The first point made by Roussel states that it is ‘premature to imply that eDNA will be an efficient tool for detecting all aquatic animal species’.

We agree with this statement and also that from Ficetola et al. ( 2008) who stated that eDNA is ‘useful for studying secretive aquatic or semi-aquatic species, which release DNA into the environment through mucus, faeces, urine and remains’.

In our review, we do not imply that eDNA can be used for ‘all’ aquatic species, but we merely point out (i) that eDNA analysis has been used as an accurate indicator of species presence in a range of environments; (ii) that techniques such as eDNA analysis are of great interest for the management and conservation of vulnerable species if traditional survey methods pose a threat to these animals; and (iii) that eDNA is particularly useful for those species that are difficult to detect using conventional methods.

We go on to state that the success rate of eDNA analysis is not always 100%, illustrating that it is not always an accurate tool for the detection of aquatic animal species.

Roussel chose Treguier et al. 2014, which was not available at the time of writing Rees et al. ( 2014b), as an example of eDNA being an inefficient tool on its own for the detection of the red swamp crayfish Procambarus clarkia.

They showed that when crayfish abundance was estimated to be low by conventional trapping, eDNA detection efficiency was poor, illustrating a limitation of eDNA detection for species–habitat combination.

However, Roussel did not mention that this same study found that eDNA had a better overall detection efficiency than conventional trapping (73% vs. 65%, respectively).

Experimental evidence suggested that false positives and/or false detections were unlikely in this case, so would not account for the higher detection efficiency.

This result suggests that in this case, eDNA was more accurate than a traditional survey.

A further example of an invertebrate that has been successfully detected using eDNA analysis is the tadpole shrimp Lepidurus apus (Thomsen et al. 2012).

This was detected with a 100% success rate in 10 ponds known to contain the species.

Both studies illustrate that detection of invertebrates is possible, and with continuing developments in PCR technologies and sampling methods, the detection efficiency of invertebrates could be improved.

In Rees et al. ( 2014b), we stated that ‘the probability of detection will vary for the chosen target species and will be dependent on the taxa, the density of organisms, and also the type of water body.’ Therefore, we completely agree with Roussel that when studying a new target species, preliminary tests to estimate the detectability of the target DNA under a range of semi-natural conditions should be a prerequisite for surveying.

A recent study which aimed to quantify the effects of UV-B, temperature and pH on eDNA degradation in aquatic microcosms (Strickler, Fremier & Goldberg 2015) may provide useful information for these preliminary tests.

The study found that aquatic environments which are ‘colder, more protected from solar radiation, and more alkaline are likely to hold detectable amounts of eDNA longer than those that are warmer, sunnier, and neutral or acidic’.

Another recent study which compared eDNA capture and extraction methods on both lake and river water (Deiner et al. 2015) highlights that the choice of molecular protocols used can strongly affect biodiversity detection.

The study showed that the species of interest could be detected but that detection rates were dependant on the protocols used.

Additionally, the capture and extraction protocols affected the total amount of DNA recovered from each sample.

As such, another preliminary test to estimate the detectability of the target DNA could be to find the best combination of capture and extraction for the species of interest (Deiner et al. 2015 recommends certain protocols for certain situations).

It is also worth noting here that a further paper (Takahara, Minamoto & Doi 2015) highlighted that freeze–thawing of water samples reduced the detection of common carp Cyprinus carpio and that there were changes in common carp eDNA concentration in freeze–thawed vs. non-frozen samples.

This study suggests that this methodology should be avoided.

Additionally, we would go further and suggest that any new method for eDNA detection should be compared directly with the established ‘gold standard’ method for the detection of the species or habitat under investigation.

As mentioned in the original review, given the developmental stage of applying eDNA to ecological surveys of aquatic animals, it is vital that new emerging methodologies are corroborated by the established survey methods for a particular species or habitat type.

This in turn will enable a robust comparison and assessment of new eDNA survey methods.


The second point made by Roussel is again correct in that it is often perceived that eDNA analysis is more accurate than traditional surveys.

As we stated in Rees et al. ( 2014b), eDNA has ‘the potential of greater sensitivity over traditional survey methods’; we do not state that it is more accurate, although eDNA analysis has been shown to be more accurate in a few studies (Dejean et al. 2012; Takahara, Minamoto & Doi 2013; Treguier et al. 2014).

Our review points out that the eDNA success rate was not always 100% when the species was known to be present (Thomsen et al. 2012), illustrating that it is not always more accurate.

Roussel is again correct to state that the abundance of the species in question is not always stated within the literature and that it is therefore ‘difficult to understand how the method might perform across a gradient of target species densities’, an important point which does add to our review.

We agree that each survey method is imperfect and we devoted a whole subsection in Rees et al. ( 2014b) to comparing the use of eDNA analysis vs. traditional survey methods.

We agree that there should be more comparisons between eDNA and traditional census methods, which is what we did in a recent study on great crested newts Triturus cristatus, in the UK (Rees et al. 2014a).

Likewise, we agree that it would be very useful for method comparison if species abundances were included in these studies, and feel that this is an important point.


The third point made by Roussel concentrates on the reporting of experimental details, and specifically false positives.

We agree with the comments made on reporting of experimental details.

The point should be made here that consideration of guidelines for Minimum Information for Publication of Quantitative Real-Time PCR Experiments (Bustin et al. 2009) should be done before publication of any study involving real-time PCR, a point which both our Rees et al. ( 2014b) review and Roussel neglected to make.

The background to these guidelines is that there is very little consensus on ‘how best to perform and interpret real time PCR (qPCR) experiments’ and that this problem is exacerbated by the fact that little experimental information is included in published studies.

These guidelines encourage the inclusion of reaction conditions, limits of detection, and many other items that should be reported to allow for useful comparison or replication of experiments (provided in the form of a useful checklist).

Also encouraged is the correct use of nomenclature that can often be confused in publications.

In terms of false positives, Roussel makes the point that we also made: false positives occur when DNA is detected, but DNA is not present in the sample (Rees et al. 2014b; Roussel et al. 2015).

Roussel is correct that the term ‘false positive’ should be carefully and unambiguously defined.

As was stated in the review, when there are positive PCR signals, you need to consider the possibility that ‘there is target DNA in the sample but no viable/live organisms in the system’.

During the peer review process of our paper (Rees et al. 2014b), we were asked to be careful about the use of the wording ‘false positive’.

We therefore suggested that a ‘false positive’ could also be termed a ‘false detection’.

Perhaps our definitions should have been made more clearly to state that a ‘false positive’ occurs when there is no target DNA in the sample and a ‘false detection’ occurs when there is target DNA in the sample but no viable/live organisms in the system.

It will be important for future publications to stipulate whether one is referring to a ‘false positive’ or a ‘false detection’.

Roussel goes on to state that ‘false detections’ need more attention if we want to understand what an eDNA detection represents.

We would like to emphasize the point that in some species that are transiently present in water (for example, move between ponds), an eDNA detection would still be valid whether the species was present or not at the time of sampling, defining species distributions.

Of course, as suggested by Roussel, consideration should be made to the fact that DNA in a water sample could originate from predator faeces or dead animals which we also stated in Rees et al. ( 2014b).


The fifth point made by Roussel is discussed here as it also relates back to false detection.

This point deals with the application of eDNA detection to running water, an area in which there have been few studies (Roussel indicates nine papers have been published on this subject).

Roussel is correct that the implications of discharge variation and dendritic organization will need to be considered when studying these environments.

We chose to discuss the sampling regimes used so far and concluded that ‘methods for sample collection will vary’ depending on the environment that is to be studied.

We did not discuss the issue of downstream transportation of DNA as there was little information on this within the published studies.

However, a recent paper on giant salamanders in Japan (Fukumoto, Ushimaru & Minamoto 2015) discussed the possibility of false positives in the lower reaches of the river systems under investigation.

The authors hope that in the future by ‘combining data on the water discharge, the eDNA concentration in the water, the eDNA release rate from an individual, and the eDNA degradation rate’, they will be able to distinguish between DNA from an upstream area and DNA from near the sampling site.

We agree with Roussel that care should be taken to avoid the inclusion of sedimentary material when taking water samples.

Sediment could contain eDNA from past site occupancy rather than recent (dependent on eDNA degradation rates) or present occupancy.

However, it should be noted that a recent study of aqueous and sedimentary eDNA from an invasive fish, big-headed Asian carp Hypophthalmichthys spp. ( Turner, Uy & Everhart 2015), suggested that sedimentary eDNA was a slightly more sensitive detector of site occupancy than eDNA derived from water samples.

The authors suggest that the prolonged retention of eDNA seen within sediments (up to 132 days after removal of carp) could be due to particle settling and/or retarded degradation of sediment-adsorbed DNA molecules.

The authors went on to suggest that the use of sediment-derived eDNA could be a better method for inferring current or past site occupancy by aquatic macrofauna, especially benthic species.

This study illustrates the importance of study design in relation to the species of interest, a point made in the original review paper.

A further point on the presence of sediment in water samples is that in our experience, sediment can act to inhibit the PCRs.

Inhibition controls can be used to monitor for inhibitors which may be present in a sample (Bustin et al. 2009; Biggs et al. 2015; Turner, Uy & Everhart 2015).

The inclusion of an inhibition control is now carried out as standard in our laboratories.


The fourth point made by Roussel relates to sampling design.

We agree that rigorous sampling design is important and site occupancy models can be used (for example, the program PRESENCE version 6.4 available from http://www.mbr-pwrc.usgs.gov/software/presence.html (MacKenzie et al. 2002)).

Roussel stated that we failed to recognize that there are two locations where imperfect detection can occur: the field and the laboratory.

We have a large section devoted to sampling in which we discuss the numerous different methods employed for sampling in the literature.

We discussed how groups have improved the sampling coverage of the water systems and noted that methods for sample collection will vary depending on the type of water body.

Additionally, we recognized that the probability of detection will depend on several factors including the taxa, the density of organisms and the type of water body.

We also note that there is considerable variation in the processing of the water samples prior to the extraction step for which we state that ‘a direct comparison of these methods to monitor the effects of different processing methods on eDNA extraction and detection would be useful’.

This implies that there are sources of imperfect detection at the field level, although we may not have stated so in so many words.

The citations in Roussel (Schmidt et al. 2013; Ficetola et al. 2014) which illustrate this imperfect detection are useful and should be considered when designing sampling regimes.

We note that many of the points raised were already covered in our original review paper although there are some additional valid considerations, which are discussed above.

Additionally, we do not believe that we neglected to discuss the potential for errors in the field sampling although we did not include discussion on the use of modelling during the study design phase, which was an important omission.

We feel that our original conclusions are valid.

eDNA analysis is fast becoming an important tool in the study of aquatic species, but there does not appear to be ‘a single study which could be used as a template for standardization of this technique’.

This is due to the very different natures of the environments being studied, the taxa, the density of organisms, and the disparity in the way samples are scored as positive.

Despite this, the technique has been used as an ‘accurate indicator of the presence of species in a range of aquatic environments’.

Alternative methods of PCR or novel methodologies could be used to provide rapid on-site detections, and next-generation sequencing of eDNA samples could become a powerful tool for monitoring species diversity.

In agreement with the conclusions of Roussel, we feel that eDNA analysis is a ‘promising method’ and that more investigations are required, and as with any method, users should be aware of potential weaknesses.

Roussel concluded that non-detection of target species DNA does not mean the absence of that species, nor does a positive detection mean that the species is present.

This is of course true for the reasons already discussed above.

However, it is also the case that traditional field survey methods are not always accurate when recording the absence of a particular species, particularly in cases where secretive or elusive species are being surveyed.

This points to the fact that neither field survey nor eDNA analysis is perfect, which supports a statement made in the original review and is our concluding remark here, that ‘eDNA should not be used to replace or disregard the knowledge and expertise of experienced field ecologists and taxon specialists, but should become an important tool to enhance limited conservation resources’.


An invited commentary on this article appears on page 919, and the authors' response appears on page 922.


The prevalence of cognitive impairment is rising in China.

In 2011, national figures estimated that around 9% of older persons in China had cognitive impairment (1).

The World Health Orga- nization projects that, by 2050, 10 million older Chinese will have dementia (2).

Cognitive impairment refers to reduction of ability in various tasks, such as memory, learning, concentration, language, and reasoning, that might affect a person's everyday life (3, 4).

People with mild cognitive impairment have a 10-fold increased risk of later developing greater levels of cognitive impairment common in Alzheimer's disease and other demen- tias (5).

Furthermore, the lifetime risk of cognitive impairment is disproportionately greater in women because they live longer and are more vulnerable to cognitive decline than men from a neurobiological standpoint (6, 7).


Recent evidence has demonstrated that exposure to second- hand smoke (SHS), also called passive smoking, is associated with greater risk of cognitive impairment in older adults (8-10).

SHS is the major source of indoor air pollution worldwide (11) and contains hundreds of carcinogenic chemicals.

The ingestion of carcinogens may lead to cognitive impairment by increasing oxidative stress (5, 12, 13).

Cross-sectional evidence has demon- strated that SHS exposure within the home is associated with a higher risk of Alzheimer's disease and other dementias (8-10, 14, 15).

Specifically, longer exposure to SHS has been reported to increase the risk of Alzheimer's disease and vascular demen- tia (9, 10, 16).


China is the world's largest producer and consumer of tobacco.

The prevalence of smoking in China is high, with 350 million active smokers and 740 million passive smokers in 2010 (17).

Chinese women are more likely to be passive smo- kers and are heavily exposed to SHS at home because many live in households with husbands or other family members who smoke cigarettes.

In 2010, nearly 50% of nonsmoking Chinese women were exposed to household SHS, and SHS exposure was even more common in rural China (18).

However, there is a paucity of research addressing the relationship between SHS The primary aim of this study was to address this gap by examining the association between household SHS exposure and changes in the cognitive function of older, married women over the course of 2 years, using longitudinal, national survey data from China.


The sec- ondary aim of the study was to examine whether the associa- tion between SHS exposure and cognitive function varies across different age cohorts.

Prior longitudinal research has suggested that cognitive decline is already evident in middle age (ages 45-49 years) (19), and as such, the inclusion of a middle-aged group in this study is a crucial addition to the literature.

Further, a descriptive study on the prevalence of age-associated memory impairment has found that the age-specific prevalence rate of age-associated memory impairment is highest in adults aged 60-64 years (45.7%) and lowest in those aged 75-78 years (20).

Also, studies of active smoking and cognitive impairment among the Chinese population suggest that older current smok- ers (with an average age of 63 years) were more likely to develop cognitive impairment compared with the oldest smok- ers or never-smokers in China (1, 21).

Thus, it is critical to investigate whether the probability of cognitive decline is also high among the older women who were exposed to SHS within similar age ranges, and to examine whether the risk of cogni- tive decline varied across different age cohorts ranging from mid-life to the oldest.


Comparing the influence of SHS expo- sure across these age groups could help to identify whether certain age groups are at higher risk of cognitive decline than others.

This study is vitally important to inform policy and pro- grams aimed at reducing household SHS exposure and ulti- mately cognitive impairment in China.


Data for this study were from 2 waves of the China Health and Retirement Longitudinal Study (CHARLS, 2011-2013), which included interviews with a nationally representative sam- ple of adults aged 45 years or older, as well as their spouses when possible.

The sample was obtained through multistage cluster sampling, with an overall response rate of 80.5% (nearly 64.15% in rural areas) (22, 23).

The national baseline survey of CHARLS was conducted between June 2011 and March 2012 and included 17,705 respondents from 10,257 households.

At baseline, there were 6,248 women who never smoked cigarettes and were aged 45 years or older, married, and living with spouses who had either smoked cigarettes in the past or smoked at the time of interview.

Of these, 5,119 respondents were inter- viewed again during the second wave of data collection in 2013.

Our final sample was composed of 2,037 respondents who had completed both surveys and provided complete data for all study variables.


CHARLS included items for cogni- tive function similar to those used in the Health and Retirement Study, which were components of the Telephone Interview of Cognitive Status battery (24).

The Telephone Interview of Cog- nitive Status is a telephone version of the Mini-Mental State Examination and assesses cognitive function (25).

An analysis by McArdle et al. ( 26) of the Health and Retirement Study data suggested 2 factors to adequately capture cognitive function: one factor related to episodic memory and a second factor related to other tasks of the Telephone Interview of Cognitive Status battery.

Based upon this suggestion and recommenda- tions from previous studies using the CHARLS data (27, 28), we constructed 2 measures of cognitive function.

In CHARLS, memory was assessed through an immediate word recall based on respondents' capacity to immediately repeat, in any order, 10 Chinese nouns just read to them, followed by a delayed- recall test of respondents' ability to repeat the same list of words 4 minutes later (24, 25).

A single score of memory was calcu- lated by averaging the immediate- and delayed-recall scores, and it ranged from 0 to 10.


In addition to the memory tests, CHARLS included some components of the mental status questions of the Telephone Interview of Cognitive Status battery.

Our second measure of cognitive function was based on these questions and included items on orientation, visuoconstruction, and numeric ability.

Orientation was assessed by asking respondents to name the cur- rent day's date (month, day, year) and season and to identify the correct day of the week.

Visuoconstruction was assessed by ask- ing respondents to accurately redraw a previously shown pic- ture.

Numeric ability was assessed through the serial sevens test, which asks respondents to subtract 7 from 100 (up to 5 times), and whether additional explanation or an aid such as a paper and pencil was needed to complete the task.

Scores on these items were aggregated into a single score that ranged from 0 to 11 and was labeled as mental status (28).

For both measures, higher scores indicate better cognitive function (27, 28).


Exposure to SHS among Chinese women was assessed through several survey items that asked husbands and wives about cigarette smoking at home.

Both husbands and wives were asked about the year they got mar- ried, whether they had ever smoked, if they were currently smoking cigarettes, and the age or year when they started and stopped smoking.

The length of SHS exposure was calculated to reflect the total number of years that never-smoking women lived with spouses who smoked cigarettes at home.


Given that cognitive function may vary across educational levels and across health and socioeconomic status, we included age in years, urban/rural residence, educa- tion (illiterate, primary education, or at least secondary educa- tion), household expenditures, and chronic diseases as control variables (27-30).


To measure financial resources, we calcu- lated the annual household expenditures.

The existing literature has shown that expenditures are a better measure of economic resources available to the family in developing countries than Downloaded from https://academic.oup.com/aje/article/187/5/911/4804402 by Institut Armand-Frappier user on 15 May 2024 is income (31).

The summed total of annual expenditures was log transformed.


Physical health conditions included hyperten- sion and diabetes, as well as whether participants were being treated for those conditions.


The measure of depressive symp- toms was based on responses to the 10-item version of the Cen- ter for Epidemiologic Studies Short Depression Scale.

Each of the items has 4 response options coded from 0 to 3.

The total score is the sum of points for all 10 items.

A dichotomous vari- able was created using a score of 10 or greater to indicate the presence of depressive symptoms (32).


To examine the association between SHS exposure and cog- nitive function, we used lagged dependent-variable regression models with ordinary least squares estimation.

The lagged dependent-variable model is superior for analyzing the effects of predictor variables on an outcome with 2-wave panel data while controlling for the influence of time-invariant variables (33, 34).

In our models, the length of SHS exposure was the predictor variable, and memory and mental status were 2 sepa- rate outcome variables with controls for the influence of age, education, household expenditures, urban/rural residence, and chronic diseases.

Prior to fitting the regression models, descrip- tive analyses were conducted to estimate the mean, frequency, and missing values of key study variables for the entire sample and for the different age groups.

All analyses and procedures were conducted in SAS, version 9.4 (SAS Institute, Inc., Cary, North Carolina).


Due to attrition from the baseline to the follow-up survey and missing data on study variables that did not occur at ran- dom, we used multiple imputation with multivariate normal distribution to replace those missing cases and adjust for this potential bias.

While 2-10 imputations are recommended (35), we chose to create 10 imputed data sets to maximize the adjust- ment for this potential bias.

The imputed data sets were analyzed separately, and the results were combined in a way that accounts for variation in the imputed values (36).


Table 1 provides a descriptive summary of the variables for all study participants and according to age group.

For the entire sample, women were 57 years old on average at baseline, and the majority completed primary education.

Nearly two-thirds of the respondents lived in rural areas, and the average duration that women were exposed to SHS was 30 years.

The group aged 65 years or older had the highest proportion of hypertension, dia- betes, and depressive symptoms, as well as the lowest education levels, compared with the other 2 groups.

The mean scores of memory and mental status significantly decreased across age groups.

In comparison with the group aged 45-54 years, the mean scores of memory and mental status among the group aged 55-64 years were lower.


Overall, changes in the scores of memory (from 3.91 to 3.87) and mental status (from 6.82 to 6.66) between the 2 waves were significant (for memory, t = 4.30, P < 0.001; for mental status, t = 4.92, P < 0.001).

Results from the regression models for the relationship between SHS exposure and each measure of cognitive function for all respondents are reported in Table 2.

Scores of memory and mental status among women in 2011 were a strong predic- tor of their corresponding cognitive function measures 2 years later.

Higher baseline scores predicted better performance in memory and mental status in 2013.

After controlling for the health and demographic variables, as well as baseline scores of memory and mental status, we found that exposure to SHS sig- nificantly predicted a decline in memory but not in mental sta- tus.

For each 1-year increase in the length of SHS exposure, there was an additional 0.01-point decline in scores of memory in the 2013 follow-up.

Therefore, SHS exposure was associ- ated with a greater decline in memory over a 2-year period.


For each 1-year increase in age, there was an additional 0.03- point decline in scores for memory.

The association between education and memory was strong, as higher levels of education protected against memory decline.

Compared with illiterate re- spondents, the memory score in 2013 for those who finished at least primary education was 0.62 higher, and for those with sec- ondary education or above, the score for memory was 1.26 high- er.

Moreover, respondents living in urban settings had higher scores in memory than respondents living in rural areas.


Depressive symptoms also significantly predicted declines in memory and mental status.

In our study sample, the magnitude of memory decline among those with depressive symptoms was 0.21 greater than among those without depressive symp- toms, and the magnitude of the decline in mental status among those with depressive symptoms was 0.30 greater than those without depressive symptoms.

Similarly, urban residence, more education, and higher household expenditures (a proxy for wealth) significantly predicted a lesser degree of decline in mental status.


We further explored whether the association between SHS exposure and memory varied by age among the 3 age groups: 45-54 years, 55-64 years, and 65 years or older.

As shown in Table 3, a significant relationship was found between SHS ex- posure and memory for the group aged 55-64 years.

In this age group, for each 1-year increase in exposure to SHS, there was an additional 0.01 decline in scores of memory.

Memory was better among the Chinese women who were living in urban en- vironments than among their rural counterparts.


To our knowledge, this study is the first investigation of cog- nitive function in relation to household SHS exposure for middle-aged and older women in China using longitudinal data.

We found that longer SHS exposure was related to greater declines over time in memory scores in this population-based sample.

On average, the negative coefficient indicated that, compared with those not exposed to SHS, women with SHS exposure have a greater decline in memory scores.

We also found that among women aged 55-64 years, greater exposure to SHS was related to greater declines in memory scores.

No association between SHS exposure and cognitive function was observed in women aged 65 years or older, perhaps due to the relatively few cases in this age group.

A larger sample may be needed to detect significant effect for this group.


Despite the small regression coefficient (0.01), our findings are not trivial given that the average score of memory declined Downloaded from https://academic.oup.com/aje/article/187/5/911/4804402 by Institut Armand-Frappier user on 15 May 2024 0.04 points over the 2-year period.

Additionally, our finding is quite similar in magnitude to prior research on the relationship between age and cognitive function (score declines of -0.043 for memory and -0.035 for mental status) using the 2011 CHARLS data (27), and a larger coefficient tends to be found in cross-sectional studies (37).

The existing literature suggests that the "significance of effect" cannot be judged only by the P value or the regression coefficient but also by the confidence interval (37).

In the present study, the 95% confidence interval for SHS on memory is small, which provides some evidence that the association between SHS and memory is high (see Tables 2 and 3).


Women between the ages of 55 and 64 years who had longer exposure to household smoking in their mar- riage also demonstrated lower scores on memory 2 years later, after controlling for levels of education, geographic residence, household expenditures, chronic diseases, and baseline memory Our study also highlighted that education protected cognitive function from decline among these older Chinese women.

Edu- cational levels for older Chinese women are generally quite low, and they are even lower within the oldest cohorts.

Findings from our investigation show that even some level of education could prevent the decline of cognitive function.

Given the socioeco- nomic development occurring in China, there have been impres- sive improvements over time in educational achievements, especially for women (27), which may lead to better cognitive function in several dimensions (38).

In line with previous studies (27, 28, 30), we found that older women with depressive symp- toms had lower cognitive function.

Thus, screening and treat- ment for depressive symptoms presents another opportunity for interventions that could improve the cognitive function of older Chinese women.


Prior research suggests that cognitive impairment tends to begin at younger ages in China than in Western countries.

Ex- isting evidence indicates that cognitive impairment is common among adults in the Chinese population starting near age 55 years (39), whereas in Western countries such cognitive impairment tends to occur approximately a decade later (around age 65 years) (9, 10).

The earlier age of onset may be one reason that could explain the significant cognitive decline among the cohort of women aged 55-64 years who were exposed to SHS over a longer period of time.

Women within this age group are at risk due to their older age and lower edu- cation.

It is also noteworthy that the majority of respondents in our sample were from rural China. Given the combination of cohort influences and inefficient cognitive screenings in China, particularly in rural areas, it is not surprising that a significant association between SHS exposure and memory decline is found among this age group.

In contrast, cognitive decline was not observed among the youngest group in our study, likely due to the combined protection of younger age and higher edu- cation.


In addition, despite minimal influence, selection bias might have influenced the results.

For the oldest group in our sample (those aged 65 years or older), it is possible that par- ticipants were cognitively and physically healthier than women of the same age who did not participate in CHARLS.

Along these same lines, those who experienced higher health risks from environmental toxins such as SHS exposure might have suffered a premature death.

The absence of a direct asso- ciation between SHS exposure and mental status suggests that clinical research is needed to investigate the exact mechanism by which SHS exposure affects cognitive function pertaining Limitations of the present study should also be noted.


First, given the small sample of male passive smokers as well as women aged 65 years and older, it is difficult to test the interac- tions of sex and SHS exposure, or age and SHS exposure, on cognitive function.

Thus, the connections between SHS expo- sure and cognitive function found in the present study should be tested more rigorously using larger samples and longer-term panel data.

Second, as with any longitudinal study, selective attrition and inadequate control of all potentially related vari- ables could have influenced the findings.

Third, self-reported responses may underestimate or overestimate exposure to SHS (40).

Future research could include additional observed or bio- logical measures of SHS and cognitive functioning to improve the precision of measurement.


Despite these limitations, findings from this study call for public health policy in tobacco control and smoking cessation to eliminate SHS exposure in households.

In many regions of China, particularly rural communities, offering a cigarette to guests is a common and highly valued social custom (41).


Policies should involve widespread public education about the harmful effects of SHS exposure to inform and change individuals' attitudes and social customs.

Sustainable efforts, including school-based anti-SHS education for children and young adults as well as community-based education for middle- aged and older adults, are needed to raise awareness of the hazards of SHS.


Many rural areas in China lack the counseling, medication, and information to address tobacco addiction.

As a result, it is difficult for smokers to quit smoking.


SHS does not have a safe level of exposure, nor can the risk be completely mitigated through ventilation or filtration (42).

The health of nonsmokers can be protected only through completely smoke- free environments (43).

Therefore, education on SHS is cru- cially important to empower every nonsmoker to prohibit smoking indoors to reduce household SHS exposure, espe- cially for women.


Furthermore, smoke-free laws and regulations should be en- forced through action by county- and city-level governments.

Results of this study showed that the cognitive function of Chi- nese women in 2011 was a strong predictor of their cognitive function 2 years later.

Therefore, cognitive health screenings for the middle-aged and younger elderly populations in urban and rural communities should be recommended, because early screening tests and behavioral interventions including SHS cessation could postpone or prevent the onset of dementia.

Campaigns for SHS cessation should be required by the gov- ernment and implemented in schools, hospitals, workplaces,


Pan et al. [ 1] reported findings that exposure to secondhand smoke (SHS) was associated with cognitive decline over the course of 2 years among middle-aged and older Chinese women who never smoked, and they also reported a dose-response relationship.

SHS exposure affects vulnerable people disproportionately because they have less control or choice over their living and working environment.

Smoking is an established risk factor for dementia, but recent evidence reports on dementia-risk increase have not included SHS.

Many epidemiologic studies collect data on smoking but not SHS exposure.

SHS may be one of the most prevalent and modifiable risk factors for cognitive decline and therefore represents a major potential target for reduction of dementia risk.

Given the high prevalence of smoking in China and other parts of the world, there is an urgent need to raise awareness of SHS reduction as part of global and national strategies to reduce cognitive decline and dementia and to introduce legislation that protects nonsmokers and vulnerable children and adults from SHS.


The current study by Pan et al. [ 1] provides new evidence on the risk of secondhand smoke (SHS) for cognitive decline in mid- and late adulthood, highlighting a risk factor that has not been included as a priority in recent influential evidence reports [2, 3] on dementia prevention.

This builds on previous cross-sectional research on adults aged ≥60 years from 5 provinces in China [4], reporting an association between dementia diagnosis and exposure to environmental tobacco smoke.

People who develop cognitive deficits in middle age are likely to have greater risk of late-life dementia than are adults who develop cognitive impairment for the first time in late life [5].

By 2030, 66 million adults globally are expected to have dementia [6].

Cognitive impairment that is not severe enough to meet criteria for a diagnosis of dementia has a prevalence of 16.8% in adults aged >65 years in Canada [7] and 20% in adults aged >70 years in the United States [8].

With the lack of any significant breakthrough in discovering an effective treatment or cure for dementia or Alzheimer disease, risk reduction is the mainstay of current dementia-prevention programs.


Findings on SHS and cognitive decline in the Chinese Health and Retirement Longitudinal Study (CHARLS) are particularly important because the study is representative of the population, is longitudinal, and has measures comparable to those of other national surveys, including the US Health and Retirement Study [9].

This enables cross-national comparison of rates of exposures for late-life disease.

Within China, exposure to SHS affects approximately 50% of the population [10, 11] and within the CHARLS data set, participants had an average of 30 years of exposure.

The impact of SHS on cognitive decline is likely compounded by other highly prevalent risk factors that reduce brain and cognitive reserve.

Globally, the most prevalent risk factor for dementia is low education (up to lower secondary school, according to the International Standard Education Classification), which affects 40% of the world population [12] and up to 70% in China [13].


Pan et al. [ 1] reported data for participants in middle age, which has been identified as a crucial period for focused lifestyle and vascular risk reduction interventions [14–17].

The authors reported that SHS was associated with more rapid memory decline but not with decline in cognitive status as measured using a telephone-interview version of the Mini-Mental State Examination (MMSE).

The MMSE is designed to detect cognitive impairment and has ceiling effects in the normal population.

It usually lacks sensitivity to detect cognitive decline in normal aging, although significant change in the sample was detected over the follow-up period of 2 years.

The extent of decline on the cognitive measures in CHARLS due to SHS per annum was one-third of that due to aging [1].

However, it is possible that there was further cognitive decline not captured by the limited cognitive test battery in the study.


Other risk factors reported by Pan et al., including low education, rural setting, and depressive symptoms, were associated with cognitive decline in the study [1] and are consistent with the wider literature.

The results provide some possible explanations for observed demographic differences in cognitive impairment/dementia [18], with rural Chinese women having higher rates of dementia than rural men and urban-dwelling women.

The incidence of dementia in China is similar to that in Europe and the United States [19], and prevalence rates vary in reports according to region and urban and rural setting, with generally higher rates of dementia in women [20–22].


Previous research from 5 provinces in China [4] has found an increased risk of dementia and Alzheimer disease associated with environmental tobacco smoke.

Findings from Pan et al. [ 1] provided a potential explanation for the observation of increasing prevalence of dementia in China, particularly in women [23], and suggest that without intervention, high rates of incident dementia [24] might occur.

The higher rates of smoking and exposure to SHS in China compared with those in the United States and Europe may in part explain differences in incidence and prevalence rates between countries.

However US smoking data [25] reveal large disparities in smoking rates that are associated with socioeconomic status and disadvantage, with the majority of US smokers now being socially disadvantaged.

Given that smoking and exposure to SHS are associated with increased risk of cognitive decline and late-life dementia, these data suggest that the long-term disparities in health and health behavior of adults, and associated environmental exposures of children to factors such as SHS [26], may translate into disparities in dementia incidence in late-life in the United States and other countries.


In addition to including older adults, Pan et al. [ 1] reported findings for middle-aged adults who had completed 2 survey interviews during 1–2 years of follow-up in CHARLS.

Middle age is also when other modifiable risk factors for cognitive decline occur, such as high serum cholesterol and overweight or obese body mass index. Previous research on the impact of SHS has focused on early-life development or very old adults.

Hence this report fills a gap in the life-course evidence base on the impact of SHS exposure.

Other recent reports of the impact of air pollution on risk of cognitive impairment/dementia [27, 28] suggest that strategies for dementia risk reduction need to focus more broadly on environmental factors that have an impact on health.


The study also raises further questions and topics for investigation.

It is limited by the restriction in sex to women and by SHS measured from husband’s smoking at home.

Data are needed to confirm a similar effect of SHS on men.

Including SHS exposure at work and in other places would give a fuller picture of the impact of SHS on cognitive impairment.


The longer-term follow-up of the participants who did not have cognitive impairment at baseline would help further clarify the causal effect of SHS on cognitive impairment.

Adjustments in the data analysis including other important confounding factors, such as body mass index and stroke, would help assess the extent to which SHS increased the risk of cognitive impairment.

The interaction between SHS and other known risk factors for dementia, such as the apolipoprotein ε4 genotype, and other vascular risk factors also needs exploration.

Concomitant neuroimaging data would provide information on whether smoking and SHS have similar impacts on vulnerable brain regions and the accumulation of tau and amyloid pathology.

Further follow-up of the sample for incident Alzheimer disease and vascular dementia would also allow for evaluation of how these results translate into risk factors for dementia.


The reversibility of risk associated with SHS is not known.

One study of older adults on a smoking cessation program found that successful quitters showed reduced brain atrophy compared with unsuccessful quitters, and they also had cognitive trajectories more similar to never smokers in a control group [29].

However there appears to be a lack of data on removal of the risk of SHS and subsequent impact on cognitive function.


SHS exposure differs from many other behavioral risk factors for chronic disease because individual behavior affects others.

Infants, children, or those with low mobility may be unable to move away from environments with SHS.

Adults with dementia or cognitive impairment may have a higher level of SHS exposure within the home because they may be less mobile or not recognize the harmful effects of SHS, compounding effects in this group.

This raises questions about public responsibility for exposure to SHS and the role of governments in regulating exposure.


In Australia, laws governing SHS exposure have been instituted to protect children.

For example, adults are not allowed to smoke while driving with children.

Most jurisdictions also prohibit smoking in workplaces, on public transport, on airplanes, and in restaurants.

However, although Chinese legislation has called for a ban on smoking in all public places over the last few years, progress has been hindered by the high prevalence of smoking [10], and there has been limited reduction in the prevalence of SHS exposure in China [30].


In the past, health concerns in relation to SHS have focused on cardiorespiratory diseases and cancer risk.

However, there is now evidence that exposure to SHS is associated with an increased risk of cognitive impairment, probably also including dementia.

At present, 93% of the world’s population live in countries that are not fully covered by smoke-free public health regulations, and 35% of people in the world are exposed to SHS [31].

Given the prevalence and length of exposure of this potential risk factor for cognitive decline, both education and regulatory approaches appear warranted to protect the future brain health of current populations, thus reducing the epidemic of dementia in the world.


The commentary with in-depth and broad perspectives by Anstey and Chen [1] provides a precise assessment of the strengths and limitations of our longitudinal study on secondhand smoke (SHS) and cognitive function among women in mid- and late adulthood in China [2].

We agree with their conclusions that the most important strength of our current study is that it provides new evidence on the risk of SHS for cognitive decline from a life-course perspective, using population-representative data.

Most existing literature focuses either on the influence of active smoking or on very old adults.

We believe ours is the first investigation providing life-course evidence for cognitive function in relation to household SHS exposure for middle-aged or older women in China.

Findings of our study strengthen the view that exposure to household SHS can hasten the onset of cognitive impairment for Chinese women and affirms middle age as a crucial period for risk reduction intervention [1, 2].

We also agree with Anstey and Chen’s comment that our study suggests that long-term environmental exposure to factors such as SHS, even at a minimal level, cumulatively results in cognitive decline and in disparities in the health of women and children [1, 3].

This life-course evidence might be replicated in the United States and other countries in order to investigate environmental risks for dementia incidence in late life [1].


Anstey and Chen pointed out that our study did not find a significant association between SHS and mental status, perhaps because the Mini-Mental State Examination (MMSE) has a ceiling effect and may lack sensitivity to detect cognitive impairment in normal aging [1].

A systematic review and meta-analysis conducted by Mitchell suggests that the ceiling effect means that “MMSE may not perform well in people with mild cognitive impairment and the test is only marginally impaired in the detection of mild dementia as compared to detection of moderate to severe dementia” [4].

Mitchell’s study also shows that MMSE performs best when screening, separating individuals with dementia from healthy cognitively unimpaired individuals, based on results from 108 studies [4].

In the China Health and Retirement Longitudinal Study (CHARLS), the MMSE was used for dementia screening rather than diagnosis, and our study aimed to assess the association between cognitive function in memory and mental status and SHS rather than grading the severity of cognitive impairment.

For this purpose, cognitive function measured by MMSE scores in our study is adequate, especially given a lower educational level among our study population.

We fully agree with Anstey and Chen that the single tool of MMSE is not sufficient to test cognitive function, and a more extensive neuropsychological and clinical evaluation should be included in CHARLS as well as in future studies.

As we suggested in our discussion, future research could include additional observed or biological measures of SHS and cognitive functioning to improve the precision of measurement [2].


In addition, we applaud Anstey and Chen for their suggestion that further investigation should substantiate the effect of SHS on both sexes, examine SHS exposure outside households, and explore the moderation effect of smoking cessation on SHS and cognitive function over the life course [1, 5].

Such studies would provide a more comprehensive understanding of the impact of SHS on cognitive impairment, to guide educational and regulatory interventions to reduce the prevalence of dementia globally.


The study is a register-based, case-control study where the study base consists of all women in Stockholm and Gotland counties in Sweden between 1976 and 1999, who were gain- fully employed according to any of the censuses performed during 1960-1990.

The subjects entered the study base either in 1976 or on their 15th birthday, whichever came first, and were followed through 1999 or to the time of breast cancer diagnosis, if that came before 1999.

All cases of breast cancer in the study base were identified through the Regional Cancer Registry in Stockholm, one of the six regis- tries responsible for recording cases of cancer and reporting them to the National Swedish Cancer Registry that thereby covers more than 98 percent of all incident cancers in Sweden (19).

A referent year was defined as the year of the case's diagnosis.

Controls were selected randomly by age and calendar year from the Register of the Total Population, operated by Statistics Sweden.

With the use of the national registration number that is unique to each individual in Sweden, all subjects in the study could be linked to other Swedish registers in order to obtain required information.

Controls were allowed to be selected only once, and cases could not be selected as controls.

The cases and eligible controls had to be resident in Stockholm or Gotland County at the referent year but not during the whole study period.


The exposure assessment was based on information about occupation obtained from the censuses performed by Statis- tics Sweden in 1960, 1970, 1975, 1980, 1985, and 1990.

Information about magnetic field exposure was assessed by linking a new job-exposure matrix developed from an elec- tromagnetic field measurement program performed in Stock- holm County between March 2001 and October 2002 to the occupational codes in the censuses (18).

The new job-expo- sure matrix includes 49 of the most common occupations among women in Stockholm County and covers about 85 percent of the gainfully employed women according to the 1980 census.

The measurements were made with personal magnetic field meters (Emdex Lite; Enertech Consultants, Campbell, California) that volunteer women in the different occupations carried on a belt by the hip for 24 hours.

During the measurement periods, the subjects kept a diary of their whereabouts from which time spent at work could be abstracted.

The number of measured subjects in each occu- pation varied between five and 24.

The meter recorded the exposure every fourth second, and a number of parameters describing different aspects of the exposure were calculated.

In this study, we evaluated the time-weighted average, maximum values, short-term variations (rate of change), and the proportion of the workday spent in levels exceeding 0.3 µT. The rate of change (RC), a measure of the short time variability of the exposure, was calculated as follows:


RC (µT/4 seconds) = , where M 1 and M 2 are successive magnetic field measure- ments registered every fourth second, and n is the number of measurements in a given time period (5).

To reduce the influence of individual extreme values, we used the geometric mean and the median when summarizing the parameters over occupations.


The exposure described as the geometric mean of the time- weighted average was divided into four categories: <0.10 µT, 0.10-0.19 µT, 0.20-0.29 µT, and ≥0.30 µT. The cutpoints were chosen to enable comparison with previous studies.

Based on the assumption that magnetic fields act as a tumor promoter (1), exposure at the last census providing information on occupation was investigated.

To further eval- uate different time windows for exposure and to compare with results from previous studies, we analyzed exposure at least 10 years before the referent year and exposure before the age of 35 years.

The study period was split in two parts to evaluate exposure before and after 1985, assuming the measurements to be more accurate for the later period.

The year 1985 was the census year that most closely splits the study period in half.

Ever versus never being exposed was defined as ever holding an occupation with magnetic field levels at or above 0.30 µT compared with never having an occupation with more than 0.10 µT. Finally, we investigated the effect of being exposed to ≥0.30 µT in at least two, three, or four consecutive censuses prior to the referent year.


In a sensitivity analysis aimed at reducing potential misclassification of the exposure caused by variation in the exposure within an occupation, we performed analyses for a subset of the occupations using the following definition: An occupation was considered as having low exposure if the median of the measurements within the occupation was 0.10 µT or less and the third quartile was lower than 0.17 µT; we similarly defined an occupation as having high exposure if the median was 0.25 µT or more and the first quartile was


Downloaded from https://academic.oup.com/aje/article/161/3/250/127045 by McGill University School of Law user on 16 May 2024 252 Forssén et al. Am J Epidemiol 2005;161:250-259 higher than 0.17 µT. In this way, the exposure contrast between the occupations was increased.

The rationale for these cutpoints was driven by the exposure distribution of the individual measurements on which the job-exposure matrix was based and chosen to refine the exposure to include only occupations that overlap as little as possible.

The distribution of the occupations is described in detail elsewhere (18).


For the maximum values, we used the cutoff points 1.5 µT, 2 µT, and 3.5 µT, and for the rate of change, we used 0.05 µT/4 seconds, 0.07 µT/4 seconds, and 0.12 µT/4 seconds.

These cutoff points followed the 10th, 50th, and 90th percen- tiles of the distribution among the controls.

The proportion of time spent above 0.30 µT was analyzed with cutoff points following the 10th, 50th, 90th, and 95th percentiles (6, 10, 35, and 40 percent of the workday).


The confounding factors that we evaluated were socioeco- nomic status, age, referent year, number of children, and age at first birth.

The information about socioeconomic status was obtained from the censuses and was categorized as shown in table 1.

Both age and referent year influence the breast cancer risk and, since their distributions varied between categories of magnetic field levels, they were considered potential confounding factors.

The socioeco- nomic status also varied between levels of electromagnetic field exposure but is more complicated as a confounder, since it is partly derived from the occupation and could be regarded as a step in the causal pathway and argued not to be controlled for (20, 21).

There is, however, no reason to believe that the actual exposure (magnetic fields) in any way would affect the formation of the socioeconomic index and, since high socioeconomic status is a well-known risk factor for breast cancer, we found it relevant to evaluate socioeco- nomic status as a potential confounder.


For women born in Sweden in 1932 or later, reliable infor- mation about the age at first child and number of children (including stillbirths) was available from the multiple-gener- ation registry operated by Statistics Sweden.

Parity at the referent year was categorized as nulliparous, one, two, three, four, or five or more children.

Age at first child was catego- rized as less than 21 years, 21-24 years, 25-29 years, 30-34 years, or 35 years or more.

Analyses on socioeconomic status and parity in this subgroup were performed both to ensure coherence with previous studies and categories chosen there- after (22) and also to evaluate potential confounding.


We performed age-specific analyses using the categories less than 50 years and 50 years or more as a crude categori- zation for menopause.

Estrogen receptor status was available through the Regional Cancer Registry for about 64 percent of the cases.

During the study period, all of the hormone receptor assays were done at one laboratory at the Karo- linska Hospital, Stockholm.

A case was considered estrogen receptor positive when the estrogen receptor content of the primary tumor was 0.05 fmol or more per µg of DNA (which corresponds to 2-3 fmol/mg of protein).


To determine risk (odds ratio), we used logistic regression models in SAS version 8.2 software (SAS Institute, Inc., Cary, North Carolina).

The random variation was assessed by 95 percent confidence intervals.

The potential confounding variables were included one at a time for evaluation in the model, and the results are presented as both crude and adjusted odds ratios.

Wherever possible, the variables were evaluated as both continuous and categorical.

Since contin- uous variables in a logistic regression model assume an expo- nential relation between the variable and the outcome, the variables were first analyzed as categorized data and expo- nential trends were explored.

No such trends were discovered and, since the large number of subjects allowed for many variables, the categorical approach was kept.


Table 2 gives the number and percentage of cases and controls according to age, estrogen receptor status, and completeness of information regarding exposure and confounding variables.

The 1 percent missing information about occupations are women that, according to the census variables, were employed but had no information about type of occupation.

Exposure information was available for about 90 percent of the subjects.

The difference in number of subjects between cases and controls with information on parity (36.8 percent for cases and 49.9 percent for controls) is due to the fact that the controls were selected with equal numbers over the age strata and not according to the age distribution among the cases.


Analyses on socioeconomic status and parity are shown in table 1.

The estimates for these variables follow well-estab- lished patterns with higher risks for higher social groups compared with lower (unskilled manual workers), lower risks for women with many children, and higher risks for those who had their first child at an older age (22).


Informa- tion about parity was available only for women born in Sweden in 1932 or later, and in this subgroup we evaluated the effect of confounding from parity and socioeconomic status on the observed association between magnetic field exposure and breast cancer.

Adjustment for number of chil- dren and age at first birth had no effect on the odds ratios (not shown), and therefore we present results only for the total number without adjustment for parity.


Table 3 displays the results for magnetic field exposure for the last census providing information on occupation prior to the referent year.

The average time to referent year was 7.6 years, with the interquartile distribution (quartile 1, median, and quartile 3) of 2, 5, and 11 years, respectively.

The data were stratified according to age at referent year (<50 and ≥50 years) and estrogen receptor status (estrogen receptor posi- tive and negative) and are presented in table 3 as both crude and adjusted odds ratios for the confounding factors that had an effect on the risk.


In the crude analyses, the odds ratios were below unity for many subgroups but, in the adjusted model, the odds ratios were close to unity.

In the highest exposure category (≥0.30 µT), the adjusted odds ratio for postmenopausal women with estrogen receptor-positive breast cancer was 0.94 (95 percent confidence interval (CI): 0.72, 1.22).

Of the potential confounding factors, age and socioeconomic status were more important than the referent year, although each contributed somewhat to the change in the risk estimates.


For women without information on estrogen receptor status, the results were similar (not shown).


The results from using the categorization with more extreme exposure contrasts are shown in table 4.

The expo- sure variation within these occupations was overlapping only with, at the most, 25 percent of their upper or lower range.

In spite of the refined categorization, the odds ratios in these analyses were also close to unity.


The exposure defined as time-weighted average was further evaluated for different time windows as shown in table 5.

No associations were found for exposure experi- enced at least 10 years before the referent year or for post- menopausal women (≥50 years) exposed before age 35 years.

The odds ratios for women highly exposed before 1985 were slightly lower than those for women exposed after 1985, although all odds ratios were close to unity.

Table 5 also shows the odds ratios for breast cancer in relation to exposure parameters other than the time-weighted average.

No associations were found for exposure defined as maximum values, short-term variation (rate of change), or the proportion of time spent in fields of 0.30 µT or more.


Having any occupation with exposure of 0.30 µT or more in at least four consecutive censuses prior to the referent year, which corresponds to 15-20 years, was not associated with an increased risk for breast cancer (table 6).

Defining exposure as ever having an occupation with magnetic field exposure of 0.30 µT or more did not show any increased risks (data not shown).


This study provides no evidence for an increased risk of breast cancer among women working in occupations with high magnetic field exposure.

The size of the study also gave odds ratios with high precision in most subgroups, but no associations were found regardless of exposure cutpoint, choice of exposure parameter, or whether the analyses were stratified by age and estrogen receptor status.


The study has several strengths.

It is population based, and the controls were selected at random from the population, minimizing the risk for selection bias.

Exposure information was collected from census registries covering the total popu- lation, and therefore the participation of cases and controls was almost complete.

The information in the Regional Cancer Registry is of high quality, and less than 2 percent of the breast cancer cases are not covered by the register (19).

Estrogen receptor status was not available for 46 percent of the cases; however, the results did not differ between women


The major concern in the study is exposure misclassifica- tion, even if exposure assessment has been improved compared with previous studies.

There are a limited number of measurements within each occupation, and the measure- ments were performed later than the actual exposure.

It is likely that magnetic field exposure may have changed over time in some occupations, but separate analyses of exposures occurring before and after 1985 did not indicate any major impact of exposure misclassification due to changes in the exposure over time.

Furthermore, the information about occupation is based on census data every fifth year, and we have no information about occupation between the census years.

Our results did not indicate that being exposed during up to four consecutive censuses was associated with any risk increase.

Too few women were exposed during five or six censuses to allow meaningful analyses.


The exposure parameter that might be biologically rele- vant is unknown, but the most commonly used is the time- weighted average, which we evaluated in several analyses.

Other suggested parameters are peak values, short-term vari- ations in exposure magnitude, and time spent at different exposure levels.

We investigated all of the discussed param- eters but found no increased risks for any of them.

Several different time windows for the exposure were also explored: exposure close in time prior to diagnosis, exposure with a 10-year time lag, and exposure before age 35 years.

None of them showed any tendency for elevated risks.


We have evaluated and controlled for several important confounding factors, but we cannot exclude the possibility that some other occupational risk factors for breast cancer are more prevalent in occupations unexposed to magnetic fields, which could hide a possible association with magnetic field exposure.

However, there are hitherto no established occupational risk factors for breast cancer that are likely to explain the lack of association.


In previous epidemiologic studies of magnetic field expo- sure and breast cancer, the exposure has been derived mainly from three different sources of magnetic fields: power lines near the residence, use of electric blankets, and occupational exposure.

The studies performed before 1999 have been evaluated in two reviews (8, 9) and in an evaluation of the carcinogenicity of nonionizing radiation in the extremely low-frequency range by the International Agency for Research on Cancer (1).

The majority of the studies on elec- tric blankets have shown no effects and, for residential expo- sure, positive findings were restricted to subgroups where the number of exposed subjects was limited and where chance could be a possible explanation for the observed risk increase.

Also, it is possible that findings in subgroups are reported more frequently if they are positive.

More recent studies of residential exposure (23-25) and use of electric blankets (26) have not reported increased risks.


The studies on occupational magnetic field exposure have showed more varying results, with several studies reporting elevated risks for industrial and electrical occupations, tele- phone installers, telegraph operators, and radio operators (8, 9).

These types of occupations are, however, not very common among women (18), and the number of exposed cases is therefore generally low.

Also, women and men with the same occupational title might not have the same expo- sure.

The lack of data on occupational magnetic field expo- sure among women has raised concern in previous occupational studies.

The crude and uncertain exposure assessment could have caused considerable exposure misclassification and a dilution of possible effects toward unity.

If this were true, one would expect that improvements of the exposure assessment would give higher risk estimates.


An attempt to improve the exposure assessment based on actual measurements was made by van Wijngaarden et al. ( 14).

They found an increased risk for premenopausal women and estrogen receptor-positive breast cancer associ- ated with long duration of high occupational exposure.

However, despite the improvements, the highest exposure category was defined as "industrial workers," a broad occu- pational group, and the authors suggest that the association could be explained by some other risk factors for breast cancer among industrial workers.


In two other recent studies where elevated risks were found, the highest exposure category comprised textile workers (15, 17), an occupational group believed to be highly exposed to magnetic fields (27, 28).

Seamstresses are included in the new job-exposure matrix but had an average exposure of 0.16 µT and were hence not included in the highest exposure category.

In this study, the odds ratio of breast cancer for women having textile occupations compared with women in occupations with exposure of less than 0.10 µT was 1.37 (95 percent CI: 1.11, 1.68) and, compared with women in all other occupations (irrespective of magnetic field exposure), the odds ratio was 1.33 (95 percent CI: 1.10, 1.62).

These results suggest that the increased risk for breast cancer in these occupations might be related to some exposure other than magnetic fields.


In our previous study of occupational magnetic fields and breast cancer (12), we based the exposure assessment on a job-exposure matrix developed from measurements performed on men (29).

In that study, we found an increased risk of 1.5 (95 percent CI: 0.6, 3.5) in the group of highly exposed (≥0.25 µT) women aged less than 50 years and of 3.6 (95 percent CI: 0.7, 18.1) for young women with estrogen receptor-positive breast cancer.

Both odds ratios are imprecise, because exposure information was missing for many of the occupations typically held by women, which limited the number of subjects available for analyses.

With the new job-exposure matrix developed for this study, both the validity of the exposure assessment and the precision were greatly improved, and an equal or even higher odds ratio would have been expected if the previously found risk increase was real.

Since no elevated risks were found in any subgroups in this study, it is most likely that the elevated risk in our earlier study was caused by random variation.


The study presented here is one of the largest studies ever on occupational electromagnetic field exposure and breast cancer risk, with an exposure assessment greatly improved compared with previous studies.

This allowed us to estimate breast cancer risk with great precision also in subgroups where increased risks have been suggested in previous studies.

The findings give no support to the hypothesis that magnetic field exposure increases the risk of female breast cancer.


Can we close the book on the issue of magnetic fields and breast cancer?

No. This is the rational answer to a question possibly implied by the very large study by Forssén et al., which found "no evidence for an increased risk of breast cancer among women working in occupations with high magnetic field exposure" [1].

Indeed, that this—and prior studies—did not detect an increased risk does not readily imply that there is none.


Here is why: In studies of possible links between nonionizing radiation and breast cancer, exposure misclassification is of paramount concern.

In their introduction, Forssén et al. [ 1] start out from the so-called melatonin hypothesis that suggests a biologically plausible and credible [2] mechanism for possible causal associations between exposures to extremely-low-frequency electric and/or magnetic fields and breast cancer in women via impaired pineal secretion of melatonin [3].

A "lack of melatonin can reasonably be anticipated to be a human carcinogen" [4], and "this hypothesis has thus far withstood 15 years of critical scrutiny and empirical research, inside the laboratory and out in the epidemiologic field" [5].

While it is to the credit of Forssén et al. that they improved assessment of women’s exposure to magnetic fields, it must be emphasized that the study’s underlying hypothesis implicates two distinct frequencies: extremely-low-frequency electric and/or magnetic fields and visible light.

Of the two, light inhibits melatonin much more strongly and more reproducibly than magnetic fields may.

It follows that light could be a much more likely culprit—at work and at home—than magnetic fields [6].


Intriguingly, findings from first studies of blind people and of shift workers are compatible with the notion that visible light may affect breast cancer risks; that is, risks may be reduced in the blind [7-11] and increased in female shift workers [12-15].

Importantly, light could be a significant cause of breast cancer because the ubiquitous nature of visible radiation implies the possibility that even small risk elevations could lead to a substantial population burden.

Moreover, since the possibly strong risk factor light was not controlled for, it remains conceivable that magnetic fields do increase breast cancer risks but that this effect has been assessed erroneously in many (or all) epidemiologic studies to date.

To illustrate this crucial point with a provocative analogy: we would be prone to miss, underestimate, or overestimate possible effects of most known lung carcinogens if we did not control for differential smoking habits in study subjects [16].


A look at experimental work may be important here.

In recent years, Thun-Battersby et al. [ 17] were able to substantiate earlier observations that magnetic field exposures enhance the development and growth of mammary tumors in the 7,12-dimethylbenz[a]anthracene (DMBA) model of breast cancer in female Sprague-Dawley rats.

Experimental work could also explain why a similar study conducted in the United States yielded different results, in particular because the substrains of rats used in the two laboratories differed in their susceptibility to DMBA-induced mammary cancers [18].

Possibly, the control of light (indeed, all animals were exposed to the same light regime) may have allowed the experimental researchers to detect magnetic field effects that could be masked in the existing epidemiologic studies by uncontrolled light exposures.


It remains to be emphasized that if we decide to find out whether the melatonin hypothesis and associated predictions are valid, epidemiologic studies must collect information about magnetic fields and light [6]. If we continue to rely on exposure assessments of magnetic fields alone, investigations shall not be sensibly interpretable and the important question for public health concerning whether magnetic fields and/or visible light may increase breast cancer risks will not be answered convincingly.


We thank Dr. Erren [1] for his comments about our study [2].

Dr. Erren discusses the possibility that exposure to light at night might confound the results found for extremely-low-frequency electromagnetic fields in relation to breast cancer.

The hypothesis that light at night might increase breast cancer risk through a mechanism involving suppression of the hormone melatonin is very interesting and has been addressed in studies of, for example, shift workers [3-5] and the blind [6].

At present, the evidence in favor of this hypothesis is suggestive but not very strong regarding either the effect of light at night on breast cancer risk or a possible protective effect of melatonin on breast cancer development [7].

However, the hypothesis is intriguing, and we continue to follow this research area with great interest.


In our study [2], we were unable to adjust for exposure to light at night because of a lack of data, but we believe it is unlikely that doing so would substantially change the results.

To our knowledge, no study has investigated the association between light at night and exposure to occupational electromagnetic fields, but there is nothing that hitherto indicates an existence of such a relation that could mask a risk from electromagnetic fields.

Furthermore, epidemiologic studies that have investigated light as a risk factor for breast cancer have observed an increase in risk that is very moderate and not entirely consistent.

At most, risks of twofold or less have been reported, substantially lower than those for the association between smoking and lung cancer, as Dr. Erren [1] brought up as an example.

Exposure to light can therefore not be considered a "potentially strong" risk factor for breast cancer, should the effect be real.


In using the experimental studies as a comparison, Dr. Erren [1] also seems to suggest an interactive effect between light and electromagnetic fields.

As far as we know, no studies on humans support this effect.

Given the level of exposure to light common in Sweden, electromagnetic fields do not seem to affect breast cancer risk.


Astrophysical ionizing radiation events affect the Earth episodically.

Effects come from the Sun through flares and solar proton events as well as interstellar supernovae and gamma-ray bursts (reviewed in Melott and Thomas, 2011).

Much of the interest regarding terrestrial effects has centered on major mass extinctions (e.g. Melott et al., 2004; Melott and Thomas, 2009).

Extreme events may eliminate all higher life on Earthlike planets, but are unlikely to cause complete global sterilization (Sloan et al. 2017).

However, less intense effects are also possible (e.g. Knipp et al., 2018), more frequent and therefore more likely to have extant evidence, and still interesting.


There is a great deal of accumulated evidence for supernovae (SN) moderately near the Earth (Knie et al., 2004; Fry et al., 2016; Mamajek, 2016; Melott, 2016; Wallner et al., 2016; Breitschwerdt et al., 2016; Binns et al., 2016; Fimiani et al., 2016; Ludwig et al., 2016; Erlykin et al., 2017).


Although the strongest signal is of an event about 2.6 Ma, which coincides within the uncertainties to the Pliocene-Pleistocene (PP) boundary, it may well be a series beginning as long as 7 Ma related to the evacuation of the Local Bubble, a large cavity in the interstellar medium (Breitschwerdt et al., 2016).


The possible terrestrial effect of a nearby SN has long been of interest.

More recently, new information on prompt and early emissions from SNe as well as the timing and distance of relatively recent proximate SNe were used as input in a series of computations (Thomas et al., 2016; Melott et al., 2017).

Although early work focused on ozone depletion from photon and cosmic ray (CR) irradiation, we have found additional increased importance for the extended arrival of cosmic rays.


We will not repeat here the description of those computations, except for a brief summary.

We refer the reader to Melott et al. ( 2019) for a summary or to Melott et al. ( 2017) for more detail.


We assumed a SN of the most common type IIP to have taken place about 2.6 Ma, at a distance of 50 pc.

This is taken to be representative, and possibly a member of a series.

The Local Bubble had been already formed and the magnetic field is assumed to have been largely expelled and residing within the walls of this structure, which would reflect cosmic rays back into the interior, extending the irradiation time for the Earth.


Cosmic ray propagation in the turbulent field leads to a time-dependent flux at the Earth.

We use here the CR flux for case B as computed in Melott et al. ( 2017) at 100y after the arrival of the first photons, and assume that magnetic reflection off the walls will later keep a similar flux inside the cavity of the Local Bubble.

The flux of cosmic rays is greatly increased beyond normal levels.


For many decades, the primary effect considered for terrestrial life from a nearby SN was ozone depletion (Gehrels et al., 2003).

We computed this effect based on a series of studies beginning with Thomas et al. ( 2005).

In Melott et al. ( 2017), it was estimated that these events would produce about a 50% increase in UVB at the surface, with a range of potential biological impacts (Thomas 2018).


Muons are highly penetrating elementary particles which penetrate up to a km in the ocean, but still by virtue of their large numbers constitute the primary component of irradiation from cosmic rays at the Earth's surface.

The large increase in cosmic rays following a nearby SN leads to a similar large increase in muon flux at the Earth's surface (Melott et al., 2017) and in the top km of the oceans (Melott et al., 2019).

Our previous work argued that a megafaunal extinction around the PP boundary (Pimiento et al., 2017) may have been induced by increased muon flux in the top few hundred meters of ocean (Melott et al., 2019).


In this short paper, we discuss additional substantial effects which could also indirectly have had a major effect on the biota, including the evolution of our species.

Atmospheric ionization and dissociation from cosmic rays and their subsequent showers of elementary particles led to the ozone depletion mentioned earlier.

The ionization may also have had other effects.


Atmospheric ionization was computed using tables from Atri et al. ( 2010), originally generated by CORSIKA.

CORSIKA (COsmic Ray SImulations for KAscade) is a package combining high- and low-energy interaction models with a specialized scheme for transport in air.

The version used for atmospheric ionization was EPOS 1.61, UrQmd 1.3.

This gives ionization rate (ions cm -2 s -1 ) as a function of altitude for various primary proton energies.


The tables in Atri et al. ( 2010) give ionization for primaries with energies from 300 MeV to 1 PeV (10 15 eV) from the ground to 90 km.

We used here primary proton energies between 1 GeV and 1 PeV. The ionization rate as a function of altitude was created by convolving the CR flux from Melott et al. ( 2017) with tables from Atri et al. ( 2010).


In Figure 1 we show the energy spectrum of galactic cosmic rays at the present time and at 100 y after the receipt of the first photons from a supernova at 50 pc (Melott et al., 2017).

Although the computations had open boundary conditions, we expect that the environment would be reflective due to the compression of the magnetic field in the walls of the Local Bubble.

Given the size of the structure, long duration of the flux at the 100 y level would be expected.

The units are such that equal areas in this plot imply equal amounts of energy deposition at the Earth.

Normally, high energy cosmic rays are too low in flux to contribute much to atmospheric ionization.


So, besides an overall increase in cosmic ray flux, there is a unique predominance of high energy primaries in the CR flux, up to about a PeV, which are normally not very important for atmospheric ionization.

It is important to understand the consequences of this change.

Figure 2 shows results of our analysis of the ionization effects of cosmic ray primaries.

We show the ionization flux (ions generated per area per time) per GeV of total incident cosmic-ray energy as a function of altitude and primary energy.

It is important to notice that the ionization rate is plotted per GeV unit of primary energy, not per primary.

Thus, the expected simple increase in ionization due to more energy per primary is factored out. But, an increase is still seen, particularly in the troposphere.

This increase is due to the greater penetration of the high energy cosmic rays in the atmosphere.

This will have consequences for activity in the troposphere; our primary interest here is lightning, especially cloud-to-ground lightning.

An energy of 1 GeV is representative of the majority of usual galactic cosmic ray primary protons, while the 10 GeV and 10 6 GeV energies bracket the range most affected by the SN.

In the lowest 2 km bin in our model, ionization caused by the higher energy primary protons (the range associated with SN CRs) is about two orders of magnitude higher than that caused by the lower energy associated with galactic CR primaries.

There is a great potential for major changes of weather near the surface.


In Figure 3 we show the altitude profile of ionization from galactic CRs under normal conditions and at our fiducial time.

( Energetic protons from the Sun mostly do not penetrate down to the troposphere and are not relevant for the issues considered here, as discussed in Usoskin et al., 2011.) The ionization "SNCR" in Figure 3 is computed by convolving the 100 yr, Case B cosmic ray flux as seen in Figure 1 with the ionization as a function of altitude and CR energy as seen in Figure 2.

It can be seen that there is a major difference, with ionization in the troposphere about 50 times greater than normal.

For comparison we also show our computed ionization profile from the 1956 solar superflare, the hardest and most energetic in the modern era (Usoskin et al., 2011) and therefore amenable to quantitative measurement.


Consequently, the great increase in TeV to PeV cosmic ray flux upon the Earth from an even moderately near supernova should have strong consequences for atmospheric physics near the surface.


It has been proposed that cosmic rays set off electron avalanches in the atmosphere which are the main initiator of lightning (Gurevich et al., 1999, 2008, 2009; Erlykin and Wolfendale, 2010; Chilingarian et al., 2017a; Kumar et al. 2018).

Until recently this was an eminently reasonable idea with only circumstantial evidence in its favor.

One of the obstacles has been the difficulty of making any in situ measurements of the processes initiating lightning.


However, Chilingarian et al. ( 2017b) reported observations on Aragats Mountain, Armenia, where the cloud layer is quite low.

They found a number of electron avalanches of duration less than a microsecond which were terminated by nearby lightning flashes.

This is a smoking gun that makes this a compelling theory, which we will take as our working model.


We note here that it has been proposed that cloud cover is related to cosmic ray flux (Svensmark et al., 2017 and references therein).

Since this proposal is highly controversial, we shall only mention it here.


A 50 times increase in atmospheric ionization in the troposphere would clearly make the breakdown and electron cascade much easier, and one could expect a great increase in lightning (e.g. Erlykin and Wolfendale, 2010).

Furthermore, the originally isotropic distribution of cosmic rays would result in showers which are much more preferentially vertical, due to the variation in atmospheric column density with angle.

So, not only would lightning be enhanced, but cloud-to-ground lightning should be preferentially enhanced.

The theory of lightning initiation is not well-developed, and we cannot say that a 50-fold increase in ionization would lead to a 50-fold increase in the number of lightning events.

However, the potential is there for a large increase.

We note that in the supernova-enhanced regime, there would be of order hundreds of high- energy primaries per km 2 per second, so that ionization would be intermediate between continuous and pulsed.


One effect of increased atmospheric ionization from supernovae CRs examined .

( A.L. Melott, A.L., B.C, Thomas, and B.D. Fields, B.D. (2019b) Climate change via CO2 drawdown from astrophysically initiated atmospheric ionization?

arXiv:1810.01995; under review; hereafter Melott et al. 2019b) was fixation of nitrogen.


Normally, the nitrogen in the atmosphere is largely unavailable to the biota due to the strength of the triple bond in N2.

For this reason, nitrogen must be converted into other forms; there is a large literature on the synthesis and deposition of nitrate as a side effect of atmospheric ionization and dissociation (e.g. Thomas et al., 2005).


Our computations suggested that the increase in available nitrogen would be relatively small, of order 10% from the cosmic ray ionization.

However, a large increase in the rate of lighting could provide a strong enhancement in the deposition of nitrate.


Lightning is the dominant souce of nitrogen fixation in the pre-industrial world (Schlesinger and Bernhardt 2013, Table 12.3).

The computational methods of Thomas et al. ( 2005) include results from nitrate deposition, mostly in the form of rain or snow.


We estimate that it would not be unreasonable for a large increase in the pre-industrial, pre-human nitrogen flux from lightning to result in a similarly large, perhaps several times over nitrate enhancement.


This might lead, as discussed in Melott et al. ( 2019b, above) to a CO2 drawdown and cooling of the climate as was observed at the onset of the Pleistocene.


We stress that this all depends on there being a large increase in the lightning frequency, which we are unable to estimate.


Lightning is the main initiator of wildfires if one excludes humans (Pausas and Keeley, 2000; Veraverbeke et al. 2017).

A large increase in lightning would again be expected to cause a large increase in the number of wildfires started.

There has been a coincident worldwide increase in the number of wildfires in since about 7 Ma (Zhou et al., 2014; Bond, 2015), which is probably responsible for the conversion of forest to savanna worldwide (Karp et al. 2018).

There has been no good explanation for this increase in fires across various continents and climatic zones (Bond, 2015).

We suggest that a global increase in lightning might provide such an explanation.


The evidence for the increase in fires after impact events can be found in soot and other carbon-related sediments (Wolbach et al., 1985, 2018).

Such evidence might be found here and is in fact cited in Zhou et al. ( 2014) and Bond (2015).

While impact- related fires would show a sudden spike, we would expect the SN-CR wildfires to take place over a much more extended period of time, 10 4 -10 5 y for each supernova.


The conversion from woodland to savannah has long been held to be a central factor in the evolution of hominins to bipedalism, although more recent thinking tends to view it as a contributing factor (Senut et al., 2018).

Thus, it is possible that nearby supernovae played a role in the evolution of humans.

This should be borne in mind as more research is done, particularly in the initiation of lightning.


The obvious question to many is the probability of such an event in our future.

The nearest apparent Type II supernova progenitor is Betelgeuse, which is likely to go off sometime in the next Myr (Meynet et al., 2013).

However, at a distance of ~200 pc, it is not likely to cause serious consequences.

We could be affected by a gamma-ray burst from an unseen nearby progenitor with only a few hours' warning (Medvedev and Loeb, 2013), but this is a very low-probability event.

The analysis of such events is most useful as a clue to understanding the geological past.

For technological humans, solar events are a more reasonable basis for immediate concern (Melott and Thomas, 2012; Knipp et al., 2018).


Several observational studies have revealed the explosion of a supernova in the early Pleistocene at about 100 pc from the Solar System.

The proof of this explosion is based on the signature associated with Fe60 deposits in Earth’s sediments and on the Moon’s regolith.

The supernova remnant was the cause of additional input of galactic cosmic rays (GCRs) in the Solar System.

Their propagation from the supernova remnant to Earth and the effects of the associated cascades of secondary particles triggered by their interaction with Earth’s atmosphere were analyzed by Melott and Thomas [1].

According to their study and previous works cited in their article, high-energy cosmic rays (above 1 TeV) caused a 20-fold increase of irradiation by muons on Earth’s surface and on the ocean and an order of magnitude increase of the atmospheric ionization that could have lasted more than 1,000 years.

The increase in irradiation could have contributed to a minor mass extinction in the Pliocene–Pleistocene transition, 2.6 My ago.

Melott and Thomas [1] analyzed the climatic consequences of the increased atmospheric ionization leading to more frequent lightning and, therefore, to an increase in nitrate deposition and in wildfires.

Increased wildfires, evidenced by an increase in soot and carbon deposits over the relevant period, would have contributed to the transition from forest to savanna in northeastern Africa, long argued to have been a factor in the evolution of hominin bipedalism.


In this discussion, we argue that the Gauss-Matuyama Earth’s magnetic field reversal, which defines the Pliocene–Pleistocene transition [6], could have enhanced the impact of the GCRs related to the early Pleistocene supernova discussed by Melott and Thomas [1].

Although they discussed various hypotheses concerning the interstellar magnetic field, they voluntarily neglected the variations of local magnetic fields on the impact of cosmic rays.

They base their choice on a work by Jackman et al. [ 5] in which the effect of GCRs on atmospheric chemistry is analyzed during the 1960–2010 epoch.

More specifically, Jackman et al. [ 5] showed that local magnetic field fluctuations of a factor of two at Earth’s poles over a solar cycle only have a moderate effect.


These conclusions are correct only if Earth’s magnetic field has a dominant dipole component, as is the case in the present epoch analyzed in Jackman et al. [ 5] and during most of Earth’s history.

By contrast, they certainly do not apply to geomagnetic excursions and magnetic field polarity reversals, which have episodically affected Earth’s magnetic field.

Paleomagnetic studies show that these events are usually short (a few thousand years) compared with the geological timescale and have occurred many times in the past but not periodically.

Most importantly for our discussion, during these events, the shielding provided by the magnetic field is lost, thus potentially increasing the impact of GCRs on the biosphere.


Magnetic field reversals and geomagnetic excursions are complex events during which the dipolar component of the magnetic field is unstable and weak [4].

In most if not all reversals, the intensity of the dipole is strongly reduced.

In addition, some reversals show complex directional changes; that is, the locations of the (virtual) geomagnetic poles change rapidly, and the polarity may swing several times before settling to normal or reverse.

The whole process may last several thousand years and in some cases up to 20,000 years.


The reduction in magnetic field intensity and the rapid changes in the direction of the dipole have important consequences on the propagation of charged particles.

Low-energy cosmic rays are partly guided by magnetic field lines and, with the present dipole magnetic field that is implicitly supposed in Melott and Thomas [1], the ratio of ionospheric ionization at the poles and at the equator is almost one order of magnitude [5].

When the dipole field was reduced or canceled or when the virtual poles were located at low or intermediate latitudes, the intertropical zone, where African savanna is situated, could have been exposed to the direct influx of cosmic rays as the poles are today.


The Gauss-Matuyama magnetic field reversal is used to define the boundary between the Pliocene and Pleistocene epochs [6] and has been dated to 2.589 ± 0.003 Ma based on lacustrine sediments [2].

This age is compatible with the age of the supernova and associated Fe60 deposits as discussed previously.


Although the Gauss-Matuyama reversal has been dated precisely, paleomagnetic studies indicate that the magnetic field has been unstable for more than 10 ky around this date.

For instance, Glen et al. [ 3] reported fluctuations of the magnetic field direction indicating a total transition time of about 15 ky.

Yang et al. [ 8] made similar observations from samples collected in a different region and concluded that the transition lasted more than 11 ky.


Also worthy of interest, Goguitchaichvili et al. [ 7] estimated that, during the Gauss-Matuyama transition, the intensity of the magnetic field in southwestern Iceland was on average 14.8 ± 4.6 mT (compared with 52.1 mT today), corresponding to a decrease of the magnetic dipole moment by a factor of two.

Therefore, during the Gauss-Matuyama transition, the biosphere may have been unprotected from cosmic rays for 10–15 ky.


The increased input of GCRs associated with a supernova may also last for a few thousand years [1].

The impacts of the arrival of supernova GCRs and the magnetic field reversal can be combined if the two phenomena occurred at the same time, as might have been the case 2.6 My ago.

The possible effect of the partial or total cancellation of Earth’s magnetic dipole component during the Gauss-Matuyama magnetic reversal could thus be included in further work following the study by Melott and Thomas [1].

This effect would most probably reinforce their conclusion regarding the impact of the supernova occurring 2.6 My ago as a possible cause of the Pliocene–Pleistocene transition.


Addendum.

We acknowledge authors A. L. Melott and B. C. Thomas for their answer, and we agree with their argument that the highly energetic particles studied in their article are not influenced by the geomagnetic field deflection.

We still consider that the increase of the lower-energy GCRs (abundant, e.g., in cases A and C in Thomas et al. [ 9]), which are influenced by Earth’s magnetic field, may have enhanced the cloud cover.

Empirical studies show the influence of the lower-energy GCRs (those below 20 GeV measured with neutron monitors) on the cloud cover [10], [11].

This finding is complementary to the process described in Melott and Thomas’s article with higher-energy cosmic rays, and it reinforces the conclusions regarding the influence of a nearby supernova as a trigger of a cooling of the climate and wildfires.

As for other geological events, the Pliocene–Pleistocene transition may result from an addition of independent causes with similar consequences.