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Evaluating Public Health Interventions: 6. Modeling Ratios or Differences? Let the Data Tell Us.

ABSTRACT: We provide an overview of the relative merits of ratio measures (relative risks, risk ratios, and rate ratios) compared with difference measures (risk and rate differences). We discuss evidence that the multiplicative model often fits the data well, so that rarely are interactions with other risk factors for the outcome observed when one uses a logistic, relative risk, or Cox regression model to estimate the intervention effect. As a consequence, additive models, which estimate the risk or rate difference, will often exhibit interactions. Under these circumstances, absolute measures of effect, such as years of life lost, disability- or quality-adjusted years of life lost, and number needed to treat, will not be externally generalizable to populations other than those with similar risk factor distributions as the population in which the intervention effect was estimated. Nevertheless, these absolute measures are often of the greatest importance in public health decision-making. When studies of high-risk study populations are used to more efficiently estimate effects, these populations will not be representative of the general population's risk factor distribution. The relative homogeneity of ratio versus absolute measures will thus have important implications for the generalizability of results across populations.

SUBMITTER: Spiegelman D

PROVIDER: S-EPMC5463222 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Evaluating Public Health Interventions: 6. Modeling Ratios or Differences? Let the Data Tell Us.

Spiegelman Donna D VanderWeele Tyler J TJ

American journal of public health 20170701 7

We provide an overview of the relative merits of ratio measures (relative risks, risk ratios, and rate ratios) compared with difference measures (risk and rate differences). We discuss evidence that the multiplicative model often fits the data well, so that rarely are interactions with other risk factors for the outcome observed when one uses a logistic, relative risk, or Cox regression model to estimate the intervention effect. As a consequence, additive models, which estimate the risk or rate ...[more]

PMID: 28590865

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Project description:Introduction: Public health (PH) interventions are crucial for ensuring sustainable healthcare services. Nevertheless, they represent a neglected area in the field of health technology assessment (HTA) due to various methodological issues and their complex design that goes beyond clinical setting. The present study provides an environmental scan of HTA initiatives related to the assessment of PH technologies on a global level. Methods: We conducted a cross-sectional survey among 85 HTA-related European and international societies, health bodies, and networks from September 2018 to January 2019. The questionnaire contained four sections and 18 questions regarding activities related to the evaluation of PH technologies, information on existing PH technologies, and methodologies of assessment as well as barriers and facilitators to reaching a decision and implementing a PH technology. Results: Among 52 survey responses, the majority of the respondents came from European countries (35%), followed by North American (27%), and South American (19%) countries. The main type of organizations covered by our survey included HTA agencies, public administrations, and research institutes. Seventy-one % of the institutions reported engagement in any aspect of HTA in the area of PH (N = 37). Among those, 81% evaluated less than 5 PH technologies from 2013 to 2018. The most common barriers for reaching a decision on PH technologies were lack of data, conflicting stakeholder priorities, and methodological issues. A total of 76 PH interventions were reported, and most cited initiatives were related to chronic disease screening, prevention of infectious diseases, and maternal, prenatal, and neonatal screening. Conclusion: Our survey reported a rather limited involvement of HTA in the evaluation of PH technologies. In particular, an evaluation of behavioral and lifestyle interventions remains extremely rare. The implementation of collaborative HTA approaches in the setting of PH practice and policy needs to be prioritized and further strengthened. Moreover, ensuring reliable data structures and consolidation of HTA methods for the evaluation of PH technologies will be crucial for tackling the enormous burden of non-communicable diseases in societies.

Project description:The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

Project description:Groundwater contamination by potentially harmful elements (PHEs) originating from the weathering of granitic and gneissic rock dissolution poses a public health concern worldwide. This study investigated physicochemical variables and PHEs in the groundwater system and mine water of the Adenzai flood plain region, in Pakistan, emphasizing the fate distribution, source provenance, chemical speciation, and health hazard using the human health risk assessment HHRA-model. The average concentrations of the PHEs, viz., Ni, Mn, Cr, Cu, Cd, Pb, Co, Fe, and Zn 0.23, were 0.27, 0.07, 0.30, 0.07, 0.06, 0.08, 0.68, and 0.23 mg/L, respectively. The average values of chemical species in the groundwater system, viz., H+, OH-, Ni2+, Mn2+, Mn3+, Cr3+, Cr6+, Cu+, Cu2+, Cd2+, Pb2+, Pb4+, Co2+, Co3+, Fe2+, Fe3+, and Zn2+, were 1.0 × 10-4 ± 1.0 × 10-6, 1.0 × 10-4 ± 9.0 × 10-7, 2.0 × 10-1 ± 1.0 × 10-3, 3.0 × 10-1 ± 1.0 × 10-3, 1.0 × 10-22 ± 1.0 × 10-23, 4.0 × 10-6 ± 2.0 × 10-6, 4.0 × 10-11 ± 2.0 × 10-11, 9.0 × 10-3 ± 1.0 × 10-2, 2.0 × 10-1 ± 2.0 × 10-3, 7.0 × 10-2 ± 6.0 × 10-2, 5.0 × 10-2 ± 5.0 × 10-2, 2.0 × 10-2 ± 1.5 × 10-2, 6.0 × 10-2 ± 4.0 × 10-2, 8.0 × 10-31 ± 6.0 × 10-31, 3.0 × 10-1 ± 2.0 × 10-4, 4.0 × 10-10 ± 3.0 × 10-10, and 2.0 × 10-1 ± 1.0 × 10-1. The mineral compositions of PHEs, viz. Ni, were bunsenite, Ni(OH)2, and trevorite; Mn viz., birnessite, bixbyite, hausmannite, manganite, manganosite, pyrolusite, and todorokite; Cr viz., chromite and eskolaite; Cu viz., CuCr2O4, cuprite, delafossite, ferrite-Cu, and tenorite; Cd viz., monteponite; Pb viz, crocoite, litharge, massicot, minium, plattnerite, Co viz., spinel-Co; Fe viz., goethite, hematite, magnetite, wustite, and ferrite-Zn; and Zn viz., zincite, and ZnCr2O4 demarcated undersaturation and supersaturation. However, EC, Ca2+, K+, Na+, HCO3-, Cr, Cd, Pb, Co, and Fe had exceeded the WHO guideline. The Nemerow's pollution index (NPI) showed that EC, Ca2+, K+, Na+, HCO3-, Mn, Cd, Pb, Co, and Fe had worse water quality. Principal component analysis multilinear regression (PCAMLR) and cluster analysis (CA) revealed that 75% of the groundwater contamination originated from geogenic inputs and 18% mixed geogenic-anthropogenic and 7% anthropogenic sources. The HHRA-model suggested potential non-carcinogenic risks, except for Fe, and substantial carcinogenic risks for evaluated PHEs. The women and infants are extremely exposed to PHEs hazards. The non-carcinogenic and carcinogenic risks in children, males, and females had exceeded their desired level. The HHRA values of PHEs exhibited the following increasing pattern: Co > Cu > Mn > Zn > Fe, and Cd > Pb > Ni > Cr. The higher THI values of PHEs in children and adults suggested that the groundwater consumption in the entire region is unfit for drinking, domestic, and agricultural purposes. Thus, all groundwater sources need immediate remedial measures to secure health safety and public health concerns.

Project description:INTRODUCTION:An interrupted time series (ITS) design is an important observational design used to examine the effects of an intervention or exposure. This design has particular utility in public health where it may be impracticable or infeasible to use a randomised trial to evaluate health system-wide policies, or examine the impact of exposures (such as earthquakes). There have been relatively few studies examining the design characteristics and statistical methods used to analyse ITS designs. Further, there is a lack of guidance to inform the design and analysis of ITS studies.This is the first study in a larger project that aims to provide tools and guidance for researchers in the design and analysis of ITS studies. The objectives of this study are to (1) examine and report the design characteristics and statistical methods used in a random sample of contemporary ITS studies examining public health interventions or exposures that impact on health-related outcomes, and (2) create a repository of time series data extracted from ITS studies. Results from this study will inform the remainder of the project which will investigate the performance of a range of commonly used statistical methods, and create a repository of input parameters required for sample size calculation. METHODS AND ANALYSIS:We will collate 200 ITS studies evaluating public health interventions or the impact of exposures. ITS studies will be identified from a search of the bibliometric database PubMed between the years 2013 and 2017, combined with stratified random sampling. From eligible studies, we will extract study characteristics, details of the statistical models and estimation methods, effect metrics and parameter estimates. Further, we will extract the time series data when available. We will use systematic review methods in the screening, application of inclusion and exclusion criteria, and extraction of data. Descriptive statistics will be used to summarise the data. ETHICS AND DISSEMINATION:Ethics approval is not required since information will only be extracted from published studies. Dissemination of the results will be through peer-reviewed publications and presentations at conferences. A repository of data extracted from the published ITS studies will be made publicly available.

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Evaluating Public Health Interventions: 6. Modeling Ratios or Differences? Let the Data Tell Us.

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Evaluating Public Health Interventions: 6. Modeling Ratios or Differences? Let the Data Tell Us.

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