Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study

The effect of a minimum price per unit of alcohol in Scotland on alcohol-related ambulance call-outs: A controlled interrupted time-series analysis

BACKGROUND AND AIMS

On 1 May 2018, Scotland introduced a minimum unit price (MUP) of £0.50 for alcohol, with one UK unit of alcohol being 10 ml of pure ethanol. This study measured the association between MUP and changes in the volume of alcohol-related ambulance call-outs in the overall population and in call-outs subsets (night-time call-outs and subpopulations with higher incidence of alcohol-related harm).

DESIGN

An interrupted time-series (ITS) was used to measure variations in the daily volume of alcohol-related call-outs. We performed uncontrolled ITS on both the intervention and control group and a controlled ITS built on the difference between the two series. Data were from electronic patient clinical records from the Scottish Ambulance Service.

SETTING AND CASES

Alcohol-related ambulance call-outs (intervention group) and total ambulance call-outs for people aged under 13 years (control group) in Scotland, from December 2017 to March 2020.

MEASUREMENTS

Call-outs were deemed alcohol-related if ambulance clinicians indicated that alcohol was a 'contributing factor' in the call-out and/or a validated Scottish Ambulance Service algorithm determined that the call-out was alcohol-related.

FINDINGS

No statistically significant association in the volume of call-outs was found in both the uncontrolled series [step change = 0.062, 95% confidence interval (CI) = -0.012, 0.0135 P = 0.091; slope change = -0.001, 95% CI = -0.001, 0.1 × 10-3 P = 0.139] and controlled series (step change = -0.01, 95% CI = -0.317, 0.298 P = 0.951; slope change = -0.003, 95% CI = -0.008, 0.002 P = 0.257). Similarly, no significant changes were found for the night-time series or for any population subgroups.

CONCLUSIONS

There appears to be no statistically significant association between the introduction of minimum unit pricing for alcohol in Scotland and the volume of alcohol-related ambulance call-outs. This was observed overall, across subpopulations and at night-time.

Association between High Ambient Temperatures and Road Crashes in an Australian City with Temperate Climate: A Time-Series Study, 2012-2021

(1) Background: High ambient temperatures are associated with increased morbidity and mortality rates, and some evidence suggests that high temperatures increase the risk of road crashes. However, little is known regarding the burden of road crashes attributable to no-optimal high temperatures in Australia. Therefore, this study examined the effects of high temperatures on road crashes using Adelaide in South Australia as a case study. (2) Methods: Ten-year daily time-series data on road crashes (n = 64,597) and weather during the warm season (October-March) were obtained between 2012 and 2021. A quasi-Poisson distributed lag nonlinear model (DLNM) was used to quantify the cumulative effect of high temperatures over the previous five days. The associations and attributable burden at moderate and extreme temperature ranges were computed as relative risk (RR) and attributable fraction. (3) Results: There was a J-shaped association between high ambient temperature and the risk of road crashes during the warm season in Adelaide, and pronounced effects were observed for minimum temperatures. The highest risk was observed at a 1 day lag and lasting for 5 days. High temperatures were responsible for 0.79% (95% CI: 0.15-1.33%) of road crashes, with moderately high temperatures accounting for most of the burden compared with extreme temperatures (0.55% vs. 0.32%). (4) Conclusions: In the face of a warming climate, the finding draws the attention of road transport, policy, and public health planners to design preventive plans to reduce the risk of road crashes attributable to high temperatures.

Comparison of various heat waves definitions and the burden of heat-related mortality in France: Implications for existing early warning systems

INTRODUCTION

In France, a heat warning system (HWS) has been implemented almost two decades ago and rely on some official heat wave (HW) definitions. However, no study has compared the burden associated with a large set of alternative HW definitions to the official definitions. Such comparison could be particularly helpful to identify HW conditions for which effective HWS would minimize the health burden across various geographical contexts and possibly update thresholds to trigger HWS. The aim of this study is to identify (and rank) definitions that drive the highest health burden in terms of mortality to inform future HWS across multiple cities in France.

METHODS

Based on weather data for 16 French cities, we compared the two official definitions used in France to: i) the Excess Heat Factor (EHF) used in Australia, and ii) 18 alternative hypothetical HW definitions based on various combinations of temperature metrics, intensity, and duration. Propensity score matching and Poisson regressions were used to estimate the effect of each HW exposure on non-accidental mortality for the May-September period from 2000 to 2015.

RESULTS

The associations between HW and mortality differed greatly depending on the definition. The greatest burden of heat was 1,055 (95% confidence interval "CI": [856; 1,302]) deaths per summer and was obtained with the EHF. The EHF identified HW with 2.46 (95% CI: [1.92; 3.58]) or 8.18 (95% CI: [6.63; 10.61]) times the global burden at the national level obtained with the climatological indicator of the French national weather service and the HW indicator of the French national HWS, respectively and was the most impactful definition pattern for both temperate oceanic and Mediterranean climate types.

CONCLUSION

Identifying the set of extreme heat conditions that drive the highest health burden in a given geographical context is particularly helpful when designing or updating heat early warning systems.

Alternative adjustment for seasonality and long-term time-trend in time-series analysis for long-term environmental exposures and disease counts

Background

Time-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.

Methods

We 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.

Results

Simulation 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.

Conclusions

Our 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-020-01199-1.

The short-term effects of air pollution on respiratory disease hospitalizations in 5 cities in Poland: comparison of time-series and case-crossover analyses

Very few publications have compared different study designs investigating the short-term effects of air pollutants on healthcare visits and hospitalizations for respiratory tract diseases. This study describes, using two different study designs (a case-crossover design and a time-series analysis), the association of air pollutants and respiratory disease hospitalizations. The study has been conducted on 5 cities in Poland on a timeline of almost 4 years. DLNM and regression models were both used for the assessment of the short-term effects of air pollution peaks on respiratory hospitalizations. Both case-crossover and time-series studies equally revealed a positive association between air pollution peaks and hospitalization occurrences. Results were provided in the form of percentage increase of a respiratory visit/hospitalization, for each 10-μg/m3 increment in single pollutant level for both study designs. The most significant estimated % increases of hospitalizations linked to increase of 10 μg/m3 of pollutant have been recorded in general with particulate matter, with highest values for 24 h PM2.5 in Warsaw (6.4%, case-crossover; 4.5%, time series, respectively) and in Białystok (5.6%, case-crossover; 4.5%, time series, respectively). The case-crossover analysis results have shown a larger CI in comparison to the results of the time-series analysis, while the lag days were easier to identify with the case-crossover design. The trends and the overlap of the results occurring from both methods are good and show applicability of both study designs to air pollution effects on short-term hospitalizations.

Understanding and using time series analyses in addiction research

Time series analyses are statistical methods used to assess trends in repeated measurements taken at regular intervals and their associations with other trends or events, taking account of the temporal structure of such data. Addiction research often involves assessing associations between trends in target variables (e.g. population cigarette smoking prevalence) and predictor variables (e.g. average price of a cigarette), known as a multiple time series design, or interventions or events (e.g. introduction of an indoor smoking ban), known as an interrupted time series design. There are many analytical tools available, each with its own strengths and limitations. This paper provides addiction researchers with an overview of many of the methods available (GLM, GLMM, GLS, GAMM, ARIMA, ARIMAX, VAR, SVAR, VECM) and guidance on when and how they should be used, sample size det ermination, reporting and interpretation. The aim is to provide increased clarity for researchers proposing to undertake these analyses concerning what is likely to be acceptable for publication in journals such as Addiction. Given the large number of choices that need to be made when setting up time series models, the guidance emphasizes the importance of pre-registering hypotheses and analysis plans before the analyses are undertaken.

Using rush hour and daytime exposure indicators to estimate the short-term mortality effects of air pollution: A case study in the Sichuan Basin, China

BACKGROUND

Daily mean concentrations of air pollutants have been widely used as exposure indicators to estimate the short-term mortality effects of outdoor air pollution. However, daily mean concentrations might insufficiently represent the true exposure level because of the diurnal variations of air pollutants and various human activity patterns. Daytime or rush-hour concentrations may lead to better estimations.

OBJECTIVE

Our study aimed to imitate the true exposure level under assumptions about human activity patterns and to examine the short-term mortality effects of the exposure to air pollution during a) the morning-evening rush hours (ME), b) the morning-lunch-evening rush hours (MLE), and c) the whole daytime (WDT) in Chengdu, Sichuan Basin, China.

METHODS

We investigated the diurnal variations of PM_2.5, SO₂, and O₃ and examined the associations between the three pollutants and nonaccidental mortality, cardiovascular mortality, respiratory mortality using generalized additive model. Three novel exposure indicators (ME, MLE, and WDT) were employed to imitate the most probable exposure levels. Relative change of excess risk (ER) was used to compare effects estimated from models with different exposure indicators.

RESULTS

In the relationship of PM_2.5 and mortality, ERs estimated from the novel-indicator models decreased by 4.88%-11.89% in comparison with ERs from the daily-indicator models. All the three novel indicators of SO₂ offered lower ERs of respiratory mortality than the daily indicator did. Significant associations were observed in O₃-nonaccidental mortality at lag0 in both winter and spring, and O₃-cardiovascular mortality at lag0 in winter. Overall, majority of effect estimates based on rush-hour or daytime indicators were lower than the estimates based on daily mean concentrations.

CONCLUSION

The use of daily mean concentrations may bias exposure assessment and thus inflating effect estimates. This study highlights the importance of rush-hour and daytime exposure and provides alternative indicators for estimating acute effects of air pollution.

Assessing the cumulative health effect following short term exposure to multiple pollutants: An evaluation of methodological approaches using simulations and real data

BACKGROUND

Assessment of the cumulative effect of correlated exposures is an open methodological issue in environmental epidemiology. Most previous studies have applied regression models with interaction terms or dimension reduction methods. The combined effect of pollutants has been also evaluated through the use of exposure scores that incorporate weights based on the strength of the component-specific associations with health outcomes.

METHODS

We compared three approaches addressing multi-pollutant exposures in epidemiological models: main effects models, the adaptive least absolute shrinkage and selection operator (LASSO) and a weighted exposure score. We assessed the performance of the methods by simulations under various scenarios for the pollutants' correlations. We further applied these methods to time series data from Athens, Greece in 2007-12 to investigate the combined effect of short-term exposure to six regulated pollutants on all-cause and respiratory mortality.

RESULTS

The exposure score provided the least biased estimate under all correlation scenarios for both mortality outcomes. The adaptive LASSO performed well in the case of low and medium correlation between exposures while the main effect model resulted in severe bias. In the real data application, the cumulative effect estimate was similar between approaches for all-cause mortality ranging from 0.7% increase per interquartile range (IQR) (score) to 1.1% (main effects), while for respiratory mortality conclusions were contradictive and ranged from - 0.6% (adaptive LASSO) to 2.8% (score).

CONCLUSIONS

Τhe use of a weighted exposure score to address cumulative effects of correlated metrics may perform well under different exposure correlation and variability in the health outcomes.

A Systematic Review of the Time Series Studies Addressing the Endemic Risk of Acute Gastroenteritis According to Drinking Water Operation Conditions in Urban Areas of Developed Countries

Time series studies (TSS) can be viewed as an inexpensive way to tackle the non-epidemic health risk from fecal pathogens in tap water in urban areas. Following the PRISMA recommendations, I reviewed TSS addressing the endemic risk of acute gastroenteritis risk according to drinking water operation conditions in urban areas of developed countries. Eighteen studies were included, covering 17 urban sites (seven in North-America and 10 in Europe) with study populations ranging from 50,000 to 9 million people. Most studies used general practitioner consultations or visits to hospitals for acute gastroenteritis (AGE) as health outcomes. In 11 of the 17 sites, a significant and plausible association was found between turbidity (or particle count) in finished water and the AGE indicator. When provided and significant, the interquartile excess of relative risk estimates ranged from 3–13%. When examined, water temperature, river flow, and produced flow were strongly associated with the AGE indicator. The potential of TSS for the study of the health risk from fecal pathogens in tap water is limited by the lack of specificity of turbidity and its site-sensitive value as an exposure proxy. Nevertheless, at the DWS level, TSS could help water operators to identify operational conditions most at risk, almost if considering other water operation indicators, in addition to turbidity, as possible relevant proxies for exposure.

Short-Term Associations between Air Pollution Concentrations and Respiratory Health-Comparing Primary Health Care Visits, Hospital Admissions, and Emergency Department Visits in a Multi-Municipality Study

Acute effects of air pollution on respiratory health have traditionally been investigated with data on inpatient admissions, emergency room visits, and mortality. In this study, we aim to describe the total acute effects of air pollution on health care use for respiratory symptoms (ICD10-J00-J99). This will be done by investigating primary health care (PHC) visits, inpatient admissions, and emergency room visits together in five municipalities in southern Sweden, using a case-crossover design. Between 2005 and 2010, there were 81,019 visits to primary health care, 38,217 emergency room visits, and 25,271 inpatient admissions for respiratory symptoms in the study area. There was a 1.85% increase (95% CI: 0.52 to 3.20) in the number of primary health care visits associated with a 10 µg/m³ increase in nitrogen dioxide (NO₂) levels in Malmö, but not in the other municipalities. Air pollution levels were generally not associated with emergency room visits or inpatient admissions, with one exception (in Helsingborg there was a 2.52% increase in emergency room visits for respiratory symptoms associated with a 10 µg/m³ increase in PM₁₀). In conclusion, the results give weak support for short-term effects of air pollution on health care use associated with respiratory health symptoms in the study area.

The effects of air pollution on asthma hospital admissions in Adelaide, South Australia, 2003-2013: time-series and case-crossover analyses

BACKGROUND

Air pollution can have adverse health effects on asthma sufferers, but the effects vary with geographic, environmental and population characteristics. There has been no long time-series study in Australia to quantify the effects of environmental factors including pollen on asthma hospitalizations.

OBJECTIVES

This study aimed to assess the seasonal impact of air pollutants and aeroallergens on the risk of asthma hospital admissions for adults and children in Adelaide, South Australia.

METHODS

Data on hospital admissions, meteorological conditions, air quality and pollen counts for the period 2003-2013 were sourced. Time-series analysis and case-crossover analysis were used to assess the short-term effects of air pollution on asthma hospitalizations. For the time-series analysis, generalized log-linear quasi-Poisson and negative binomial regressions were used to assess the relationships, controlling for seasonality and long-term trends using flexible spline functions. For the case-crossover analysis, conditional logistic regression was used to compute the effect estimates with time-stratified referent selection strategies.

RESULTS

A total of 36,024 asthma admissions were considered. Findings indicated that the largest effects on asthma admissions related to PM_2.5 , NO₂ , PM₁₀ and pollen were found in the cool season for children (0-17 years), with the 5-day cumulative effects of 30.2% (95% CI: 13.4-49.6%), 12.5% (95% CI: 6.6-18.7%), 8.3% (95% CI: 2.5-14.4%) and 4.2% (95% CI: 2.2-6.1%) increases in risk of asthma hospital admissions per 10 unit increments, respectively. The largest effect for ozone was found in the warm season for children with the 5-day cumulative effect of an 11.7% (95% CI: 5.8-17.9%) increase in risk of asthma hospital admissions per 10 ppb increment in ozone level.

CONCLUSION

Findings suggest that children are more vulnerable and the associations between exposure to air pollutants and asthma hospitalizations tended to be stronger in the cool season compared to the warm season, with the exception of ozone. This study has important public health implications and provides valuable evidence for the development of policies for asthma management.