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Modifiable reporting unit problems and time series of long-term human activity.

ABSTRACT: This paper responds to a resurgence of interest in constructing long-term time proxies of human activity, especially but not limited to models of population change over the Pleistocene and/or Holocene. While very much agreeing with the need for this increased attention, we emphasize three important issues that can all be thought of as modifiable reporting unit problems: the impact of (i) archaeological periodization, (ii) uneven event durations and (iii) geographical nucleation-dispersal phenomena. Drawing inspiration from real-world examples from prehistoric Britain, Greece and Japan, we explore their consequences and possible mitigation via a reproducible set of tactical simulations. This article is part of the theme issue 'Cross-disciplinary approaches to prehistoric demography'.

SUBMITTER: Bevan A

PROVIDER: S-EPMC7741099 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Modifiable reporting unit problems and time series of long-term human activity.

Bevan A A Crema E R ER

Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20201130 1816

This paper responds to a resurgence of interest in constructing long-term time proxies of human activity, especially but not limited to models of population change over the Pleistocene and/or Holocene. While very much agreeing with the need for this increased attention, we emphasize three important issues that can all be thought of as modifiable reporting unit problems: the impact of (i) archaeological periodization, (ii) uneven event durations and (iii) geographical nucleation-dispersal phenome ...[more]

PMID: 33250029

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Project description:BackgroundThe importance of reporting research evidence to stakeholders in ways that balance complexity and usability is well-documented. However, guidance for how to accomplish this is less clear. We describe a method of developing and visualising dimension-specific scores for organisational context (context rank method). We explore perspectives of leaders in long-term care nursing homes (NHs) on two methods for reporting organisational context data: context rank method and our traditionally presented binary method-more/less favourable context.MethodsWe used a multimethod design. First, we used survey data from 4065 healthcare aides on 290 care units from 91 NHs to calculate quartiles for each of the 10 Alberta Context Tool (ACT) dimension scores, aggregated at the care unit level based on the overall sample distribution of these scores. This ordinal variable was then summed across ACT scores. Context rank scores were assessed for associations with outcomes for NH staff and for quality of care (healthcare aides' instrumental and conceptual research use, job satisfaction, rushed care, care left undone) using regression analyses. Second, we used a qualitative descriptive approach to elicit NH leaders' perspectives on whether the methods were understandable, meaningful, relevant, and useful. With 16 leaders, we conducted focus groups between December 2017 and June 2018: one in Nova Scotia, one in Prince Edward Island, and one in Ontario, Canada. Data were analysed using content analysis.ResultsComposite scores generated using the context rank method had positive associations with healthcare aides' instrumental research use (p < .0067) and conceptual research use and job satisfaction (p < .0001). Associations were negative between context rank summary scores and rushed care and care left undone (p < .0001). Overall, leaders indicated that data presented by both methods had value. They liked the binary method as a starting point but appreciated the greater level of detail in the context rank method.ConclusionsWe recommend careful selection of either the binary or context rank method based on purpose and audience. If a simple, high-level overview is the goal, the binary method has value. If improvement is the goal, the context rank method will give leaders more actionable details.

Project description:BackgroundTime-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts.MethodsWe discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002-2013, and time-series analysis using real data for seven major South Korean cities, 2006-2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods.ResultsSimulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency.ConclusionsOur findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.

Project description:Reversing ecological degradation through restoration activities is a key societal challenge of the upcoming decade. However, lack of evidence on the effectiveness of restoration interventions leads to inconsistent, delayed, or poorly informed statements of success, hindering the wise allocation of resources, representing a missed opportunity to learn from previous experiences. This study contributes to a better understanding of spatial and temporal dynamics of ecosystem services at ecological restoration sites. We developed a method using Landsat satellite images combined with a Before-After-Control-Impact (BACI) design, and applied this to an arid rural landscape, the Baviaanskloof in South Africa. Since 1990, various restoration projects have been implemented to halt and reverse degradation. We applied the BACI approach at pixel-level comparing the conditions of each intervened pixel (impact) with 20 similar control pixels. By evaluating the conditions before and after the restoration intervention, we assessed the effectiveness of long-term restoration interventions distinguishing their impact from environmental temporal changes. The BACI approach was implemented with Landsat images that cover a 30-year period at a spatial resolution of 30 meter. We evaluated the impact of three interventions (revegetation, livestock exclusion, and the combination of both) on three ecosystem services; forage provision, erosion prevention, and presence of iconic vegetation. We also evaluated whether terrain characteristics could partially explain the variation in impact of interventions. The resulting maps showed spatial patterns of positive and negative effects of interventions on ecosystem services. Intervention effectiveness differed across vegetation conditions, terrain aspect, and soil parent material. Our method allows for spatially explicit quantification of the long-term restoration impact on ecosystem service supply, and for the detailed visualization of impact across an area. This pixel-level analysis is specifically suited for heterogeneous landscapes, where restoration impact not only varies between but also within restoration sites.

Project description:BackgroundMacrolides are recommended as an adjunctive treatment for patients with moderate to severe chronic obstructive pulmonary disease (COPD) who experience recurrent exacerbations. The objective of this study was to examine temporal trends in the provision of long-term macrolide therapy, specifically before and after publication of the landmark MACRO trial in August 2011 showing efficacy of macrolides for this indication.MethodsWe performed an interrupted time series analysis using population-level health administrative data. The study cohort consisted of all Ontario residents who had COPD, were using at least 1 long-acting inhaler, and were aged 65 years and older between Apr. 1, 2004, and Mar. 31, 2018. We compared the baseline characteristics of eligible patients before and after publication of the MACRO trial. Our primary outcome was overall prevalence of long-term macrolide therapy; secondary outcomes were incidence of COPD-related hospitalizations, emergency department visits and outpatient exacerbations requiring high-dose steroids in each quarter. We performed an interrupted time series analysis to assess for changes in the incidence of macrolide prophylaxis by quarter-year over the study period.ResultsThe rate of long-term macrolide use increased from 0.8 per 1000 people in 2004 to 13.8 per 1000 people in 2018 (in the severe COPD group, the rate increased from 1.3 to 32.3 per 1000 people). The interrupted time series analysis showed that, before 2011, the prevalence of macrolide prophylaxis increased at a rate of 0.44 (95% confidence interval [CI] 0.39-0.50) per 1000 people per year; after 2011, the rate of increase grew by 1.18 (95% CI 1.07-1.29) per 1000 people to 1.63 (95% CI 1.56-1.69) per 1000 people per year. The seasonal pattern of COPD-related health care visits remained stable over the study period, and there was no detectable reduction in hospitalizations or emergency department visits at the population level.InterpretationIn the past decade, there has been a significant rise in the use of long-term macrolide therapy for patients with COPD. As this practice becomes increasingly common, it will be important to monitor its potential benefits on COPD exacerbations but also its potential effects on adverse events and antimicrobial resistance patterns.

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Modifiable reporting unit problems and time series of long-term human activity.

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Modifiable reporting unit problems and time series of long-term human activity.

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