Editing IPCC:AR6/WGII/Chapter-7 (section)

==== 7.2.2.4 Respiratory Tract Infections ====

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Climatic risk factors for respiratory tract infections (RTIs) due to multiple pathogens (bacteria, viruses and fungi) include temperature and humidity extremes, dust storms, extreme precipitation events and increased climate variability. Amongst a range of RTIs, pneumonia and influenza represent a significant disease burden ( [[#Ferreira-Coimbra--2020|Ferreira-Coimbra et al., 2020]] ; [[#Lafond--2021|Lafond et al., 2021]] ; [[#McAllister--2019|McAllister et al., 2019]] ; [[#Wang--2020|Wang et al., 2020]] c). The drivers of pneumonia incidence are complex and include a range of possible non-climate as well as climate factors. For example, chronic diseases (e.g., lung disease, chronic obstructive pulmonary disease (COPD) and asthma), other comorbidities, a weak immune system, age, gender, community, passive smoking, air pollution and childhood immunisation may confound the climate pneumonia relationship ( [[#Miyayo--2021|Miyayo et al., 2021]] ).

In temperate regions, the incidence of pneumonia is higher in winter months, but the exact causes of this seasonality remain debated ( [[#Mirsaeidi--2016|Mirsaeidi et al., 2016]] ). With regards to temperature, various J-shaped, U-shaped or V-shaped temperature–pneumonia relationships have been reported in the literature ( [[#Huang--2018|]] [[#Huang--2018|Huang et al., 2018]] ; [[#Kim--2016|Kim et al., 2016]] ; [[#Liu--2014|Liu et al., 2014]] ; [[#Qiu--2016|Qiu et al., 2016]] ; [[#Sohn--2019|Sohn et al., 2019]] ) with such relationships dependent on location. Humidity also appears important but, like temperature, its effect is not consistent across studies – low temperatures and low humidity ( [[#Davis--2016|Davis et al., 2016]] ), high temperatures and high humidity ( [[#Lam--2020|Lam et al., 2020]] ) and low temperatures and high humidity ( [[#Miyayo--2021|Miyayo et al., 2021]] ) have all been found to be associated with an increased incidence of pneumonia.

Day-to-day variations in temperature also appear important. For Australia, increases in emergency room visits for childhood pneumonia are associated with sharp temperature drops ( [[#Xu--2014|Xu et al., 2014]] ). Large inter-daily changes in temperature are important for respiratory disease incidence in Guangzhou, China ( [[#Lin--2013|Lin et al., 2013]] ) and Shanghai ( [[#Lei--2021|Lei et al., 2021]] ) while rapidly changing and extreme temperatures during pregnancy have been linked to childhood pneumonia ( [[#Miao--2017|Miao et al., 2017]] ; [[#Zeng--2017|Zeng et al., 2017]] ; [[#Zheng--2021|Zheng et al., 2021]] ). In tropical and subtropical areas of Africa and Asia, pneumonia incidence has been reported to be higher during the rainy season, pointing to a positive association between pneumonia patterns and temperature and precipitation ( [[#Chowdhury--2018a|Chowdhury et al., 2018a]] ; [[#Lim--2018|Lim and Siow, 2018]] ; [[#Paynter--2010|Paynter et al., 2010]] ).

The degree to which the timing, duration and magnitude of local influenza virus epidemics is dependent on climate factors is poorly understood ( [[#Lam--2020|Lam et al., 2020]] ). Further, a host of non-climate confounders are ''likely'' to influence the incidence of seasonal influenza ( [[#Caini--2018|Caini et al., 2018]] ). This poses a number of challenges for making reliable climate-based epidemiological forecasts for influenza ( [[#Gandon--2016|Gandon et al., 2016]] ). Although no association between anomalous climate conditions and influenza have been reported in some locations ( [[#Lam--2020|Lam et al., 2020]] ), generally, low winter temperatures and humidity in temperate regions and periods of high humidity and precipitation in the tropical and subtropical regions have been linked to outbreaks of influenza ( [[#Deyle--2016|Deyle et al., 2016]] ; [[#Soebiyanto--2015|Soebiyanto et al., 2015]] ; [[#Tamerius--2013|Tamerius et al., 2013]] ). However, the climate sensitivity of influenza may be more complex than this, with both high and low humidity; the amount and intensity of precipitation; solar activity and/or sunshine; and latitude also being important ( [[#Axelsen--2014|Axelsen et al., 2014]] ; [[#Chong--2020b|Chong et al., 2020b]] ; [[#Geier--2018|Geier et al., 2018]] ; [[#Park--2019|Park et al., 2019]] ; [[#Qu--2016|Qu, 2016]] ; [[#Smith--2017|Smith et al., 2017]] ; [[#Wang--2017|Wang et al., 2017]] c; [[#Zhao--2018a|Zhao et al., 2018a]] ). Moreover, the shape of the climate variable influenza relationship may be conditioned on influenza type ( [[#Chong--2020a|Chong et al., 2020a]] ). Further, distinct periods of weather variability characterised by rapid inter-daily changes in temperature may act as precursors to influenza epidemics as has been demonstrated for the marked 2017–2018 influenza season and others across the USA ( [[#Liu--2020a|Liu et al., 2020a]] ; [[#Zhao--2018a|Zhao et al., 2018a]] ). For the eastern Mediterranean, such rapid weather changes are associated with the ‘Cyprus Low’, with the timing and magnitude of seasonal influenza related to the interannual frequency of this particular weather regime ( [[#Hochman--2021|Hochman et al., 2021]] ). Potentially, large-scale modes of climatic variability such as ENSO and the Indian Ocean Dipole, which strongly moderate the frequency of weather regimes in some parts of the world, could affect influenza pandemic dynamics. However, studies conducted to date report inconsistent results. Some point to an increased (decreased) severity of seasonal influenza during El Niño (La Niña) ( [[#Oluwole--2015|Oluwole, 2015]] ; [[#Oluwole--2017|Oluwole, 2017]] ), while others find influenza to be more severe and frequent when coinciding with La Niña events ( [[#Chun--2019|Chun et al., 2019]] ; [[#Flahault--2016|Flahault et al., 2016]] ; [[#Shaman--2013|Shaman and Lipsitch, 2013]] ). This raises the possibly of non-stationary associations between large-scale modes of climatic variability and influenza dynamics ( [[#Onozuka--2015|Onozuka and Hagihara, 2015]] ) as found for other diseases ( [[#Kreppel--2014|Kreppel et al., 2014]] ), something that might be expected given El Niño’s time-varying impact on global precipitation and temperature fields and associated impacts on health outcomes ( [[#McGregor--2018|McGregor and Ebi, 2018]] ).

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