Microcephaly measurement in adults and its association with clinical variables

Abstract

Objective:
To establish a microcephaly cut-off size in adults using head circumference as an indirect measure of brain size, as well as to explore factors associated with microcephaly via data mining.

Methods:
In autopsy studies, head circumference was measured with an inelastic tape placed around the skull. Total brain volume was also directly measured. A linear regression was used to determine the association of head circumference with brain volume and clinical variables. Microcephaly was defined as head circumference that were two standard deviations below the mean of significant clinical variables. We further applied an association rule mining to find rules associating microcephaly with several sociodemographic and clinical variables.

Results:
In our sample of 2,508 adults, the mean head circumference was 55.3 ± 2.7cm. Head circumference was related to height, cerebral volume, and sex (p < 0.001 for all). Microcephaly was present in 4.7% of the sample (n = 119). Out of 34,355 association rules, we found significant relationships between microcephaly and a clinical dementia rating (CDR) > 0.5 with an informant questionnaire on cognitive decline in the elderly (IQCODE) ≥ 3.4 (confidence: 100% and lift: 5.6), between microcephaly and a CDR > 0.5 with age over 70 years (confidence: 42% and lift: 2.4), and microcephaly and males (confidence: 68.1% and lift: 1.3).

Conclusion:
Head circumference was related to cerebral volume. Due to its low cost and easy use, head circumference can be used as a screening test for microcephaly, adjusting it for gender and height. Microcephaly was associated with dementia at old age.

Full text

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https://doi.org/10.11606/s1518-8787.2022056004175
Original ArticleRev Saude Publica. 2022;56:38
Microcephaly measurement in adults and
its association with clinical variables
Nicole Rezende da CostaI, Livia MancineII,III , Rogerio SalviniII , Juliana de Melo
TeixeiraII , Roberta Diehl RodriguezI, Renata Elaine Paraizo LeiteI, Camila NascimentoI,
Carlos Augusto PasqualucciI, Ricardo NitriniI, Wilson Jacob-FilhoI, Beny LaferI, Lea
Tenenholz GrinbergI,IV , Claudia Kimie SuemotoI, Paula Villela NunesI
I Universidade de São Paulo. Faculdade de Medicina. São Paulo, SP, Brasil
II Universidade Federal de Goiás. Instituto de Informática. Goiânia, GO, Brasil
III Instituto Federal Goiano. Departamento de Ensino. Iporá, GO, Brasil
IV University of California. Memory and Aging Center. San Francisco, California, United States
ABSTRACT
OBJECTIVE: To establish a microcephaly cut-o size in adults using head circumference as
an indirect measure of brain size, as well as to explore factors associated with microcephaly
via data mining.
METHODS: In autopsy studies, head circumference was measured with an inelastic tape
placed around the skull. Total brain volume was also directly measured. A linear regression
was used to determine the association of head circumference with brain volume and clinical
variables. Microcepha ly was dened as head circu mference that were two standard deviations
below the mean of signicant clinical variables. We further applied an association ru le mining
to nd rules associating microcephaly with several sociodemographic and clinical variables.
RESULTS: In our sample of 2,508 adults, the mean head circumference was 55.3 ± 2.7cm. Head
circumference was related to height, cerebral volume, and sex (p < 0.001 for all). Microcephaly
was present in 4.7% of the sample (n = 119). Out of 34,355 association ru les, we found signica nt
relationships bet ween microcephaly and a cl inical dementia ratin g (CDR) > 0.5 with an i nformant
questionnaire on cognitive decline in the elderly (IQCODE) ≥ 3.4 (condence: 100% and lift: 5.6),
between microcephaly and a CDR > 0.5 with age over 70 years (condence: 42% and lift: 2.4),
and microcephaly and males (condence: 68.1% and lift: 1.3).
CONCLUSION: Head circumference was related to cerebral volume. Due to its low cost and
easy use, head circumference can be used as a screening test for microcephaly, adjusting it for
gender and height. Microcephaly was associated with dementia at old age.
DESCRIPTORS: Adult. Microcephaly, classication. Cephalometry. Dementia. Data Mining.
Correspondence:
Paula Villela Nunes
Universidade de São Paulo
Rua Dr. Ovídio Pires de Campos,
785
01060-970 São Paulo, SP, Brasil
E-mail: paula@formato.com.br
Received: Aug 27, 2021
Approved: Dec 1, 2021
How to cite: Costa NR, Mancine
L, Salvini R, Teixeira JM, Rodriguez
RD, Leite REP, et al. Microcephaly
measurement in adults and its
association with clinical variables.
Rev Saude Publica. 2022;56:38.
https://doi.org/10.11606/s1518-
8787.2022056004175
Copyright: This is an open-access
article distributed under the
terms of the Creative Commons
Attribution License, which permits
unrestricted use, distribution, and
reproduction in any medium,
provided that the original author
and source are credited.
http://www.rsp.fsp.usp.br/

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Microcephaly in adults Costa NR et al.
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INTRODUCTION
Head circumference (HC) is an anthropometric parameter highly correlated with brain
size1,2. A usual standard for microcephaly is an HC more than two standard deviations
below the mea n3–5. Microcephaly can be present at birth - primary m icrocephaly - or develop
postnatally - secondary microcephaly6. Some causes of congenital microcephaly are genetic
mutations, whereas other modiable causes include prenatal infections (e.g., exposure to
the Zika virus), maternal alcohol and substance abuse, and inadequate nutrition during
pregnancy7. Secondary microcephaly can occur due to deceleration of brain growth in face
of infection, trauma, intoxication, metabolic disease, and Rett syndrome, among other
examples4. Moreover, microcephaly may lead to various developmental abnormalities and
decreased cognitive reserve, with long-term consequences such as the increased risk for
forms of dementia in vulnerable individuals8 –10.
Microcephaly pa rameters are well establi shed for children a ged 0 to 18 months11, and the World
Health Organization makes available charts for the HC growth in children from birth to the
age of ve years, plotted as standard deviations from the mean12. Nevertheless, even though
HC is an accessible and inex pensive measure, we lack dened para meters for microcephaly i n
adults, and little is known about the clinical implications of microcephaly in this population.
is study aimed to establish a microcephaly cut-o size in adults using HC as an indirect
measure of brain volume, as well as to investigate factors related to microcephaly via data
mining to elicit several possible associations.
METHODS
Data Source
is cross-sectional study was conducted on subjects who underwent autopsy at the
São Paulo Autopsy Service (SVOC-USP) between 2004 and 2019. In Brazil, autopsies are
mandatory for all individuals whose cause of death was unidentied after death. e
SVOC-USP is a community-based general autopsy service.
Study Population
Data were derived from the collection of the Biobank for Aging Studies at the Universidade
de São Paulo (BAS-USP). Our study protocol was reviewed and approved by the ethics
committee of the Faculdade de Medicina at the Universidade de São Paulo (approval number
458.272), following the World Medical Association Declaration of Helsinki. Subjects were
included in our study after its procedures had been explained to family members and they
had signed an informed consent form, thus agreeing to participate in our research.
e methodological procedures of the BAS-USP have been described elsewhere13–15 . Subjects
who were aged 50 years or older and who had died from natural (non-traumatic) causes
were included. Cases without reliable informants, with a medical history of advanced
chronic diseases or a prolonged agonal state were excluded. Subjects with signicant
cerebral lesions, including stroke and cerebral tumors, were excluded from the BAS-USP
cohort because an immediate brain examination is required to conrm the cause of death.
Nurses with expertise in gerontology invited knowledgeable informants to participate in
our study. Knowledgeable informants were close family members or caregivers who had at
least weekly contact with the deceased in the last six months before their death and could
recount and provide details on subjects’ health information.
Clinical Post-Mortem Evaluation
Clinical evaluation consisted of assessing subjects’ clinical and functional status three
months before death. A validated semi-structured cl inical interv iew16 assessed demograph ic

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variables (age, sex, and educational attainment), conditions related to death, past medical
history, and cognitive status. For the association rule mining (ARM), age was also
categorized according to the median of the sample as < 70 and ≥ 70 years old, and education
was stratied into illiterate, 1–4 years of study, and ve years or more. Cognitive status in
the three months before death was assessed by the informant questionnaire on cognitive
decline in the elderly (IQCODE)17, and informants aided in the clinical dementia rating
(CDR)18, validated for post-mortem use16. e IQCODE assesses the cognitive decline in
the elderly in the past ten years, and the IQCODE cut-o was ≥ 3.419. e CDR was used to
identify the presence and stages of dementia, and a CDR > 0.5 was considered indicative
of cognitive impairment18.
Clinical medical history was assessed in detail during the interviews with informants,
including history of hypertension, diabetes mellitus, coronary artery disease, congestive
heart failure, dyslipidemia, cardiac arrhythmia, stroke, alcohol abuse/alcoholism, and
tobacco use.
During the clinical evaluation, the interviewer continuously checked for data consistency
and exclusion criteria to detect any conditions that might lead to the exclusion of the case
at hand.
Morphometric Measurements
Head circumference was obtained before opening the skull. An inelastic tape was
placed around the skull to obtain the largest perimeter when across the glabella and
opstocranium2 ,20. Brain volume (in mL) was obtained by estimating the volume of water
displaced by the submerged brain, according to Archimedes’ principle, a standard procedure
for accurately measuring the volume of body regions21.
In this study, the microcephaly cut-o was dened in two steps. First, we analyzed which
variables were associated with dierent HC measures. e variables tested were sex,
height, age, and educational attainment. Second, microcephaly was set at two standard
deviations below the mean HC for each group of clinical variables found to have a
correlation with HC.
Statistical Analysis
Spearman correlation test was used to determine the association of HC with brain volume,
height, age, and education. Moreover, dierences in HC between sexes were tested with
independent sample t-Tests. e signicant associations or di erences found were included
in a multivariate linear regression analysis. e entire sample was divided into quartile
measures of height to obtain the HC adjusted for height, with 10cm divisions; a sample
division followed this step according to sex. e level of signicance of the two-tailed tests
was set at 0.05. e software Stata 12.0 (College Station, TX: StataCorp LP) was used to
perform the statistical analyses.
Association Rule Mining
Association rule mining is a suitable method for discovering patterns or extracting
co-occurrences of events from databases. It is a rule-based machine learning method
for discovering multiple concomitant relations between variables in large databases.
We can derive association rules from the frequency of variable sets, called itemsets,
in an ordinal data set. An item is any variable characterizing a particular individual.
A frequent itemset is any set of items with a frequency greater than or equal to a user’s
predened minimum threshold22.
An association rule has the form (X ⇒ Y) with the logical meaning “IF X, TH EN Y”; in which
X and Y are sets of non-overlapping items, i.e., X implies the occurrence of Y. X and Y are
called the antecedent and consequent of the rule, respectively.

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Association rule mining is probabilistic, and its primary measure assessments are values
for support, condence, and lift. Support is dened by the joint probability of X and Y in the
data set, i.e., the percentage of records containing X and Y (Equation 1).
Equation 1: Support (X ⇒ Y) = Occurrences of both X ∧ Y
Total number of records
Condence is dened by the conditional probability of Y occurring given X in the data set,
i.e., the percentage of times both X and Y occur (Equation 2).
Equation 2: Condence (X ⇒ Y) = Occurrences of both X ∧ Y
Occurences of X
e lift measures the dependency relationship between X and Y (Equation 3) by assessing
how many times more often X and Y occur together than expected if they were statistically
independent. A lift value of one indicates X and Y are independent. A lift value greater than
one means that X and Y are positively correlated, and a lift value lower than one, that X and
Y are negatively correlated.
Equation 3: Lift (X ⇒ Y) = Occurrences of both X ∧ Y
Occurrences of X × Occurrences of Y
Association rule mining aims to discover frequent and reliable association rules, i.e., rules
with user-specied minimum thresholds of support and condence. Additionally, it can
also specify the maximum size of a rule, dened as the number of items comprising it. For
example, a rule of size three means that it consists of two items in the antecedent and one
item in the consequent, whereas a rule of size two means that it consists of one item in the
antecedent and one item in the consequent.
e most used ARM is the Apriori algorithm, introduced by Agrawal et al.22 in 1993.
It consists of two steps. In the rst step, frequent patterns (itemsets with support greater
than the predened minimal support) are generated. In the second step, frequent pattern
condences are estimated, and those with condence greater than the set minimal
condence are selected as the nal rules. In this study, a 1% minimum support and a
30% minimum condence were established. ese thresholds were chosen due to the low
frequency of microcephaly i n the data set, thus requir ing lower values to obtain microcepha ly
associations. Moreover, we set the maximum size of a rule as three; this meant that the
generated rules were either size two or three. is rule size was chosen to help us interpret
the associations since, with more extensive rules, it would be more challenging to analyse
how microcephaly is associated with other variables. e package arules of the R language
was used to perform the Apriori algorithms.
Ethics
Study approval statement: the data was derived from the collection of the Biobank
for Aging Studies at the Universidade de São Paulo (BAS-USP). is study protocol
was reviewed and approved by the ethics committee of the Escola de Medicina at the
Universidade de São Paulo (approval number 458.272), following the World Medical
Association Declaration of Helsinki.
Consent statement: a knowledgeable informant was a close family member or caregiver who
had at least weekly contact with the deceased in the last six months before their death and
could recount and provide details on the deceased’s health information.

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RESULTS
From 2004 to 2019, we obtained data from 2,508 individuals. Mean age was 70.8 (± 12.7)
years, and average education, 4.7 (± 3.8) years. Mean brain volume was 1,169 (± 163)
mL; mean HC, 55.3 (± 2.7) cm; and mean height, 1.68 (±10.3) m. We found smaller brain
volumes in individuals with a CDR > 0.5 than in individuals with a CDR ≤ 0.5 (1,090 ± 160;
1,179 ± 161 mL, respectively, p < 0.001). Table 1 shows the categorized sociodemographic
and clinical variables for the ARM.
Head circumference was correlated to brain volume (rho = 0.466, p < 0.001), height
(rho = 0.394, p < 0.001), age (rho = -0.25, p < 0.001), and education (rho = 0.169, p < 0.001), and
diered between sexes (men = 56.3 ±2 .6 cm, women = 54.2 ± 2.4 cm, p < 0.001).
In the multivariate ana lysis with brain volume, sex , height, a ge, and education as covariables,
HC was related to brain volume, sex, height, but not age or education, as Table 2 shows. If we
excluded cases with a CDR > 0.5 (as HC remains constant, but brain volume can atrophy),
the signicance of brain volume, sex, and height would have remained p < 0.001. Table 3
shows the number of participants with microcephaly according to height and sex. If we
consider microcephaly to be an HC two standard deviations below the mean, according to
height and sex, it was present in 4.7% of the sample (n = 119).
Table 1. Sociodemographic and clinical variables of the sample (n = 2,508).
Covariables n (%)
Age ≥ 70 years 1,349 (53.8)
Female 1,195 (47.7)
Education 394 (15.8)
Illiterate
1–4 years 1,368 (54.8)
≥ 5 years 734 (29.4)
Hypertension 1,482 (64.7)
Diabetes mellitus 696 (27.8)
Coronary artery disease 511 (20.4)
Congestive heart failure 422 (16.8)
Dyslipidemia 244 (9.7)
Cardiac arrhythmia 174 (6.9)
Stroke 298 (11.9)
Alcohol abuse/alcoholism 375 (15.0)
Tobacco use 790 (31.5)
CDR > 0.5 436 (17.5)
IQCODE ≥ 3.4 446 (17.9)
CDR > 0.5: clinical dementia rating indicative of cognitive impairment; IQCODE ≥ 3.4: cut-off for the informant
questionnaire on cognitive decline in the elderly in the past 10 years.
Table 2. Association of head circumference with sociodemographic and clinical variables (n = 2,508).
Covariables β (95%CI) p
Brain volume (mL) 0.005 (0.005 to 0.006) < 0.001
Age (years) -0.009 (-0.020 to 0.002) 0.10
Sex (female) 0.849 (0.573 to 1.124) < 0.001
Education (years) 0.012 (-0.019 to 0.044) 0.44
Height (cm)a0.043 (0.029 to 0.058) < 0.001
a Multivariate linear regression analysis.

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Association Rules with Microcephaly
All variables assessed, via ARM, for their relationship to microcephaly were ordinal.
ose variables included age, sex, education, hypertension, diabetes mellitus, coronary
artery disease, congestive heart failure, dyslipidemia, cardiac arrhythmia, stroke,
alcohol abuse/alcoholism, tobacco use, a CDR > 0.5, and an IQCODE ≥ 3.4. e ARM
produced 34,355 candidate rules that satised the required thresholds in the discovery
data set. en, we selected only the rules which included microcephaly to analyze
possible associations with the other variables, resulting in 258 association rules. Finally,
we evaluated the rules with the highest degree of association between microcephaly and
the other variables by their lift values.
Analysis of the Rules of Size Three
Initially, we analyzed the rules of size three, i.e., rules with associations between three
variables, one of them being microcephaly. ree rules had lift values of more than two,
expressing the most signicant associations: IF a CDR > 0.5 AND microcephaly, THEN an
IQCODE ≥ 3.4 (condence: 100%, lift: 5.6); IF an IQCODE ≥ 3.4 AND microcephaly, THEN a
CDR > 0.5 (condence: 100%, lift: 5.6); and IF age over 70 years AND microcephaly, THEN
CDR > 0.5 (condence: 42%, lift: 2.4).
After this initial analysis, we sought rules which excluded microcephaly to investigate how
it inuenced these associations. Table 4 shows a comparison between the association rules
with and without microcephaly. Condence and lift values were higher if microcephaly
were present in the association.
Analysis of the Rules of Size Two
We also analysed rules of size two, nding signicant associations between lift values
greater than one and males: IF microcephaly, THEN male (condence: 68.1%, lift: 1.3).
en, we examined the association between microcephaly and females: IF microcephaly,
Table 3. Head circumference and microcephaly cut-off values according to sex and height (n = 2,508).
Distribution
of height (m)
Female Male
nHC
(cm), mean ± SD
Microcephaly
cut-off (cm) nHC
(cm)
Microcephaly
cut-off (cm)
Up to 1.59 282 53.5 ± 2.4 < 48.7 46 55.2 ± 2.3 < 50.6
1.60–1.69 301 54.3 ± 2.2 < 49.9 198 55.4 ± 2.4 < 51.0
1.70–1.79 327 54.7 ± 2.2 < 50.3 427 56.2 ± 2.4 < 51.8
1.8 or more 45 55.6 ± 2.6 < 50.4 374 56.7 ± 2.5 < 51.7
HC: head circumference.
Table 4. Comparison between rules that show associations among CDR > 0.5, IQCODE ≥ 3.4, and age
over 70 years with and without the presence of microcephaly.
Rules with microcephaly Condence Lift Rules without
microcephaly Condence Lift
IF CDR > 0.5 AND
microcephaly,
THEN IQCODE ≥ 3.4
100% 5.6 IF CDR > 0.5
THEN IQCODE ≥ 3.4 95.4% 5.4
IF IQCODE ≥ 3.4 AND
microcephaly,
THEN CDR > 0.5
100% 5.6 IF IQCODE≥3.4
THEN CDR>0.5 93.3% 5.4
IF age over 70 years AND
microcephaly,
THEN CDR > 0.5
42.0% 2.4 IF age over 70 years
THEN CDR > 0.5 28.4% 1.6
CDR > 0.5: clinical dementia rating indicative of cognitive impairment; IQCODE ≥ 3.4: cut-off for the informant
questionnaire on cognitive decline in the elderly in the past 10 years.

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THEN female (condence: 31.9%, lift: 0.7), in which lift values below one indicate a negative
association between microcephaly and females.
Finally, besides the associations related to microcephaly (the focus of this study), other rules
produced associations, such as IF diabetes, THEN hypertension (condence: 81.8%, lift:
1.4); IF stroke, THEN diabetes (condence: 39.1%, lift: 1.4); IF stroke, THEN hypertension
(condence: 80.2%, lift: 1.4); IF an IQCODE ≥ 3.4, THEN female (condence: 63.7%, lift: 1.3);
and IF a CDR > 0.5, THEN female (condence: 63.5%, lift: 1.3).
DISCUSSION
Our study is one of the few analyzing the relationship of microcephaly with clinical and
sociodemographic variables and the only one that used ARM, a data mining approach. We
found that microcephaly was associated with dementia or cognitive impai rment, especia lly
in individuals older than 70 years.
Head circumference correlated to adult brain volume without dementia, and it is a
non-invasive, fast, and inexpensive method to indirectly measure ch ild or adult brain volume.
As HC remains constant across the life span, these results suggest that microcephaly might
be a risk factor for dementia at old age, as structural changes in the brain may impact
cognition across the older age span23.
e results of the association of microcephaly with a CDR > 0.5 and an IQCODE ≥ 3.4 agree
with previous studies. A longitudinal study evaluating 1,569 individuals, aged 60 and
over, from a Korean community showed that the clinical expression of dementia related
to brain volume. People with larger brains were more likely to remain nondemented24.
Another frequently cited longitudinal study, in which 294 catholic sisters were assessed
annual ly for dementia, found that high educational attainment and larger head size, either
by themselves or in combination, may reduce the risk of the expression of dementia in
later life25. A population study based on the Well-being of the Singapore Elderly survey
assessed associations between dementia, HC, and leg length among the older adult
population and found that HC is independently associated with dementia among that
population, suggesting that the risk factors for dementia exert their inuence since early
life9. With more neurons and synapses, maximum brain volume may be an important
variable associated with brain reserve20,24,26.
Machine learning algorithms can complement classical statistics27, helping researchers
to create new hypotheses28. We used ARM in our study for two main reasons. First, ARM
enabled us to observe all associations among the clinic and sociodemographic variables
available in our database. Second, we could verify, by the ARM metrics, how strong the
associations were when we compared the rules in the presence or absence of microcephaly.
e signicant association we found between microcephaly and males agrees with the
literature, as mental retardation is more frequent in boys than girls, a nding attributed
to mutations in X-linked genes28, 29. Besides the associations related to microcephaly (the
focus of this study), ARM also produced associations which are well established in the
literature, such as the ones between diabetes and hypertension, stroke and diabetes, stroke
and hypertension, cognitive decline and females, and dementia and females30, reinforcing
the use of this method in showing reliable associations.
In our study, we found that HC correlated with brain volume, sex, and height. Individuals
with dementia showed a smaller brain volume, an expected atrophy due to their condition.
Measuring HC has advantages since it is a non-invasive, fast, and inexpensive method to
indirectly measure child or adult brain volume. To indirectly determine microcephaly via
HC, we must, ideally, consider sex and height. In our sample, men with an HC < 51cm and
women with an HC < 49cm are indicative of microcephaly; if height > 1.7m, one should
add 1cm to the HC. We also nd signicant associations of HC with brain volume in the

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few studies conducted in adults1,2 ,31–3 3. e relationship of HC with age and sex is well
established in children aged 0 to 18 months11. In this study, we considered the corrections
for sex and height, but not age, appropriate to determ ine microcephaly in adults, despite the
century-long growth trend many countries show34 — attr ibuted to improved environmental
conditions34. As a result, we now have progressively larger adults than in previous decades.
Taller people often have larger brains and heads35,36. In th is study, however, after the logistic
regression, height and sex, but not age, related to HC and brain size.
Strengths of our study include its large sample size, community basis, and an ethnically
and educationally diverse population. However, our study has some limitations: its
cross-sectional nature fails to allow for causal relationships. Moreover, the use of
informant-reported data is a concern, as informants can be unaware of some of the
treatments and disorders the deceased may have had. However, we used a validated
semi-structured clinical interview16 which several other publications accept13–15,37,38. Our
study assessed a community sample in Brazil. Samples from multiracial countries, such
as ours, can add valuable data to the literature, but the validity of our ndings to other
populations needs further testing. Furthermore, to the best of our knowledge, this is
the rst study that applied ARM to detect rules associated with microcephaly in adults.
is strategy has the advantage of setting high-accuracy standards and the analysis of
multiple variables at the same time.
CONCLUSION
is population-based cross-sectional study suggests that HC not only relates to cerebral
volume but could also function as an accessible and inexpensive screening test for
microcephaly, in conjunction with individuals’ height and sex. Moreover, we found an
association between microcephaly and clinical variables often present in cognitive decline
at older age which might be a risk factor for dementia.
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Funding: Fundação de Amparo à Pesquisa do Estado de São Paulo (Fapesp - Process 2018/16626-0; 2016/24326-0
and 2017-07089-8). Conselho Nacional de Desenvolvimento Cientíco e Tecnológico (CNPq - Process 466763/2014-0).
Alzheimer’s Association Research Fellowship (AARF - 18-566005). Instituto Federal Goiano – Campus Iporá (grant
number 23220.001846.2021-19). Private donation from Paulo Sérgio Galvão to University of São Paulo Bipolar
Disorder Research program (Promam) for funding this research.
Authors’ Contribution: Study design and planning: PVN, RS, CAP, RN, WJF, LG, CKS. Data collection, analysis
and interpretation: PVN, RS, RDR, REPL, CN, CAP, RN, LM, WJF, LG, BL, CKS. Manuscript drafting or review:
NRC, LM, RS, JMT, RDR, REPL, CN, CAP, RN, WJF, BL, LG, CKS, PVN. Approval of the nal version: NRC, LM, RS,
JMT, RDR, REPL, CN, CAP, RN, WJF, BL, LG, CKS, PVN. Public responsibility for the content of the article: NRC,
LM, RS, JMT, RDR, REPL, CN, CAP, RN, WJF, BL, LG, CKS, PVN.
Conict of Interest: e authors declare no conict of interest.
Data Availability: e data that support the ndings of this study are publicly unavailable due to information that
could compromise the privacy of research participants, but data are available from the data sharing committee
(gerolab@gmail.com) upon reasonable request.