1

PHASE IV (2014/2015)

COMMUNITY HEALTH PROJECT 2015

GROUP 2

KHAN95 Study: Knowledge, Attitudes and Practices of Haze and

N95 mask use amongst Singaporeans

Authors

ABIGAIL LEE

ALEX LUA

ALEXANDRA YUNG

AMANDA CHIN XIN YI

ANG KAI YUN

CHAN HUI CHING NICOLE

CHAUNG JIAQUAN

CHERYL LAM

CHUA JIA LONG

CLAUDIA CHONG YING XIA

FIONA NG YEE LIN

HOA MIN HUI, LYRIA

JASMINE CHANG

JIANG BOCHAO

JULIA-ANN LEE TING YAN

KENNEDY NG YAO YI

KEVIN LIM

LEE BING HOWE

LEONG YUN HAO

LIANG SAI

LIE JIA LING CHERYL

LIM WAN JUN, DEBORAH

LIU REN WEI

LOOK XINQI

LORRAINE YONG

LOW YU HAN KRISTABELLA

LOW ZHAO KAI

MARIANNE TSANG

MICHAEL CHEE YEN KIT

NICHOLAS NGIAM

NICOLE CHEW

NIGEL FONG

RYAN LEE

SHAINA NEO

TAN HUI MING GEORGE

TAN WEI PING MARCUS

WESLEY YEUNG

Supervisors

A/Prof Chia Sin Eng Dr. Judy Sng

Yong Loo Lin School of Medicine

Saw Swee Hock School of Public Health

National University of Singapore

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INTRODUCTION

Singapore suffers from yearly bouts of haze. In 2013, the 3-hour Pollution Standard Index (PSI)

peaked at a hazardous level of 401. This haze is due to land clearing by fire in neighbouring

Indonesia1, which is used extensively to clear land for agriculture or to settle disputes over land

rights2.

The most consistent pollutant in biomass smoke is micro-particulate matter (PM). These are

small combustion particles, classified by size as <10µm (PM10) and <2.5µm (PM2.5)3. Gaseous

pollutants are also produced, including CO, CO2, CH4, NOx and NH34. Studies in Kuala Lumpur

found high PM but low gaseous pollutants in haze5.

Exposure to PM has been reported to have small but significant associations to a range of health

effects, both in the short term and long term. It has been linked to an increased incidence of

respiratory tract infections and conjunctivitis and an increase in outpatient and accident and

emergency attendance in Singapore6 and neighbouring countries like Malaysia7 and Thailand8. In

particular, PM10 has been found to be positively associated with blurred vision (AOR 1.009,

95% CI 1.004–1.014)8. Exposure to PM10 has been linked to an increase in emergency hospital

admissions in people with underlying respiratory diseases, such as asthma and chronic

obstructive pulmonary disease (COPD) in a dose dependent manner9,10. Exposure to PM air

pollution also influences a range of cardiovascular health measures, including self-reported

symptoms, mean arterial pressure and maximal ST segment depression in susceptible patients

with underlying coronary heart disease11, and has been linked to heart failure-related

hospitalizations and mortality12 with the highest effects seen on the day of maximal exposure13.

These cardiorespiratory effects have been attributed to the production of cytokines during

exposure to PM1014.

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N95 masks protect against PM10, PM2.5

Face masks are regarded by both the government and the general population as a logical defence

against haze. N95 masks provide effective protection against particulate matter, and can reduce

exposure by 10-fold with proper fit and usage15.

In 2013, Singaporeans emptied pharmacy shelves of surgical and N95 masks, which had to be

rationed16,17. In response, the Government distributed 4.15 million stockpiled N95 masks, of

which 1 million masks were allocated to low-income households18. In 2014, the Government

provided each household with an ‘emergency preparedness starter kit’, inclusive of N95 masks,

in order to enhance community preparedness in the event of a severe haze episode, or a flu

pandemic19. The ‘duck bill’ foldable design was selected over the standard cup-shaped design for

ease of mailing.

N95 respirators are only effective when donned properly. Hence, it is unclear whether the

aforementioned measures will truly protect Singaporeans from haze particles. There is little data

on mask donning proficiency in the community, with only 24% of participants demonstrating

proper mask donning techniques in a study done in the United States20.

User training to perform a fit-check has been shown to increase the likelihood of a proper fit15,21.

Numerous public education modalities are available for this purpose, including use of the

internet, television, radio, printed media, and face-to-face demonstrations. In a study performed

by Harber et al., video training was found to be significantly superior to print and computer-based

training methods22. No similar studies have been performed evaluating the effectiveness of

various training modalities in the local context.

Given that Singapore can do little to prevent the haze from recurring, masks will be an important

mitigation strategy for years to come. However, the provision of masks will be ineffective

without knowledge of proper mask fit techniques. Hence, this study seeks to evaluate the mask

donning proficiency of the Singapore community.

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Personal Protective Behaviors During Haze

Exposure to the haze, and its associated adverse health consequences, is a unique public health

problem that cannot be meaningfully addressed by local public policy measures. Prevention of

adverse health consequences from exposure to haze relies on personal protective behaviors.

However, past research has suggested that more than half of respondents did not take any

measures to protect themselves from air pollution23. Other studies have also examined the

relationships between knowledge, risk perception and personal protective behaviors in other air

pollution settings. Although it seems intuitive that people who have more knowledge of haze

would have a higher perceived risk and hence take more personal protective behaviors, previous

research had mixed results.

Evans et al. investigated psychological reactions to air pollution in Los Angeles24. The

participants’ beliefs about air pollution were measured by asking respondents to rate: (1) the

severity of smog in the city they lived; (2) perceived smog on the day of interview; (3) whether

they believed that smog had negative effects on their personal health. Knowledge of smog was

assessed by asking respondents to rank in order the principle causes of smog. Finally, they

assessed compliance to two health advisories: (1) Utilise transportation alternatives other than

driving; (2) Restrict outdoor activities during high pollution periods. Similar to other studies23

only about half of participants followed state advisories. They also found that personal beliefs of

negative consequences were associated with compliance to state advisories.

Skov, Torben, Torben Cordtz, Lilli Kirkeskov Jensen, and Peter Theilade studied modifications

of health behavior during air pollution in Copenhagen in response to notifications25. They

measured beliefs of health consequences by the following questions: (1) Do you think the air in

the Copenhagen area may cause health problems on a short term basis (hours or days)? ; (2) Do

you think the air in the Copenhagen area may cause health problems on a long term basis. Similar

to the study in Los Angeles, they assessed knowledge of smog by asking respondents to rank, in

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order, prime emission sources. The two coping behaviors measured were the avoidance of

outdoor activities, and avoidance of driving. Unlike Evans et al., they did not find any significant

association between knowledge or beliefs of health consequences and avoidance of outdoor

activity.

Evans et al. measured the construct of knowledge by the correct ranking of sources of emissions

and did not include the nature of harm caused by air pollution. In fact, knowledge of the sources

of emissions is neither sufficient, nor indicative of knowledge of the health effects of haze. The

findings by Skov et al. were confounded by whether residents noticed an air pollution

notification. They also acknowledged that Copenhagen experiences relatively mild pollution

levels, and hence, the effect size observed might be attenuated. In both studies, perceived risk and

coping behaviors were specific to urban air pollution. Urban air pollution differs from haze in an

important way: where urban air pollution is concerned, residents might see themselves both as

contributors and victims from the pollution; in contrast residents suffering the haze see

themselves as victims of foreign forces which they have no direct part in. Hence, health behaviors

studied in urban air pollution often include a component of reducing personal contribution to

pollution. Urban air pollution is a more chronic problem whereas haze tends to affect residents in

an episodic manner lasting at most a few weeks at a time. In addition, the above two studies were

conducted more than two decades ago (in late 1988 and 1991 respectively). Since then,

information has become increasingly more accessible. For instance, Singapore’s National

Environment Agency website provides the public with information about haze, its effects on

health, and protective measures that one can take26. Pollution levels (Pollution Standards Index)

are updated hourly, and beyond certain levels, certain protective measures are advised, such as

reducing prolonged or strenuous outdoor physical exertion, are advised. We postulate that these

developments have improved public knowledge of haze, causing a change in health behaviors.

There are no existing questionnaire instruments developed for haze and new measures need to be

devised.

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OBJECTIVES

The primary objectives for this study are twofold. First, we aim to assess the knowledge, attitudes

and practices of Singapore residents with regard to the haze, and N95 mask usage in particular.

This includes assessing the level of knowledge of harmful effects from the haze and possible

protective measures, as well as the actual preventive measures undertaken by Singapore

residents. The focus of this study is on the 2013 haze episode, given that it is the most severe

haze episode in recent memory. Next, we aim to assess the prevalence and proficiency of N95

mask use in the community during haze.

Secondary objectives for this study include exploring underlying factors that influence an

individual’s personal protective behaviors during haze episodes, and identifying factors

associated with N95 mask usage, and N95 mask fit proficiency.

METHODOLOGY

We conducted a cross-sectional study from 9th to 15th February 2015 in Jurong East district in the

western part of Singapore. This area was most affected during the haze period of 2013, as

measured by the National Environmental Agency of Singapore. The survey included an

interviewer administered questionnaire measuring Knowledge, Attitude and Practices on the haze

situation in June to July 2013 and on the usage of N95 Respirator, and a visual mask-fit test

assessing participants’ proficiency of donning the N95 Respirator.

Sampling

We used a two-stage cluster sampling technique. In the first stage, all 167 public housing

apartment blocks in the Jurong East district were identified using publicly available information

from the Singapore Housing Development Board. 120 HDB blocks were randomly selected via

simple random sampling without replacement. In the second stage, all residential units were

identified from the apartment blocks selected in the first stage. 20% of the residential units within

each HDB block were selected by simple random sampling without replacement. The first

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individual answering the door who met the inclusion criteria was enrolled. If that individual did

not fulfill the inclusion criteria, the interviewer team would then sample the next eligible resident

in the household. Inclusion criteria for the survey were: (1) Singapore Citizen/Permanent

Resident; (2) age 21 years or older; (3) lived in the estate during the month of June & July 2013;

(4) able to communicate in English or Mandarin; (5) physically able to independently don the

N95 mask.

Survey (Refer to Appendix 1 for survey protocol)

Data was collected electronically using a secure online survey platform on tablet computers. All

data were digitally captured, stored and made accessible only on password-protected terminals.

No identifiable information were collected. A secondary backup mode of data collection was in

the form of hardcopy printed questionnaires in case of technical failure. The online digital

platform reduced the chance of human error that could happen during the data entry from

questionnaire forms. It also allowed for real time data analysis and quality control.

Surveys were conducted in the evenings between 5pm to 9pm on weekdays and 2pm to 8pm on

weekends when most residents would have returned home from their daily activities. If there

were no respondents, households were given a non-responder notice (Refer to Appendix 2) and

re-visited on another occasion (same day or another day).

Two unique interviewer teams were randomized during each day of fieldwork to undergo an

auditing process. The teams were assessed by a dedicated auditor based on a checklist covering

the key points in the project’s protocol. The purpose of the audit was to ensure that no protocol

breach occurred and to provide continuous daily feedback on good practices as well as minor

mistakes made. The internal audit yielded a small number (n=6) of protocol breaches (e.g.

completed surveys that did not meet the inclusion criteria). These entries were promptly traced

out and removed from the collected data.

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Mask Fit Testing

There are several models of the N95 respirator mask available. The 3M VFlex 9105 N95 mask

was used in our study as it is the model distributed by the government for free to households in

Singapore. (Refer to Appendix 3)

Qualitative or Quantitative Fit Testing are the most accurate methods for assessing if a N95 mask

fits a particular individual, and are approved by the Occupational Safety and Health

Administration, USA27. However, these methods are time-consuming and require bulky

equipment that may not be practical in the community. A more feasible method is the Visual N95

Fit Testing which includes visual assessment and a User Seal Check. It sacrifices accuracy for

convenience - 25 - 30% of those who pass the Visual N95 Fit Test fail a subsequent Qualitative

or Quantitative Test28,29. However, it may be the most appropriate method in a community study

like ours.

An individually sealed N95 Mask was given to the respondent with an attached pictorial

instructions sheet containing instructions in English, Chinese, Malay and Tamil languages.

Participants were not prompted to refer to the attached instructions. Interviewers noted whether

the participant spontaneously referenced the instructions. The respondent was then asked to put

on the mask as he/she deemed fit. A visual mask-fit test was conducted, as per the protocol used

in a previous study20 (Refer to Appendix 4). We also included a User Seal Check which is a self-

administered test and is performed in accordance with manufacturer instructions from 3M30.

Regardless of the outcome of the mask fit assessment, the interviewer would conclude with a

mask fit demonstration for educational purposes. The mask-fit procedure is taught according to

instructions from 3M30. (Refer to Appendix 5)

Questionnaire Development

Questions on demographics were adapted from the Singapore General Household survey and

2010 Singapore census. Questions on tobacco use were adapted from the WHO Global Adult

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Tobacco Survey31. Diseases chosen to represent medical history of chronic diseases were chosen

based on published studies13,32,33. Health advisories and information published by the Singapore

National Environment Agency were used to design questions on protective actions. Factual

questions on haze were designed using information from published studies5,9,10. Domain experts

at the Saw Swee Hock School of Public Health were consulted for feedback and expert opinion.

Refer to Appendices 6 and 7 for English and Mandarin questionnarires respectively.

In the present study, we propose a measurement instrument that addresses the limitations of

previous studies and is specific to haze. Knowledge is measured by a series of 6 statements,

Perceived risk by 4 statements, and Personal protective behaviours by 4 statements (Table I).

Reliability & Validity

Overall reliability of our questionnaire was assessed using the McDonald’s Omega Hierarchical

(ωh) coefficient on a 3 factor confirmatory factor analysis model34. The latter was estimated using

a tetrachoric correlation matrix suitable for binary variables35. This was chosen over the more

commonly reported Cronbach’s α coefficient as our measures were based on a multidimensional

factor model and violates the assumptions of unidimensionality and tau-equivalence required for

Cronbach’s α36. Reliability of individual subscales were estimated using the McDonald’s Omega

Total (ωt) coefficient37 and presented along with correlation of scores with factors. Reliability

estimates were computed using the “psych” package in R38.

We define the validity of the questionnaire as to whether it measures what it purports to39.

Specifically, we looked at the construct validity of our questionnaire, which is how adequately

our questionnaire items represent their underlying latent traits. This was assessed by the direction

and magnitude of item factor-loadings to our hypothesized constructs.

Translation

The English questionnaire was translated into Chinese by multiple bilingual experts and

subsequently back-translated to ensure semantic equivalence.

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Pictorial reference

A standardized visual aid template pictorial reference (eg. pictures of air purifiers, surgical, N95

masks, etc) was used whenever possible to aid in the interview process and to reduce recall

bias40. (Refer to Appendix 8)

Training and Pilot Study

Interviewer training was conducted to ensure standardization of survey execution, minimizing

bias introduced by inconsistent interview styles. Two compulsory sessions with full attendance

were conducted three months apart: one prior to the Pilot study and the second just before

initiation of fieldwork. Interviewers were taught the survey protocol for identification of study

participants and a Manual of Procedures was issued to each interviewer as a reference. Regarding

the visual mask-fit assessment, a Senior Resident in Occupational Medicine from the National

University Hospital, Singapore trained interviewers on proper mask-fit technique and assessment.

All interviewers were assessed at the end of the training session to ensure competency and

consistency in administration of the survey and visual mask fit. This was conducted in the form

of eight case studies featuring different possible scenarios that may be encountered during

fieldwork based on Pilot study observations.

A pilot study was conducted in December 2014 in the neighbouring district of Clementi on 278

households in public housing blocks. Data from the pilot study was used to (1) help identify

inadequacies in the protocol and (2) refine the questionnaire. Improvements were made and these

were highlighted to interviewers in the second training session.

Ethics

Institutional Review Board (IRB) approval was granted for our study under NUS-IRB no. B-14-

250. Verbal consent was obtained for all participants and a participant information sheet (PIS)

provided. (Refer to Appendix 9)

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Data analysis

Data were analysed using using R: A Language and Environment for Statistical Computing41 and

SPSS 22.0 for Windows42. Sample proportions and means of demographic characteristics were

presented in a descriptive Table. Outcome variables were presented as proportions together with

binomial 95% confidence intervals for key variables. Percentile 95% confidence interval

estimates for the median of sum of practice scores were obtained by ordinary nonparametric

bootstrap with 100000 replicates. Bias corrected and accelerated 95% confidence interval

estimates43 for ωh were obtained by an ordinary nonparametric bootstrap with 1000 replicates44.

Bootstrapping was performed using the “boot” package in R45,46.

Hypothesis testing for factors influencing and mask fit success were performed using the

Pearson’s chi-squared test for categorical variables and the Welch's t test for age. Testing of

differences in proportions across income group was performed using the Chi-squared Test for

Trend in Proportions. Subsequently, we fit a binary logistic regression model to examine

variables associated with passing the visual mask fit test and to control for possible confounders.

The nonparametric Kruskal Wallis H test was used to examine for differences in practice score

rankings between levels of perceived severity of haze. Subsequently, pairwise Mann-Whitney U

tests were performed between the perceived severity level "Neutral" and the other severity

groups. The p-values for the latter were adjusted for family-wise error rate using the Bonferroni

correction. A p-value of <0.05 was taken to indicate statistical significance for all hypothesis

testing.

To determine the relationship between the participant’s perceived severity of haze (indicated on a

Likert scale) and the frequency of personal protective practices, a total “practice score” was

calculated for each individual. The practice score was calculated by taking the sum of Likert

scores for all 7 practices within our questionnaire. Likert scores for each item range from 1

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(Frequency= Not at all) to 5 (Frequency= Almost Every Day), with 7 and 35 being the minimum

and maximum total scores respectively.

We fit 2 hypothesized three-factor Item Response Theory (IRT) measurement models to our three

latent variables of Knowledge, Risk and Behaviour*. A chi-square difference test was conducted

between a 1 parameter (1P) and 2 parameter (2P) IRT model to choose for the better fitting

model. The chosen measurement model was then incorporated into 2 candidate IRT-Structural

Equation Models (IRT-SEMs)†. Likewise, a chi-square difference test was conducted to select for

the better final model using objective criteria. Item Response Theory and Structural Equation

Modelling were performed with MPLUS47. We used the Means and Variance Adjusted Weighted

Least Squares Estimation (WLSMV) estimator with a probit link function as our data contained a

mixture of continuous and binary variables48‡. Unstandardized and standardized path coefficients

were presented together with direct, indirect effects and standard errors. Absolute and relative fit

indices were used to assess model fit: (1) the χ2 goodness-of-fit statistic; (2) Root Mean Square

Error of Approximation (RMSEA); (3) Comparative Fit Index.

RESULTS

We attempted to survey 2,499 households. Of these, 268 (10.7%) did not meet inclusion criteria

and were excluded. Among households who met the inclusion criteria, 714 (32.0%) completed

the survey, 541 (24.2%) declined to be surveyed, and 976 (43.7%) did not respond. (Figure I)

The demographics of our study respondents were comparable to that of the Singapore 2010

census (Table II) with the exception that the Indian race was slightly over-sampled. Median age

differs because respondents less than 21 years of age were excluded.

                                                                                                               * IRT is suitable for binary responses in questionnaire items (unlike confirmatory factor analysis), accounts for measurement errors inherent in our observed variables when estimating latent variables scores and provides additional information such as item difficulties and discriminations. † A detailed discussion of Modern Test Theory, Item Response Theory and Structural Equation modeling is beyond the scope of this paper but we have included a brief overview in Appendix 12. ‡ Parameter estimation techniques for IRT-SEM models include full information maximum likelihood (MLE) or limited information Means and Variances Adjusted Weighted Least Squares (WLSMV).

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N95 Mask Wearing and Mask-Fit Pass Rates

For a respondent to be effectively protected from haze via N95 masks, he/she must (1) own an

N95 masks, (2) if so, wear the mask, (3) if so, wear the mask correctly. We analysed each of

these in turn.

During the 2013 haze episode, 78.2% of respondents owned masks (Figure 1). Common barriers

to owning N95 masks (Table III-A) include discomfort (36.0%) and masks being out of stock

(33.8%). 20% of respondents who did not own masks reported that the cost of N95 masks was a

barrier, but there was no trend in proportions who reported that cost was a barrier among the

income groups (p = 0.79, Table III-B).

Among respondents who owned N95 masks, 55.6% wore the masks (Figure II). Masks being

troublesome (47.7%) or uncomfortable (53.8%) to wear were the common barriers to mask

wearing (Table III-C). Bivariate analysis found that respondents with a past medical history of

chronic disease, or who perceived that they were at greater risk of haze, were 1.33 times (p =

0.044, Table IV-A) and 1.63 times (p =0.001, Table IV-B) respectively more likely to wear a

mask.

Wearing N95 masks alone does not ensure protection if the mask is improperly donned. Only

12.6% (95% CI 10.3-15.3%) of all respondents passed the visual mask-fit test. The most

commonly failed mask-fit criteria (Table V) were correct strap placement (73% fail), leaving a

visible gap between the mask and skin (62% fail), and tightening the nose clip (60%).

Younger age (p<0.0001, Table VI-A), receipt of previous mask-fit training (p<0.001, Table VI-

B) and previous ownership of N95 masks (p=0.008, Table VI-C) all significantly improved

mask-fit pass rates. Such univariate analysis, however, was subject to confounding – for instance,

when respondents were stratified into groups with and without previous mask-fit training,

ownership of N95 masks ceased to significantly improve pass rates. The binary logistic

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regression model (Table VI-D) found that younger age (AOR 0.97, 95%CI 0.95-0.99) and past

mask-fit training (AOR 3.34, 95%CI 2.10-5.62) were independent predictors of improved pass

rates. Notably, referring to the multilingual instruction leaflet (provided with the mask; Appendix

10) was not associated with improved pass rates in both the chi-squared test (p=0.59, Table VI-E)

and the logistic regression model (AOR 0.94, 95%CI 0.54-1.63). Having worn a mask during the

2013 haze was also not significantly associated with improved pass rates (Table VI-F).

Furthermore, 98.4% of all respondents had at least 1 significant misconception about the usage of

N95 masks (out of 4 questions asked, Table VII). Even in the presence of correct mask-wearing

technique, such misconceptions may compromise effective protection.

In terms of mask fit education strategies, 79.5% of respondents agreed that mask-fit education

should be carried out. In terms of the medium for such education, the most popular choice across

all age groups was television, although internet-based education appealed to younger age groups

(<40 years), and face-to-face demonstration at Community Centers or door-to-door visits were

more popular in older age groups (especially those >65 years) (Figure III).

Knowledge, Attitudes, Practices towards haze

Our study also analyzed knowledge of and attitudes towards haze, as well as personal protective

behaviour taken during the 2013 haze episode. These included (Table VIII) seeking daily news

updates on severity of haze (72%), or avoiding outdoor activities at least weekly (71%).

To determine the relationship between the participant’s perceived severity of haze (indicated on a

Likert scale) and the frequency of personal protective practices, a total “practice score” was

calculated for each individual. The practice score was calculated by taking the sum of Likert

scores for all 7 practices within our questionnaire. Likert scores for each item range from 1

(Frequency= Not at all) to 5 (Frequency= Almost Every Day), with 7 and 35 being the minimum

and maximum total scores respectively. A Kruskal-Wallis H test showed that there was a

statistically significant difference in practice scores between the different levels of perceived

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severity, χ2 = 52.589, p <0.001, with the mean rank practice scores as demonstrated in the Table

above for the respective levels. Figure IV summarizes the median practice scores of all 5 severity

groups. The results are demonstrated in Table IX-A. There was no statistically significant

difference in practice score between the lower adjacent levels of perceived severity (corrected p-

value ≥0.05). However, the group reporting perceived severity as “Very severe” appeared to have

a statistically significantly higher practice score compared to all other groups.

Measurement model

The overall reliability of our 3 factor measurement model was borderline with a ωh of 0.65 (BCa

95% CI 0.50-0.75). Individual subscales had varying reliability (Table IX-B). The “Perceived

risk” subscale had high reliability (ωt = 0.90), while the “Knowledge of haze” subscale had

adequate reliability (ωt = 0.78) . The “Personal protective behaviors” subscale had particularly

low reliability (ωt = 0.56).

A chi-squared difference test between the 1P and 2P IRT models was performed, yielding a chi-

squared value of 208.954 on 13 degrees of freedom with a p-value < 0.0001. This indicates a

statistically significant better fit for the 2P IRT model. In addition, a higher CFI and lower

RMSEA was in favour of the 2P IRT model. The chosen 2P IRT model had good fit indices: χ2/df

= 1.98; CFI = 0.959; RMSEA = 0.037 (0.028-0.046). The hypothesis test of the RMSEA being ≤

0.05 yielded a p-value of 0.993. Overall, our measurement model had a good fit to the data. The

2P IRT model was used in subsequent analyses. The results are summarized in Table IX-C. Item

factor loadings onto their respective factors were appropriate and highly significant and is

evidence for construct validity. The measurement model was then incorporated into two

candidate IRT-SEMs.

Model assessment

Various assessment indices for our SEM model are presented in Table IX-D, with their

acceptable ranges49. A chi-squared difference test between the two candidate structural equation

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models was performed, yielding a chi-squared value of 1.635 on 1 degree of freedom with a p-

value 0.2010. This indicated that there was no significantly better fit provided by M2 over M1 by

the additional free parameters and the more parsimonious M1 was chosen. M1 had adequate to

good fit indices: χ2/df = 1.67; CFI = 0.932; RMSEA = 0.031 (0.024-0.037). The hypothesis test

of the RMSEA being ≤ 0.05 yielded a p-value of >0.999. Overall, M1 had an adequate fit to the

data and was our final chosen model.

Effects

The final fitted structural equation model is presented in Figure V. A discussion of item difficulty

and discrimination results are available in Appendix 11. The standardized (direct, indirect and

total) effects and unstandardized effects (with two-tailed p-values) of demographic and health

factors on individual Knowledge, Perceived Risk and Personal Protective Behaviour scores are

presented in Table X-A, X-B, and X-C respectively. Some salient findings included: (1)

Knowledge had a positive effect on both personal protective behaviours and perceived risk, (2)

Education had a negative direct effect on personal protective behaviours, but a larger positive

effect on knowledge, such that it had a net positive effect on personal protective behaviour, and

(3) increased perceived risk had a slightly positive effect on personal protective behaviours

DISCUSSION

Periodic bouts of haze is likely to be a continuing geopolitical reality in Southeast Asia.

Indonesia’s 2014 ratification of the ASEAN agreement on transboundary haze pollution in 201450

appears cosmetic when it does little to address the root causes of haze – the financial incentive for

slash-and-burn farming has not been disarmed, and the political will for enforcement remains

weak, with widespread graft among those tasked with enforcement51, and the country’s anti-

corruption agency embroiled in high-level political conflict52. There appears to be no durable

solution in sight to the yearly haze affliction.

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Effective usage of N95 masks

Therefore, the use of N95 masks will remain an important mitigation measure in the years to

come, especially for individuals who cannot avoid going outdoors, or those who are at increased

risk of adverse health effects from haze15. Our study findings that the vast majority of

respondents failed to protect themselves from the 2013 haze episode through effective use of N95

masks is therefore worrying. We found that this was due to:

(1) Some 21.8% of respondents did not own N95 masks, mainly due to a perception of discomfort

(36.0%), masks being out of stock (33.8%), or queues for masks being too long (22.7%).

(2) A further 34.7% of respondents owned, but did not wear N95 masks. Reasons cited for not

wearing N95 masks include finding masks ‘troublesome’ (47.7%) or uncomfortable (53.8%).

(3) Respondents who owned and wore N95 masks might not have attained effective protection

due to improper mask fit. We note that a paltry 12.6% of respondents passed the visual mask-

fit test, with pass rates being adversely affected by increasing age and lower education levels.

(4) Furthermore, 98.4 % (n = 703) of all respondents had at least 1 significant misconception (out

of 4 questions asked) about the correct use of N95 masks, which may compromise their

effective protection.

While diplomatic efforts to eliminate haze at source continue, the government’s interim measure

of mailing 3 duck-billed N95 masks to every household in 2014 immediately address (1). On the

other hand, the lack of public education or mask-fit training makes this an incomplete policy

solution which fails to address (2) – (4). Mailing masks to every household does not ensure that

they will use the masks; even those who use masks have a only 12.6% chance of achieving

effective protection. It does not help that duck-billed model of N95 masks were chosen for

mailing to every household; while these masks are more compact to mail, anecdotally, many

respondents found them more challenging to don.

19

While arming every household with masks is a laudable initial step, we propose the following

measures:

(1) Population-wide mask-fit training, which most respondents welcome. As self-reported

previous training was effective in improving the pass rate, we have reason to hope that

training may improve pass rates. While the pass rate among respondents with prior mask-fit

training was superior to those without prior training, it was still not excellent (26%); this may

be due to prior training being with the traditional cup-style N95 mask, and unfamiliarity with

duckbill N95 masks.

We hypothesize that this will also benefit respondents who cited ‘discomfort’ as a reason for

not owning or wearing N95 masks. Such respondents may have found masks uncomfortable

because of an improper fit; learning proper mask-fit may therefore improve comfort levels.

This will also benefit respondents who did not own or did not wear masks because they ‘did

not know how to use’ it.

The exact medium and form of mask-fit training is a subject for further study. As television

is the educational medium of greatest interest to all age groups (55.2 - 70.0%), mass outreach

via public service advertisement can be considered in future haze episodes. Targeted

outreach to different demographic groups can be done via different media – younger

respondents favour internet-based education, while older respondents favour training in

community centres or door-to-door visits. Targeting older residents may be important given

their lower mask-fit pass rates. Whatever the medium, this training will likely have to be in

greater depth than simply providing an instruction sheet, which we found made no difference

to mask-fit pass rates.

(2) Public education: This addresses several aspects. Firstly, it tackles those whose use of N95

masks may be compromised by misconceptions about proper mask use. Secondly, it may

help the group who owned but did not wear masks make a more informed decision on

whether to wear masks.

20

(3) Mask design: given low mask-fit pass rates, even among respondents with prior training,

coupled with feedback that duck-billed masks is more difficult to don correctly, it may be

worthwhile for manufacturers to consider modifying the duckbill N95 mask to be easier to

don. Alternatively, if future studies find a significantly higher mask-fit pass rates with cup-

shaped N95 masks be used, this model can be used for future mask distribution. In addition,

as we found the instruction leaflet of little help in improving mask-fit pass rates, we propose

a redesign of this leaflet, perhaps in particular to focus on the common errors we have

highlighted. Printing instructions on the mask itself, e.g. “this strap goes above your ear”,

could also be considered.

Personal protective behaviours beyond N95 masks

Apart from N95 masks, additional personal protective behaviours that could protect one from the

effects of haze. There are good reasons to seek to empower the population to take personal

protective behaviours against haze, especially with growing number of elderly Singaporeans with

lung or heart disease, who may be more adversely affected by haze. If this is desired, our findings

suggest that public education to increase knowledge about haze may be the way to go, as this has

a large and significant effect on personal protective behaviours, and is also a modifiable factor.

Education levels, history of asthma, lung or heart disease have significant effects but are non-

modifiable factors.

Surprisingly, perceived risk exerted only a small effect on the amount of personal protective

behaviours taken, suggesting that it might not be a sufficient factor in influencing health

behaviors. Despite this knowledge had a large effect on perceived risk and is congruent with the

idea that having greater understanding of haze is associated with feeling more at risk from it.

On the other hand, while the link between haze and adverse health outcomes in the short term

(e.g. asthma exacerbation) has been investigated6,7,8, there have been few studies investigating the

association between adopting personal protective behaviours and a decrease in adverse acute

21

health outcomes from haze on a population level15. Encouraging personal protective behaviours,

therefore, is at present a logical but not evidence-based strategy. Caution against overwhelmingly

prescriptive value judgements is warranted, and whether to adopt personal protective behavior is

ultimately an informed individual choice.

Evaluation of our study

This is the first local and second global study assessing the community’s proficiency in donning

the N95 mask. We expose what we believe to be an important policy issue (in distributing N95

masks that the population largely will not wear correctly) requiring remedial action. Despite the

novelty of our subject matter, we have succeeded in obtaining a large and generally

representative sample and yielded a satisfactory model of a complex issue. We believe that the

conduct of our survey is largely of high quality due to the multiple quality assurance checkpoints

such as training and audits, and a negligible rate of missing data.

On the other hand, our study may be subject to several sources of bias including (1) recall bias

given a 1.5 year period between the 2013 haze and the survey, (2) a selection bias due to the

exclusion of non-English or Mandarin-speaking households and the selection of the first

respondent who opens the door, (3) non-response bias, and (4) a non-representative population in

that respondents from Jurong East, our survey area, may not represent the population of

Singapore as a whole.

Our study has several other limitations. Due to safety concerns and logistic limitations, we were

unable to use the more accurate qualitative or quantitative N95 mask-fit28. The visual mask fit we

used in its place is not a validated instrument although it has been used in another published

study20. Receipt of past training is respondent-reported, which poses accuracy concerns, and we

could have better characterised what ‘past training’ entailed.

Our analysis is also subject to various limitations. Due to the low mask-fit pass rate (only 90

respondents passed), our analysis of the factors affecting the pass rate may be underpowered.

22

Overall reliability of our questionnaire was limited and was especially affected by the “Personal

protective behaviors” subscale. We postulate that this is could be due inadequate coverage of the

domains due to limited number of questions for each construct we could feasibly include in our

survey questionnaire. However, our research area is relatively unexplored and we assert that the

limited overall reliability reflects an early stage of theory building and should not preclude

subsequent interpretation of our adequately fitting final model. There remains unexplained

variance within our latent constructs and items suggesting that there are additions factors that we

may not have considered. Furthermore, the SEM only allows for inferences on associated and

cannot be used prove causality, Subgroup analyses of vulnerable populations such as the elderly

and those with chronic diseases remain an area for further study.

If population wide mask-fit training is desired, we suggest conducting further studies before

embarking on this intervention. A single-blind interventional trial, comparing pre-intervention

and post-intervention mask-fit pass rates, and/or the effectiveness of different education methods

and media can be considered. It may also be worthwhile to compare duck-bill vs cup-shaped N95

masks, in terms of differences in pass rates, and comfort levels after proper mask-fit training.

Finally, it may be important to explore whether mask-fit education has a lasting effect on mask-

fit pass rates, i.e. how rapidly and significantly do pass rates decay after an educational

intervention. While our study highlights this important issue of N95 mask-fit proficiency, more

work has to be done to ensure the success of any remedial intervention.

DECLARATION

This study received the sponsorship of 3M Singapore, who provided free 3M Vflex 9105 N95

Masks.

  23

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  26

TABLES

Table I: Questions items used in SEM model and the rationale for dichotomy into binary responses

No. Question Dichotomy Reason

Perceived Risk

1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree

1. I believe haze has a damaging effect on my health

1 = 4, 5

0 = 1, 2, 3

These questions had their responses divided into those who believe haze has a damaging effect on their health from those who are not concerned or are unaware of the health effects of haze.

2. I am at risk of lung disease from haze

1 = 4, 5

0 = 1, 2, 3

3. I am at risk of heart disease from haze

1 = 4, 5

0 = 1, 2, 3

4. I am at risk of eye disease from haze

1 = 4, 5

0 = 1, 2, 3

Knowledge of Haze

1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree

1. Haze is caused by forest fires in neighbouring countries

1 = 4, 5

0 = 1, 2, 3

These questions had their responses divided into those with correct responses and those with incorrect responses. Correct responses indicate correct knowledge of the subject matter. Note that question 3 is reverse-coded as agreeing to the statement is an incorrect response.

2. PSI can measure severity of haze

1 = 4, 5

0 = 1, 2, 3

3. The elderly and children do not have higher risk of harm during the haze

1 = 4, 5

0 = 1, 2, 3

5. Health effects of haze depends on how long one has been exposed to it

1 = 4, 5

0 = 1, 2, 3

7. The main pollutant during haze is particulate matter (e.g. PM 10, PM 2.5)

1 = 4, 5

0 = 1, 2, 3

8. Individuals who spend a lot of time outdoors need to be protected

1 = 4, 5

0 = 1, 2, 3

  27

Personal Protective Behaviours

1 = Not at all, 2 = Less than weekly, 3 = Weekly, 4 = Once every few days, 5 = Almost every day

1. I sought updates about the severity of haze

1 = 4, 5

0 = 1, 2, 3

We took seeking for updates at least once every few days as indicative of such behaviour. Hence, these questions had their responses divided into those who sought for updates at least once every few days and those who did so less frequently.

2. I wore an N95 mask 1 = 2, 3, 4, 5

0 = 1

This question had its responses divided into those who had worn N95 masks before from those who had never worn one before.

3. I stayed indoors and avoided outdoor activities

1 = 2, 3, 4, 5

0 = 1

This question had its responses divided into those who had some degree of outdoor activity limitation and those who had no outdoor activity limitation as a direct consequence of haze.

5. I used an air purifier 1 = 2, 3, 4, 5

0 = 1

We took using an air purifier at least once as indicative of such behaviour. Hence, this question had its responses divided into those who used air purifier at least once and those who had never used an air purifier before.

  28

Table II: Demographics of study population versus Singapore 2010 Census

Our Study (n = 714) Singapore 2010 Census

Age (years)

Mean ± SD

Range

Age by groups: n (%)

21-40

41-65

>65

49 ± 16

21 - 89

242 (33.9)

356 (49.9)

116 (16.2)

37.4

Gender: n (%)

Male

Female

332 (46.5)

382 (53.5)

49.3%

50.7%

Ethnicity n (%)

Chinese

Malay

Indian

Others

514 (72)

86 (12)

106 (14.8)

8 (1.1)

74.1%

13.4%

9.2%

3.3%

Type of Residence n (%)

Rental Flat

1-2 Room

3 Room

4 Room

5 Room or Exec

33 (4.6)

12 (1.7)

192 (26.9)

248 (34.7)

229 (32.1)

5.6%

24.3%

38.7%

31.1%

*(Percentage among all HDB housing and excluding private housing)

Education Level n (%)

None

Primary

31 (4.3)

119 (16.7)

32.4%

18.9%

  29

Secondary

Tertiary

University

213 (29.8)

183 (25.6)

168 (23.5)

25.9%

22.8%

Household Income n (%)

No income

<$3000

$3000-$5000

$5000-$8000

$8000-$12000

>$12000

Refuse to answer

139 (19.5)

100 (14.0)

166 (23.2)

120 (16.8)

113 (15.8)

58 (8.1)

18 (2.5)

Employment strata n (%)

Employed

Manufacturing

Hotels & Restaurant

Financial Services

Construction

Transport & Storage

Business Services

Wholesale & Retail Trade

Information & Comms

Community, Social and Personal Services

Unemployed

Full time student

Housewife

Retired

425 (59.5)

39 (5.5)

12 (1.7)

30 (4.2)

27 (3.8)

36 (5.0)

25 (3.5)

26 (3.6)

15 (2.1)

40 (5.6)

288 (40.3)

37 (5.2)

84 (11.8)

136 (19.0)

  30

Table III-A: Common barriers to owning N95 masks Did not Own (n = 156, missing data = 2)

Reasons Not a barrier Was a barrier Long queue 119 (77.3%) 35 (22.7%) Expensive 123 (79.9%) 31 (20.1%) Out of stock 102 (66.2%) 52 (33.8%) Not useful 116 (75.3%) 38 (24.7%) Did not look nice 132 (85.7%) 21 (14.3%) Not comfortable 97 (63%) 57 (36.0%) Did not know how to use 123 (79.9%) 31 (20.1%)

Table III-B: Cross-tabulation between Monthly Household Income and Cost Being a Concern

Monthly Household Income

Total < 3000 3000 – 8000

> 8000

Cost being a concern

Not a concern

Count

% within monthly income

44

80.0%

79

77.5%

25

78.1%

148

78.3%

Is a concern

Count

% within monthly income

11

20.0%

23

22.5%

7

21.9%

41

21.7%

Total

Count

% within monthly income

55

100.0%

102

100.0%

32

100.0%

189

100.0%

Table III-C: Common reasons for not wearing N95 masks in spite of owning them Own, but did not wear (n = 248, missing data = 51)

Reasons Not a barrier Was a barrier Troublesome 103 (52.3%) 94 (47.7%) Not useful 152 (77.2%) 45 (22.8%) Did not look nice 173 (87.8%) 24 (12.2%) Not comfortable 91 (46.2%) 106 (53.8%) Did not know how to use 173 (87.8%) 24 (12.2%)

  31

Table IV-A: Cross-tabulation between chronic disease and wore mask WOREMASK Total

No Yes

CHRONICDZ

No

Count 381 280 661 % within CHRONICDZ 57.6% 42.4% 100.0%

Yes

Count 23 30 53 % within CHRONICDZ 43.4% 56.6% 100.0%

Total

Count 404 310 714 % within CHRONICDZ 56.6% 43.4% 100.0%

% of Total 56.6% 43.4% 100.0% Chi-squared Tests

Value df Asymp. Sig. (2-sided)

Exact Sig. (2-sided)

Exact Sig. (1-sided)

Pearson Chi-squared 4.052a 1 .044 Continuity Correctionb 3.493 1 .062 Likelihood Ratio 4.009 1 .045 Fisher's Exact Test .060 .031 Linear-by-Linear Association 4.046 1 .044

N of Valid Cases 714 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 23.01. b. Computed only for a 2x2 table

  32

Table IV-B: Cross-tabulation between perceived risk and mask wearing

Crosstab WOREMASK Total

No Yes

Is Haze risk to health?

Not severe

Count 76 30 106 % within Is Haze risk to health? 71.7% 28.3% 100.0%

severe

Count 328 280 608 % within Is Haze risk to health? 53.9% 46.1% 100.0%

Total

Count 404 310 714 % within Is Haze risk to health? 56.6% 43.4% 100.0%

% of Total 56.6% 43.4% 100.0%

Chi-squared Tests Value df Asymp. Sig.

(2-sided) Exact Sig. (2-

sided) Exact Sig. (1-

sided) Pearson Chi-squared 11.577a 1 .001 Continuity Correctionb 10.866 1 .001 Likelihood Ratio 12.023 1 .001 Fisher's Exact Test .001 .000 Linear-by-Linear Association 11.561 1 .001

N of Valid Cases 714 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 46.02. b. Computed only for a 2x2 table

  33

Table V: Failure rates for each mask fit assessment item No. Mask Fit Test Item Failure Count

(Percentage)

1 Mask donned right side up 194 (27.2)

2 Mask is straight (not tilted) 139 (19.5)

3 Both straps were used 246 (34.5)

4 Both straps were correctly placed 521 (73.0)

5 Nose clip was tightened 431 (60.4)

6 No visible gap between respirator and skin 442 (61.9)

7 No visible beard 128 (17.9)

8 No leakage of air around mask when subject is asked to blow out 482 (67.5)

9 Breathing in to create vacuum 496 (69.5)

  34

Table VI-A: Mean age of participants who passed or failed the mask fit test

N Mean Age ± SD (years) Pass 90 41.4 ± 13.8 Fail 624 50.2 ± 16.3 T-test results P-value Mean difference 95%CI <0.001 8.84 5.70 – 12.00 Table VI-B: Cross-tabulation between Past Training for N95 mask and Visual Mask Fit Test Result Visual Mask Fit Test Result Total

Fail Pass

Past Training No Count

% within participants with past training

506

91.3%

48

8.7%

554

100.0%

Yes Count

% within participants with past training

118

73.8%

42

26.3%

160

100.0%

Total Count

% within participants with past training

624

87.4%

90

12.6%

714

100.0%

  35

Table VI-C: Cross-tabulation between mask ownership and mask fit result

OWNMASK * MASKFITTESTRESULT Crosstabulation MASKFITTESTRESU

LT Total

Fail Pass

OWNMASK

No Count 146 10 156 % within OWNMASK 93.6% 6.4% 100.0%

Yes Count 478 80 558 % within OWNMASK 85.7% 14.3% 100.0%

Total Count 624 90 714 % within OWNMASK 87.4% 12.6% 100.0%

Chi-squared Tests Value df Asymp. Sig.

(2-sided) Exact Sig. (2-

sided) Exact Sig. (1-

sided) Pearson Chi-squared 6.954a 1 .008 Continuity Correctionb 6.253 1 .012 Likelihood Ratio 7.937 1 .005 Fisher's Exact Test .009 .004 Linear-by-Linear Association 6.944 1 .008

N of Valid Cases 714 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 19.66. b. Computed only for a 2x2 table

  36

Table VI-D: Log-Binomial Regression Model of 714 Residents of Jurong-East for Passing the Visual Mask Fit Test (Total n= 714, n passed= 90)

Table 6D -

Predictor Adjusted Prevalence Ratio

95% C.I p-value

lower upper

Intercept 0.175 0.165

Past training 2.74*** 1.87 4.01 <0.0001

Wore masks before 1.04 0.714 1.53 0.843

Read Instructions 0.959 0.615 1.43 0.846

Age 0.980* 0.965 0.996 0.0129

Education level

No qualification Reference

Primary School 1.11 0.199 20.6 0.923

Secondary School 1.61 0.341 28.8 0.642

Tertiary Education 1.80 0.358 32.8 0.575

University 2.65 0.537 48.0 0.351

Female Gender 0.884 0.609 1.28 0.512

  37

Table VI-E: Cross-tabulation between Referral to Visual Mask Fit Instructions (VMF) and Visual Mask Fit Result Visual Mask Fit Assessment

Result Total Fail Pass

Referred to VMF

Instructions

No

Count

% within VMF

instructions

482

87.8%

67

12.2%

549

100.0%

Yes

Count

% within VMF

instructions

142

86.1%

23

13.9%

165

100.0%

Total

Count

% within VMF

Instructions

624

87.4%

90

12.6%

714

100.0%

Table VI-F: Cross-tabulation between Having Worn a Mask During the 2013 Haze period and Visual Mask Fit Result Visual Mask Fit Assessment

Result Total Fail Pass

Wore mask during 2013 haze period

No Count

% within wore mask

360

89.1%

44

10.9%

404

100.0%

Yes Count

% within wore mask

264

85.2%

46

14.8%

310

100.0%

Total Count

% within wore mask

624

87.4%

90

12.6%

714

100.0%

  38

Table VII: Beliefs about N95 masks Knowledge Questions (n = 714) Questions Correct Wrong Unsure

Q4 (can be used for children) 297 (41.60%) 228 (31.93%) 189 (26.47%)

Q6 (More than 1 can be used) 139 (19.47%) 462 (64.71%) 113 (15.83%)

Q9 (can be reused) 245 (34.31%) 383 (53.64%) 86 (12.04%)

Q10 (surgical mask can be used)

372 (52.10%) 241 (33.75%) 101 (14.15%)

  39

Table VIII: Frequency of Personal Protective Behaviours during 2013 haze (n=714)

No. Personal Protective Behaviour

Frequency (Percent)

Not at all Less than weekly

Weekly Once every few days

Almost daily

1 Sought updates about the severity of haze (e.g. news, internet, radio)

80 (11.2) 21 (2.9) 18 (2.5) 84 (11.8) 511 (71.8)

2 Wore an N95 mask 404 (56.6) 63 (8.8) 24 (3.4) 128 (17.9) 95 (13.3)

3 Stayed indoors and avoided outdoor activities

167 (23.4) 37 (5.2) 37 (5.2) 190 (26.6) 283 (39.6)

4 Cleaned house more frequently than usual

305 (42.7) 18 (2.5) 51 (7.1) 113 (15.8) 227 (31.8)

5 Used an air purifier at home

540 (75.6) 17 (2.4) 5 (0.7) 33 (4.6) 119 (16.7)

6 Took showers more frequently

424 (59.8) 21 (2.9) 13 (1.8) 63 (8.8) 193 (27.0)

7 Kept oneself more hydrated than usual

166 (23.2) 8 (1.1) 8 (1.1) 67 (9.4) 465 (65.1)

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Table IX-A: Post-hoc Analysis with Pair-wise comparison using Mann-Whitney U Test

Reference group: Neutral

Perceived Severity

Mann-Whitney U value

P-value (Asymp. Sig. 2-tailed)

Corrected P-value^

Neutral Not severe at all 391.0 0.638 -

Somewhat not severe

1584.0 0.310 -

Somewhat severe

5523.5 0.000228 <0.01

Very Severe 3924.0 0.0000001 <0.01

^Bonferroni correction applied

Table IX-B: Reliability of subscales Subscale Reliability coefficient ωt Correlation of scores with

factors

Perceived risk 0.90 0.91

Knowledge of haze 0.78 0.72

Personal protective behaviors 0.56 0.57

Table IX-C – Comparison of goodness-of-fit between 1P and 2P Item Response Theory models

χ2 df p-value χ2/df CFI RMSEA (90% CI) Probability RMSEA ≤ .05

T≤ <0.05 - >0.05 <5 >0.90 <0.05 -

1P IRT 487.379 87 <0.001 5.60 0.772 0.080 (0.073-0.087) <0.001

2P IRT 146.631 74 <0.001 1.98 0.959 0.037 (0.028-0.046) 0.993

df= degrees of freedom; P= probability value; NFI = normed fit index; RFI = relative fit index; IFI =incremental fit index; TLI = Tucker–Lewis coefficient; CFI = comparative fit index; RMSEA = root mean square error of approximation.

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Table IX-D: Measure of fit for the structured equation model

χ2 df p-value χ2/df CFI RMSEA Probability RMSEA ≤ .05

T≤ <0.05 <5 >0.05 <5 >0.90 0.05-0.08

M1 M2

257.667 280.661

154 167

<0.001 <0.001

1.67 1.68

0.932 0.927

0.031 (0.024-0.037) 0.031 (0.024-0.037)

>0.999 >0.999

df= degrees of freedom; P= probability value; NFI = normed fit index; RFI = relative fit index; IFI =incremental fit index; TLI = Tucker–Lewis coefficient; CFI = comparative fit index; RMSEA = root mean square error of approximation.

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Table X-A: Standardized and unstandardized effects of exogenous variables and perceived risk on knowledge about haze. (SD of knowledge = 1; hence StdY = Std/1) Variable Unstandardized Effects Standardized Effects

Estimate (S.E) Two-tailed p-value

Direct Effects Indirect Effects Total

Age - - - - -

Gender - - - - -

Education1 Primary Secondary Tertiary University

0.903** (0.347) 1.316*** (0.351) 1.722*** (0.391) 1.935*** (0.394)

0.009 <0.001 <0.001 <0.001

0.817 1.191 1.558 1.751

- 0.817 1.191 1.558 1.751

Past Medical Hx - - - - -

Perceived Risk - - - - - 1 relative to “No qualification” Table X-B: Standardized effects of exogenous variables and knowledge on haze on perceived risk. (SD of risk = 1; hence StdY = Std/1) Variable Unstandardized Effects Standardized Effects

Estimate (S.E) Two-tailed p-value

Direct Effects Indirect Effects

Total

Age 0.010* (0.005) 0.025 0.141 - 0.141

Gender1 -0.064 (0.112) 0.570 -0.053 - -0.053

Education2 Primary Secondary Tertiary University

-0.422 (0.270) -0.490 (0.283) -0.609 (0.336) -0.595 (0.348)

0.118 0.084 0.070 0.087

-0.351 -0.408 -0.506 -0.495

0.488 0.711 0.930 1.045

0.137 0.304 0.424 0.551

Past Medical Hx3 0.007 (0.196) 0.973 0.006 - 0.006

Knowledge 1.650*** (0.092) <0.001 0.597 - 0.597 1 relative to Men; 2 relative to “No qualification”; 3 relative to no past medical history

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Table X-C: Standardized and unstandardized effects of exogenous variables, perceived risk and knowledge about haze on personal protective behaviour (SD of protective behavior = 1; hence StdY = Std/1) Variable Unstandardized Effects Standardized Effects

Estimate (S.E) Two-tailed p-value

Direct Effects Indirect Effects Total

Age -0.0001 (0.008) 0.989 -0.001 0.022 0.021

Gender1 0.365 (0.197) 0.064 0.221 -0.008 0.213

Education2 Primary Secondary Tertiary University

-0.630 (0.448) -0.828 (0.508) -0.686 (0.569) -0.772 (0.586)

0.160 0.103 0.228 0.188

-0.382 -0.502 -0.416 -0.468

0.611 0.907 1.190 1.350

0.230 0.405 0.775 0.882

Past Medical Hx3 0.520 (0.359) 0.148 0.315 0.001 0.316

Perceived Risk 0.212 (0.127) 0.094 0.155 - 0.155

Knowledge 1.078*** (0.279) <0.001 0.722 - 0.722 1 relative to Men; 2 relative to “No qualification”; 3 relative to no past medical history

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FIGURES Figure I: Flow chart of surveyed households

Figure II: Pie chart of mask possession and mask usage

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Figure III: Proportions of residents who indicated interest in respective modalities of N95 mask fit education by age group

Figure IV: Median practice frequency scores in each severity group with 95% CI as error bar

0  

10  

20  

30  

40  

50  

60  

70  

80  

Internet   Television   Printed  Material  

Community  Centre  

Door-­‐to-­‐door   Not  interested  in  any  

%  response  by  age  group  

Types  of  training  

Types  of  training  participants  are  interested  in    

21  -­‐  40   41  -­‐  65   >  65  

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Figure V: Structural Equation Model

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APPENDICES Appendix 1: Survey Protocol

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Appendix 2: Non-Responder Notice

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Appendix 3: Picture of N95 Mask

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Appendix 4: Mask-fit pass criteria Once the respondent was satisfied with the fitting, the mask was visually inspected for the following parameters: orientation (1) right side-up and (2) not tilted, (3) position, (4) position of straps, (5) tightening of nose clip, (6) visible gaps between mask and face, (7) presence of facial hair. Subsequently, the respondent was asked to place his hands over the mask periphery and report the (8) presence of air leakage upon forceful exhalation. The respondent was finally asked to inhale deeply and the interviewer checked visually for the (9) generation of a vacuum indicating correct mask fit – indicated by gentle inward denting of the mask during inhalation. Failure to meet any of the above criteria was deemed an overall fail in mask fit.

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Appendix 5: Mask Donning Training Steps of donning the N95 Masks were standardized as follows: (1) Place thumb at center of nosepiece and unfold the respirator by pulling top part (with nosepiece) up and bottom part down so the respirator is open all the way. (2) Bend nosepiece slightly around thumb at center of nosepiece. (3) Place respirator against your face with the bottom under your chin, and the nosepiece across the bridge of your nose. (4) Hold respirator on your face with one hand. With your other hand pull the bottom securely under your chin. (5) Pull one strap over your head and position it around the neck below your ears. (6) Pull second strap over your head and position it high on the back of your head. (7) If desired, tabs on side of respirator can be used to adjust for a comfortable fit. (8) Make certain facial hair, hair, jewelry and clothing are not between your face and the respirator as they will interfere with fit. (9) Make certain respirator is completely opened and edges lay flat against the face. (10) Adjust for a comfortable fit by pulling bottom edge under chin while holding top edge on nose. (11) Place your fingertips from both hands at the top of the nosepiece. Use both hands to bend the nose-piece to fit snugly against your nose and face. (12) Perform a user seal check by placing both hands completely over the respirator and exhale. Be careful not to disturb the position of the respirator. If air leaks around the nose, readjust the nosepiece. If air leaks around respirator edges, adjust position of straps and make certain respirator edges fit snugly against the face.

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Appendix 6: English Questionnaire

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56

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Appendix 7: Chinese Questionnaire

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61

62

63

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Appendix 8: Pictorial Reference Sheet

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Appendix 9: Participant Information Sheet

Understanding the Haze and N95 Mask use in Singapore 1. Principal Investigator: Dr Judy Sng Gek Khim, Senior Lecturer, Saw Swee Hock

School of Public Health, National Universtiy Singapore Co-investigator(s): Kennedy Ng and Wesley Yeung, Yong Loo Lin School of Medicine.

2. What is the purpose of this research? You are invited to participate in this research study. This information sheet provides you with information about the research. The Principal Investigator (the research doctor or person in charge of this research) or his/her representative will also describe this research to you and answer all of your questions. Read the information below and ask questions about anything you don’t understand before deciding whether or not to take part. This project aims to: 1. Establish Knowledge, Attitudes and Practices (KAP) of Haze among Singapore residents. 2. Establish KAP of Singapore residents with respect to N95 Mask use during Haze periods. 3. Estimate prevalence of improper N95 Mask Fit and explore factors affecting proficiency

of N95 Mask fitting. 3. Who can participate in the research? What is the expected duration of my

participation? What is the duration of this research? Anyone that fulfills the inclusion criteria stated below in this housing estate can participate in the research. Anyone that meets any of the exclusion criteria stated below will not be able to participate in the research. Inclusion criteria

• Singaporean or Permanent Residents • At or above the age of 21 • Residents in the Estate for at least 24 months • English or Mandarin speaking • Able to perform the Mask Fit Test independently

Exclusion criteria

• Non Singaporean/Permanent Residents • Below the age of 21 • Not residents of the estate for at least 24 months • Not able to converse in English or Mandarin • Not able to perform the Mask Fit Test independently

Expected duration of your participation is approximately 45 minutes. There will only one visit to your household, no further visits are expected over the 6 month duration of our research.

4. What is the approximate number of participants involved? 1000-1500 Particpants

5. What will be done if I take part in this research? You will be given a survey that assesses your knowledge on haze and N95 mask use. Following that, you will be put through a visual N95 Mask Fit where your technique of donning a N95 mask would be assessed using a checklist. Our trained surveyors will provide an on-the-spot demonstration of proper Mask Fitting Technique should you require help in achieving an adequate N95 mask fit.

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6. How will my privacy and the confidentiality of my research records be protected? Only your unit number will be recorded in the event the survey is incomplete or if you contact us with a request to withdraw your data. Your name, IC number or any other personal identifiers will not be recorded. All the research data will be coded (i.e. subjects will be assigned a study ID, with the link between their study IDs and unit numbers kept separate from the data) from the earliest point of data collection. Only the principal investigator and his/her co-investigators will have access to the raw research data. Research data used in publication will be kept for a minimum of 10 years before being discarded.

7. What are the possible discomforts and risks for participants? Due to the nature of the research methodology, we do not forsee any possibele discomforts or risks for participants.

8. What is the compensation for any injury? No injury is expected from participating in this study, hence there will be no compensation for injury.

9. Will there be reimbursement for participation? No reimbursement is given for participating in this study.

10. What are the possible benefits to me and to others? The possible benefit is that of learning how to use an N95 mask effectively in the event of the need for its use (ie, during haze periods or epidemics). Furthermore, with the information gathered, gaps in the knowledge and inaccurate perceptions of both Haze and N95 Mask use can be identified, allowing more targeted educational programs that could address them. Better educational programs could in turn potentially lead to an increase awareness of the detrimental effects of Haze and raise the proficiency of proper N95 Mask donning to improve protection against Haze.

11. Can I refuse to participate in this research? Yes, you can. Your decision to participate in this research is voluntary and completely up to you. You can also withdraw from the research at any time without giving any reasons, by informing the principal investigator of your unit number and all your data collected will be discarded.

12. Whom should I call if I have any questions or problems? Please contact the Principal Investigator, (judy_sng@nuhs.edu.sg) or Co-investigator (kennedy.ng@gmail.com, 9138 7657) for all research-related matters and in the event of research-related injuries. For an independent opinion regarding the research and the rights of research participants, you may contact a staff member of the National University of Singapore Institutional Review Board (Attn: Mr Chan Tuck Wai, at telephone (+65) 6516 1234 or email at irb@nus.edu.sg).  

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Appendix 10: Instruction Leaflet

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Appendix 11: IRT Difficulty and Discriminations for individual items Item difficulties and discriminations for each latent variable were plotted on separate Item Characteristic Curves.

Item difficulty Higher positive values of the item difficulty parameter estimate indicate higher levels of ability being measured before a respondent, answer a question correctly (Knowledge), for feels he/she is at risk of (Perceived Risk) or engages in the behavior (Personal Protective Behaviors). Items with relatively high item difficulties (>1) include “The main pollutant during haze is particulate matter (for example PM10 and PM2.5)” (Knowledge), “I am at risk of heart disease from haze” (Perceived Risk) and “I used an air purifier at home” (Personal Protective Behaviors). Items with relative low item difficulties include (<1) include “Haze is caused by forest fires in neighboring countries” (Knowledge), “The elderly and children do not have a higher risk of harm during haze” (Knowledge, reverse coded) and “Individuals who spend a lot time outdoors need to be protected” (Knowledge). Overall, these item difficulty estimates make theoretical sense. Item discrimination The discrimination parameter provides information on how an item differentiates between individuals at different ability levels. Items with relatively high discrimination parameters (>1) include “Individuals who spend a lot time outdoors need to be protected” (Knowledge), “ I am at risk of lung disease from haze” (Perceived Risk), “I am at risk of heart disease from haze” (Perceived Risk), “ I am at risk of eye disease from haze” (Perceived Risk). Items with relatively low discrimination parameters (<0.5) include “The elderly and children do not have a higher risk of harm during haze”, “The main pollutant during haze is particulate matter (for example PM10 and PM2.5)” (Knowledge), “I sought updates about the severity of haze (for example, on the news, internet or radio)” (Personal Protective Behaviors), “I wore an N95 mask” (Personal Protective Behaviors) and “I used an air purifier at home” (Personal Protective Behaviors).

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Appendix 12: Modern Test Theory, Item Response Theory and Structural Equation Modeling We chose a modern test theory approach over classical test theory (CTT) to model our latent variables. In the latter, raw scores computed by the sum or means of item responses are analyzed against outcome variables. However, such raw scores actually have an ordinal relationship to the latent variable we actually desire to measure. This ordinal relationship does not allow for analyses which require continuous variables and can introduce bias into hypothesis testing of differences of means or estimation of slopes in regression analysis. In addition, these techniques are susceptible to floor and ceiling effects when respondents have maximal or minimal test scores respectively. Due to limitations of the survey instrument, variation at these extremes are not accounted for, possibly resulting in spurious associations and biased estimates. In IRT models, the scale of final calculated ability (theta) is a linear, continuous variable. In our study, theta can be interpreted as an individual’s knowledge of haze, perceived risk of haze and degree of practicing personal protective behaviors. The use of theta preserves the ordering of individuals by ability just like in CTT but allows for variable distances at the extremes. There are two popular techniques in modern test theory that can model latent variables: Confirmatory Factor Analysis (CFA) and IRT. We chose IRT over CFA as the latter assumes continuous, normally distributed item responses, an assumption that does not hold for our binary data. Although factor analysis techniques, using polychoric or tetrachoric correlation matrices, that work with ordinal and binary data with good results have been described, IRT provides a few additional advantages described below. In IRT, a linear model between ability (theta) and getting a correct/incorrect response on each item is modeled with a logit or probit link function in a manner analogous to logistic and probit regression. While a fitted IRT model can generate individual ability scores for the 3 domains of interest in our study, subsequent analysis using those factor scores by techniques such as ordinary least squares linear regression and ANOVA do not take into account the measurement error implicit in the latent variables which are only estimated from the set of observed variables present in our questionnaire. This omission has been shown to result in nontrivial bias and underestimation of the effects contributed by latent variables. As such, we performed full structural equation modelling (SEM) which accounts for these measurement errors. A structural equation model consists of a measurement model, containing our 3 factor IRT model, and a structural model, containing the paths between factors and exogenous covariates. The hypothesized 2 parameter normal ogive IRT (2P IRT) measurement model includes an item difficulty and item discrimination parameters. Item difficulty indicates the likelihood that an individual will answer an item correctly at given ability level while item discrimination provides information about how well the item differentiates between individuals of different ability levels. Both these parameters are scaled on the ability level. For item difficulty, a large negative value represents an easy item in that even individuals of low ability have a high likelihood of answering the item correctly while the converse is true for large positive values, indicating a difficult item. For item discrimination, a larger value indicates an item that better distinguishes individuals of different ability levels for the constructs described earlier. We tested this model against an 1 parameter Rasch model (1P IRT) which assumes equal item discrimination and chose the final measurement model based on statistical criteria. The accepted measurement model was incorporated into a hypothesized structural model and an alternative model. We selected the final model by statistical criteria to produce the resultant IRT-SEM model.