Avoiding bias from aggregate measures of exposure

Long-term variation in exposure to NO2 concentrations in the city of Naples, Italy: Results of a citizen science project

The objective of this study is to evaluate long-term changes in the level of exposure to NO2 among the population living in the urban area of Naples (south Italy). This has been achieved by integrating data from the regional reference monitoring network with information collected during the citizen science initiative called 'NO2, NO grazie!' conducted in February 2020 and coordinated by the Non-Governmental Organisation 'Cittadini per l'aria'. This citizen science campaign was based on low-cost passive samplers (Palmes tubes), providing the ability to obtain unprecedented high-resolution NO2 levels. Using a Land Use Random Forest (LURF), we extrapolated the experimental data obtained from the citizen science campaign and evaluated the changes in population exposure from 2013 to 2022 and the uncertainty associated with this assessment was quantified. The results indicate that a large proportion of the inhabitants of Naples are still exposed to high NO2 concentrations, even if strict emission containment measures are enforced. The average levels remain higher than the new interim and air quality targets suggested by the World Health Organisation. The implementation of co-created citizen science projects, where NGO and citizens actively participate alongside scientists, can significantly improve the estimation and the interpretation of official reference data.

A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

BACKGROUND

Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities.

METHODS

NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose-response function on the counterfactual concentrations of 10 μg/m3.

RESULTS

The Land Use Random Forest models were able to capture 41-42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604.

CONCLUSIONS

Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

Overdiagnosis in mammographic screening for breast cancer in Europe: a literature review

OBJECTIVES

Overdiagnosis, the detection through screening of a breast cancer that would never have been identified in the lifetime of the woman, is an adverse outcome of screening. We aimed to determine an estimate range for overdiagnosis of breast cancer in European mammographic service screening programmes.

METHODS

We conducted a literature review of observational studies that provided estimates of breast cancer overdiagnosis in European population-based mammographic screening programmes. Studies were classified according to the presence and the type of adjustment for breast cancer risk (data, model and covariates used), and for lead time (statistical adjustment or compensatory drop). We expressed estimates of overdiagnosis from each study as a percentage of the expected incidence in the absence of screening, even if the variability in the age range of the denominator could not be removed. Estimates including carcinoma in situ were considered when available.

RESULTS

There were 13 primary studies reporting 16 estimates of overdiagnosis in seven European countries (the Netherlands, Italy, Norway, Sweden, Denmark, UK and Spain). Unadjusted estimates ranged from 0% to 54%. Reported estimates adjusted for breast cancer risk and lead time were 2.8% in the Netherlands, 4.6% and 1.0% in Italy, 7.0% in Denmark and 10% and 3.3% in England and Wales.

CONCLUSIONS

The most plausible estimates of overdiagnosis range from 1% to 10%. Substantially higher estimates of overdiagnosis reported in the literature are due to the lack of adjustment for breast cancer risk and/or lead time.

Overdiagnosis in breast cancer: design and methods of estimation in observational studies

OBJECTIVES

In recent years observational epidemiological studies have been used to estimate overdiagnosis in breast cancer screening. These estimates vary widely. In this paper we present some of the methodological issues which explain the large variability of the reported findings.

METHODS

Different types of observational studies were identified according to study design, definition of the population, adjustment for breast cancer risk and adjustment for lead time.

RESULTS

The majority of observational studies that have estimated breast cancer overdiagnosis have analyzed temporal trends or geographical differences in breast cancer incidence. Estimates of overdiagnosis in a dynamic population vary widely, from 4% to 52%. Only a few studies have used the cohort approach and they found estimates varying from 1% to 5%.

CONCLUSIONS

The cohort approach is preferable to the analysis of a dynamic population because it allows the follow-up of a group of women who have had the opportunity for screening and evaluates if there is sufficient follow-up after the last screen.

Complexities in the estimation of overdiagnosis in breast cancer screening

There is interest in estimating and attributing temporal changes in incidence of breast cancer in relation to the initiation of screening programmes, in particular to estimation of overdiagnosis of breast cancer as a result of screening. In this paper, we show how screening introduces complexities of analysis and interpretation of incidence data. For example, lead time brings forward time- and age-related increases in incidence. In addition, risk factors such as hormone replacement therapy use have been changing contemporaneously with the introduction of screening. Although we do not indicate exactly how such complexities should be corrected for, we use some simple informal adjustments to show how they may account for a substantial proportion of increased incidence, which might otherwise erroneously have been attributed to overdiagnosis. We illustrate this using an example of analysis of breast cancer incidence data from Sweden.