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Preplanned Studies: Disparities of Heatwave-Related Preterm Birth in Climate Types — China, 2012–2019

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  • Summary

    What is already known about this topic?

    An association between prenatal heatwave exposure and the risk of preterm birth was found. However, the disparities in heatwave-related preterm birth across different climate types have not been examined.

    What is added by this report?

    This nationwide case-crossover study investigated the association between heatwave exposure and preterm birth across different Köppen-Geiger climate types. Among pregnant women residing in the arid-desert-cold climate type, exposure to compound heatwaves was found to be associated with a significantly higher risk of preterm birth {adjusted odds ratios (AORs) ranged from 1.55 [95% confidence interval (CI): 1.21–1.97] to 2.11 (95% CI: 1.35–3.31)}. In contrast, among pregnant women residing in the tropical monsoonal climate type, exposure to daytime-only heatwaves was associated with an increased risk of preterm birth [AORs ranged from 1.25 (95% CI: 1.03–1.51) to 1.37 (95% CI: 1.05–1.77)].

    What are the implications for public health practice?

    Specific interventions should be implemented in China to mitigate the risk of preterm birth related to heatwaves, particularly for pregnant women residing in arid-desert-cold and tropical monsoonal climates.

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  • Funding: Project on Mechanism-Based Precise and Integrated Strategies for Preventing and Managing Preterm Birth (2022YFC2704600, 2022YFC2704605) funded by The National Key Research and Development Program of China. Public Health Issues Arising from Climate Change (grant 202046) funded by the Chinese Ministry of Ecology and Environment. Project on the Establishment of China-ASEAN Science and Technology Cooperation Center for Public Health (KY202101004) funded by The National Key Research and Development Program of China
  • [1] Chersich MF, Pham MD, Areal A, Haghighi MM, Manyuchi A, Swift CP, et al. Associations between high temperatures in pregnancy and risk of preterm birth, low birth weight, and stillbirths: systematic review and meta-analysis. BMJ 2020;371:m3811. http://dx.doi.org/10.1136/bmj.m3811CrossRef
    [2] Zhang YQ, Yu CH, Wang L. Temperature exposure during pregnancy and birth outcomes: an updated systematic review of epidemiological evidence. Environ Pollut 2017;225:700 − 12. http://dx.doi.org/10.1016/j.envpol.2017.02.066CrossRef
    [3] Sun SZ, Weinberger KR, Spangler KR, Eliot MN, Braun JM, Wellenius GA. Ambient temperature and preterm birth: a retrospective study of 32 million US singleton births. Environ Int 2019;126:7 − 13. http://dx.doi.org/10.1016/j.envint.2019.02.023CrossRef
    [4] Wang YY, Li Q, Guo YM, Zhou H, Wang QM, Shen HP, et al. Ambient temperature and the risk of preterm birth: a national birth cohort study in the mainland China. Environ Int 2020;142:105851. http://dx.doi.org/10.1016/j.envint.2020.105851CrossRef
    [5] Kent ST, McClure LA, Zaitchik BF, Smith TT, Gohlke JM. Heat waves and health outcomes in Alabama (USA): the importance of heat wave definition. Environ Health Perspect 2014;122(2):151 − 8. http://dx.doi.org/10.1289/ehp.1307262CrossRef
    [6] Chen Y, Zhai PM. Revisiting summertime hot extremes in China during 1961-2015: overlooked compound extremes and significant changes. Geophys Res Lett 2017;44(10):5096 − 103. http://dx.doi.org/10.1002/2016GL072281CrossRef
    [7] Guo YF, Chen PR, Xie YX, Wang YP, Mu Y, Zhou RB, et al. Association of daytime-only, nighttime-only, and compound heat waves with preterm birth by urban-rural area and regional socioeconomic status in China. JAMA Netw Open 2023;6(8):e2326987. http://dx.doi.org/10.1001/jamanetworkopen.2023.26987CrossRef
    [8] Climatologies at high resolution for the earth’s land surface areas. 1 km global Köppen–Geiger climate classification for present and future. https://chelsa-climate.org/1-km-global-koppen-geiger-climate-classification-for-present-and-future/. [2021-11-18].https://chelsa-climate.org/1-km-global-koppen-geiger-climate-classification-for-present-and-future/
    [9] Richardson DB, Cole SR, Chu HT, Langholz B. Lagging exposure information in cumulative exposure-response analyses. Am J Epidemiol 2011;174(12):1416 − 22. http://dx.doi.org/10.1093/aje/kwr260CrossRef
    [10] Guo TJ, Wang YY, Zhang HG, Zhang Y, Zhao J, Wang Y, et al. The association between ambient temperature and the risk of preterm birth in China. Sci Total Environ 2018;613-614:439 − 46. http://dx.doi.org/10.1016/j.scitotenv.2017.09.104CrossRef
    [11] Bouchama A, Abuyassin B, Lehe C, Laitano O, Jay O, O'Connor FG, et al. Classic and exertional heatstroke. Nat Rev Dis Primers 2022;8(1):8. http://dx.doi.org/10.1038/s41572-021-00334-6CrossRef
    [12] Ebi KL, Capon A, Berry P, Broderick C, de Dear R, Havenith G, et al. Hot weather and heat extremes: health risks. Lancet 2021;398(10301):698 − 708. http://dx.doi.org/10.1016/S0140-6736(21)01208-3CrossRef
    [13] Basagaña X, Michael Y, Lensky IM, Rubin L, Grotto I, Vadislavsky E, et al. Low and high ambient temperatures during pregnancy and birth weight among 624,940 singleton term births in Israel (2010–2014): an investigation of potential windows of susceptibility. Environ Health Perspect 2021;129(10):107001. http://dx.doi.org/10.1289/EHP8117CrossRef
    [14] Hondula DM, Balling RC, Vanos JK, Georgescu M. Rising temperatures, human health, and the role of adaptation. Curr Clim Change Rep 2015;1(3):144 − 54. http://dx.doi.org/10.1007/s40641-015-0016-4CrossRef
  • FIGURE 1.  AORs of preterm birth associated with heat waves during the last week before delivery among climate types.

    Note: 18 definitions of heat waves with three types, daytime-only (only daily maximum temperature exceeds thresholds), nighttime-only (only daily minimum temperature exceeds thresholds), and compound (both daily maximum and minimum temperature exceeds thresholds) heat waves, and six indexes, 75th-D2, 75th-D3, 75th-D4, 90th-D2, 90th-D3, and 90th-D4 (periods equal to or more than two, three, or four consecutive days above the daily thresholds of temperature as 75th or 90th percentiles). Climate types and descriptions followed the updated Köppen-Geiger climate classification. All models adjusted moving average of relative humidity and PM2.5 in the last gestational week (lag06), calculated across the time window, using a natural cubic spline with 3 df. We examined the climate zonal variation with an interaction term of heatwave exposure variable and climate types’ category variable.

    Abbreviation: Ref=reference; AOR=adjusted odds ratios; CI=confidence interval; Am=Tropical-monsoon; Aw=Tropical-savannah; BSk=Arid-steppe-cold; BWk=Arid-desert-cold; Cfa=Temperate-fully humid-hot summer; Cwa=Temperate-dry winter-hot summer; Cwb=Temperate-dry winter-warm summer; Dfa=Cold-fully humid-hot summer; Dwa=Cold-dry winter-hot summer; Dwb=Cold-dry winter-warm summer.

    * Statistically significant.

    TABLE 1.  Climate zonal characteristics of participants.

    CharacteristicParticipants, nPreterm births,
    n (%)
    Sampled sites (health facilities), nSampled counties, n§
    Total5,446,088310,384 (5.70)438325
    Climate type and descriptions*
    ATropical134,6537,606 (5.65)118
    AmTropical-monsoon57,2212,685 (4.69)64
    AwTropical-savannah77,4324,921 (6.36)54
    BArid777,35145,465 (5.85)8263
    BSkArid-steppe-cold640,73038,082 (5.94)6652
    BWkArid-desert-cold136,6217,383 (5.40)1611
    CTemperate3,754,084216,811 (5.78)267191
    CfaTemperate-fully humid-hot summer2,147,735129,056 (6.01)145106
    CwaTemperate-dry winter-hot summer1,455,03978,931 (5.42)10877
    CwbTemperate-dry winter-warm summer151,3108,824 (5.83)148
    DCold780,00040,502 (5.19)7865
    DfaCold-fully humid-hot summer42,1622,633 (6.24)42
    DwaCold-dry winter-hot summer648,88935,134 (5.41)5848
    DwbCold-dry winter-warm summer88,9492,735 (3.07)1615
    * Climate types and descriptions followed the updated Köppen-Geiger climate classification.
    Percentages are calculated from participants’ number of all singleton births during the warm season (April to October) in China in each category of climate types.
    § Two sampled counties covered both arid BSk and cold Dwa climate types.
    Download: CSV

    TABLE 2.  Summary of climate zonal heat waves in the warm season during 2012–2019.

    Heatwave definitions*Köppen-Geiger Climate Types
    TropicalAridTemperateCold
    TypesIndexesCut off Tmax (℃)Cut off Tmin (℃)Heatwave days/site/
    year§ (%)
    Cut off Tmax (℃)Cut off Tmin (℃)Heatwave days/site/
    year§ (%)
    Cut off Tmax (℃)Cut off Tmin (℃)Heatwave days/site/
    year§ (%)
    Cut off Tmax (℃)Cut off Tmin (℃)Heatwave days/site/
    year§ (%)
    Daytime-only heat wave75th-D232.9817.9518.50 (8.64)26.2518.1326.40 (12.34)29.5417.9622.53 (10.53)25.5917.4722.57 (10.55)
    75th-D332.9817.9511.92 (5.57)26.2518.1315.84 (7.40)29.5417.9614.07 (6.57)25.5917.4712.75 (5.96)
    75th-D432.9817.958.48 (3.96)26.2518.139.17 (4.28)29.5417.969.16 (4.28)25.5917.477.63 (3.57)
    90th-D233.8819.5013.34 (6.23)28.2419.5713.23 (6.18)31.2419.3512.85 (6.01)27.6418.8411.35 (5.30)
    90th-D333.8819.508.20 (3.83)28.2419.576.87 (3.21)31.2419.357.16 (3.35)27.6418.845.65 (2.64)
    90th-D433.8819.505.58 (2.61)28.2419.573.64 (1.70)31.2419.354.03 (1.88)27.6418.842.79 (1.30)
    Nighttime-only heat wave75th-D232.9817.9536.16 (16.90)26.2518.1326.55 (12.41)29.5417.9621.09 (9.85)25.5917.4722.38 (10.46)
    75th-D332.9817.9523.99 (11.21)26.2518.1314.93 (6.98)29.5417.9611.88 (5.55)25.5917.4711.85 (5.54)
    75th-D432.9817.9516.74 (7.82)26.2518.138.95 (4.18)29.5417.966.66 (3.11)25.5917.476.14 (2.87)
    90th-D233.8819.5023.35 (10.91)28.2419.5712.67 (5.92)31.2419.3511.35 (5.30)27.6418.8410.21 (4.77)
    90th-D333.8819.5014.65 (6.84)28.2419.576.55 (3.06)31.2419.356.03 (2.82)27.6418.844.76 (2.23)
    90th-D433.8819.508.78 (4.10)28.2419.573.28 (1.53)31.2419.353.08 (1.44)27.6418.842.17 (1.02)
    Compound heat wave75th-D232.9817.9549.45 (23.11)26.2518.1320.25 (9.46)29.5417.9630.77 (14.38)25.5917.4722.36 (10.45)
    75th-D332.9817.9541.01 (19.16)26.2518.1314.07 (6.57)29.5417.9623.76 (11.10)25.5917.4715.31 (7.15)
    75th-D432.9817.9535.34 (16.51)26.2518.139.62 (4.49)29.5417.9617.44 (8.15)25.5917.479.96 (4.65)
    90th-D233.8819.5019.44 (9.09)28.2419.574.39 (2.05)31.2419.359.66 (4.52)27.6418.845.67 (2.65)
    90th-D333.8819.5013.94 (6.52)28.2419.572.33 (1.09)31.2419.356.47 (3.02)27.6418.842.88 (1.34)
    90th-D433.8819.5011.42 (5.34)28.2419.571.31 (0.61)31.2419.354.25 (1.98)27.6418.841.59 (0.74)
    * 18 definitions of heat waves with three types, daytime-only (only daily maximum temperature exceeds thresholds), nighttime-only (only daily minimum temperature exceeds thresholds), and compound (both daily maximum and minimum temperature exceeds thresholds) heat waves, and six indexes, 75th-D2, 75th-D3, 75th-D4, 90th-D2, 90th-D3, and 90th-D4 (periods equal to or more than two, three, or four consecutive days above the daily thresholds of temperature as 75th or 90th percentiles).
    Tmax, daily maximum temperature; Tmin, daily minimum temperature.
    § Percentages are calculated using 214 days in the warm season (April to October) as the denominator.
    Download: CSV

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Disparities of Heatwave-Related Preterm Birth in Climate Types — China, 2012–2019

View author affiliations

Summary

What is already known about this topic?

An association between prenatal heatwave exposure and the risk of preterm birth was found. However, the disparities in heatwave-related preterm birth across different climate types have not been examined.

What is added by this report?

This nationwide case-crossover study investigated the association between heatwave exposure and preterm birth across different Köppen-Geiger climate types. Among pregnant women residing in the arid-desert-cold climate type, exposure to compound heatwaves was found to be associated with a significantly higher risk of preterm birth {adjusted odds ratios (AORs) ranged from 1.55 [95% confidence interval (CI): 1.21–1.97] to 2.11 (95% CI: 1.35–3.31)}. In contrast, among pregnant women residing in the tropical monsoonal climate type, exposure to daytime-only heatwaves was associated with an increased risk of preterm birth [AORs ranged from 1.25 (95% CI: 1.03–1.51) to 1.37 (95% CI: 1.05–1.77)].

What are the implications for public health practice?

Specific interventions should be implemented in China to mitigate the risk of preterm birth related to heatwaves, particularly for pregnant women residing in arid-desert-cold and tropical monsoonal climates.

  • 1. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
  • 2. Chinese Center for Disease Control and Prevention Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
  • 3. National Office for Maternal and Child Health Surveillance of China, West China Second University Hospital, Sichuan University, Chengdu City, Sichuan Province, China
  • 4. Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu City, Sichuan Province, China
  • 5. School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China
  • 6. Chinese Center for Disease Control and Prevention, Beijing, China
  • Corresponding authors:

    Qiyong Liu, liuqiyong@icdc.cn

    Juan Liang, liangjuan@scu.edu.cn

  • Funding: Project on Mechanism-Based Precise and Integrated Strategies for Preventing and Managing Preterm Birth (2022YFC2704600, 2022YFC2704605) funded by The National Key Research and Development Program of China. Public Health Issues Arising from Climate Change (grant 202046) funded by the Chinese Ministry of Ecology and Environment. Project on the Establishment of China-ASEAN Science and Technology Cooperation Center for Public Health (KY202101004) funded by The National Key Research and Development Program of China
  • Online Date: December 08 2023
    Issue Date: December 08 2023
    doi: 10.46234/ccdcw2023.205
  • Recent systematic reviews have identified a need for studies investigating the association between high temperatures and preterm birth (PTB) across different climate types (1-2). Previous research has suggested that the association between extreme heat and PTB may vary depending on the climate (3-4), and there is also variability in the definition of heatwaves used in different studies (5). Furthermore, recent studies have revealed differences in the dominant subtypes of heat episodes across regions in China (6). To enhance our understanding of this topic, we conducted a large, nationally representative case-crossover study using data from China’s national maternal surveillance system encompassing 5,446,088 participants from 2012 to 2019. Our study aimed to examine the risk of PTB associated with 18 different definitions of heatwaves in various climate types according to the Köppen-Geiger classification. Our findings indicate that pregnant women in the arid-desert-cold climate type faced a higher risk of PTB when exposed to compound heatwaves, while those in the tropical monsoonal climate type experienced an increased risk with daytime-only heatwaves. These results provide valuable evidence for the development of targeted strategies for heat-PTB prevention in China, taking into account the disparities in heatwave-related PTB among different climate types.

    We obtained data on singleton live births from China’s National Maternal Near Miss Surveillance System (NMNMSS) for the period between January 1, 2012, and December 31, 2019, as data for 2020–2022 were not available during the study period. The data included information from 438 health facilities in 325 counties or districts across China. We applied four exclusion criteria and extracted a final analytic sample of 5,446,088 births in the warm season (April to October), as described elsewhere (7). The NMNMSS was approved by the Ethics Committee of West China Second University Hospital, Sichuan University, China (Protocol ID: 2012008), and adhered to the principles of the Declaration of Helsinki. The ethical approval (Protocol ID: 2012008) also authorized the use of NMNMSS data for subsequent studies, including the current study, on maternal health.

    We defined PTB as births occurring before 37 completed weeks of gestation. To assign climate types for eligible birth records, we used the addresses of the delivery health facilities for each pregnant woman, as residential addresses were not available in the NMNMSS. Climate types were classified based on the updated Köppen-Geiger climate classification. We obtained climate classification data from the 1 km global Köppen-Geiger raster product for the time period 1981–2010 from Climatologies at high resolution for the earth’s land surface areas (CHELSA) (8). Daily maximum temperature (Tmax), minimum temperature (Tmin), relative humidity, and fine particulate matter levels with an aerodynamic diameter less than or equal to 2.5 (PM2.5) were extracted. To assign exposure, we calculated the mean grid from a zone with a 25-km radius around each pregnant woman’s delivery address. Detailed information on the calculation method can be found elsewhere (7). We defined 18 types of heatwaves, categorized into three distinct types: daytime-only (Tmax exceeds thresholds only), nighttime-only (Tmin exceeds thresholds only), and compound (both Tmax and Tmin exceed thresholds). We used six indexes, namely 75th-D2, 75th-D3, 75th-D4, 90th-D2, 90th-D3, and 90th-D4, which represent periods of equal to or more than two, three, or four consecutive days above the daily temperature thresholds at the 75th or 90th percentiles. (Supplementary Material).

    In this multisite study, a space-time-stratified case-crossover design was used to examine the relationship between heatwave events and PTB. This design allowed each participant to serve as her own control and compared exposure on case days to control days (7). Time-invariant individual level confounders, as well as long-term and seasonal trends, were controlled for in the design. Conditional logistic regression models were employed to assess the association between heatwave events and PTB. The models were adjusted for the moving average of relative humidity and PM2.5 in the last gestational week (lag06), calculated across the time window, using a natural cubic spline with 3 df. The analysis explored the variation in climate zones using an interaction term between the heatwave exposure variable and the category variable for climate types. The reference group for this analysis was the tropical monsoonal (Am) climate type. The significance of the interaction term was tested using a two-sided P-value of <0.05. Each of the three types of heatwave definitions and six indexes were modeled individually. Additionally, the lag effects of the final week prior to delivery were investigated. We estimated the odds ratio employing the maximized model goodness of fit in the seven lag days (lag0, lag1, lag2, lag3, lag4, lag5, lag6) for each heatwave definition (9).

    All analyses were performed using R software (version 4.1.1, R Project for Statistical Computing, Vienna, Austria). The “survival” package (version 3.2.11) and the “splines” package (version 4.1.1) were employed for conducting the conditional logistic regression analysis.

    In the final analytic sample, which included a total of 5,446,088 participants, we observed coverage of 10 different Köppen-Geiger climate types. These types encompassed two tropical [Tropical-monsoon (Am) and Tropical-savannah (Aw)], two arid [Arid-steppe-cold (BSk) and Arid-desert-cold (BWk)], three temperate [Temperate-fully humid-hot summer (Cfa), Temperate-dry winter-hot summer (Cwa), and Temperate-dry winter-warm summer (Cwb)], and three cold [Cold-fully humid-hot summer (Dfa), Cold-dry winter-hot summer (Dwa), and Cold-dry winter-warm summer (Dwb)] climate types (Supplementary Table S1). The majority of participants (68.93%) resided in temperate climate types (Table 1). The rate of PTB did not vary significantly among tropical, arid, temperate, and cold climate types (Table 1). However, pregnant women living in tropical climate types experienced higher exposure to compound and nighttime heat waves during the study period. Conversely, women in arid climate types experienced less exposure to compound heat waves (Table 2).

    CharacteristicParticipants, nPreterm births,
    n (%)
    Sampled sites (health facilities), nSampled counties, n§
    Total5,446,088310,384 (5.70)438325
    Climate type and descriptions*
    ATropical134,6537,606 (5.65)118
    AmTropical-monsoon57,2212,685 (4.69)64
    AwTropical-savannah77,4324,921 (6.36)54
    BArid777,35145,465 (5.85)8263
    BSkArid-steppe-cold640,73038,082 (5.94)6652
    BWkArid-desert-cold136,6217,383 (5.40)1611
    CTemperate3,754,084216,811 (5.78)267191
    CfaTemperate-fully humid-hot summer2,147,735129,056 (6.01)145106
    CwaTemperate-dry winter-hot summer1,455,03978,931 (5.42)10877
    CwbTemperate-dry winter-warm summer151,3108,824 (5.83)148
    DCold780,00040,502 (5.19)7865
    DfaCold-fully humid-hot summer42,1622,633 (6.24)42
    DwaCold-dry winter-hot summer648,88935,134 (5.41)5848
    DwbCold-dry winter-warm summer88,9492,735 (3.07)1615
    * Climate types and descriptions followed the updated Köppen-Geiger climate classification.
    Percentages are calculated from participants’ number of all singleton births during the warm season (April to October) in China in each category of climate types.
    § Two sampled counties covered both arid BSk and cold Dwa climate types.

    Table 1.  Climate zonal characteristics of participants.

    Heatwave definitions*Köppen-Geiger Climate Types
    TropicalAridTemperateCold
    TypesIndexesCut off Tmax (℃)Cut off Tmin (℃)Heatwave days/site/
    year§ (%)
    Cut off Tmax (℃)Cut off Tmin (℃)Heatwave days/site/
    year§ (%)
    Cut off Tmax (℃)Cut off Tmin (℃)Heatwave days/site/
    year§ (%)
    Cut off Tmax (℃)Cut off Tmin (℃)Heatwave days/site/
    year§ (%)
    Daytime-only heat wave75th-D232.9817.9518.50 (8.64)26.2518.1326.40 (12.34)29.5417.9622.53 (10.53)25.5917.4722.57 (10.55)
    75th-D332.9817.9511.92 (5.57)26.2518.1315.84 (7.40)29.5417.9614.07 (6.57)25.5917.4712.75 (5.96)
    75th-D432.9817.958.48 (3.96)26.2518.139.17 (4.28)29.5417.969.16 (4.28)25.5917.477.63 (3.57)
    90th-D233.8819.5013.34 (6.23)28.2419.5713.23 (6.18)31.2419.3512.85 (6.01)27.6418.8411.35 (5.30)
    90th-D333.8819.508.20 (3.83)28.2419.576.87 (3.21)31.2419.357.16 (3.35)27.6418.845.65 (2.64)
    90th-D433.8819.505.58 (2.61)28.2419.573.64 (1.70)31.2419.354.03 (1.88)27.6418.842.79 (1.30)
    Nighttime-only heat wave75th-D232.9817.9536.16 (16.90)26.2518.1326.55 (12.41)29.5417.9621.09 (9.85)25.5917.4722.38 (10.46)
    75th-D332.9817.9523.99 (11.21)26.2518.1314.93 (6.98)29.5417.9611.88 (5.55)25.5917.4711.85 (5.54)
    75th-D432.9817.9516.74 (7.82)26.2518.138.95 (4.18)29.5417.966.66 (3.11)25.5917.476.14 (2.87)
    90th-D233.8819.5023.35 (10.91)28.2419.5712.67 (5.92)31.2419.3511.35 (5.30)27.6418.8410.21 (4.77)
    90th-D333.8819.5014.65 (6.84)28.2419.576.55 (3.06)31.2419.356.03 (2.82)27.6418.844.76 (2.23)
    90th-D433.8819.508.78 (4.10)28.2419.573.28 (1.53)31.2419.353.08 (1.44)27.6418.842.17 (1.02)
    Compound heat wave75th-D232.9817.9549.45 (23.11)26.2518.1320.25 (9.46)29.5417.9630.77 (14.38)25.5917.4722.36 (10.45)
    75th-D332.9817.9541.01 (19.16)26.2518.1314.07 (6.57)29.5417.9623.76 (11.10)25.5917.4715.31 (7.15)
    75th-D432.9817.9535.34 (16.51)26.2518.139.62 (4.49)29.5417.9617.44 (8.15)25.5917.479.96 (4.65)
    90th-D233.8819.5019.44 (9.09)28.2419.574.39 (2.05)31.2419.359.66 (4.52)27.6418.845.67 (2.65)
    90th-D333.8819.5013.94 (6.52)28.2419.572.33 (1.09)31.2419.356.47 (3.02)27.6418.842.88 (1.34)
    90th-D433.8819.5011.42 (5.34)28.2419.571.31 (0.61)31.2419.354.25 (1.98)27.6418.841.59 (0.74)
    * 18 definitions of heat waves with three types, daytime-only (only daily maximum temperature exceeds thresholds), nighttime-only (only daily minimum temperature exceeds thresholds), and compound (both daily maximum and minimum temperature exceeds thresholds) heat waves, and six indexes, 75th-D2, 75th-D3, 75th-D4, 90th-D2, 90th-D3, and 90th-D4 (periods equal to or more than two, three, or four consecutive days above the daily thresholds of temperature as 75th or 90th percentiles).
    Tmax, daily maximum temperature; Tmin, daily minimum temperature.
    § Percentages are calculated using 214 days in the warm season (April to October) as the denominator.

    Table 2.  Summary of climate zonal heat waves in the warm season during 2012–2019.

    Pregnant women in arid BWk climate type endure a higher risk of PTB {adjusted odds ratio (AOR) range, 1.55 [95% confidence interval (CI): 1.21–1.97] to 2.11 (95% CI: 1.35–3.31)} than tropical Am climate type during exposure to compound heat waves in the 90th-D3 and 90th-D4 indexes (Figure 1). When exposed to daytime-only heat waves, pregnant women in tropical Am climate type also face a higher risk of PTB [AOR range, 1.25 (95% CI: 1.03–1.51) to 1.37 (95% CI: 1.05–1.77)] than other climate types in the 75th-D2, 75th-D3, 90th-D3, and 90th-D4 indexes. The risk of PTB for pregnant women in arid BWk climate type is associated with exposure to nighttime-only heat waves in the 90th-D4 index [AOR, 1.23 (95% CI: 1.00–1.51)], with no significant difference compared with tropical Am pregnant women.

    Figure 1. 

    AORs of preterm birth associated with heat waves during the last week before delivery among climate types.

    Note: 18 definitions of heat waves with three types, daytime-only (only daily maximum temperature exceeds thresholds), nighttime-only (only daily minimum temperature exceeds thresholds), and compound (both daily maximum and minimum temperature exceeds thresholds) heat waves, and six indexes, 75th-D2, 75th-D3, 75th-D4, 90th-D2, 90th-D3, and 90th-D4 (periods equal to or more than two, three, or four consecutive days above the daily thresholds of temperature as 75th or 90th percentiles). Climate types and descriptions followed the updated Köppen-Geiger climate classification. All models adjusted moving average of relative humidity and PM2.5 in the last gestational week (lag06), calculated across the time window, using a natural cubic spline with 3 df. We examined the climate zonal variation with an interaction term of heatwave exposure variable and climate types’ category variable.

    Abbreviation: Ref=reference; AOR=adjusted odds ratios; CI=confidence interval; Am=Tropical-monsoon; Aw=Tropical-savannah; BSk=Arid-steppe-cold; BWk=Arid-desert-cold; Cfa=Temperate-fully humid-hot summer; Cwa=Temperate-dry winter-hot summer; Cwb=Temperate-dry winter-warm summer; Dfa=Cold-fully humid-hot summer; Dwa=Cold-dry winter-hot summer; Dwb=Cold-dry winter-warm summer.

    * Statistically significant.

    • In our nationwide study examining the relationship between heat waves and PTB across ten different climate types as classified by the Köppen-Geiger system, we observed varying associations. Specifically, among pregnant women exposed to daytime-only heat waves, those residing in the tropical Am climate type faced an elevated risk of PTB [AOR range, 1.25 (95% CI: 1.03–1.51) to 1.37 (95% CI: 1.05–1.77)]. Additionally, for pregnant women exposed to compound heat waves, those living in the arid BWk climate type experienced a higher risk of PTB [AOR range, 1.55 (95% CI: 1.21–1.97) to 2.11 (95% CI: 1.35–3.31)].

      Previous studies have examined the association between extreme heat exposure during pregnancy and PTB. These studies exploring associations in the last week before delivery have observed stronger associations in hot-dry/mixed-dry climate zones in the US, with a relative risk of 1.057 (95% CI: 1.030–1.083), and in comparative hot areas of China, with an AOR of 1.069 (95% CI: 1.010–1.132) (3,10). Another study conducted in China found AORs of 2.48 (95% CI: 2.37–2.59), 1.62 (95% CI: 1.36–1.93), and 1.39 (95% CI: 1.33–1.46) for PTB in temperate, tropical, and subtropical zones (4), respectively, when exposed to extreme heat throughout the entire pregnancy. Studies mentioned above reported climatic zonal disparities; meanwhile, the AOR of PTB was higher with extreme heat exposure during the entire pregnancy than in the last week before delivery. In comparison to the risk of PTB associated with exposure to heat waves nationwide in China, AORs ranging from 1.02 (95% CI: 1.00–1.03) to 1.04 (95% CI: 1.01–1.07) for compound heat waves and AORs ranging from 1.03 (95% CI: 1.01–1.05) to 1.04 (95% CI: 1.01–1.08) for daytime-only heat waves (7), our findings provide further evidence of higher associations in specific climate types compared to nationwide estimates.

      The association between acute prenatal exposure and PTB is still unclear. Heat-induced PTB can occur due to heat-related dehydration, impaired body temperature regulation, and cardiovascular changes (11-12). Differences in the impact of heat on health across geographic regions may be explained by physiological adaptations and adaptive capacities at the individual and community levels, including behavioral, infrastructure, and technological adaptations (3,13-14). Our research suggests that lower levels of physiological, behavioral, and technological adaptations in arid climates and during daytime-only heat waves in tropical regions may contribute to the observed findings. Further investigation into the climatic variations of heat-induced PTB could shed light on the underlying mechanisms and inform the development of adaptation services to reduce risks for pregnant women exposed to extreme temperatures.

      Our study has several strengths. First, we used finer domains to determine localized heat extremes by utilizing the 25-km radius surrounding each health facility. This allowed us to accurately assess temperature distribution and percentiles. Additionally, we incorporated considerations for human climate adaptation in various climate types by utilizing the temperature distribution from the recent climate state period (1981–2010) as a reference for defining threshold values in each domain. Second, we conducted an analysis of the disparities in heat wave-related preterm births across different climate types. We examined 18 different definitions of heat waves and utilized a comprehensive national sampling database that covered ten diverse climate types. Our findings contribute to the global heat-PTB studies using unified climate classification.

      The study has certain limitations that should be acknowledged. First, the study only obtained the delivery hospital addresses of pregnant women from the NMNMSS database. The climate type for each pregnant woman was determined based on the addresses of the delivery health facilities. Although efforts were made to reduce misclassification by calculating the mean grid from a 25-km radius around each address, there may still be potential exposure misclassification. Second, this study is exploratory in nature, specifically investigating the associations between heat waves and PTB in different climate types. Further research is needed to validate these findings.

      In conclusion, this study conducted at a national level found that pregnant women residing in arid BWk climate types were at a higher risk of PTB when exposed to compound heat waves during the final week before delivery. Similarly, in tropical regions with an Am climate type, exposure to daytime-only heat waves was associated with an increased risk of PTB. These findings underscore the need for the implementation of heat-PTB prevention strategies that take into account the climate disparities between regions.

    • YG and QL declare funding from the Chinese Ministry of Ecology and Environment. QL declares a grant from The National Key Research and Development Program of China. JL reports grants from The National Key Research and Development Program of China. All other authors declare no conflicts of interest.

    • We also thank the National Maternal Near Miss Surveillance System facilities and staff for the data collection, reports, and review, without which this research would not be possible.

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