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International Journal ofBiometeorology ISSN 0020-7128 Int J BiometeorolDOI 10.1007/s00484-014-0848-y

How much does latitude modifytemperature–mortality relationship in 13eastern US cities?

Jianpeng Xiao, Ji Peng, Yonghui Zhang,Tao Liu, Shannon Rutherford, HualiangLin, Zhengmin Qian, Cunrui Huang,Yuan Luo, Weilin Zeng, et al.

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ORIGINAL RESEARCH PAPER

How much does latitude modify temperature–mortalityrelationship in 13 eastern US cities?

Jianpeng Xiao & Ji Peng & Yonghui Zhang & Tao Liu & Shannon Rutherford &

Hualiang Lin & Zhengmin Qian & Cunrui Huang & Yuan Luo & Weilin Zeng & Cordia Chu &

Wenjun Ma

Received: 21 October 2013 /Revised: 11 May 2014 /Accepted: 12 May 2014# ISB 2014

Abstract Although several studies have documented thatlatitude might be an effect modifier of the association betweentemperature and mortality, little is known about how muchlatitude modifies the temperature–mortality relationship. Inthis study, we examined this research question using a distrib-uted lag non-linear model and meta-regression analysis basedon data from 13 large cities of eastern US from the USNational Morbidity, Mortality, and Air Pollution Study. Wefound that cold effects lasted about 1 month while hot effectswere acute and short-term. Meta-regression analysis showedthat latitude modified both the cold and hot effects withstatistical significance. The cold effect decreased with thelatitude increment, with −0.11 % change of mortality effectfor 1° increment, while the hot effect increased with the

latitude increment, with 0.18 % change of mortality effectfor 1° increment. This finding indicates the importance oflatitude on temperature-related mortality risk, which is helpfulfor city to develop localized effective adaptation strategy inthe context of climate change.

Keywords Latitude . Temperature . Mortality . Distributedlag non-linear model

Introduction

There has been increasing interest in the last two decades toassess the relationship between temperature and mortality,particularly in light of climate change (Medina-Ramon andSchwartz 2007; Ren et al. 2008; Hajat and Kosatky 2010;Martin et al. 2012; Lin et al. 2013). A U-, V-, or J-shapedrelationship between temperature and mortality has been re-ported in previous studies (Armstrong 2006; Anderson andBell 2009; Guo et al. 2011), and this association could bemodified by several factors such as socioeconomic status,demographic characteristics, weather patterns, and air pollu-tion (Ren et al. 2008; Anderson and Bell 2009; Hajat andKosatky 2010).

Acclimatization refers to physiological and/or behav-ioral adaptation to local weather conditions. The healthimpacts of temperatures are likely to depend on localconditions and may be modified by sex, age, education,income, unemployment, and other socioeconomic factors,which represent different capacity for acclimatization(Ren et al. 2008; Anderson and Bell 2009; Hajat andKosatky 2010). Moreover, some studies have reportedthe acclimatization to local weather varied across timeand region (Keatinge and Donaldson 1997; Carson et al.2006; Anderson and Bell 2009). For instance, one studyreported that heat-related mortality was greater during

Jianpeng Xiao and Ji Peng are co-first authors.

Electronic supplementary material The online version of this article(doi:10.1007/s00484-014-0848-y) contains supplementary material,which is available to authorized users.

J. Xiao : T. Liu :H. Lin :Y. Luo :W. Zeng :W. Ma (*)Guangdong Provincial Institute of Public Health, GuangdongProvincial Center for Disease Control and Prevention, 160#, QunxianRoad, Panyu District, Guangzhou 511430, Chinae-mail: mwj68@vip.tom.com

J. PengShenzhen Center for Chronic Disease Control, Shenzhen, China

Y. ZhangGuangdong Provincial Center for Disease Control and Prevention,Guangzhou, China

S. Rutherford : C. Huang :C. ChuCenter for Environment and Population Health, School ofEnvironment, Griffith University, Brisbane, Australia

Z. QianDepartment of Epidemiology, School of Public Health, Saint LouisUniversity, Saint Louis, MO, USA

Int J BiometeorolDOI 10.1007/s00484-014-0848-y

Author's personal copy

early summer heat waves than during late summer heatwaves, indicating seasonal acclimatization (Anderson andBell 2011). Another study found that there was a progres-sive reduction in temperature-related deaths over time,which likely reflected long-term climate acclimatization(Carson et al. 2006). For the regional variation, theEurowinter group found that the effect of cold weatheron mortality was larger in warm countries (Keatinge andDonaldson 1997). Curriero et al. revealed that there was agreater effect of colder temperatures on mortality risk inmore-southern cities and of warmer temperatures in more-northern cities in the US (Curriero et al. 2002). Similarly,another European study showed greater cold effects inseven Mediterranean cities than in eight North–centralEuropean cities (Analitis et al. 2008). These findingsimplied that different acclimatization of the communitiesto their local weather conditions and that latitude mightalso play a role in the variation of the temperature–mor-tality relationship (Curriero et al. 2002; Gasparrini et al.2012; Turner et al. 2012). However, few studies havequantitatively assessed the effect of latitude on the tem-perature–mortality relationship.

To better understand the effect modification of latitude ontemperature–mortality relationship, we used a two-stage ap-proach to estimate the change of cold- or hot-related mortalityeffects for per 1° latitude change across the 13 large cities ineastern United States. In the first stage, we explored the dose–response relationship for cold and hot temperatures for eachcity. In the second stage, we pooled the city-specific relativerisk (RR) and examined how latitude modified the tempera-ture–mortality relationship.

Materials and methods

Data collection

Daily weather, air pollution, and mortality data were collectedfor 13 large metropolitan areas in the eastern United States forthe period of 1987–2000 (Boston, Massachusetts; New York,New York; Philadelphia, Pennsylvania; Baltimore, Maryland;Jacksonville, Florida; Washington, District of Colum; Atlanta,Georgia; Newark, New Jersey; Providence, Rhode Island;Raleigh, North Carolina; Rochester, New York; Tampa, Flor-ida; and Miami, Florida) (Fig. 1). These metropolitan areasrepresent the principal population centers on the eastern sea-board. The data set was extracted from the National Morbid-ity, Mortality, and Air Pollution Study (NMMAPS) (Sametet al. 2000), which was publicly available through theInternet-based health and air pollution surveillance system(http://www.ihapss.jhsph.edu). Daily mortality excluded non-residents and deaths from injuries or external causes

(International Classification of Diseases, Ninth Revision(ICD-9) Codes 800 and above, ICD-10 Codes S and above).

Weather variables included city-specific daily mean, max-imum, and minimum temperatures. Days missing weatherdata (<2.5% of total days) were excluded. Air pollutantsincluded average particulate matter <10 μm in aerodynamicdiameter (PM10), nitrogen dioxide (NO2), and average ozone(O3). Based on previous studies (Anderson and Bell 2009;Hajat and Kosatky 2010; Yu et al. 2012), we examined severalcity-level characteristics (such as the percentage of populationover 65 years old, poverty of individuals, education level lessthan ninth grade, and latitude of city) as potential effectmodifiers of the temperature–mortality relationship. Thecity-level characteristics were obtained from the 2000 UScensus (Table 1).

Statistical analysis

Analyses were conducted in two stages. In the first stage,the distributed lag non-linear model (DLNM) for each cityproduced an estimated RR for different lag days(Gasparrini 2011). In the second stage, meta-analyseswere conducted to estimate the overall effect of tempera-ture on mortality. Then, we used meta-regression analysisto explore the modification effect of latitude.

Fig. 1 The location of the 13 of cities from which data were used in thisstudy

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Stage 1: city-specific relative risk estimate

In this stage, the relative risks of temperature on mortalitywere calculated for each city. A DLNM model can be speci-fied as:

Log E Y tð Þ½ � ¼ αþ cb tm; 3; lag; 3ð Þ þ s PM10; 3ð Þ þ s NO2; 3ð Þ

þ s O3; 3ð Þ þ s rh; 3ð Þ þ s time; 7 � 14ð Þ þ DOW

where E(Yt) is expected number of deaths on day t; α is themodel intercept. cb means the “cross-basis” function; s(.) is anatural cubic spline function. The model has B-splines with 3degrees of freedom (df) for both temperature (mean tempera-ture (tm)) and lag, which allows us to analyze the same-dayeffects of temperature and lag structures on mortality simul-taneously. We used 3 df for PM10, NO2, O3, and relativehumidity. The df for long-term trend and seasonality wasidentified as 7/year. DOW is a dummy variable representingday of the week.

Our initial results showed that the temperature–mortalityrelationships were generally U- and V-shaped, with potentialthresholds. Thus, we used a threshold–natural cubic splinemodel for the analysis in accordance with previous studies(Medina-Ramon and Schwartz 2007; Gasparrini et al. 2010),assuming that the effect of cold temperature was linear belowthe special threshold and the effect of high temperature waslinear above the same threshold. The threshold was deter-mined as the point of minimum mortality temperature(MMT). We then estimated the linear relationships belowand above the city-specific temperature threshold. Specifical-ly, the mortality risk was calculated for 1 °C decrease intemperature below the MMT (cold effect) and for 1 °C

increase above the MMT (heat effect). Previous work hasshown that cold effects persisted for about 1 month(Armstrong 2006; Guo et al. 2011). To capture the overalltemperature effect and adjust for any potential harvest effect,the maximum lag was set to 34 days. In this stage, the relativerisks of temperature onmortality were calculated for each city.The effects were estimated for six lag periods, lag 0, lag 0–6,lag 0–13, lag 0–20, lag 0–27, and lag 0–34, and thresholdtemperatures were estimated at different lag periods. In ourstudy, we found cold effect and hot effects were the greatest atlag 0–27 and lag 0–6, respectively. So, we reported the coldeffect for lag 0–27 and hot effect for lag 0–6.

Stage 2: random-effect meta-analyses

A second-stage analysis was performed to provide a quanti-tative summary of overall mortality effects of temperaturesusing random effect meta-analysis. In the current stage, wequantified the effect of heterogeneity using the formula I2,which described the percentage of total variation across stud-ies (Patsopoulos et al. 2008). In addition, the τ2-statistic, anestimate of residual amount of heterogeneity, was employed toquantify the heterogeneity (Viechtbauer 2010).

Mixed-effects meta-regression analysis was piloted toexplore the city heterogeneity. Variables representing po-tential effect modifiers, latitude, the percentage of popu-lation over 65 years old, poverty of individuals, andeducation level less than ninth grade, which reflected thesocioeconomic status (SES) were included in the meta-regression analysis. We estimated how much latitudemodified the mortality effect. SES and MMT for everycity, which represented population acclimatization, werealso adjusted for in the model. We calculated the mortalityeffect (RR) change for per 1° increment in latitude.

Table 1 Summary characteristics for the 13 cities selected in United States

City Latitude, °N Meantemperature, °C

2000 population, ×103 Average dailyno. of deaths

Aged >65 years, % Education lessthan 9th grade, %

Poverty ofindividuals, %

Miami, Florida 25.78 25.12 2,253.36 45 13.30 14.70 18.00

Tampa, Florida 27.96 22.98 998.95 18 12.49 5.20 12.51

Jacksonville, Florida 30.32 20.41 778.88 12 10.30 4.40 12.20

Atlanta, Georgia 33.76 18.52 417.93 8 9.73 7.20 13.50

Raleigh, North Carolina 35.82 15.67 627.85 14 8.30 4.20 7.82

Washington, District ofColumbia

38.91 12.78 572.06 16 11.20 4.30 20.20

Baltimore, Maryland 39.30 13.28 651.15 20 13.20 9.40 22.90

Philadelphia, Pennsylvania 40.01 13.40 1,517.55 42 14.10 7.50 22.90

New York, New York 40.67 12.83 8,931.74 190 11.70 7.10 19.96

Newark, New Jersey 40.72 13.15 793.63 19 12.20 7.10 15.55

Providence, RhodeIsland

41.82 10.91 621.60 15 14.60 8.50 15.47

Boston, Massachusetts 42.34 10.90 689.81 13 10.40 9.10 19.00

Rochester, New York 43.90 9.08 735.34 16 12.90 4.50 11.15

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We used the 95 % confidence interval (95 % CI), and pvalues<0.05 were significant. All the analyses were conduct-ed using the “dlnm” and “metafor" package in the software R,version 3.0.0 (R Development Core Team 2013).

Results

Table 1 summarizes characteristics of the 13 cities, or-dered by latitude. The data included education levels,poverty, and proportion of elderly residents in 2000, aswell as mean temperatures and average daily number ofdeaths from 1987 to 2000. Generally, northern cities havelower mean temperature than southern cities, rangingfrom 9.08 °C to 25.12 °C. The other socio-economiccharacteristics for population, education, and povertywere various across the 13 cities.

The random-effect meta-analysis found statistically sig-nificant associations between temperatures and mortalityin different lag periods (Fig. 2). For the cold effect, theeffect increased as the number of lag days increased up toa maximum overall effect which appeared at the lag 0–27(RR, 1.012, 95 % CI, 1.008–1.016). On the contrary, there

was a decreasing trend with the increment of lag days forhot effect, with the maximum overall effect observed atthe lag 0–6 (RR, 1.012, 95 % CI, 1.004–1.019). The resultof heterogeneity test showed that both cold and hot effectswere quite different across cities in different lag periods.For instance, the proportions of total variation in mortalityattributed to between-cities differences were 98.11 % forthe cold effect (I2=98.11 %) at lag 0–27 and 93.46 % forthe hot effect (I2=93.46 %) at lag 0–6.

The large heterogeneity across cities suggested thatthere would be some modifiers for the temperature effects.We introduced the city characteristics and latitude inmeta-regression analysis. Results showed that the percent-age of individuals over 65 years, poverty of individuals,and education level less than ninth grade were not foundto modify the estimate except for the poverty characteris-tic on hot effect (Table 2). However, latitude was found tobe a factor to modify both the cold and hot effects. Ingeneral, the cold effect decreased with the latitude incre-ment, with −0.11 % change of mortality effect for 1°increment at lag 0–27. Conversely, the hot effect in-creased with the latitude increment, with 0.18 % changeof mortality effect at lag 0–6 (Table 2). Figure 3 graphi-cally illustrated the relationship between mortality risk

Fig. 2 The overall lagged effect in different periods by cold and hot temperature. a Cold effect. b Hot effect (RR, relative risk, indicates temperature-related effect increase due to per 1 °C decrease or increase)

Table 2 Changes in cold- and hot-related mortality effect estimates per interval increase in a city-specific characteristic

Predictor Interval Change in mortality effect (%, 95 %CI)

Cold effect Hot effect

Age >65 years 1 % 0.10 (−0.5 to 0.24) 0.01 (−0.27 to 0.28)Poverty 1 % 0.01 (−0.07 to 0.08) 0.15* (0.05 to 0.25)

Less than ninth grade 1 % 0.09 (−0.02 to 0.20) 0.01 (−0.20 to 0.22)Latitude of city 1° −0.11* (−0.15 to −0.08) 0.18* (0.11 to 0.25)

*p<0.05

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and latitude. The heterogeneity test also supported themodification of latitude. After adjustment for latitude,the heterogeneity across cities decreased significantly.For cold effect at lag 0–27, the change of τ2 is 96.42 %,suggesting that 96.42 % of the total heterogeneity can beattributed to latitude. With regard to the hot effect at lag0–6, latitude could account for 78.4 % of heterogeneity(Table 3).

To further quantitatively estimate the modification effect oflatitude on temperature-related relationship, city-specific char-acteristics were adjusted for in the meta-regression models.After adjustment for the SES, the modification effect of lati-tude did not change dramatically. For example, after adjust-ment for the three socioeconomic variables, cold effect at lag0–27 increased by 0.11 % with 1° latitude increment. And thehot effect at lag 0–6 decreased by 0.14 % with 1° latitudeincrement (Table 4).

We also studied the effect modification for other lag pe-riods using similar analysis approach. The similar effect mod-ification of latitude on cold and hot effects was found (Elec-tronic supplementary material, Figure S1).

Discussion

A number of studies have identified the associations betweentemperature and mortality (Medina-Ramon and Schwartz2007; Analitis et al. 2008; Anderson and Bell 2009; Arm-strong et al. 2011). Using existing data from 13 cities in theeastern United States, we examined the effect modification oflatitude on the relationship. To the best of our knowledge, thisstudy might be the first study to specifically quantify themodification effect of latitude on temperature–mortalityrelationship.

Consistent with many previous studies (Anderson and Bell2009; Guo et al. 2011), we found that temperature was signif-icantly associated with mortality. Our results showed that coldeffects increased with the lag day increment and lasted forabout 1 month, whereas hot effect was acute and short-term.The long lag effect of cold temperature on mortality suggestedthat cold weather presented as an important public healthproblem and should not be underestimated, despite muchattention recently being given to the impacts of higher tem-peratures (Hajat and Kosatky 2010; Anderson and Bell 2011).

In the present study, we found cold- or hot-related mortalityeffects varied greatly across the 13 US cities. Previous studieshave reported that the effect of temperature on mortality canbe impacted by city level characteristics, such as education,poverty, and elderly (Carson et al. 2006; Anderson and Bell2009; Hajat and Kosatky 2010). In this study, we found thatmost of these SES variables did not significantly change theeffect of temperature-related mortality. However, latitudeshowed a great modification effect on both cold and hoteffects. This finding indicated that latitude might be a keyfactor for explaining variability in temperature–mortality re-lationship across cities. Furthermore, after controlling for thesocioeconomic status, the modification effect of latituderemained robust. It was generally known that the latitudedictates the intensity and duration of sun exposure, whichwas strongly correlated with temperature (Cheng et al.

Fig. 3 Relative risk oftemperature on mortality bylatitude (Cold effect for lag 0–27.and Hot effect for lag 0–6)

Table 3 Comparison of heterogeneity test before and after adjustmentfor latitude

Heterogeneity test Cold effect(at lag 0–27)

Hot effect(at lag 0–6)

I2, %, unadjusted 98.11 93.46

I2, %, adjusted for latitude 9.07 73.75

τ2, ×10-6, unadjusted 54.76 161.13

τ2, ×10-6, adjusted for latitude 1.96 34.81

Change of τ2, % 96.42 78.40

I2 , I2 =100 %×(Q−df)/Q, describes the percentage of total variation inpoint and estimates attributable to genuine variation rather than samplingerror

τ2 the estimate of residual amount of heterogeneity

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2008). Previous studies have reported that temperature thresh-olds were related to local latitude, with lower temperaturethresholds in high-latitude areas and higher temperaturethresholds in low-latitude areas (Armstrong 2006; Loughnanet al. 2010; Ye et al. 2012). In our study, the correlationbetween latitude and temperature threshold was −0.29(p>0.05) for cold effect at lag 0–27 and is −0.86 (p<0.05)for hot effect at lag 0−6. This suggested that MMT may alsobe a modification for temperature effect, which was in agree-ment with some previous studies (Loughnan et al. 2010; Yeet al. 2012). Nonetheless, in the present study, the modifyingeffect of latitude on temperature effect did not change afteradjusting for the MMT. These suggested that latitude could bean important modifier of temperature-related mortality effect,which may be as important as understanding citycharacteristics.

Our finding showed that cold effect decreased as the lati-tude increased, while the hot effect increased as the latitudesincreased, which was in accordance with previous studies.One study showed that the percentage increased in all-causemortality per 1 °C fall in temperature was greater in warmerregions in Europe (Keatinge and Donaldson 1997). Anotherstudy reported the cold slope was steeper for the southernmostcities and the hot slopes were steeper for the northernmostcities in US (Curriero et al. 2002). Gasparrini also reportedthat the effect of extreme cold temperature was larger in lowlatitude cities while the effect of extreme cold temperature waslarger in high-latitude cities (Gasparrini et al. 2012).

Our findings were biologically and behaviorally plausible.A range of studies have reported that marked changes intemperature influenced the physiological reaction of humansand were associated with health outcomes (Medina-Ramonand Schwartz 2007; Anderson and Bell 2009; Ye et al. 2012).Systolic and diastolic blood pressure, mean heart rate, andmaximal oxygen consumption of humans would change withambient temperature variation, which may lead to fatal out-comes (Ibald-Mulli et al. 2001). People in low-latitude areas

tended to be more biologically acclimatized to heat, and thoseresiding in high-latitude areas were more sensitive to hotweather (Ren et al. 2008; Guo et al. 2011). Behaviorally,people residing in low-latitude regions were more likely totake action to acclimate to the local warm temperatures suchas using air-conditioning, ventilation, and drinking more wa-ter in hot weather. However, when temperature fell unexpect-edly in warm regions, residents may be not ready to respond tocold temperature, and this may be particularly important forvulnerable populations like the elderly and infants. Converse-ly, people residing in the high-latitude regions physiologicallyand behaviorally were more likely to adapt to the local coldweather by using central heating and wearing warmer cloth-ing, etc.

The current study indicated that some important publichealth considerations were necessary when planning toadapt to climate change or variability. Firstly, thoughmuch emphasis and resources were focused on extremehigh-temperature events, the health impacts of extremecold events in warm regions should not be neglected.Secondly, when health impacts of climate change wereassessed, geographical adaptation should be considered inthe modeling method for projections, hence reducing theuncertainty of temperature-related mortality risk assess-ments. Thirdly, people residing in low-latitude regionsshould pay more attention to the cold temperature, espe-cially the sudden cold, as the present study and ourprevious study has shown that cold temperature has largeimpacts in low-latitude warm regions (Xie et al. 2012).Moreover, people residing in cold regions should takemore care of hot temperature. Fourth, health promotionefforts need to focus on preparing vulnerable citizens forbehavioral adaptation measures that are most appropriatefor responding to these future changes in temperature,particularly in those regions likely to experience moreextreme hot or cold temperatures where such extremeshave been relatively rare in the past.

Table 4 Modification of temperature-related mortality effect by latitude with city-specific characteristic

Model Change in mortality effect for per 1° increment in latitude, % (95 % CI)

Cold effect Hot effect

Adjusted for age >65 years −0.11* (−0.14 to −0.08) 0.19* (0.10 to 0.27)

Adjusted for poverty −0.12* (−0.15 to −0.08) 0.15* (0.09 to 0.21)

Adjusted for less than ninth grade −0.11* (−0.15 to −0.07) 0.19* (0.12 to 0.26)

Adjusted for SESa −0.11* (−0.14 to −0.07) 0.14* (0.06 to 0.21)

Adjusted for MMTb −0.12* (−0.17 to −0.07) 0.13* (0.01 to 0.26)

Adjusted for SES and MMT −0.12* (−0.17 to −0.08) 0.06 (−0.07 to 0.18)

a SES socioeconomic status, In the present study, it contained percentage of population over 65 years, education attainment less than ninth grade, andpoverty bMMT minimum mortality temperature

*p<0.05

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The study had several limitations. Our study was based ononly one US region. Other studies were needed to furtherconfirm these findings in different countries using the sameprotocol. The other limitation was using the ambient temper-ature as the personal exposure indicator, which may not beperfectly precise, for the use of central heating and air condi-tioning may impact the adaptive mechanisms (Basu 2009;Ostro et al. 2010). Additionally, due to limitations with thedatabase accessed, seasonal confounders such as influenzaepidemics were not controlled for in our analysis.

Conclusion

In conclusion, this study found that latitude might be animportant effect modifier of temperature–mortality relation-ships, and there is a greater effect of colder temperatures onmortality risk in low-latitude regions and of warmer temper-atures in high-latitude regions. It is necessary to consider themodifying role of latitude when planning adaptation policyand assessing health risk of temperature.

Acknowledgments This study was partly funded by the Special Cli-mate Change Research Program of China Meteorological Administration(No. CCSF201211) and the Guangdong Provincial Medical ResearchFoundation (No. C2012030).

Competing interests None declared.

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Int J Biometeorol

Author's personal copy