OR: 1.28; 95% CI: 1.216-1.341).
CONCLUSION: While the majority of high school students do not become regular smokers after initiating smoking, earlier initiation of smoking is associated with subsequent regular smoking irrespective of sex or race/ethnicity. These findings have potentially important implications for intervention targeting.
KEYWORDS: Adolescent; Epidemiology; Smoking
Fulltext Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

Orimaye SO, Wong JS, Golden KJ, Wong CP, Soyiri IN

BMC Bioinformatics, 2017 Jan 14;18(1):34.
PMID: 28088191 DOI: 10.1186/s12859-016-1456-0

BACKGROUND: The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.
RESULTS: Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).
CONCLUSIONS: Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.
Fulltext HDSS Profile: The South East Asia Community Observatory Health and Demographic Surveillance System (SEACO HDSS)

Partap U, Young EH, Allotey P, Soyiri IN, Jahan N, Komahan K, et al.

Int J Epidemiol, 2017 Oct 01;46(5):1370-1371g.
PMID: 29024948 DOI: 10.1093/ije/dyx113
Fulltext Burden of injury along the development spectrum: associations between the Socio-demographic Index and disability-adjusted life year estimates from the Global Burden of Disease Study 2017

Haagsma JA, James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, et al.

Inj Prev, 2020 Oct;26(Supp 1):i12-i26.
PMID: 31915273 DOI: 10.1136/injuryprev-2019-043296

BACKGROUND: The epidemiological transition of non-communicable diseases replacing infectious diseases as the main contributors to disease burden has been well documented in global health literature. Less focus, however, has been given to the relationship between sociodemographic changes and injury. The aim of this study was to examine the association between disability-adjusted life years (DALYs) from injury for 195 countries and territories at different levels along the development spectrum between 1990 and 2017 based on the Global Burden of Disease (GBD) 2017 estimates.
METHODS: Injury mortality was estimated using the GBD mortality database, corrections for garbage coding and CODEm-the cause of death ensemble modelling tool. Morbidity estimation was based on surveys and inpatient and outpatient data sets for 30 cause-of-injury with 47 nature-of-injury categories each. The Socio-demographic Index (SDI) is a composite indicator that includes lagged income per capita, average educational attainment over age 15 years and total fertility rate.
RESULTS: For many causes of injury, age-standardised DALY rates declined with increasing SDI, although road injury, interpersonal violence and self-harm did not follow this pattern. Particularly for self-harm opposing patterns were observed in regions with similar SDI levels. For road injuries, this effect was less pronounced.
CONCLUSIONS: The overall global pattern is that of declining injury burden with increasing SDI. However, not all injuries follow this pattern, which suggests multiple underlying mechanisms influencing injury DALYs. There is a need for a detailed understanding of these patterns to help to inform national and global efforts to address injury-related health outcomes across the development spectrum.
Fulltext Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study

James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Liu Z, et al.

Inj Prev, 2020 Oct;26(Supp 1):i125-i153.
PMID: 32839249 DOI: 10.1136/injuryprev-2019-043531

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria.
METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced.
RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes.
CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.
Fulltext Global injury morbidity and mortality from 1990 to 2017: results from the Global Burden of Disease Study 2017

James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Liu Z, et al.

Inj Prev, 2020 10;26(Supp 1):i96-i114.
PMID: 32332142 DOI: 10.1136/injuryprev-2019-043494

BACKGROUND: Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries.
METHODS: We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs).
FINDINGS: In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505).
INTERPRETATION: Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.

Filters

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links