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HEALTH AND SAFETY (530 journals)                  1 2 3 | Last

Showing 1 - 200 of 203 Journals sorted alphabetically
16 de Abril     Open Access  
A Life in the Day     Hybrid Journal   (Followers: 9)
Acta Informatica Medica     Open Access   (Followers: 1)
Acta Scientiarum. Health Sciences     Open Access  
Adultspan Journal     Hybrid Journal  
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10)
Advances in Public Health     Open Access   (Followers: 23)
African Health Sciences     Open Access   (Followers: 2)
African Journal for Physical, Health Education, Recreation and Dance     Full-text available via subscription   (Followers: 6)
African Journal of Health Professions Education     Open Access   (Followers: 6)
Afrimedic Journal     Open Access   (Followers: 2)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4)
AJOB Primary Research     Partially Free   (Followers: 3)
American Journal of Family Therapy     Hybrid Journal   (Followers: 11)
American Journal of Health Economics     Full-text available via subscription   (Followers: 13)
American Journal of Health Education     Hybrid Journal   (Followers: 30)
American Journal of Health Promotion     Hybrid Journal   (Followers: 24)
American Journal of Health Sciences     Open Access   (Followers: 6)
American Journal of Health Studies     Full-text available via subscription   (Followers: 10)
American Journal of Preventive Medicine     Hybrid Journal   (Followers: 26)
American Journal of Public Health     Full-text available via subscription   (Followers: 230)
American Journal of Public Health Research     Open Access   (Followers: 29)
American Medical Writers Association Journal     Full-text available via subscription   (Followers: 2)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 4)
Annals of Global Health     Open Access   (Followers: 9)
Annals of Health Law     Open Access   (Followers: 3)
Annals of Tropical Medicine and Public Health     Open Access   (Followers: 15)
Applied Biosafety     Hybrid Journal  
Applied Research In Health And Social Sciences : Interface And Interaction     Open Access   (Followers: 2)
Archive of Community Health     Open Access  
Archives of Medicine and Health Sciences     Open Access   (Followers: 3)
Arquivos de Ciências da Saúde     Open Access  
Asia Pacific Journal of Counselling and Psychotherapy     Hybrid Journal   (Followers: 8)
Asia Pacific Journal of Health Management     Full-text available via subscription   (Followers: 3)
Asia-Pacific Journal of Public Health     Hybrid Journal   (Followers: 8)
Asian Journal of Gambling Issues and Public Health     Open Access   (Followers: 3)
Association of Schools of Allied Health Professions     Full-text available via subscription   (Followers: 6)
Atención Primaria     Open Access   (Followers: 1)
Australasian Journal of Paramedicine     Open Access   (Followers: 3)
Australian Advanced Aesthetics     Full-text available via subscription   (Followers: 4)
Australian Family Physician     Full-text available via subscription   (Followers: 3)
Australian Indigenous HealthBulletin     Free   (Followers: 6)
Autism & Developmental Language Impairments     Open Access   (Followers: 5)
Behavioral Healthcare     Full-text available via subscription   (Followers: 6)
Best Practices in Mental Health     Full-text available via subscription   (Followers: 8)
Bijzijn     Hybrid Journal   (Followers: 2)
Bijzijn XL     Hybrid Journal   (Followers: 1)
Biomedical Safety & Standards     Full-text available via subscription   (Followers: 8)
BLDE University Journal of Health Sciences     Open Access  
BMC Oral Health     Open Access   (Followers: 5)
BMC Pregnancy and Childbirth     Open Access   (Followers: 20)
BMJ Simulation & Technology Enhanced Learning     Full-text available via subscription   (Followers: 7)
Brazilian Journal of Medicine and Human Health     Open Access  
Buletin Penelitian Kesehatan     Open Access   (Followers: 2)
Buletin Penelitian Sistem Kesehatan     Open Access  
Bulletin of the World Health Organization     Open Access   (Followers: 17)
Cadernos de Educação, Saúde e Fisioterapia     Open Access   (Followers: 1)
Cadernos Saúde Coletiva     Open Access   (Followers: 1)
Canadian Family Physician     Partially Free   (Followers: 12)
Canadian Journal of Community Mental Health     Full-text available via subscription   (Followers: 12)
Canadian Journal of Human Sexuality     Hybrid Journal   (Followers: 1)
Canadian Journal of Public Health     Full-text available via subscription   (Followers: 20)
Case Reports in Women's Health     Open Access   (Followers: 3)
Case Studies in Fire Safety     Open Access   (Followers: 13)
Central Asian Journal of Global Health     Open Access   (Followers: 2)
Central European Journal of Public Health     Full-text available via subscription   (Followers: 4)
CES Medicina     Open Access  
Child Abuse Research in South Africa     Full-text available via subscription   (Followers: 1)
Child's Nervous System     Hybrid Journal  
Childhood Obesity and Nutrition     Open Access   (Followers: 10)
Children     Open Access   (Followers: 2)
CHRISMED Journal of Health and Research     Open Access  
Christian Journal for Global Health     Open Access  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
Ciencia y Cuidado     Open Access  
Ciencia, Tecnología y Salud     Open Access  
ClinicoEconomics and Outcomes Research     Open Access   (Followers: 2)
CME     Hybrid Journal   (Followers: 1)
CoDAS     Open Access  
Community Health     Open Access   (Followers: 2)
Conflict and Health     Open Access   (Followers: 8)
Contraception and Reproductive Medicine     Open Access  
Curare     Open Access  
Current Opinion in Behavioral Sciences     Hybrid Journal   (Followers: 3)
Day Surgery Australia     Full-text available via subscription   (Followers: 2)
Digital Health     Open Access   (Followers: 2)
Dramatherapy     Hybrid Journal   (Followers: 2)
Drogues, santé et société     Full-text available via subscription  
Duazary     Open Access   (Followers: 1)
Early Childhood Research Quarterly     Hybrid Journal   (Followers: 15)
East African Journal of Public Health     Full-text available via subscription   (Followers: 3)
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity     Hybrid Journal   (Followers: 17)
EcoHealth     Hybrid Journal   (Followers: 4)
Education for Health     Open Access   (Followers: 5)
electronic Journal of Health Informatics     Open Access   (Followers: 6)
ElectronicHealthcare     Full-text available via subscription   (Followers: 4)
Elsevier Ergonomics Book Series     Full-text available via subscription   (Followers: 5)
Emergency Services SA     Full-text available via subscription   (Followers: 2)
Ensaios e Ciência: Ciências Biológicas, Agrárias e da Saúde     Open Access  
Environmental Disease     Open Access   (Followers: 2)
Environmental Sciences Europe     Open Access   (Followers: 2)
Epidemics     Open Access   (Followers: 4)
Epidemiologic Perspectives & Innovations     Open Access   (Followers: 5)
Epidemiology, Biostatistics and Public Health     Open Access   (Followers: 19)
Ethics, Medicine and Public Health     Full-text available via subscription   (Followers: 2)
Ethiopian Journal of Health Development     Open Access   (Followers: 8)
Ethiopian Journal of Health Sciences     Open Access   (Followers: 7)
Ethnicity & Health     Hybrid Journal   (Followers: 13)
European Journal of Investigation in Health, Psychology and Education     Open Access   (Followers: 2)
European Medical, Health and Pharmaceutical Journal     Open Access  
Evaluation & the Health Professions     Hybrid Journal   (Followers: 10)
Evidence-based Medicine & Public Health     Open Access   (Followers: 6)
Evidência - Ciência e Biotecnologia - Interdisciplinar     Open Access  
Expressa Extensão     Open Access  
Face à face     Open Access   (Followers: 1)
Families, Systems, & Health     Full-text available via subscription   (Followers: 8)
Family & Community Health     Partially Free   (Followers: 12)
Family Medicine and Community Health     Open Access   (Followers: 6)
Family Relations     Partially Free   (Followers: 11)
Fatigue : Biomedicine, Health & Behavior     Hybrid Journal   (Followers: 2)
Food and Public Health     Open Access   (Followers: 11)
Frontiers in Public Health     Open Access   (Followers: 7)
Gaceta Sanitaria     Open Access   (Followers: 3)
Galen Medical Journal     Open Access  
Geospatial Health     Open Access  
Gesundheitsökonomie & Qualitätsmanagement     Hybrid Journal   (Followers: 9)
Giornale Italiano di Health Technology Assessment     Full-text available via subscription  
Global Health : Science and Practice     Open Access   (Followers: 5)
Global Health Promotion     Hybrid Journal   (Followers: 16)
Global Journal of Health Science     Open Access   (Followers: 9)
Global Journal of Public Health     Open Access   (Followers: 12)
Global Medical & Health Communication     Open Access   (Followers: 1)
Globalization and Health     Open Access   (Followers: 5)
Hacia la Promoción de la Salud     Open Access  
Hastings Center Report     Hybrid Journal   (Followers: 3)
HEADline     Hybrid Journal  
Health & Place     Hybrid Journal   (Followers: 16)
Health & Justice     Open Access   (Followers: 5)
Health : An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine     Hybrid Journal   (Followers: 7)
Health and Human Rights     Free   (Followers: 8)
Health and Social Care Chaplaincy     Hybrid Journal   (Followers: 7)
Health and Social Work     Hybrid Journal   (Followers: 52)
Health Behavior and Policy Review     Full-text available via subscription   (Followers: 1)
Health Care Analysis     Hybrid Journal   (Followers: 14)
Health Inform     Full-text available via subscription  
Health Information Management Journal     Hybrid Journal   (Followers: 15)
Health Issues     Full-text available via subscription   (Followers: 2)
Health Notions     Open Access  
Health Policy     Hybrid Journal   (Followers: 38)
Health Policy and Technology     Hybrid Journal   (Followers: 2)
Health Professional Student Journal     Open Access   (Followers: 2)
Health Promotion International     Hybrid Journal   (Followers: 21)
Health Promotion Journal of Australia : Official Journal of Australian Association of Health Promotion Professionals     Full-text available via subscription   (Followers: 10)
Health Promotion Practice     Hybrid Journal   (Followers: 15)
Health Prospect     Open Access   (Followers: 1)
Health Psychology     Full-text available via subscription   (Followers: 48)
Health Psychology Research     Open Access   (Followers: 18)
Health Psychology Review     Hybrid Journal   (Followers: 41)
Health Renaissance     Open Access  
Health Research Policy and Systems     Open Access   (Followers: 11)
Health SA Gesondheid     Open Access   (Followers: 2)
Health Science Reports     Open Access  
Health Sciences and Disease     Open Access   (Followers: 2)
Health Services Insights     Open Access   (Followers: 2)
Health Systems     Hybrid Journal   (Followers: 3)
Health Voices     Full-text available via subscription  
Health, Culture and Society     Open Access   (Followers: 13)
Health, Risk & Society     Hybrid Journal   (Followers: 11)
Healthcare     Open Access   (Followers: 1)
Healthcare in Low-resource Settings     Open Access   (Followers: 1)
Healthcare Quarterly     Full-text available via subscription   (Followers: 8)
Healthy-Mu Journal     Open Access  
HERD : Health Environments Research & Design Journal     Full-text available via subscription  
Highland Medical Research Journal     Full-text available via subscription  
Hispanic Health Care International     Full-text available via subscription  
HIV & AIDS Review     Full-text available via subscription   (Followers: 11)
Home Health Care Services Quarterly     Hybrid Journal   (Followers: 6)
Hong Kong Journal of Social Work, The     Hybrid Journal   (Followers: 2)
Hospitals & Health Networks     Free   (Followers: 4)
IEEE Journal of Translational Engineering in Health and Medicine     Open Access   (Followers: 3)
IMTU Medical Journal     Full-text available via subscription  
Indian Journal of Health Sciences     Open Access   (Followers: 2)
Indonesian Journal for Health Sciences     Open Access   (Followers: 1)
Inmanencia. Revista del Hospital Interzonal General de Agudos (HIGA) Eva Perón     Open Access  
Innovative Journal of Medical and Health Sciences     Open Access  
Institute for Security Studies Papers     Full-text available via subscription   (Followers: 5)
interactive Journal of Medical Research     Open Access  
International Health     Hybrid Journal   (Followers: 5)
International Journal for Equity in Health     Open Access   (Followers: 7)
International Journal for Quality in Health Care     Hybrid Journal   (Followers: 34)
International Journal of Applied Behavioral Sciences     Open Access   (Followers: 2)
International Journal of Behavioural and Healthcare Research     Hybrid Journal   (Followers: 8)
International Journal of Circumpolar Health     Open Access   (Followers: 1)
International Journal of Community Medicine and Public Health     Open Access   (Followers: 5)
International Journal of E-Health and Medical Communications     Full-text available via subscription   (Followers: 2)
International Journal of Environmental Research and Public Health     Open Access   (Followers: 20)
International Journal of Evidence-Based Healthcare     Hybrid Journal   (Followers: 8)
International Journal of Food Safety, Nutrition and Public Health     Hybrid Journal   (Followers: 16)
International Journal of Health & Allied Sciences     Open Access   (Followers: 3)
International Journal of Health Care Quality Assurance     Hybrid Journal   (Followers: 10)

        1 2 3 | Last

Journal Cover Epidemiologic Perspectives & Innovations
  [SJR: 0.905]   [H-I: 21]   [5 followers]  Follow
  This is an Open Access Journal Open Access journal
   ISSN (Online) 1742-5573
   Published by Springer-Verlag Homepage  [2355 journals]
  • Social network analysis and agent-based modeling in social epidemiology

    • Authors: Abdulrahman M El-Sayed; Peter Scarborough; Lars Seemann; Sandro Galea
      First page: 1
      Abstract: Abstract The past five years have seen a growth in the interest in systems approaches in epidemiologic research. These approaches may be particularly appropriate for social epidemiology. Social network analysis and agent-based models (ABMs) are two approaches that have been used in the epidemiologic literature. Social network analysis involves the characterization of social networks to yield inference about how network structures may influence risk exposures among those in the network. ABMs can promote population-level inference from explicitly programmed, micro-level rules in simulated populations over time and space. In this paper, we discuss the implementation of these models in social epidemiologic research, highlighting the strengths and weaknesses of each approach. Network analysis may be ideal for understanding social contagion, as well as the influences of social interaction on population health. However, network analysis requires network data, which may sacrifice generalizability, and causal inference from current network analytic methods is limited. ABMs are uniquely suited for the assessment of health determinants at multiple levels of influence that may couple with social interaction to produce population health. ABMs allow for the exploration of feedback and reciprocity between exposures and outcomes in the etiology of complex diseases. They may also provide the opportunity for counterfactual simulation. However, appropriate implementation of ABMs requires a balance between mechanistic rigor and model parsimony, and the precision of output from complex models is limited. Social network and agent-based approaches are promising in social epidemiology, but continued development of each approach is needed.
      PubDate: 2012-02-01
      DOI: 10.1186/1742-5573-9-1
      Issue No: Vol. 9, No. 1 (2012)
  • Use of the integrated health interview series: trends in medical provider
           utilization (1972-2008)

    • Authors: Mike Davern; Lynn A Blewett; Brian Lee; Michel Boudreaux; Miriam L King
      First page: 2
      Abstract: Abstract The Integrated Health Interview Series (IHIS) is a public data repository that harmonizes four decades of the National Health Interview Survey (NHIS). The NHIS is the premier source of information on the health of the U.S. population. Since 1957 the survey has collected information on health behaviors, health conditions, and health care access. The long running time series of the NHIS is a powerful tool for health research. However, efforts to fully utilize its time span are obstructed by difficult documentation, unstable variable and coding definitions, and non-ignorable sample re-designs. To overcome these hurdles the IHIS, a freely available and web-accessible resource, provides harmonized NHIS data from 1969-2010. This paper describes the challenges of working with the NHIS and how the IHIS reduces such burdens. To demonstrate one potential use of the IHIS we examine utilization patterns in the U.S. from 1972-2008.
      PubDate: 2012-03-30
      DOI: 10.1186/1742-5573-9-2
      Issue No: Vol. 9, No. 1 (2012)
  • Extending the sufficient component cause model to describe the Stable Unit
           Treatment Value Assumption (SUTVA)

    • Authors: Sharon Schwartz; Nicolle M Gatto; Ulka B Campbell
      First page: 3
      Abstract: Abstract Causal inference requires an understanding of the conditions under which association equals causation. The exchangeability or no confounding assumption is well known and well understood as central to this task. More recently the epidemiologic literature has described additional assumptions related to the stability of causal effects. In this paper we extend the Sufficient Component Cause Model to represent one expression of this stability assumption--the Stable Unit Treatment Value Assumption. Approaching SUTVA from an SCC model helps clarify what SUTVA is and reinforces the connections between interaction and SUTVA.
      PubDate: 2012-04-03
      DOI: 10.1186/1742-5573-9-3
      Issue No: Vol. 9, No. 1 (2012)
  • WINPEPI updated: computer programs for epidemiologists, and their teaching

    • Authors: Joseph H Abramson
      First page: 1
      Abstract: Background The WINPEPI computer programs for epidemiologists are designed for use in practice and research in the health field and as learning or teaching aids. The programs are free, and can be downloaded from the Internet. Numerous additions have been made in recent years. Implementation There are now seven WINPEPI programs: DESCRIBE, for use in descriptive epidemiology; COMPARE2, for use in comparisons of two independent groups or samples; PAIRSetc, for use in comparisons of paired and other matched observations; LOGISTIC, for logistic regression analysis; POISSON, for Poisson regression analysis; WHATIS, a "ready reckoner" utility program; and ETCETERA, for miscellaneous other procedures. The programs now contain 122 modules, each of which provides a number, sometimes a large number, of statistical procedures. The programs are accompanied by a Finder that indicates which modules are appropriate for different purposes. The manuals explain the uses, limitations and applicability of the procedures, and furnish formulae and references. Conclusions WINPEPI is a handy resource for a wide variety of statistical routines used by epidemiologists. Because of its ready availability, portability, ease of use, and versatility, WINPEPI has a considerable potential as a learning and teaching aid, both with respect to practical procedures in the planning and analysis of epidemiological studies, and with respect to important epidemiological concepts. It can also be used as an aid in the teaching of general basic statistics.
      PubDate: 2011-02-02
      DOI: 10.1186/1742-5573-8-1
      Issue No: Vol. 8, No. 1 (2011)
  • Disease-specific prospective family study cohorts enriched for familial

    • Authors: John L Hopper
      First page: 2
      Abstract: Abstract Most common diseases demonstrate familial aggregation; the ratio of the risk for relatives of affected people to the risk for relatives of unaffected people (the familial risk ratio)) > 1. This implies there are underlying genetic and/or environmental risk factors shared by relatives. The risk gradient across this underlying 'familial risk profile', which can be predicted from family history and measured familial risk factors, is typically strong. Under a multiplicative model, the ratio of the risk for people in the upper 25% of familial risk to the risk for those in the lower 25% (the inter-quartile risk gradient) is an order of magnitude greater than the familial risk ratio. If familial risk ratio = 2 for first-degree relatives, in terms of familial risk profile: (a) people in the upper quartile will be at more than 20 times the risk of those in the lower quartile; and (b) about 90% of disease will occur in people above the median. Historically, therefore, epidemiology has compared cases with controls dissimilar for underlying familial risk profile. Were gene-environment and gene-gene interactions to exist, environmental and genetic effects could be stronger for people with increased familial risk profile. Studies in which controls are better matched to cases for familial risk profile might be more informative, especially if both cases and controls are over-sampled for increased familial risk. Prospective family study cohort (ProF-SC) designs involving people across a range of familial risk profile provide such a resource for epidemiological, genetic, behavioural, psycho-social and health utilisation research. The prospective aspect gives credibility to risk estimates. The familial aspect allows family-based designs, matching for unmeasured factors, adjusting for underlying familial risk profile, and enhanced cohort maintenance.
      PubDate: 2011-02-27
      DOI: 10.1186/1742-5573-8-2
      Issue No: Vol. 8, No. 1 (2011)
  • Clustering based on adherence data

    • Authors: Sylvia Kiwuwa-Muyingo; Hannu Oja; Sarah A Walker; Pauliina Ilmonen; Jonathan Levin; Jim Todd
      First page: 3
      Abstract: Abstract Adherence to a medical treatment means the extent to which a patient follows the instructions or recommendations by health professionals. There are direct and indirect ways to measure adherence which have been used for clinical management and research. Typically adherence measures are monitored over a long follow-up or treatment period, and some measurements may be missing due to death or other reasons. A natural question then is how to describe adherence behavior over the whole period in a simple way. In the literature, measurements over a period are usually combined just by using averages like percentages of compliant days or percentages of doses taken. In the paper we adapt an approach where patient adherence measures are seen as a stochastic process. Repeated measures are then analyzed as a Markov chain with finite number of states rather than as independent and identically distributed observations, and the transition probabilities between the states are assumed to fully describe the behavior of a patient. The patients can then be clustered or classified using their estimated transition probabilities. These natural clusters can be used to describe the adherence of the patients, to find predictors for adherence, and to predict the future events. The new approach is illustrated and shown to be useful with a simple analysis of a data set from the DART (Development of AntiRetroviral Therapy in Africa) trial in Uganda and Zimbabwe.
      PubDate: 2011-03-08
      DOI: 10.1186/1742-5573-8-3
      Issue No: Vol. 8, No. 1 (2011)
  • Attributing the burden of cancer at work: three areas of concern when
           examining the example of shift-work

    • Authors: Thomas C Erren; Peter Morfeld
      First page: 4
      Abstract: Abstract This commentary intends to instigate discussions about epidemiologic estimates and their interpretation of attributable fractions (AFs) and the burden of disease (BOD) of cancers due to factors at workplaces. By examining recent work that aims to estimate the number of cancers attributable to shift-work in Britain, we suggest that (i) causal, (ii) practical and (iii) methodological areas of concern may deter us from attributable caseload estimations of cancers at this point in time. Regarding (i), such calculations may have to be avoided as long as we lack established causality between shift-work and the development of internal cancers. Regarding (ii), such calculations may have to be avoided as long as we can neither abandon shift-work nor identify personnel that may be unaffected by shift-work factors. Regarding (iii), there are at least four methodological pitfalls which are likely to make AF calculations uninterpretable at this stage. The four pitfalls are: (1) The use of Levin's 1953 formula in case of adjusted relative risks; (2) The use of broad definitions of exposure in calculations of AFs; (3) The non-additivity of AFs across different levels of exposure and covariables; (4) The fact that excess mortality counts are misleading due to the fact that a human being dies exactly once - a death may occur earlier or later, but a death cannot occur more than once nor can it be avoided altogether for any given individual. Overall, causal, practical and methodological areas of concern should be diligently considered when performing and interpreting AF or BOD computations which - at least at the present time - may not be defensible.
      PubDate: 2011-09-30
      DOI: 10.1186/1742-5573-8-4
      Issue No: Vol. 8, No. 1 (2011)
  • The use of complete-case and multiple imputation-based analyses in
           molecular epidemiology studies that assess interaction effects

    • Authors: Manisha Desai; Denise A Esserman; Marilie D Gammon; Mary B Terry
      First page: 5
      Abstract: Background In molecular epidemiology studies biospecimen data are collected, often with the purpose of evaluating the synergistic role between a biomarker and another feature on an outcome. Typically, biomarker data are collected on only a proportion of subjects eligible for study, leading to a missing data problem. Missing data methods, however, are not customarily incorporated into analyses. Instead, complete-case (CC) analyses are performed, which can result in biased and inefficient estimates. Methods Through simulations, we characterized the performance of CC methods when interaction effects are estimated. We also investigated whether standard multiple imputation (MI) could improve estimation over CC methods when the data are not missing at random (NMAR) and auxiliary information may or may not exist. Results CC analyses were shown to result in considerable bias and efficiency loss. While MI reduced bias and increased efficiency over CC methods under specific conditions, it too resulted in biased estimates depending on the strength of the auxiliary data available and the nature of the missingness. In particular, CC performed better than MI when extreme values of the covariate were more likely to be missing, while MI outperformed CC when missingness of the covariate related to both the covariate and outcome. MI always improved performance when strong auxiliary data were available. In a real study, MI estimates of interaction effects were attenuated relative to those from a CC approach. Conclusions Our findings suggest the importance of incorporating missing data methods into the analysis. If the data are MAR, standard MI is a reasonable method. Auxiliary variables may make this assumption more reasonable even if the data are NMAR. Under NMAR we emphasize caution when using standard MI and recommend it over CC only when strong auxiliary data are available. MI, with the missing data mechanism specified, is an alternative when the data are NMAR. In all cases, it is recommended to take advantage of MI's ability to account for the uncertainty of these assumptions.
      PubDate: 2011-10-06
      DOI: 10.1186/1742-5573-8-5
      Issue No: Vol. 8, No. 1 (2011)
  • A method to predict breast cancer stage using Medicare claims

    • Authors: Grace L Smith; Ya-Chen T Shih; Sharon H Giordano; Benjamin D Smith; Thomas A Buchholz
      First page: 1
      Abstract: Background In epidemiologic studies, cancer stage is an important predictor of outcomes. However, cancer stage is typically unavailable in medical insurance claims datasets, thus limiting the usefulness of such data for epidemiologic studies. Therefore, we sought to develop an algorithm to predict cancer stage based on covariates available from claims-based data. Methods We identified a cohort of 77,306 women age ≥ 66 years with stage I-IV breast cancer, using the Surveillence Epidemiology and End Results (SEER)-Medicare database. We formulated an algorithm to predict cancer stage using covariates (demographic, tumor, and treatment characteristics) obtained from claims. Logistic regression models derived prediction equations in a training set, and equations' test characteristics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value [NPV]) were calculated in a validation set. Results Of the entire sample of women diagnosed with invasive breast cancer, 51% had stage I; 26% stage II; 11% stage III; and 4% stage IV disease. The equation predicting stage IV disease achieved sensitivity of 81%, specificity 89%, positive predictive value (PPV) 24%, and negative predictive value (NPV) 99%, while the equation distinguishing stage I/II from stage III disease achieved sensitivity 83%, specificity 78%, PPV 98%, and NPV 31%. Combined, the equations most accurately identified early stage disease and ascertained a sample in which 98% of patients were stage I or II. Conclusions A claims-based algorithm was utilized to predict breast cancer stage, and was particularly successful when used to identify early stage disease. These prediction equations may be applied in future studies of breast cancer patients, substantially improving the utility of claims-based studies in this group. This method may similarly be employed to develop algorithms permitting claims-based epidemiologic studies of patients with other cancers.
      PubDate: 2010-01-15
      DOI: 10.1186/1742-5573-7-1
      Issue No: Vol. 7, No. 1 (2010)
  • Can we use biomarkers in combination with self-reports to strengthen the
           analysis of nutritional epidemiologic studies'

    • Authors: Laurence S Freedman; Victor Kipnis; Arthur Schatzkin; Nataša Tasevska; Nancy Potischman
      First page: 2
      Abstract: Abstract Identifying diet-disease relationships in nutritional cohort studies is plagued by the measurement error in self-reported intakes. The authors propose using biomarkers known to be correlated with dietary intake, so as to strengthen analyses of diet-disease hypotheses. The authors consider combining self-reported intakes and biomarker levels using principal components, Howe's method, or a joint statistical test of effects in a bivariate model. They compared the statistical power of these methods with that of conventional univariate analyses of self-reported intake or of biomarker level. They used computer simulation of different disease risk models, with input parameters based on data from the literature on the relationship between lutein intake and age-related macular degeneration. The results showed that if the dietary effect on disease was fully mediated through the biomarker level, then the univariate analysis of the biomarker was the most powerful approach. However, combination methods, particularly principal components and Howe's method, were not greatly inferior in this situation, and were as good as, or better than, univariate biomarker analysis if mediation was only partial or non-existent. In some circumstances sample size requirements were reduced to 20-50% of those required for conventional analyses of self-reported intake. The authors conclude that (i) including biomarker data in addition to the usual dietary data in a cohort could greatly strengthen the investigation of diet-disease relationships, and (ii) when the extent of mediation through the biomarker is unknown, use of principal components or Howe's method appears a good strategy.
      PubDate: 2010-01-20
      DOI: 10.1186/1742-5573-7-2
      Issue No: Vol. 7, No. 1 (2010)
  • Using variable importance measures from causal inference to rank risk
           factors of schistosomiasis infection in a rural setting in China

    • Authors: Sylvia EK Sudat; Elizabeth J Carlton; Edmund YW Seto; Robert C Spear; Alan E Hubbard
      First page: 3
      Abstract: Background Schistosomiasis infection, contracted through contact with contaminated water, is a global public health concern. In this paper we analyze data from a retrospective study reporting water contact and schistosomiasis infection status among 1011 individuals in rural China. We present semi-parametric methods for identifying risk factors through a comparison of three analysis approaches: a prediction-focused machine learning algorithm, a simple main-effects multivariable regression, and a semi-parametric variable importance (VI) estimate inspired by a causal population intervention parameter. Results The multivariable regression found only tool washing to be associated with the outcome, with a relative risk of 1.03 and a 95% confidence interval (CI) of 1.01-1.05. Three types of water contact were found to be associated with the outcome in the semi-parametric VI analysis: July water contact (VI estimate 0.16, 95% CI 0.11-0.22), water contact from tool washing (VI estimate 0.88, 95% CI 0.80-0.97), and water contact from rice planting (VI estimate 0.71, 95% CI 0.53-0.96). The July VI result, in particular, indicated a strong association with infection status - its causal interpretation implies that eliminating water contact in July would reduce the prevalence of schistosomiasis in our study population by 84%, or from 0.3 to 0.05 (95% CI 78%-89%). Conclusions The July VI estimate suggests possible within-season variability in schistosomiasis infection risk, an association not detected by the regression analysis. Though there are many limitations to this study that temper the potential for causal interpretations, if a high-risk time period could be detected in something close to real time, new prevention options would be opened. Most importantly, we emphasize that traditional regression approaches are usually based on arbitrary pre-specified models, making their parameters difficult to interpret in the context of real-world applications. Our results support the practical application of analysis approaches that, in contrast, do not require arbitrary model pre-specification, estimate parameters that have simple public health interpretations, and apply inference that considers model selection as a source of variation.
      PubDate: 2010-07-14
      DOI: 10.1186/1742-5573-7-3
      Issue No: Vol. 7, No. 1 (2010)
  • Fitting additive Poisson models

    • Authors: Hendriek C Boshuizen; Edith JM Feskens
      First page: 4
      Abstract: Abstract This paper describes how to fit an additive Poisson model using standard software. It is illustrated with SAS code, but can be similarly used for other software packages.
      PubDate: 2010-07-20
      DOI: 10.1186/1742-5573-7-4
      Issue No: Vol. 7, No. 1 (2010)
  • Redundant causation from a sufficient cause perspective

    • Authors: Nicolle M Gatto; Ulka B Campbell
      First page: 5
      Abstract: Abstract Sufficient causes of disease are redundant when an individual acquires the components of two or more sufficient causes. In this circumstance, the individual still would have become diseased even if one of the sufficient causes had not been acquired. In the context of a study, when any individuals acquire components of more than one sufficient cause over the observation period, the etiologic effect of the exposure (defined as the absolute or relative difference between the proportion of the exposed who develop the disease by the end of the study period and the proportion of those individuals who would have developed the disease at the moment they did even in the absence of the exposure) may be underestimated. Even in the absence of confounding and bias, the observed effect estimate represents only a subset of the etiologic effect. This underestimation occurs regardless of the measure of effect used. To some extent, redundancy of sufficient causes is always present, and under some circumstances, it may make a true cause of disease appear to be not causal. This problem is particularly relevant when the researcher's goal is to characterize the universe of sufficient causes of the disease, identify risk factors for targeted interventions, or construct causal diagrams. In this paper, we use the sufficient component cause model and the disease response type framework to show how redundant causation arises and the factors that determine the extent of its impact on epidemiologic effect measures.
      PubDate: 2010-08-02
      DOI: 10.1186/1742-5573-7-5
      Issue No: Vol. 7, No. 1 (2010)
  • How many are affected' A real limit of epidemiology

    • Authors: Charles Poole
      First page: 6
      Abstract: Abstract A person can experience an effect on the occurrence of an outcome in a defined follow-up period without experiencing an effect on the risk of that outcome over the same period. Sufficient causes are sometimes used to deepen potential-outcome explanations of this phenomenon. In doing so, care should be taken to avoid tipping the balance between simplification and realism too far toward simplification. Death and other competing risks should not be assumed away. The time scale should be explicit, with specific times for the occurrence of specified component causes and for the completion of each sufficient cause. Component causes that affect risk should occur no later than the start of the risk period. Sufficient causes should be allowed to have component causes in common. When individuals experience all components of two or more sufficient causes, the outcome must be recurrent. In addition to effects on rates and risks, effects on incidence time itself should be considered.
      PubDate: 2010-08-24
      DOI: 10.1186/1742-5573-7-6
      Issue No: Vol. 7, No. 1 (2010)
  • Author's response to Poole, C. Commentary: How Many Are Affected' A
           Real Limit of Epidemiology

    • Authors: Nicolle M Gatto; Ulka B Campbell; Sharon Schwartz
      First page: 7
      PubDate: 2010-08-26
      DOI: 10.1186/1742-5573-7-7
      Issue No: Vol. 7, No. 1 (2010)
  • Population attributable fraction: comparison of two mathematical
           procedures to estimate the annual attributable number of deaths

    • Authors: Bernard C K Choi
      First page: 8
      Abstract: Objective The purpose of this paper was to compare two mathematical procedures to estimate the annual attributable number of deaths (the Allison et al procedure and the Mokdad et al procedure), and derive a new procedure that combines the best aspects of both procedures. The new procedure calculates attributable number of deaths along a continuum (i.e. for each unit of exposure), and allows for one or more neutral (neither exposed nor nonexposed) exposure categories. Methods Mathematical derivations and real datasets were used to demonstrate the theoretical relationship and practical differences between the two procedures. Results of the comparison were used to develop a new procedure that combines the best features of both. Findings The Allison procedure is complex because it directly estimates the number of attributable deaths. This necessitates calculation of probabilities of death. The Mokdad procedure is simpler because it estimates the number of attributable deaths indirectly through population attributable fractions. The probabilities of death cancel out in the numerator and denominator of the fractions. However, the Mokdad procedure is not applicable when a neutral exposure category exists. Conclusion By combining the innovation of the Allison procedure (allowing for a neutral category) and the simplicity of the Mokdad procedure (using population attributable fractions), this paper proposes a new procedure to calculate attributable numbers of death.
      PubDate: 2010-08-31
      DOI: 10.1186/1742-5573-7-8
      Issue No: Vol. 7, No. 1 (2010)
  • Categorisation of continuous risk factors in epidemiological publications:
           a survey of current practice

    • Authors: Elizabeth L Turner; Joanna E Dobson; Stuart J Pocock
      First page: 9
      Abstract: Background Reports of observational epidemiological studies often categorise (group) continuous risk factor (exposure) variables. However, there has been little systematic assessment of how categorisation is practiced or reported in the literature and no extended guidelines for the practice have been identified. Thus, we assessed the nature of such practice in the epidemiological literature. Two months (December 2007 and January 2008) of five epidemiological and five general medical journals were reviewed. All articles that examined the relationship between continuous risk factors and health outcomes were surveyed using a standard proforma, with the focus on the primary risk factor. Using the survey results we provide illustrative examples and, combined with ideas from the broader literature and from experience, we offer guidelines for good practice. Results Of the 254 articles reviewed, 58 were included in our survey. Categorisation occurred in 50 (86%) of them. Of those, 42% also analysed the variable continuously and 24% considered alternative groupings. Most (78%) used 3 to 5 groups. No articles relied solely on dichotomisation, although it did feature prominently in 3 articles. The choice of group boundaries varied: 34% used quantiles, 18% equally spaced categories, 12% external criteria, 34% other approaches and 2% did not describe the approach used. Categorical risk estimates were most commonly (66%) presented as pairwise comparisons to a reference group, usually the highest or lowest (79%). Reporting of categorical analysis was mostly in tables; only 20% in figures. Conclusions Categorical analyses of continuous risk factors are common. Accordingly, we provide recommendations for good practice. Key issues include pre-defining appropriate choice of groupings and analysis strategies, clear presentation of grouped findings in tables and figures, and drawing valid conclusions from categorical analyses, avoiding injudicious use of multiple alternative analyses.
      PubDate: 2010-10-15
      DOI: 10.1186/1742-5573-7-9
      Issue No: Vol. 7, No. 1 (2010)
  • Carcinogen metabolism, cigarette smoking, and breast cancer risk: a Bayes
           model averaging approach

    • Authors: Nadine Stephenson; Lars Beckmann; Jenny Chang-Claude
      First page: 10
      Abstract: Background Standard logistic regression with or without stepwise selection has the disadvantage of not incorporating model uncertainty and the dependency of estimates on the underlying model into the final inference. We explore the use of a Bayes Model Averaging approach as an alternative to analyze the influence of genetic variants, environmental effects and their interactions on disease. Methods Logistic regression with and without stepwise selection and Bayes Model Averaging were applied to a population-based case-control study exploring the association of genetic variants in tobacco smoke-related carcinogen pathways with breast cancer. Results Both regression and Bayes Model Averaging highlighted a significant effect of NAT1*10 on breast cancer, while regression analysis also suggested a significant effect for packyears and for the interaction of packyears and NAT2. Conclusions Bayes Model Averaging allows incorporation of model uncertainty, helps reduce dimensionality and avoids the problem of multiple comparisons. It can be used to incorporate biological information, such as pathway data, into the analysis. As with all Bayesian analysis methods, careful consideration must be given to prior specification.
      PubDate: 2010-11-16
      DOI: 10.1186/1742-5573-7-10
      Issue No: Vol. 7, No. 1 (2010)
  • Shift work, cancer and "white-box" epidemiology:
           Association and causation

    • Authors: Thomas C Erren
      First page: 11
      Abstract: Abstract This commentary intends to instigate discussions about upcoming epidemiologic research, and its interpretation, into putative links between shift work, involving circadian disruption or chronodisruption [CD], and the development of internal cancers. In 2007, the International Agency for Research on Cancer (IARC) convened an expert group to examine the carcinogenicity of shift work, inter alia characterized by light exposures at unusual times. After a critical review of published data, the following was stated: "There is sufficient evidence in experimental animals for the carcinogenicity of light during the daily dark period (biological night)". However, in view of limited epidemiological evidence, it was overall concluded: "Shiftwork that involves circadian disruption is probably carcinogenic to humans (Group 2A)". Remarkably, the scenario around shift work, CD and internal cancers provides a unique case for "white-box" epidemiology: Research at many levels - from sub-cellular biochemistry, to whole cells, to organs, to organisms, including animals and humans - has suggested a series of quite precise and partly related causal mechanisms. This is in stark contrast to instances of "black box" or "stabs in the dark" epidemiology where causal mechanisms are neither known nor hypothesized or only poorly defined. The overriding theme that an adequate chronobiological organization of physiology can be critical for the protection against cancer builds the cornerstone of biological plausibility in this case. We can now benefit from biological plausibility in two ways: First, epidemiology should use biologically plausible insights into putative chains of causation between shift work and cancer to design future investigations. Second, when significant new data were to become available in coming years, IARC will re-evaluate cancer hazards associated with shift work. Biological plausibility may then be a key viewpoint to consider and, ultimately, to decide whether (or not) to pass from statistical associations, possibly detected in observational studies by then, to a verdict of causation. In the meantime, biological plausibility should not be invoked to facilitate publication of epidemiological research of inappropriate quality. Specific recommendations as to how to design, report and interpret epidemiological research into biologically plausible links between shift work and cancer are provided. Epidemiology is certainly a poor tool for learning about the mechanism by which a disease is produced, but it has the tremendous advantage that it focuses on the diseases and the deaths that actually occur, and experience has shown that it continues to be second to none as a means of discovering links in the chain of causation that are capable of being broken. -Sir Richard Doll [1]
      PubDate: 2010-11-30
      DOI: 10.1186/1742-5573-7-11
      Issue No: Vol. 7, No. 1 (2010)
  • Reporting errors in infectious disease outbreaks, with an application to
           Pandemic Influenza A/H1N1

    • Authors: Laura F White; Marcello Pagano
      First page: 12
      Abstract: Background Effectively responding to infectious disease outbreaks requires a well-informed response. Quantitative methods for analyzing outbreak data and estimating key parameters to characterize the spread of the outbreak, including the reproductive number and the serial interval, often assume that the data collected is complete. In reality reporting delays, undetected cases or lack of sensitive and specific tests to diagnose disease lead to reporting errors in the case counts. Here we provide insight on the impact that such reporting errors might have on the estimation of these key parameters. Results We show that when the proportion of cases reported is changing through the study period, the estimates of key epidemiological parameters are biased. Using data from the Influenza A/H1N1 outbreak in La Gloria, Mexico, we provide estimates of these parameters, accounting for possible reporting errors, and show that they can be biased by as much as 33%, if reporting issues are not accounted for. Conclusions Failure to account for missing data can lead to misleading and inaccurate estimates of epidemic parameters.
      PubDate: 2010-12-15
      DOI: 10.1186/1742-5573-7-12
      Issue No: Vol. 7, No. 1 (2010)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
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