Ambient PM2.5 and Daily Hospital Admissions for Acute Respiratory Infections: Effect Modiﬁcation by Weight Status of Child

atmosphere

Article Ambient PM2.5 and Daily Hospital Admissions for Acute Respiratory Infections: Effect Modiﬁcation by Weight Status of Child

Hironori Nishikawa 1,* , Chris Fook Sheng Ng 1 , Lina Madaniyazi 1,2, Xerxes Tesoro Seposo 1 , Bhim Gopal Dhoubhadel 1,3 , Dhiraj Pokhrel 4, Amod K. Pokhrel 4, Sharat Chandra Verma 4, Dhruba Shrestha 5, Ganendra Bhakta Raya 5 and Masahiro Hashizume 1,6

1 Department of Global Health, School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; [email protected] (C.F.S.N.); [email protected] (L.M.); [email protected] (X.T.S.); [email protected] (B.G.D.); [email protected] (M.H.) 2 Department of Pediatric Infectious Disease, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan 3 Department of Respiratory Infections, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan 4 LEADERS Nepal, P.O. Box 8846, 3rd Floor Radhakuti Arcade, Putalisadak, Kathmandu 44600, Nepal; [email protected] (D.P.); [email protected] (A.K.P.); [email protected] (S.C.V.) 5 Siddhi Memorial Hospital, Siddhi Memorial Foundation, P.O. Box 40, Bhimsensthan-7, Bhaktapur 44800, Nepal; [email protected] (D.S.); [email protected] (G.B.R.) 6 Department of Global Health Policy, School of International Health, Graduate School of Medicine, Citation: Nishikawa, H.; Ng, C.F.S.; The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan Madaniyazi, L.; Seposo, X.T.; * Correspondence: [email protected] Dhoubhadel, B.G.; Pokhrel, D.; Pokhrel, A.K.; Verma, S.C.; Shrestha, Abstract: The high level of ambient particulate matter in many developing countries constitutes a D.; Raya, G.B.; et al. Ambient PM2.5 major health burden, but evidence on its impact on children’s health is still limited in these regions. and Daily Hospital Admissions for We conducted a time-stratified case-crossover analysis to quantify the short-term association between Acute Respiratory Infections: Effect Modification by Weight Status of fine particulate matter (PM2.5) and hospital admissions due to acute respiratory infections (ARI) Child. Atmosphere 2021, 12, 1009. among children in Bhaktapur district, Nepal, and to investigate the potential modification of the https://doi.org/10.3390/ effect by nutritional characteristic. We analyzed 258 children admitted to the pediatric hospital for atmos12081009 ARI between February 2014 to February 2015. We observed evidence of increased risk on the same (lag 0) and preceding day (lag 1). The cumulative estimate of their average (lag 01) suggested each Academic Editor: Hwan-Cheol Kim 3 10 µg/m increase in PM2.5 was associated with a relative risk (RR) of 1.16 (95% confidence interval [CI]: 1.02–1.31). The strongest evidence from a stratified analysis of three categories of weights was Received: 3 July 2021 observed in the overweight group (RR: 1.77; 95% CI: 1.17–2.69) at lag 01, while the estimates for the Accepted: 2 August 2021 normal weight and underweight groups were closer to the non-stratified estimates for all-ARI cases. Published: 5 August 2021 The findings suggests that pediatric ARI is an important morbidity associated with inhalable PM2.5 and that more research is needed to elucidate and validate the observed dissimilarity by weight. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Keywords: fine particulate matter; inhalation exposure; acute respiratory infection; body weight; published maps and institutional affil- nutritional status iations.

1. Introduction Copyright: © 2021 by the authors. In Kathmandu Valley, the capital of Nepal, the air pollution level has been estimated Licensee MDPI, Basel, Switzerland. to be ﬁve times higher than the WHO Air Quality Guidelines [1], constituting a signiﬁcant This article is an open access article public health issue due to rapid urbanization. The lack of good air quality data, notably the distributed under the terms and conditions of the Creative Commons highly inhalable particulate matter of less than 2.5 micrometer in diameter (PM2.5), and the Attribution (CC BY) license (https:// subpar quality of medical record keeping have resulted in a scarcity of studies important to creativecommons.org/licenses/by/ inform policies [2]. Although the association between air pollutants and human health has 4.0/). been well-documented [3–5], most of the studies were conducted in high-income countries

Atmosphere 2021, 12, 1009. https://doi.org/10.3390/atmos12081009 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, x FOR PEER REVIEW 2 of 10

important to inform policies [2]. Although the association between air pollutants and hu- Atmosphere 2021, 12, 1009 man health has been well-documented [3–5], most of the studies were conducted in 2high- of 10 income countries where results may not be directly applicable to understand the health burden of high air pollution level in cities such as Kathmandu Valley. More importantly, whereknowledge results gaps may remain not be given directly the applicable different toem understandission sources the healththat can burden lead to of different high air pollutiontoxicities levelin different in cities locations such as and Kathmandu the different Valley. population More importantly, characteristics knowledge in the lower gaps remainmiddle-income given the countries different (LMICs emission especially sources that poor can countries lead to differentwith limited toxicities economic in different ability locationsto mitigate and exposure the different [6]. population characteristics in the lower middle-income countries (LMICsPoor especially air quality poor affects countries growing with children limited economic differently ability [7]. In to this mitigate age subgroup, exposure [6sus-]. ceptibilityPoor airto qualityair pollution affects can growing be influenced children by differently the health [ 7status]. In thisof a agechild subgroup, [1]. In Nepal, sus- ceptibilityacute respiratory to air pollution infection can(ARI) be was influenced responsi byble the for health about status15% of of deaths a child among [1]. In children Nepal, acuteaged under respiratory five in infection 2013 [8]. (ARI)However, was susceptibility responsible for of aboutthis subgroup 15% of deathsto PM2.5 among is not well- chil- drenunderstood aged under despite five previous in 2013 reports [8]. However, linking susceptibilityPM2.5 and ARI of [9,10]. this subgroup A second to important PM2.5 is notrisk well-understoodfactor affecting children despite previousin the region reports is malnutrition, linking PM2.5 whichand ARIis a [significant9,10]. A second health importantburden with risk a substantial factor affecting impact children on the in Disabi the regionlity Adjusted is malnutrition, Life Year which (DALY) is a in significant the coun- healthtry [11]. burden Thirty-six with percent a substantial of children impact under on the age Disability five are Adjustedstunted (short Life Year for their (DALY) age), in 10% the countryare wasted [11]. (thin Thirty-six for their percent height), of children 27% are under underweight age five are(thin stunted for their (short age), for and their 1% age), are 10%overweight are wasted (heavy (thin for for their their height) height), in 27%Nepal are [12]. underweight Likewise, (thinthe influence for their of age), the andnutritional 1% are overweightstatus on the (heavy health for effects their of height) PM2.5 inis Nepallittle studied [12]. Likewise, and not thewell-understood. influence of the nutritional statusAgainst on the healththis backdrop, effects of we PM conducted2.5 is little studieda study and(1) to not quantify well-understood. the short-term association betweenAgainst this ambient backdrop, PM2.5 we and conducted daily ARI a hospital study (1) admissions to quantify among the short-term children, association and (2) to betweeninvestigate ambient the potential PM2.5 andmodification daily ARI ofhospital health effects admissions by weight among status. children, and (2) to investigate the potential modification of health effects by weight status. 2. Materials and Methods 2. Materials and Methods 2.1. Study Site 2.1. Study Site The study location is in Bhaktapur district, one of the three districts that makes up The study location is in Bhaktapur district, one of the three districts that makes up KathmanduKathmandu Valley (Figure 11).). This area is is a a mixture mixture of of urban urban and and rural rural areas. areas. Outdoor Outdoor air airpollution pollution comes comes from from multiple multiple sources, sources, such such as as the the burning burning of of biomass biomass fuel fuel in homes,homes, moderatemoderate traffictraffic onon thethe nearestnearest roads,roads, andand brickbrick kilnskilns onon thethe outskirtsoutskirtsof ofthe thecity city[ 13[13].].

35° N

30° N Kathmandu

25° N Bhaktapur

20° N Latitude

15° N Lalitpur

10° N

5° N 70° E 75° E 80° E 85° E 90° E 95° E Longitude FigureFigure 1.1. MapMap ofof KathmanduKathmandu ValleyValley showingshowing BhaktapurBhaktapur andand thethe locationslocations ofof thethe hospital,hospital, thethe PMPM monitormonitor (on(on thethe rooftoprooftop ofof thethe hospital),hospital), andand thethe NOAANOAA stationstation (at(at TribhuvanTribhuvan International International Airport). Airport).

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2.2. Study Design Data on health outcomes and exposure were available from 13 February 2014 to 12 February 2015. PM2.5 data after our study period cannot be investigated since they contain lots of missing periods due to the April 2015 Nepal earthquake. We employed a time-stratiﬁed case-crossover (TSCCO) study design to estimate the short-term associations between daily PM2.5 and ARI admissions [14–16]. The method is commonly used in epidemiological studies of air pollution and health [17–19]. Brieﬂy, with this method, each patient serves as his/her own control, and the exposure level on the day of hospitalization (or a few days before) is compared to the exposure level on the same days of the week within the same month of hospitalization. Since control periods are derived from the same individual, the method accounts for unmeasured variables that remain constant over the study period. Seasonality is adjusted for by design using calendar months as time strata [20,21].

2.3. Data Collection We collected the data on hospital admissions due to ARI retrospectively from the clinicians’ discharge summary and laboratory records at Siddhi Memorial Hospital. In- clusion criteria for study subjects were children under 10 years of age (n = 258), which is the recommended age range for weight-for-age categorization [22]. Those children were included who were living in Bhaktapur district and admitted to the pediatric ward with symptomatic diagnosis of ARI by clinicians. A total of 258 ARI-related hospital admissions were extracted from 1415 all-cause hospital admissions after excluding patients from other districts (n = 162) or diagnosed with other diseases (n = 984), and ineligible subjects for stratiﬁed analysis (n = 11, i.e., no-weight data (n = 2), outliers of z-score (n = 4) and above 10 years old (n = 5); Figure S1). For each study subject, we extracted the weight and age in months, and calculated the Z-score for weight-for-age (WFA) based on this information (Epi Info Ver 7.2, CDC). The WFA represents the deviation from optimal growth by body mass in relation to chronological age, which reﬂects the long-term health and nutritional experience of an individual up to 10 years old [22,23]. Standard Z-score cut-off points of ±2 standard deviation (SD) were applied to classify weight into three categories—underweight (Z-score ≤ −2), normal weight (−2 < Z-score < 2), and overweight (Z-score ≥ 2). Subjects with a Z-score below −6 or above +5 (n = 4) were also excluded as those results were likely due to mismeasurement [24]. We obtained daily data on PM2.5 from the study of Nepal Health Research Council (NHRC) [25]. Ambient concentrations of PM2.5 and temperature were measured at 15-min intervals using a PM monitor (E-SAMPLER by Met One Instruments, Inc., Grants Pass, OR, U.S.A.) located on the rooftop of the hospital from where we collected the health outcome data. Daily means of ambient PM2.5 and temperature were calculated from 15-min interval data. The proportion of missing values was less than 0.1% for the two variables. Instead of relative humidity, daily data of dewpoint temperature (absolute humidity) were obtained from the NOAA station at the nearest airport.

2.4. Statistical Analysis A conditional Poisson regression with a scale parameter for overdispersion (quasi- Poisson link function) was used to estimate the relative risk (RR) of ARI hospital admission and the 95% conﬁdence interval (CI) for a 10 µg/m3 increase in the ambient concentration of PM2.5 [26]. First, we assumed a linear relationship between PM2.5 and ARI admissions and examined the attenuation of the association using a single-pollutant single-lag model for exposure on the same day until one week before hospitalization (lag 0 to lag 6), as well as cumulatively using the average of lags covering multiple days (lag 01 to lag 06). Our preliminary analysis suggested an association between lags 0 and 1. Based on this, we restricted all subsequent analyses to these lag days and reported results for lag 0, lag 1, and lag 01. All estimations were adjusted for potential the confounding effect of Atmosphere 2021, 12, 1009 4 of 10

temperature [27,28] by including the previous two weeks of temperature lags using a natural cubic spline with three degrees of freedom based on the results from preliminary analysis and the possible length of lags from previous studies [29–31]. To examine if weight status moderated the PM2.5-ARI association, we performed stratiﬁed analyses for the three WFA categories described above. We performed additional analyses to check the sensitivity by extending the length of temperature adjustment, by additional adjustment for dewpoint temperature, and by assessing the nonlinear association. All statistical analyses were conducted using statistical software R, version 3.6.0 (R Core Team, 2019).

3. Results 3.1. Descriptive Summary The characteristics of 258 subjects are summarized in Table1. The majority were male, ﬁve years old or younger, and had a normal weight.

Table 1. Descriptive statistics for the ARI hospital admissions.

Frequency (n = 258) Characteristics n (%) Sex Male 181 (70.2) Female 77 (29.8) Infant (≤1) 132 (51.2) Underweight 16 (12.1) Normal weight 109 (82.6) Overweight 7 (5.3) Toddler (2–5) 114 (44.2) Age (years) Underweight 17 (14.9) Normal weight 94 (82.5) Overweight 3 (2.6) Child (6–10) 12 (4.7) Underweight 4 (33.3) Normal weight 8 (66.7) Overweight 0 (0.0) Nutritional Underweight 37 (14.3) status a Normal weight 211 (81.8) Overweight 10 (3.9) a Categorization is based on the recommended weight-for-age Z-score cut-off values ± 2 (underweight: Z-score ≤ −2, normal weight: −2 < Z-score < 2, overweight: Z-score ≥ 2). Values < −6 or >5 were excluded as an outlier [24].

Summary statistics are presented in Table2. ARI admissions during the study period occurred in 171 days (46.8%) while there were no ARI admissions on the remaining 194 days (53.2%). The proportion of holidays (85, i.e., 27 national holidays and 58 Saturdays) to zero-admission days (24.2%) was slightly higher than that of non-zero-admission days (22.2%). Atmosphere 2021, 12, x FOR PEER REVIEW 5 of 10 Atmosphere 2021, 12, 1009 5 of 10

Table 2. Summary statistics for daily acute respiratory infection (ARI) hospital admission, mean concentrationTable 2. Summary of PM statistics2.5, and 24-h for daily mean acute temperature respiratory in infectionBhaktapur (ARI) district hospital of Nepal admission, between mean 13 Feb- con- ruary 2014 to 12 February 2015. centration of PM2.5, and 24-h mean temperature in Bhaktapur district of Nepal between 13 February 2014 to 12 February 2015. 25th Percen- 75th Percen- Min. Median Max. Mean SD tile25th Per- 75thtile Per- Min. Median Max. Mean SD ARI admissions 0.0 0.0centile 0.0 centile 1.0 5.0 0.7 0.9 3 PMARI2.5 admissions (μg/m ) 2.0 0.0 11.6 0.0 41.0 0.0 63.6 1.0 213.7 5.0 43.5 0.7 35.9 0.9 3 TemperaturePM2.5 (µg/m (°C)) 9.4 2.0 15.1 11.6 22.4 41.0 27.1 63.6 213.729.8 43.521.2 35.9 6.4 Temperature (◦C) 9.4 15.1 22.4 27.1 29.8 21.2 6.4 3.2. Main Results 3.2.1.3.2. Main Results Results for All ARI Admissions 3.2.1.Figure Results 2 shows for All the ARI estimates Admissions of RR and 95% CI for single and multi-day lags up to one week.Figure There2 shows was theevidence estimates of associations of RR and between 95% CI forPM single2.5 and andARI multi-dayadmissions lags at lag up 0 (RR:to one 1.13; week. 95% There CI: 1.01–1.26) was evidence and lag of 1 associations (RR: 1.12; 95% between CI: 1.00–1.25). PM2.5 and The ARI estimated admissions associ- at ationlag 0 (RR:attenuated 1.13; 95% after CI: lag 1.01–1.26) 1. The largest and lagcumu 1 (RR:lative 1.12; estimate 95% CI: for 1.00–1.25). multi-day Theexposure estimated was observedassociation at attenuatedlag 01 for the after average lag 1. Theconcentration largest cumulative of PM2.5 on estimate the current for multi-day and the exposureprevious daywas (RR: observed 1.16; 95% at lag CI: 01 1.02–1.31). for the average concentration of PM2.5 on the current and the previous day (RR: 1.16; 95% CI: 1.02–1.31).

Figure 2. Estimates of relative risks (RR) and 95% confidence intervals of acute respiratory infection Figure 2. Estimates of relative risks (RR) and 95% confidence3 intervals of acute respiratory infection (ARI) hospital admission among children for a 10 µg/m increase in the daily PM2.5 concentration (ARI) hospital admission among children for a 10 μg/m3 increase in the daily PM2.5 concentration by by single- and multi-day lags of up to one week. single- and multi-day lags of up to one week. 3.2.2. Results by Weight Status 3.2.2.Figure Results3 showsby Weight the estimates Status by weight status at lags 0 and 1 and their average denoted by lagFigure 01. There 3 shows was nothe evidence estimates of by an weight association status in theat lags underweight 0 and 1 and group. their In average the normal de- notedweight by group, lag 01. an There association was no was evidence observed of an for association average lag in 01the (RR: underweight 1.14; 95% CI:group. 1.01–1.30). In the normalThe strongest weight evidence group, an was association observed inwas the observed overweight for groupaverage at alllag lag 01 specifications—lag(RR: 1.14; 95% CI: 1.01–1.30).0 with RR The = 1.53 strongest (95% CI: evidence 1.05–2.22), was lagobserv 1 withed in RR the = overweight 1.74 (95% CI: group 1.14–2.64), at all lag and speci- lag fications—lag01 with RR = 0 1.77 with (95% RR = CI: 1.53 1.17–2.69). (95% CI: 1.05–2.22), The RR and lag 1 95% with CI RR for = 1.74 each (95% weight CI: 1.14–2.64), group are andreported lag 01 in with Table RR S1. = The1.77 overall (95% CI: and 1.17–2.69). weight group-specific The RR and CRFs95% CI are for illustrated each weight in Figure group4. areThe reported slopes for in all Table ARI inS1. the The normal overall weight and weig and overweightht group-specific groups CRFs are positive are illustrated and above in Figurezero, with 4. The the slopes latter beingfor all theARI steepest. in the normal weight and overweight groups are positive and above zero, with the latter being the steepest.

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Figure 3. Estimates of relative risks (RR) and 95% conﬁdence intervals of acute respiratory infection FigureFigure 3.3. EstimatesEstimates ofof relativerelative risksrisks (RR)(RR) andand 95%95% confidenceconfidence intervalsintervals ofof acuteacute respiratoryrespiratory infectioninfection (ARI) hospital admission by weight status (underweight [n = 37], normal weight [n = 211], overweight (ARI)(ARI) hospitalhospital admissionadmission byby weweightight statusstatus (underweight(underweight [[nn == 37],37], normalnormal weightweight [[nn == 211],211], over-over- n weightweight[ = 10]) [[nn at == lag10])10]) 0, atat lag laglag 1, 0,0, and laglag the 1,1, andand average thethe averageaverage lag 01. laglag 01.01.

FigureFigure 4.4. 4. All-subjectAll-subjectAll-subject andand and weight-specificweight-specific weight-speciﬁc concentration-responseconcentration-response concentration-response functionsfunctions functions (CRFs)(CRFs) (CRFs) forfor thethe for associa-associa- the as- tiontionsociation betweenbetween between PMPM2.52.5 (average(average PM2.5 (average laglag 01)01) andand lag dailydaily 01) and acuteacute daily respiratoryrespiratory acute respiratory infectioninfection (ARI)(ARI) infection hospitalhospital (ARI) admissionsadmissions hospital ininadmissions children.children. in children.

WeWe assessedassessed thethe the sensitivitysensitivity sensitivity ofof of ourour our results.results. results. ExtendingExtending Extending thethe the durationduration duration ofof adjustmentadjustment of adjustment forfor temperaturetemperaturefor temperature upup toto up fourfour to four weeksweeks weeks diddid didnotnot notaffectaffect affect thethe theeffecteffect effect estimates.estimates. estimates. AssociationsAssociations Associations diddid didnotnot changechangenot change whenwhen when additionaladditional additional adjustmentadjustment adjustment waswas made wasmade made forfor dewpointdewpoint for dewpoint temperature.temperature. temperature. ToTo checkcheck To check forfor aa possiblepossiblefor a possible nonlinearnonlinear nonlinear association,association, association, wewe expressedexpressed we expressed thethe mainmain the exposureexposure main exposure inin thethe formform in the ofof formaa naturalnatural of a cubiccubicnatural splinespline cubic withwith spline fivefive with degreesdegrees five degrees ofof freedom,freedom, of freedom, bubutt foundfound but no foundno evidenceevidence no evidence supportingsupporting supporting non-linearnon-linear non- concentration-responseconcentration-responselinear concentration-response functionsfunctions functions (CRFs;(CRFs; (CRFs; differencedifference difference ofof AkaikeAkaike of Akaike InformationInformation Information CriterionCriterion Criterion be-be- tweentweenbetween thethe the linearlinear linear andand and non-linearnon-linear non-linear modelmodel model == 0.6,0.6, = 0.6, FigureFigure Figure S2).S2). S2). 4. Discussion 4.4. DiscussionDiscussion We investigated the short-term association between ambient PM2.5 and daily ARI WeWe investigatedinvestigated thethe short-termshort-term associationassociation betweenbetween ambientambient PMPM2.52.5 andand dailydaily ARIARI hospital admissions among children under 10 years old in Bhaktapur district, Nepal. hospitalhospital admissionsadmissions amongamong childrenchildren underunder 1010 yearsyears oldold inin BhaktapurBhaktapur district,district, Nepal.Nepal. WeWe We observed evidence of associations on the day of hospital admission and one day observedobserved evidenceevidence ofof associationsassociations onon thethe dadayy ofof hospitalhospital admissionadmission andand oneone dayday beforebefore before (i.e., lag 0 and lag 1). Our study suggests a relative risk of ARI hospital admission (i.e.,(i.e., laglag 00 andand laglag 1).1). OurOur studystudy suggestssuggests aa relativerelative riskrisk ofof ARIARI hospitalhospital admissionadmission amongamong among the children of 1.16 (95% CI: 1.02–1.31) for an average of a 10 µg/m3 increase in thethe childrenchildren ofof 1.161.16 (95%(95% CI:CI: 1.02–1.31)1.02–1.31) forfor anan averageaverage ofof aa 1010 μμg/mg/m33 increaseincrease inin thethe con-con- the concentration of PM2.5 on the same and preceding days. The weight status among centrationcentration ofof PMPM2.52.5 onon thethe samesame andand precedingpreceding days.days. TheThe weightweight statusstatus amongamong studystudy sub-sub- study subjects was slightly higher than national statistics, which might be due to the jectsjects waswas slightlyslightly higherhigher thanthan nationalnational statistics,statistics, whichwhich mightmight bebe duedue toto thethe socioeconomicsocioeconomic socioeconomic gap between urban-rural regions within the country. Further analysis by gapgap betweenbetween urban-ruralurban-rural regionsregions withinwithin thethe country.country. FurtherFurther analysisanalysis byby stratificationstratification stratification suggested that the overweight children might be more susceptible to the suggestedsuggested thatthat thethe overweightoverweight childrenchildren mighmightt bebe moremore susceptiblesusceptible toto thethe respiratoryrespiratory respiratory health effects of PM2.5 than children in the normal weight or underweight health effects of PM2.5 than children in the normal weight or underweight categories. The healthcategories. effects The of riskPM2.5 estimate than children for the in overweight the normal group weight at lagor underweight 01 was higher categories. than the other The risk estimate for the overweight group at lag 01 was higher than the other two groups, risktwo estimate groups, while for the the overweight estimates for group the normal at lag weight01 was andhigher underweight than the other groups two were groups, closer while the estimates for the normal weight and underweight groups were closer to the non- whileto the the non-stratified estimates for estimates the normal for all-ARIweight and cases. underweight Our study underscoresgroups were thecloser importance to the non- of stratifiedstratified estimatesestimates forfor all-ARIall-ARI cases.cases. OurOur ststudyudy underscoresunderscores thethe impoimportancertance ofof elucidatingelucidating elucidating heterogeneity in the short-term health effects of PM2.5, especially in the LMICs heterogeneity in the short-term health effects of PM2.5, especially in the LMICs where heterogeneitywhere children in often the short-term suffer from health multiple effects disease of burdens.PM2.5, especially Nevertheless, in the furtherLMICs studieswhere

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with a longer time-series and larger sample size, which may involve multiple hospitals or locations, are needed to corroborate our results. In Nepal, NHRC [25] firstly collected year-round PM2.5 data and demonstrated a descriptive analysis of air quality and the health burden, with combined data from 13 major hospitals in the region. However, the estimated health risk, especially for children in highly polluted areas of the country, remains unclear. Therefore, this study used a different approach to confirm the possibly high health burden attributable to a higher level of ambient PM2.5 in the region. In Asia, the annual average concentrations of PM2.5 in both urban and rural areas were estimated to be higher than those in North America or Europe [32]. However, limited evidence is available on the health impact of PM2.5 in these locations due to the lack of measurement of the PM2.5 concentration. Furthermore, the estimates from the previous assessments vary between locations and age groups [32,33]. For example, in Jinan, China, a comparable study [34] reported a 0.22% increase of respiratory hospital admissions among 3 children aged 0–17 years per 10 µg/m increase in daily PM2.5 levels in the same lag period (average lag 01) with our study. Liu et al. used eight local hospitals’ data of 40,172 admissions due to respiratory diseases categorized into upper infection, pneumonia, and acute bronchitis based on the ICD-10 code (International Classifications of Diseases, 10th Revision). The reason why the risk estimates were higher in our study might be attributable to differences of emission sources or chemical composition, and the basic health status of the population. On the other hand, two studies in North America where the annual mean concentration level of PM2.5 was about half of that in our study area found no increased risk of hospitalization for bronchiolitis in infants [35,36]. For overweight children, increased susceptibility to the pulmonary effects of indoor PM2.5 was previously reported by an observational prospective-cohort study, which investigated indoor air pollutants among urban minority children with asthma in Baltimore, U.S.A. [37]. Lu et al. followed 148 children aged 5–17 years with persistent asthma for a year while associations between the indoor PM2.5 level and respiratory symptoms were assessed every three months. As with our study finding higher risk estimates in the overweight group, they found that overweight or obese children had more symptoms associated with indoor PM2.5 exposure than normal weight children. Two possible mechanisms of excess weight conferring susceptibility have been suggested. First, as substantial evidence reported that air pollution exposure results in increased oxidative stress, which could mediate allergic disorders [38,39], being overweight may also be associated with an under- lying state of oxidative stress and inflammation, which potentially reduces the antioxidant defenses of overweight individuals [40,41]. Second, lower-airway exposure to a given airborne particle is greater in overweight than normal weight children since overweight or obese children may have greater pulmonary deposition [37,42–44], which might be due to s higher breathing frequency, as observed in an experiment on mice [44]. From these perspectives, weight reduction or adequate nutritional management for children might have the possibility of reducing their susceptibility to adverse respiratory health effects from the exposure to ambient air pollution. While the current study did not find any evidence supporting a risk difference between the under- and normal-weight groups, more investigation is needed given the small number of cases. Some studies have suggested that underweight children might present a vulnerable and/or susceptible sub-population in terms of their possible lower health status or immunity level. They may also have a lower socioeconomic status or poor living conditions with potential exposure to higher environmental risks [45]. There are a few limitations worth noting. In relation to the small number of daily admissions, our findings require further investigation with more samples and a longer period, as discussed. We obtained PM2.5 data from a single monitoring station and used single-pollutant models to estimate the health effects without adjusting for other air pollutants because other monitoring station or other air pollutant data were not available in the district. Multi-pollutant models would be helpful to rule out the possibility of influences of Atmosphere 2021, 12, 1009 8 of 10

other air pollutants on the observed associations. The hospital admission for ARI in the current study was based on a clinical diagnosis not validated by pathogen information. Hence, the ARI admissions might potentially include other diagnoses, ones not categorized into a standard disease classiﬁcation such as the ICD-10.

5. Conclusions

An ambient level of PM2.5 in Bhaktapur, Nepal was associated with daily hospital admissions for ARI among children aged 10 years and below. We observed a higher risk among the overweight children who were likely to be more susceptible to the health effects of PM2.5. Our ﬁndings suggest more investigations in different populations are necessary to understand the heterogenous effects of PM2.5 among children with different weight statuses. Studies are also needed to identify the speciﬁc chemical components and emission sources that may be harmful to the subgroup in the region.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/atmos12081009/s1, Figure S1: Inclusion and exclusion criteria of ARI admissions for the study, Figure S2: Linier and non-linear forms of concentration-response functions (CRFs) (difference of Akaike Information Criterion between the linear and non-linear model = 0.6), Figure S3: Time-series plot of PM2.5 and ARI hospital admissions, Table S1: The relative risks (RR) and 95% Confidence Intervals (CI) for all ARI admissions and each weight group (Underweight, Normal Weight, Overweight). Author Contributions: Conceptualization, H.N.; methodology, C.F.S.N.; software, C.F.S.N.; valida- tion, C.F.S.N.; formal analysis, H.N. and C.F.S.N.; investigation, H.N.; resources, B.G.D., D.P., A.K.P., S.C.V., D.S. and G.B.R.; data curation, B.G.D., D.P., A.K.P., S.C.V., D.S. and G.B.R.; writing—original draft preparation, H.N.; writing—review and editing, C.F.S.N., L.M., X.T.S. and M.H.; visualization, H.N.; supervision, C.F.S.N., B.G.D. and M.H.; project administration, M.H.; funding acquisition, H.N. and M.H. All authors have read and agreed to the published version of the manuscript. Funding: This research was partially supported by the Japan Student Services Organization (JASSO) for Student Exchange Support Program (Scholarship for Short-term study abroad) organized by Nagasaki University (Registrant No. HTB1817301008001). Institutional Review Board Statement: Ethical approvals for the study were obtained from the Nepal Health Research Council (Ref. No. 771) and the Institutional Review Board of the School of Tropical Medicine and Global Health, Nagasaki University (Ref. No. 56). Informed Consent Statement: Patient consent was waived since clinical data were retrospectively retrieved from discharge sheets and laboratory records without obtaining informed consent from each inpatient of the hospital. Research information has been posted on the notification board of the hospital from 28 October 2018 to secure the opportunity for opt-out from the study participants instead of obtaining Informed Consent directly. Research information contained key information of the study such as the purpose and procedure of using clinical data, health information to be used in the study, the scope of the data, name of a principal investigator who is responsible for data management, the procedure for rejecting participation, and contact information. Data Availability Statement: Not applicable. Acknowledgments: We appreciate our colleagues at Siddhi Memorial Hospital and LEADERS Nepal for providing technical support and advice on data handling. Conflicts of Interest: The authors declare no conflict of interest.

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