Epidemiological burden and trends of neonatal and under-five mortality from lower respiratory infections associated with PM2.5 pollutions in India: A systematic analysis of the Global Burden of Disease Study (1990-2021)

Epidemiological burden and trends of neonatal and under-five mortality from lower respiratory infections associated with PM2.5 pollutions in India: A systematic analysis of the Global Burden of Disease Study (1990-2021) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epidemiological burden and trends of neonatal and under-five mortality from lower respiratory infections associated with PM 2.5 pollutions in India: A systematic analysis of the Global Burden of Disease Study (1990-2021) Chandan Roy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6015754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lower respiratory infections (LRIs) caused by PM 2.5 pollution are a major factor in neonatal and under-five mortality across India. Therefore, this study explores the linkage between PM 2.5 pollution on neonatal and under-five mortality from LRIs. Materials and Methods This study utilized ambient PM 2.5 geographic mean estimates from Washington University and a household air pollution dataset from the fifth round of the National Family Health Survey (NFHS-5). Furthermore, child mortality data were extracted from the Global Burden of Disease 2021 to assess the impact of PM 2.5 on child mortality attributable from LRIs in India. The study employed 'Getis-Ord-Gi*' statistics in ArcMap 10.4 to identify PM 2.5 hotspots and cold spots. Temporal trends for neonatal and under-five mortality were analyzed using joinpoint regression analysis, and risk factors of LRIs were visualized through a heat map using MS Excel. Results From 1990 to 2021, the neonatal mortality rate (NMR) per 100,000 live births declined significantly by 66%, from 6,989.96 in 1990 to 2,377.36 in 2021. Similarly, the under-five mortality rate (U5MR) per 100,000 live births declined by 74%, from 358.52 to 94.15 per 100,000 live births. Additionally, from 2019 to 2021, a notable decline in mortality was observed for both sexes (NMR: -11.56%; U5MR: -16.21%). However, states such as Rajasthan, Haryana, Uttar Pradesh, and Bihar had notably experienced elevated PM 2.5 concentrations, which were likely contributing factors to the higher burden of neonatal and under-five mortality. Additionally, HAP was a major contributor to PM 2.5 concentrations in the Indo-Gangetic Plain region (IGP), largely due to the limited usage of clean fuels. Conclusion The study revealed that elevated PM 2.5 concentrations are likely linked to contributing factors for higher child mortality, particularly in the IGP region. To address this issue, the study suggests increasing public awareness and implementing targeted policies to reduce neonatal and under-five mortality across India. Health Policy Child mortality Fine particulate matter Health and well-being India Lower respiratory infections Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Air pollution, particularly from PM 2.5 , has become a major global environmental and public health concern, strongly associated with adverse health effects and premature deaths[1–3]. Long-term exposure to PM 2.5 not only harms human health[4] and shortens lifespan[5] but also negatively affects economic productivity[6]. Besides particulate matter consists of tiny solid and liquid particles suspended in the air, with diameters smaller than 2.5 micrometres (PM 2.5 ) or 10 micrometres (PM 10 ). Exposure to PM 2.5 adversely impacts child health[7], leading to acute respiratory infections[2], increased risk of stroke[8], cardiovascular diseases[9], and lung cancer[2]. Children are particularly more vulnerable to the adverse health effects of PM 2.5 pollution due to their developing respiratory and immune systems. The Sustainable Development Goal (SDG) target 3.9.1 aims to reduce morbidity and mortality linked to air pollution, while SDG target 7.1.2 focuses on ensuring clean energy access in households. Additionally, SDG target 11.6.2 aims to mitigate the environmental impact of urban areas by improving air quality to reduce the burden of morbidity and premature deaths from PM 2.5 [1, 10]. Globally, air pollution is responsible for 6.7 million premature deaths annually[11, 12], including 2.89 million deaths attributable to PM 2.5 pollution exposures in 2019[13]. Several studies found that there are 0.94 million deaths responsible for air pollution among under-five children, particularly in low-and middle-income countries (LMICs) [3, 14]. In LMICs, air pollution is widespread, driven by high population density, unplanned urbanization, vehicular emissions, and rapid industrialization[15, 16]. The problem is especially severe in countries with pronounced socio-economic disparities, limited access to clean fuels, and insufficient sustainable environmental management practices[17, 18]. In Sub-Saharan Africa, a 10 mg/m³ increase in PM 2.5 exposures among children leads to a nearly 22% increase in health risks associated with air pollution. A study by Chatterjee et al. (2023) revealed that South Asia experienced 1.02 million deaths attributable to PM 2.5 in 2019, primarily originating from industrial activities, household combustion, and vehicular emissions[19]. Air pollution in India has become an inevitable public health issue, particularly for neonatal and under-five children[20]. India's cities are among the most polluted globally, with elevated concentrations of PM 2.5 and other hazardous pollutants[21], which have been extensively correlated with various adverse health outcomes. Studies by Ghosh et al. (2024) and Saharan et al. (2024) indicate that the Indo-Gangetic Plain (IGP) experiences severe air pollution, with PM 2.5 being the predominant pollutant. This is largely attributed to industrial activities, crop residue burning, and the widespread use of unclean household fuels[22, 23]. As a result, PM 2.5 levels frequently exceed the National Ambient Air Quality Standards (NAAQS), posing serious health risks, particularly to vulnerable groups[24]. Several studies have highlighted the regional disparities in child mortality attributable to air pollution in India[25]. Notably, states such as Uttar Pradesh, Bihar, and Haryana have consistently experienced both high levels of air pollution and child mortality[23, 26]. Despite the substantial burden of child mortality from LRIs attributable to PM 2.5 , there are lack of comprehensive updated studies on neonatal and under-five mortality using the recently published Global Burden of Disease (GBD) 2021 study in India. The GBD 2021 dataset is one of the most comprehensive and extensive epidemiological datasets to assess the burden and trends of neonatal and under-five mortality. Additionally, the IGP region is a significant contributor to PM 2.5 , which could contribute to neonatal and under-five mortality[2, 27]. Therefore, the study examines the association between PM 2.5 pollution and neonatal and under-five mortality from LRIs in India. Understanding the burden and trends of child mortality from LRIs attributable to PM 2.5 exposure is essential for policymakers to implement a targeted healthcare interventions and shape future research directions. 2. Methods 2.1 Ambient fine particulate matter (PM 2.5 ) concentration data This study extracted annual geographic mean estimates of surface-level PM 2.5 concentrations for the year 2021, from the Atmospheric Composition Analysis Group at Washington University. The estimates of PM 2.5 were generated by integrating aerosol optical depth (AOD) data from several satellite platforms, including NASA's MODIS C6.1, VIIRS, MISR v23, MAIAC C6, and SeaWiFS[28]. These satellite measurements were further refined using the GEOS-Chem (Goddard Earth Observing System and Chemistry) chemical transport model, which applied a residual convolutional neural network (CNN) to calibrate global ground-based observations. Geographically weighted regression (GWR) was then employed to analyse the relationship between surface PM 2.5 concentrations and AOD data. To enhance accuracy, ground-based monitoring station data were used to adjust the geophysical PM 2.5 estimates throughout the study period. The resulting data, expressed in micrograms per cubic meter of air (µg/m³), were evaluated at a high spatial resolution of 0.01° × 0.01° (nearly 1 km × 1 km) for exposure assessment. The dataset is publicly accessible at: https://sites.wustl.edu/acag/datasets/surface-pm2-5/ . 2.2 Data on household air pollution The assessment of household air pollution (HAP) was conducted using two variables: the types of cooking fuel, and the availability of a separate kitchen within households. These factors were considered as a proxy indicator for HAP. The pollution from households depends on the types of fuel used for cooking, with unclean fuels causing more pollution per meal than clean fuels. Therefore, the types of fuels used were considered indicators of HAP. For this study, we gathered data from the NFHS-5[29], on types of cooking fuels by asking, “What sort of fuel does your household mostly use for cooking?”. Types of cooking fuels included electricity, natural gas, liquefied petroleum gas (LPG), kerosene, biogas, lignite (coal), various forms of biomass such as charcoal, wood, straw, agricultural residues, and animal manure, along with other types of cooking fuel. Furthermore, we recoded ‘hv226 variable: types of cooking fuels consumption by households’ into two groups: (a) usage of clean fuels (coded as “1”), such as electricity, LPG, natural gas, and biogas, and (b) usage of unclean fuels (coded as “0”), such as kerosene, coal and lignite, and biomass. Previous studies have indicated a relationship between the availability of separate kitchens and HAP. In the NFHS-5, participants were asked, “Do you have a separate area that is utilized as a kitchen?” to gather data on cooking fuel exposure. The presence of a separate kitchen (hv242) was recoded into a new variable and categorized into two groups: (a) households that have a separate room for a kitchen (coded as '1') and (b) households without a separate room for a kitchen (coded as '0'). Households lacking a separate kitchen are considered more vulnerable to HAP exposure. The dataset is publicly available at: https://dhsprogram.com/methodology/survey/survey-display-541.cfm . 2.3 Data on child mortality This study extracted data on child mortality (neonatal and under-five mortality) from the Global Burden of Disease (GBD) 2021 dataset, recently published by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington[30]. The GBD, 2021 dataset provides comprehensive information on 288 causes of mortality, 371 diseases and injuries, and 88 risk factors across 204 countries. For this study, we collected child mortality due to LRIs data to investigate the association between fine particulate matter (PM 2.5 ) and child mortality in India from 1990 to 2021. The mortality rates of neonatal and under-five children attributable to LRIs were reported in terms of per 100,000 live births. Additionally, data on risk factors associated with child mortality were obtained from the GBD 2021 study. The GBD dataset is publicly accessible at: https://vizhub.healthdata.org/gbd-results/ . 2.4. Data Analysis For this study, we collected PM 2.5 data for 2021, along with information on HAP and child mortality, to analyse mortality patterns from lower respiratory illnesses in India. For our analysis, we focused on neonatal (< 28 days) and under-five (< 5 years) mortality rates, expressed per 100,000 live births, to assess the impact of PM 2.5 on child mortality in India. Trends of child mortality were performed to present annual percentage change (APC) with 95% uncertainty intervals (UI) and annual percentage change (AAPC) using version 5.2.0 of the Joinpoint Trend Analysis Software developed by the Cancer Control and Population Sciences division of the US National Cancer Institute from 1990 to 2021. The heat map of risk factors was generated using Excel 2016. dispersed. Spatial autocorrelation analysis, using Global Moran's I statistics, was conducted to assess the degree of clustering or dispersion in the spatial data. The Moran's I value ranged from + 1 to − 1, where + 1 indicated strong spatial clustering of PM 2.5 , while − 1 signified spatial dispersion of PM 2.5 . Furthermore, the Moran’I index value of 0 indicates that the PM 2.5 was randomly distributed ( Figure S1 ) . Several studies have shown that the Inverse Distance Weighting (IDW) method is useful for assessing the spatio-temporal variation of PM 2.5 . However, it is limited in its ability to identify statistically significant spatial clusters. Therefore, the present study applied the ‘Getis-Ord-Gi*’ statistics to identify hotspots and coldspots of PM 2.5 . The analysis of the statistical methods was performed using ArcMap 10.4 software, based on the following mathematical equations: The ‘Getis-Ord Gi*’ local statistics are given as: $$Gi*=\frac{{\sum\limits_{{j=1}}^{n} {{w_i}{,_j}{x_j}--\overline {X} } \sum\limits_{{j=1}}^{n} {{w_{i,j}}} }}{{S\sqrt {\frac{{\left[ {n\sum\limits_{{j=1}}^{n} {w_{{i,j}}^{2}--} {{\left( {\sum\limits_{{j=1}}^{n} {{w_{i,j}}} } \right)}^2}} \right]}}{{n--1}}} }}$$ 1 Where x j is the attributable value for feature j , w i,j is the spatial weight between feature i and j. n is equal to the total number of features: $$\overline {X} =\frac{{\sum\limits_{{j=1}}^{n} {{x_j}} }}{n}$$ 2 $$S=\sqrt {\frac{{\sum\limits_{{j=1}}^{n} {{x_i}^{2}} }}{n}} --{(\overline {X} )^2}$$ 3 The Getis-Ord Gi* statistical method is simple to apply and interpret, with high cluster values indicating hotspots and low cluster values indicating cold spots. 3. Results 3.1 Neonatal and under-five mortality rates in India (Insert Table 1) In 2021, LRIs were responsible for 0.04 million neonatal deaths and 0.10 million under-five deaths in India. Table 1 illustrates the trends in neonatal mortality rate (NMR) and under-five mortality rate (U5MR) from 1990 to 2021. The data reveals a significant decline in both indicators over this period. NMR decreased by 65.99%, from 6989.96 (95% UI: 5719.32 to 8445.18) in 1990 to 2377.36 (95% UI: 1777.43 to 3104.95) per 100,000 live births in 2021. Similarly, U5MR declined by 73.74%, a decline from 358.52 (95% UI: 289.87 to 430.25) to 94.15 (95% UI: 72.02 to 120.6) per 100,000 live births during the same period. Among the states, Telangana, Maharashtra, and Tripura recorded the highest declines in NMR, with annual percentage changes (APC) of 82.72%, 81.84%, and 80.98%, respectively. In contrast, Sikkim exhibited a slower decline, with an APC of 55.38%, as NMR declined from 8382.78 (95% UI: 6665.27 to 10,412.01) in 1990 to 3238.59 (95% UI: 2057.45 to 4987.03) in 2021. Regarding U5MR, Kerala (85.96%), Tamil Nadu (87.3%), and Maharashtra (86.21%) experienced the most significant decrease, while Sikkim experienced the slowest decline at 54.38%. Within the IGP states (Figure S2), Rajasthan reported the highest NMR in 2021 at 4560.87 (95% UI: 3251.81 to 6475.76), followed by Uttar Pradesh at 3599.66 (95% UI: 2517.11 to 4805.51) and Madhya Pradesh at 3374.92 (95% UI: 2140.84 to 4879.41). Similarly, the highest U5MR in 2021 was observed in Rajasthan (173.94), Uttar Pradesh (158.02), and Madhya Pradesh (148.66). Kerala recorded the highest ratio of NMR to U5MR in 2021, indicating a substantial reduction in post-neonatal child mortality, while Meghalaya had the lowest ratio, suggesting that neonatal mortality was a more prominent contributor to under-five mortality in the state. 3.2 Trends in neonatal and under-five mortality in India (Insert Figure 1) (Insert Table 2) Table 1 presented the joinpoint regression analysis of NMR and U5MR per 100,000 live births due to LRIs in India from 1990 to 2021. Each trend segment is represented with a distinct colour, and the APC is depicted in Figure 1 . The analysis revealed a substantial overall decline in both NMR and U5MR per 100,000 live births throughout the study period. NMR decreased significantly from 6,989.96 in 1990 to 2,377.36 in 2021, with key inflection points identified in 2002, 2013, and 2019. The APC for NMR consistently declined, with the most notable decrease of 11.59% between 2019 and 2021 (p<0.05). Similarly, U5MR reduced from 358.52 to 94.12 deaths per 100,000 live births over the same period. The analysis identified three key joint points for U5MR, with a consistent downward trend, and the highest decline observed from 2019 to 2021, with an APC of -16.21% (p<0.05). 3.3 Distribution of pollution parameters (Insert Figure 2) Figure 2 (Section A) illustrates PM 2.5 concentrations across India. Regions with PM 2.5 levels below the NAAQS threshold of 40 µg/m³ are considered to have relatively better air quality. The map highlights that the highest concentrations of PM 2.5 were recorded in the IGP states, including Bihar, Uttar Pradesh, Haryana, and Punjab, where the levels exceeded the NAAQS threshold. Furthermore, Figure 2 (Section B) presents the distribution of PM 2.5 levels across India, using Getis-Ord Gi* statistics to identify significant hotspots and cold spots. States such as Rajasthan, Haryana, Uttar Pradesh, and Bihar are identified as major hotspots with a 99% confidence level, while the national capital, Delhi, is also recognized as a significant hotspot of PM2.5 pollution at the same confidence level. In contrast, the northern union territory of Ladakh is detected as a cold spot with 95% confidence. The remaining states did not show statistically significant deviations in PM 2.5 levels. Figure 2 (Section C) reveals that over 45% of households in Uttar Pradesh, Bihar, and Mizoram lack separate kitchen facilities. In comparison, Gujarat, Madhya Pradesh, Telangana, Jharkhand, West Bengal, Odisha, and Arunachal Pradesh showed a lower percentage, with 30% to 45% of households lacking separate kitchen facilities. Furthermore, Figure 2 (Section D) highlights the variation in unclean cooking fuel usage across India. The northern, central, and eastern states showed a significantly higher prevalence of unclean fuel used for household cooking. Specifically, states such as Rajasthan, Madhya Pradesh, Chhattisgarh, Bihar, Jharkhand, Odisha, West Bengal, Assam, Meghalaya, Nagaland, and Tripura report over 60% of households relying on unclean cooking fuels. In contrast, states like Goa, Telangana, and Andhra Pradesh have much lower usage, with less than 20% of households using unclean cooking fuel. 3.4 Patterns of neonatal and under-five mortality in India (Insert Figure 3) Figure 3 illustrates the patterns of NMR and U5MR per 100,000 live births, highlighting regional disparities due to LRIs in India. The map shows that the highest NMR is predominantly in the northern and northeastern states in Figure 3 (Section A). Notably, Rajasthan, Uttar Pradesh, Madhya Pradesh, Bihar, Jharkhand, and Sikkim report significant NMR per 100,000 live births. In contrast, states like Maharashtra and Telangana exhibit the lowest NMR, below 1,002 per 100,000 live births in 2021. Furthermore, Figure 3 (Section B) reveals significant regional disparities in under-five mortality rates (U5MR) across India. The geo-spatial map indicates that the highest U5MR, ranging between 102 and 174 per 100,000 live births, is concentrated in the northern and northeastern states. States such as Rajasthan, Haryana, Uttar Pradesh, Madhya Pradesh, and Bihar have experienced considerable U5MR attributable to LRIs. However, western and southern states including Maharashtra, Goa, Kerala, and Tamil Nadu reported the lowest U5MR, ranging from 8.08 to 29.02 per 100,000 live births due to the infections. 3.5 Heat map depicting neonatal and under-five mortality rate in India (Insert Figure 4) Figure 4 presents a heat map of the critical risk factors associated with NMR and U5MR due to LRIs in India. The figure highlighted five key risk factors contributing to LRI-related mortality. A colour gradient was used, ranging from green to red, where green represented lower mortality rates and red indicated higher mortality rates due to LRIs. Each cell in the heat map ranked Indian states based on the influence of specific risk factors on mortality outcomes. It was found that Rajasthan, Uttar Pradesh, Madhya Pradesh, Bihar, and Jharkhand had notably high NMR linked to these factors in Figure 4 (Section A). Rajasthan was the most vulnerable, with the highest neonatal mortality influenced by household air pollution and extreme temperatures (both high and low). It also ranked second for mortality due to exposure to ambient particulate matter. In contrast, Kerala had the lowest NMR from all identified risk factors. A similar pattern was observed for U5MR related to LRIs. While Rajasthan had the highest U5MR per 100,000 live births, household air pollution and extreme temperature fluctuations (both high and low) were the main contributing factors in Figure 4 (Section B). Conversely, Kerala had the lowest U5MR, showing minimal impact from these risk factors. 4. Discussion The study reveals a strong probable association between PM 2.5 concentrations on neonatal and under-five mortality due to LRIs in India. The study findings revealed that significant declines in NMR and U5MR per 100,000 live births were observed between 1990 and 2021 in India, with reductions of nearly 66% and 74%, respectively. Furthermore, a significant decline in mortality was observed for both sexes from 2019 to 2021. The study also found that Rajasthan, Haryana, Uttar Pradesh, Bihar, and Delhi are major PM 2.5 pollution hotspots region. Household-level factors such as using unclean cooking fuels and the lack of separate kitchen facilities significantly elevate indoor particulate matter pollution, leading to increased health risks among children. The states such as Rajasthan, Uttar Pradesh, Madhya Pradesh, and Bihar recorded high NMR and U5MR related to LRIs. Key contributors to these mortality rates include household air pollution, extreme temperature fluctuations, and ambient particulate matter exposure, particularly in northern and eastern states. India has experienced a significant decline in neonatal and under-five mortality, particularly from lower respiratory illnesses in the last three decades[31]. This improvement can be primarily attributed to enhanced healthcare access, expanded vaccine coverage, and reduced childhood wasting[31, 32]. Additionally, the introduction of critical vaccines like Haemophilus influenzae type b (Hib) and pneumococcal conjugate vaccines (PCV) has helped decrease severe respiratory infections among children[33, 34]. However, the significant decline in LRIs among neonatal and under-five children in India between 2019 and 2021 can be attributed to several factors. These include reducing atypical and bacterial pneumonia cases, the impact of lockdown restrictions, and decreased exposure to respiratory pathogens[35, 36]. However, regional heterogeneity is observed across states in India. States like Rajasthan, Haryana, Uttar Pradesh, and Bihar have reported elevated neonatal and under-five mortality from LRIs, likely linked to exposure to fine particulate matter[25, 37] and household-level determinants[38]. The study finds that elevated PM 2.5 levels in Rajasthan are mainly caused by secondary sulphate, resuspended dust, emissions from household cooking fuels, and vehicular emissions, with concentrations peaking during the winter season [39, 40]. Similarly, in the IGP, industrial emissions from factories dependent on fossil fuel combustion are major contributors to PM 2.5 pollution [41, 42]. Despite the tireless efforts by the government, the region has a limited adaptation of the Pradhan Mantri Ujjwala Yojana (PMUY) due to a lack of awareness and socio-economic factors[43]. The limited adoption of cleaner cooking fuels in the IGP region can be attributed to the widespread availability and affordability of traditional fuels, such as firewood, which remain abundant, easily accessible, and low-cost energy sources[44, 45]. The concentrations are further intensified by the region's high population density, rapid urbanization, and heavy traffic congestion [46, 47]. Furthermore, a study by Shupler et al. (2024) emphasizes that the combination of unclean cooking fuels and inadequate kitchen facilities is a key driver of elevated household air pollution levels [48]. These pollutants significantly increase indoor PM 2.5 levels, which can penetrate deep into the respiratory tract and cross the alveolar barrier into the bloodstream, causing systemic effects [49, 50]. Children are especially vulnerable due to their higher respiratory rates and developing respiratory systems, making them more susceptible to PM 2.5 -related health impacts [2]. Prolonged exposure to PM 2.5 can precipitate severe respiratory conditions, including asthma[51], bronchitis[52], and pneumonia[53], and may also diminish lung function and compromise the immune system[54]. The findings of the present study align with the past studies. The findings of the study revealed that states like Rajasthan, Haryana, Uttar Pradesh, Bihar, and Jharkhand had significantly high concentrations of PM 2.5 , which are associated with elevated NMR and UMR per 100,000 live births. Similarly, George et al. (2024), revealed that PM 2.5 and unclean fuel exposure significantly increase child mortality due to LRIs in India [20]. Adhikary et al. (2024) further reported that elevated PM 2.5 concentrations, along with household-level pollution, contributed to the high mortality rates among children in these states[2]. There are several factors, such as ambient air pollution[2, 55], household air pollution[55], and temperatures (both high and low)[56], play a crucial role in elevating PM 2.5 levels, which are significant contributors to the high mortality among neonatal and under-five children in these states[57]. The objective of this study is to examine the relationship between PM 2.5 pollution on child mortality from LRIs in India. Additionally, it investigates child mortality linked to LRI risks associated with various contributing factors. Our study has several strengths, including the use of advanced model-based satellite data for PM 2.5 concentrations across the country. Furthermore, the study extracted data from a large-scale, nationally representative survey with a high household response rate. The integration of data from the GBD study provides a comprehensive analysis of child mortality and its association with PM 2.5 exposures in India. Nonetheless, this study has certain limitations. Firstly, the use of model-based state-level PM 2.5 satellite data may result in misclassification and introduce bias regarding the actual concentrations of fine particulate matter[2]. Secondly, the lack of cohort data limits our ability to establish a strong causal link between PM 2.5 exposure and child mortality. It also confines our ability to accurately monitor the actual years of child mortality, as the study primarily relies on cross-sectional data to explore the relationship. Despite these limitations, our study robustly demonstrated a causal association between PM 2.5 on child mortality, strongly supported by findings from several studies [20, 58, 59]. To reduce PM 2.5 pollutants and enhance the quality of life, the study emphasizes the importance of promoting clean fuel usage in the IGP region through the implementation of various flagship schemes, particularly in vulnerable areas of the country. Additionally, it emphasizes prioritizing the NAAQS over the WHO air quality guidelines to significantly reduce anthropogenic particulate matter pollution and prevent premature mortality among children. 5. Conclusion This study revealed a significant association between PM 2.5 pollution and mortality from LRIs among neonatal and under-five children in India. Our findings also indicate that states within the IGP region are critical zones for PM 2.5 , where high child mortality is more likely to link with elevated PM 2.5 concentrations. Additionally, household-level factors such as the use of unclean cooking fuels are crucial in elevating indoor air pollutants, significantly impacting children's health in the IGP region. Therefore, it is imperative to implement strict air quality guidelines, adopt targeted policies, and raise public awareness to prevent long-term health complications among neonatal and under-five. Abbreviations LRIs - Lower Respiratory Infections. NMR - Neonatal Mortality Rate. U5MR - Under-Five Mortality Rate. SDG - Sustainable Development Goals. NAAQS - National Ambient Air Quality Standards. IGP - Indo-Gangetic Plain. GBD - Global Burden of Disease. NFHS: National Family Health Survey HAP - Household Air Pollution. DHS: Demographic and Health Survey Declarations Acknowledgments The authors acknowledge the Global Burden of Disease Study 2021 for providing the data. Ethical Considerations This study utilized publicly available data from the GBD 2021 database, and no individual-level data were analysed. Therefore, ethical approval was not required. Consent for publication None Consent to Participate Not applicable. Data Availability Statement The GBD data is publicly available for research purposes and restricted from commercial use. It can be accessed through the open-access repository of the Institute for Health Metrics and Evaluation (IHME) (https://vizhub.healthdata.org/gbd-results/). The PM 2.5 dataset is publicly available on the following website: https://sites.wustl.edu/acag/datasets/surface-pm2-5/. The socio-economic datasets generated or analysed in this study are available in the DHS Program repository: https://dhsprogram.com/methodology/survey/survey-display-541.cfm. The maps in the article were created using ArcGIS desktop version 10.4.1 and the software is available at: https://www.esri.com/en-us/arcgis/products/index. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 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Tables Table 1: Neonatal and under-five mortality per 100,000 live births across Indian states from 1990 to 2021 States Neonatal mortality rate per 100,000 live births Under-five mortality rate per 100,000 live births Ratio of NMR to U5MR, 2021 1990 (95%UI) 2021 (95%UI) APC (95%UI) 1990 (95%UI) 2021 (95%UI) APC (95%UI) India 6989.96 (5719.32 to 8445.18) 2377.36 (1777.43 to 3104.95) -65.99 (-75.28 to -52.65) 358.52 (289.87 to 430.25) 94.15 (72.02 to 120.6) -73.74 (-80.69 to -64.4) 25.25 Andhra Pradesh 4882.75 (3371.24 to 6658.85) 987.8 (654.11 to 1444.45) -79.77 (-87.34 to -64.72) 280.02 (207.06 to 361.79) 43.6 (31.49 to 58.12) -84.43 (-89.75 to -76.18) 22.65 Arunachal Pradesh 4109.97 (3132.66 to 5274.83) 1120.44 (692.13 to 1694.12) -72.74 (-84.08 to -53.44) 322.49 (243.04 to 417.58) 50.5 (30.8 to 75.85) -84.34 (-90.83 to -74.31) 22.19 Assam 6036.65 (4480.78 to 8030.03) 2285.23 (1559.79 to 3151.33) -62.14 (-77.01 to -39.33) 340.08 (263.6 to 435.39) 83.91 (57.03 to 117.27) -75.33 (-84.55 to -61.97) 27.23 Bihar 8819.32 (6418.81 to 11814.19) 2679.17 (1808.06 to 3891.87) -69.62 (-80.97 to -51.11) 444.13 (325.74 to 579.77) 93.71 (68.76 to 125.87) -78.9 (-85.85 to -67.17) 28.59 Chhattisgarh 10311.35 (7810.83 to 13182.32) 3124.96 (2128.41 to 4411.53) -69.69 (-80 to -53.4) 468.17 (380.3 to 580.23) 102.14 (62.97 to 143.3) -78.18 (-86.17 to -68.87) 30.59 Delhi 3011.61 (2156.67 to 4109.18) 638.82 (392.64 to 963.69) -78.79 (-87.9 to -63.92) 201.08 (142.53 to 256.83) 22.49 (14.15 to 33.66) -88.82 (-93.39 to -81.8) 28.41 Goa 3142.83 (2288.24 to 4101.5) 699.79 (452.58 to 998.66) -77.73 (-86.04 to -63.42) 118.23 (60.29 to 154.82) 21.59 (14.47 to 30.21) -81.74 (-89.21 to -64.72) 32.42 Gujarat 5288.76 (3877.71 to 6896.97) 1740.27 (1154.76 to 2582.4) -67.09 (-80.44 to -46.52) 283.75 (216.01 to 351.66) 69.81 (49.94 to 95.68) -75.4 (-84.26 to -62.97) 24.93 Haryana 4632.32 (3505.37 to 6124.75) 1357.23 (941.44 to 1882.15) -70.7 (-81.03 to -55.27) 269.69 (210.31 to 345.34) 78.87 (56.05 to 107.29) -70.75 (-81.25 to -56.55) 17.21 Himachal Pradesh 4042.48 (2794.85 to 5258.06) 1316.24 (851.76 to 1969.84) -67.44 (-80.59 to -43.8) 200.93 (147.47 to 259.02) 57.76 (40.44 to 80.08) -71.25 (-81.01 to -56.65) 22.79 Jammu and Kashmir 6543.43 (4960.51 to 8109.9) 1801.14 (1273.72 to 2482.14) -72.47 (-80.94 to -59.23) 279.21 (217.62 to 339.67) 64.04 (46.41 to 83.99) -77.06 (-83.65 to -67.91) 28.13 Jharkhand 8404.58 (6180.78 to 10980.14) 2118.38 (1420.23 to 3182.45) -74.79 (-83.61 to -58.48) 387.54 (289.61 to 494.45) 70.92 (46.94 to 103.81) -81.7 (-87.8 to -72.99) 29.87 Karnataka 2920.91 (2101.78 to 3944.16) 820.82 (554.01 to 1133.88) -71.9 (-82.96 to -55.7) 183.88 (133.84 to 238.61) 41.68 (28.91 to 57.36) -77.33 (-85.49 to -62.48) 19.69 Kerala 1654.13 (1248.93 to 2100.28) 364.72 (250.78 to 516.23) -77.95 (-85.95 to -67.2) 74.38 (53.69 to 98.21) 10.44 (7.22 to 14.82) -85.96 (-91.11 to -77.67) 34.93 Madhya Pradesh 10815.44 (7491.36 to 14749.87) 3374.92 (2140.84 to 4879.41) -68.8 (-82.1 to -46.96) 607.1 (464.17 to 762.74) 148.66 (103.09 to 200.88) -75.51 (-83.59 to -63.89) 22.7 Maharashtra 4768.9 (3619.11 to 6165.03) 866 (595.53 to 1212.7) -81.84 (-88.55 to -73.07) 210.52 (163.59 to 260.77) 29.02 (20.71 to 39.45) -86.21 (-90.67 to -79.8) 29.84 Manipur 3690.6 (2814.75 to 4632.62) 1002.5 (620.93 to 1553.15) -72.84 (-83.62 to -55.63) 202.86 (153.75 to 257.45) 47.69 (31.79 to 73.28) -76.49 (-85.53 to -61.49) 21.02 Meghalaya 6018.97 (4657.26 to 7534.92) 1281.94 (861.53 to 1906.89) -78.7 (-86.57 to -66.7) 323.84 (245.46 to 408.09) 82.31 (55.03 to 118.23) -74.58 (-83.53 to -59.81) 15.57 Mizoram 5762.24 (4520.54 to 7389.94) 1961.51 (1311.1 to 2840.02) -65.96 (-78.38 to -47.85) 214.2 (167.62 to 269.51) 75.45 (44.58 to 115.68) -64.78 (-78.26 to -45.62) 26 Nagaland 5849.62 (4306.6 to 7757.14) 1493.01 (986.97 to 2231.4) -74.48 (-84.19 to -57.42) 204.8 (155.78 to 266.83) 65.66 (44.65 to 91.97) -67.94 (-79.26 to -50.01) 22.74 Odisha 9576.61 (7217.95 to 12262.24) 1795.74 (1195 to 2487.91) -81.25 (-88.17 to -70.01) 434.67 (334.69 to 539.67) 74.27 (50.69 to 103.18) -82.91 (-88.62 to -74.61) 24.18 Punjab 3476.6 (2598.53 to 4681.84) 874.44 (592.63 to 1283.96) -62.91 (-78.02 to -37.5) 183.29 (140.52 to 230.58) 39.82 (27.46 to 55.68) -78.28 (-85.26 to -65.42) 21.96 Rajasthan 10222.54 (7700.44 to 12819.86) 4560.87 (3251.81 to 6475.76) -74.85 (-84.22 to -56.98) 534.28 (412.74 to 659.95) 173.94 (127.47 to 226.51) -67.44 (-77.36 to -53.69) 26.22 Sikkim 8382.78 (6665.27 to 10412.01) 3238.59 (2057.45 to 4987.03) -55.38 (-70.85 to -35) 279.46 (221.6 to 342.8) 127.48 (83.45 to 191.58) -54.38 (-71.61 to -23.42) 25.4 Tamil Nadu 3416.69 (2356.88 to 4551.41) 590.53 (403.35 to 842.08) -61.37 (-77.22 to -36.57) 208.56 (154.72 to 264.19) 26.49 (18.51 to 37.23) -87.3 (-91.62 to -81.16) 22.29 Telangana 4638.73 (3326.98 to 6235.37) 882.19 (609.02 to 1271.9) -82.72 (-89 to -71.86) 276.22 (208.76 to 364.22) 39.21 (28.59 to 51.81) -85.8 (-90.82 to -78.82) 22.5 Tripura 9758.73 (7216.6 to 12650.25) 1892.65 (1228.74 to 2754.26) -80.98 (-87.78 to -69.61) 372.96 (271.1 to 481.3) 66.48 (43.58 to 97.18) -82.17 (-88.11 to -72.13) 28.47 Union Territories other than Delhi 2867.78 (2143.63 to 3739) 1063.58 (649.06 to 1614.34) -80.61 (-87.69 to -69.02) 112.1 (82.69 to 148.13) 40.42 (24.92 to 61.08) -63.94 (-78.41 to -38.14) 26.31 Uttar Pradesh 9119.35 (6658.67 to 12209.69) 3599.66 (2517.11 to 4805.51) -60.53 (-74.09 to -39.32) 507.71 (368.2 to 657.38) 158.02 (116.1 to 204.99) -68.88 (-78.23 to -53.2) 22.78 Uttarakhand 5686.27 (4175.71 to 7418.26) 1630.95 (1068.39 to 2306.16) -71.32 (-82.43 to -55.83) 250.33 (194.66 to 308.99) 58.34 (39.85 to 79.19) -76.7 (-84.57 to -66.56) 27.96 West Bengal 6703.69 (4976.1 to 8562.5) 1694.89 (1190.72 to 2350.72) -74.72 (-83.06 to -61.48) 267.1 (199.73 to 347.79) 53.7 (39.08 to 71.6) -79.89 (-86.44 to -69.2) 31.56 Table 2: Trends in neonatal and under-five mortality rates per 100,000 live births due to lower respiratory illness in India using joinpoint regression analysis (1990–2021) Segment Neonatal Mortality Under-five Mortality Year APC (95% CI) Year APC (95% CI) 1 1990–2002 -2.64 (-3.89 to -2.36)*** 1990-2013 -2.97 (-3.12 to -2.79)*** 2 2002–2013 -1.82 (-2.15 to -0.39)** 2013-2019 -5.64 (-6.63 to -4.48)*** 3 2013–2019 -5.44 (-6.06 to -4.48)*** 2019-2021 -16.21 (-19.96 to -12.08)*** 4 2019–2021 -11.56 (-14.29 to -8.80)*** Full Range (AAPC) 1990-2021 -3.50 (-3.69 to -3.39)*** 1990-2021 -4.40 (-4.66 to -4.27)*** Notes: *APC: Annual Percentage change; **AAPC: Annual Percentage Change Significant at: *p<0.10; **p<0.05; ***p<0.01 CI: Confidence Interval Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6015754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414810025,"identity":"116b4ab6-fcec-44c9-befa-9e34431f4911","order_by":0,"name":"Chandan Roy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBADORBx4AEpWozBWhJI0ZLYACKJ0mLe3mP84kdFXfr8sMMPgbbYyek2ENAic+aMmWXPmcO5G2+nGQC1JBubHSCgRUIix8yYse1A7sbZCSAtBxK3EaflX1264ez0D0RrMX7M2MCcIC+dQ6wtPMfKGHuOHTbcIJ1TcCDBgBi/sDdv/vCjpk5efnb65g8fKuzkCGphYOAwkwBRBmCVBgSVgwD74w8gSr6BKNWjYBSMglEwEgEAzV5FZwU2GIIAAAAASUVORK5CYII=","orcid":"","institution":"Mizoram University","correspondingAuthor":true,"prefix":"","firstName":"Chandan","middleName":"","lastName":"Roy","suffix":""}],"badges":[],"createdAt":"2025-02-12 13:52:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6015754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6015754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76284511,"identity":"9b5c3337-cd71-4263-babc-7bfab03a6774","added_by":"auto","created_at":"2025-02-14 10:56:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118116,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in child mortality rates per 100,000 live births attributable to lower respiratory infections in India (1990–2021): (\u003cstrong\u003eA\u003c/strong\u003e) Neonatal mortality, (\u003cstrong\u003eB\u003c/strong\u003e) Under-five mortality\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6015754/v1/dc31e051950d1faba29d714c.png"},{"id":76284512,"identity":"172ca39a-271e-4dfa-a8da-8bc6a4a44e32","added_by":"auto","created_at":"2025-02-14 10:56:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":609580,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of pollution parameters, (Subfigures: \u003cstrong\u003e(A) \u003c/strong\u003eConcentration of PM\u003csub\u003e2.5\u003c/sub\u003e level in India, 2021; (\u003cstrong\u003eB\u003c/strong\u003e) Map showing the hot spots and cold spots of ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in 2021; (\u003cstrong\u003eC\u003c/strong\u003e) Absence of separate kitchens in households in India, 2019–2021; and (\u003cstrong\u003eD\u003c/strong\u003e) Usage of unclean fuel in India, 2019–2021).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6015754/v1/65885dd4bf8f8d2ca0d46863.png"},{"id":76284497,"identity":"14e07348-bd3e-4671-85dc-4b60eecf4033","added_by":"auto","created_at":"2025-02-14 10:56:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":868753,"visible":true,"origin":"","legend":"\u003cp\u003eNeonatal and under-five mortality rates per 1,00,000 live births attributable to lower respiratory infections in India: (\u003cstrong\u003eA\u003c/strong\u003e) Neonatal mortality rate and (\u003cstrong\u003eB\u003c/strong\u003e) Under-five mortality rate.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6015754/v1/222a17eaf1d2255f4ab26f78.png"},{"id":76285139,"identity":"cba840ad-046d-457d-89b7-1f15b48cf5a6","added_by":"auto","created_at":"2025-02-14 11:04:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99206,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of lower respiratory infections ranked by child mortality per 100,000 live births in India, 2021: (\u003cstrong\u003eA\u003c/strong\u003e) Neonatal mortality and (\u003cstrong\u003eB\u003c/strong\u003e) Under-five mortality\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6015754/v1/d6438278248960182b552223.png"},{"id":76285949,"identity":"f29fd3c8-a3a3-40be-b925-513b5f38d4cf","added_by":"auto","created_at":"2025-02-14 11:12:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2856005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6015754/v1/12c0646a-461f-4289-bb90-a20887500e56.pdf"},{"id":76284494,"identity":"2845d7a4-482e-42f4-9f3f-1f2f30d5b1c3","added_by":"auto","created_at":"2025-02-14 10:56:53","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":435215,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-6015754/v1/05d9c51d6601b92d91d0c525.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEpidemiological burden and trends of neonatal and under-five mortality from lower respiratory infections associated with PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e pollutions in India: A systematic analysis of the Global Burden of Disease Study (1990-2021)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e \u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAir pollution, particularly from PM\u003csub\u003e2.5\u003c/sub\u003e, has become a major global environmental and public health concern, strongly associated with adverse health effects and premature deaths[1\u0026ndash;3]. Long-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e not only harms human health[4] and shortens lifespan[5] but also negatively affects economic productivity[6]. Besides particulate matter consists of tiny solid and liquid particles suspended in the air, with diameters smaller than 2.5 micrometres (PM\u003csub\u003e2.5\u003c/sub\u003e) or 10 micrometres (PM\u003csub\u003e10\u003c/sub\u003e). Exposure to PM\u003csub\u003e2.5\u003c/sub\u003e adversely impacts child health[7], leading to acute respiratory infections[2], increased risk of stroke[8], cardiovascular diseases[9], and lung cancer[2]. Children are particularly more vulnerable to the adverse health effects of PM\u003csub\u003e2.5\u003c/sub\u003e pollution due to their developing respiratory and immune systems. The Sustainable Development Goal (SDG) target 3.9.1 aims to reduce morbidity and mortality linked to air pollution, while SDG target 7.1.2 focuses on ensuring clean energy access in households. Additionally, SDG target 11.6.2 aims to mitigate the environmental impact of urban areas by improving air quality to reduce the burden of morbidity and premature deaths from PM\u003csub\u003e2.5\u003c/sub\u003e[1, 10].\u003c/p\u003e \u003cp\u003eGlobally, air pollution is responsible for 6.7\u0026nbsp;million premature deaths annually[11, 12], including 2.89\u0026nbsp;million deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e pollution exposures in 2019[13]. Several studies found that there are 0.94\u0026nbsp;million deaths responsible for air pollution among under-five children, particularly in low-and middle-income countries (LMICs) [3, 14]. In LMICs, air pollution is widespread, driven by high population density, unplanned urbanization, vehicular emissions, and rapid industrialization[15, 16]. The problem is especially severe in countries with pronounced socio-economic disparities, limited access to clean fuels, and insufficient sustainable environmental management practices[17, 18]. In Sub-Saharan Africa, a 10 mg/m\u0026sup3; increase in PM\u003csub\u003e2.5\u003c/sub\u003e exposures among children leads to a nearly 22% increase in health risks associated with air pollution. A study by Chatterjee et al. (2023) revealed that South Asia experienced 1.02\u0026nbsp;million deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e in 2019, primarily originating from industrial activities, household combustion, and vehicular emissions[19].\u003c/p\u003e \u003cp\u003eAir pollution in India has become an inevitable public health issue, particularly for neonatal and under-five children[20]. India's cities are among the most polluted globally, with elevated concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and other hazardous pollutants[21], which have been extensively correlated with various adverse health outcomes. Studies by Ghosh et al. (2024) and Saharan et al. (2024) indicate that the Indo-Gangetic Plain (IGP) experiences severe air pollution, with PM\u003csub\u003e2.5\u003c/sub\u003e being the predominant pollutant. This is largely attributed to industrial activities, crop residue burning, and the widespread use of unclean household fuels[22, 23]. As a result, PM\u003csub\u003e2.5\u003c/sub\u003e levels frequently exceed the National Ambient Air Quality Standards (NAAQS), posing serious health risks, particularly to vulnerable groups[24]. Several studies have highlighted the regional disparities in child mortality attributable to air pollution in India[25]. Notably, states such as Uttar Pradesh, Bihar, and Haryana have consistently experienced both high levels of air pollution and child mortality[23, 26].\u003c/p\u003e \u003cp\u003eDespite the substantial burden of child mortality from LRIs attributable to PM\u003csub\u003e2.5\u003c/sub\u003e, there are lack of comprehensive updated studies on neonatal and under-five mortality using the recently published Global Burden of Disease (GBD) 2021 study in India. The GBD 2021 dataset is one of the most comprehensive and extensive epidemiological datasets to assess the burden and trends of neonatal and under-five mortality. Additionally, the IGP region is a significant contributor to PM\u003csub\u003e2.5\u003c/sub\u003e, which could contribute to neonatal and under-five mortality[2, 27]. Therefore, the study examines the association between PM\u003csub\u003e2.5\u003c/sub\u003e pollution and neonatal and under-five mortality from LRIs in India. Understanding the burden and trends of child mortality from LRIs attributable to PM\u003csub\u003e2.5\u003c/sub\u003e exposure is essential for policymakers to implement a targeted healthcare interventions and shape future research directions.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ambient fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) concentration data\u003c/h2\u003e \u003cp\u003eThis study extracted annual geographic mean estimates of surface-level PM\u003csub\u003e2.5\u003c/sub\u003e concentrations for the year 2021, from the Atmospheric Composition Analysis Group at Washington University. The estimates of PM\u003csub\u003e2.5\u003c/sub\u003e were generated by integrating aerosol optical depth (AOD) data from several satellite platforms, including NASA's MODIS C6.1, VIIRS, MISR v23, MAIAC C6, and SeaWiFS[28]. These satellite measurements were further refined using the GEOS-Chem (Goddard Earth Observing System and Chemistry) chemical transport model, which applied a residual convolutional neural network (CNN) to calibrate global ground-based observations. Geographically weighted regression (GWR) was then employed to analyse the relationship between surface PM\u003csub\u003e2.5\u003c/sub\u003e concentrations and AOD data. To enhance accuracy, ground-based monitoring station data were used to adjust the geophysical PM\u003csub\u003e2.5\u003c/sub\u003e estimates throughout the study period. The resulting data, expressed in micrograms per cubic meter of air (\u0026micro;g/m\u0026sup3;), were evaluated at a high spatial resolution of 0.01\u0026deg; \u0026times; 0.01\u0026deg; (nearly 1 km \u0026times; 1 km) for exposure assessment. The dataset is publicly accessible at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sites.wustl.edu/acag/datasets/surface-pm2-5/\u003c/span\u003e\u003cspan address=\"https://sites.wustl.edu/acag/datasets/surface-pm2-5/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data on household air pollution\u003c/h2\u003e \u003cp\u003eThe assessment of household air pollution (HAP) was conducted using two variables: the types of cooking fuel, and the availability of a separate kitchen within households. These factors were considered as a proxy indicator for HAP. The pollution from households depends on the types of fuel used for cooking, with unclean fuels causing more pollution per meal than clean fuels. Therefore, the types of fuels used were considered indicators of HAP. For this study, we gathered data from the NFHS-5[29], on types of cooking fuels by asking, \u0026ldquo;What sort of fuel does your household mostly use for cooking?\u0026rdquo;. Types of cooking fuels included electricity, natural gas, liquefied petroleum gas (LPG), kerosene, biogas, lignite (coal), various forms of biomass such as charcoal, wood, straw, agricultural residues, and animal manure, along with other types of cooking fuel. Furthermore, we recoded \u0026lsquo;hv226 variable: types of cooking fuels consumption by households\u0026rsquo; into two groups: (a) usage of clean fuels (coded as \u0026ldquo;1\u0026rdquo;), such as electricity, LPG, natural gas, and biogas, and (b) usage of unclean fuels (coded as \u0026ldquo;0\u0026rdquo;), such as kerosene, coal and lignite, and biomass.\u003c/p\u003e \u003cp\u003ePrevious studies have indicated a relationship between the availability of separate kitchens and HAP. In the NFHS-5, participants were asked, \u0026ldquo;Do you have a separate area that is utilized as a kitchen?\u0026rdquo; to gather data on cooking fuel exposure. The presence of a separate kitchen (hv242) was recoded into a new variable and categorized into two groups: (a) households that have a separate room for a kitchen (coded as '1') and (b) households without a separate room for a kitchen (coded as '0'). Households lacking a separate kitchen are considered more vulnerable to HAP exposure. The dataset is publicly available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/methodology/survey/survey-display-541.cfm\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/methodology/survey/survey-display-541.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 \u003cb\u003eData on child mortality\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThis study extracted data on child mortality (neonatal and under-five mortality) from the Global Burden of Disease (GBD) 2021 dataset, recently published by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington[30]. The GBD, 2021 dataset provides comprehensive information on 288 causes of mortality, 371 diseases and injuries, and 88 risk factors across 204 countries. For this study, we collected child mortality due to LRIs data to investigate the association between fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) and child mortality in India from 1990 to 2021. The mortality rates of neonatal and under-five children attributable to LRIs were reported in terms of per 100,000 live births. Additionally, data on risk factors associated with child mortality were obtained from the GBD 2021 study. The GBD dataset is publicly accessible at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vizhub.healthdata.org/gbd-results/\u003c/span\u003e\u003cspan address=\"https://vizhub.healthdata.org/gbd-results/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data Analysis\u003c/h2\u003e \u003cp\u003eFor this study, we collected PM\u003csub\u003e2.5\u003c/sub\u003e data for 2021, along with information on HAP and child mortality, to analyse mortality patterns from lower respiratory illnesses in India. For our analysis, we focused on neonatal (\u0026lt;\u0026thinsp;28 days) and under-five (\u0026lt;\u0026thinsp;5 years) mortality rates, expressed per 100,000 live births, to assess the impact of PM\u003csub\u003e2.5\u003c/sub\u003e on child mortality in India. Trends of child mortality were performed to present annual percentage change (APC) with 95% uncertainty intervals (UI) and annual percentage change (AAPC) using version 5.2.0 of the Joinpoint Trend Analysis Software developed by the Cancer Control and Population Sciences division of the US National Cancer Institute from 1990 to 2021. The heat map of risk factors was generated using Excel 2016. dispersed. Spatial autocorrelation analysis, using Global Moran's I statistics, was conducted to assess the degree of clustering or dispersion in the spatial data. The Moran's I value ranged from +\u0026thinsp;1 to \u0026minus;\u0026thinsp;1, where +\u0026thinsp;1 indicated strong spatial clustering of PM\u003csub\u003e2.5\u003c/sub\u003e, while \u0026minus;\u0026thinsp;1 signified spatial dispersion of PM\u003csub\u003e2.5\u003c/sub\u003e. Furthermore, the Moran\u0026rsquo;I index value of 0 indicates that the PM\u003csub\u003e2.5\u003c/sub\u003e was randomly distributed \u003cb\u003e(\u003c/b\u003eFigure S1\u003cb\u003e)\u003c/b\u003e. Several studies have shown that the Inverse Distance Weighting (IDW) method is useful for assessing the spatio-temporal variation of PM\u003csub\u003e2.5\u003c/sub\u003e. However, it is limited in its ability to identify statistically significant spatial clusters. Therefore, the present study applied the \u0026lsquo;Getis-Ord-Gi*\u0026rsquo; statistics to identify hotspots and coldspots of PM\u003csub\u003e2.5\u003c/sub\u003e. The analysis of the statistical methods was performed using ArcMap 10.4 software, based on the following mathematical equations:\u003c/p\u003e \u003cp\u003eThe \u0026lsquo;Getis-Ord Gi*\u0026rsquo; local statistics are given as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Gi*=\\frac{{\\sum\\limits_{{j=1}}^{n} {{w_i}{,_j}{x_j}--\\overline {X} } \\sum\\limits_{{j=1}}^{n} {{w_{i,j}}} }}{{S\\sqrt {\\frac{{\\left[ {n\\sum\\limits_{{j=1}}^{n} {w_{{i,j}}^{2}--} {{\\left( {\\sum\\limits_{{j=1}}^{n} {{w_{i,j}}} } \\right)}^2}} \\right]}}{{n--1}}} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is the attributable value for feature \u003cem\u003ej\u003c/em\u003e, \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,j\u003c/em\u003e\u003c/sub\u003e is the spatial weight between feature \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej. n\u003c/em\u003e is equal to the total number of features:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\overline {X} =\\frac{{\\sum\\limits_{{j=1}}^{n} {{x_j}} }}{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$S=\\sqrt {\\frac{{\\sum\\limits_{{j=1}}^{n} {{x_i}^{2}} }}{n}} --{(\\overline {X} )^2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Getis-Ord Gi* statistical method is simple to apply and interpret, with high cluster values indicating hotspots and low cluster values indicating cold spots.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Neonatal and under-five mortality rates in India\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Insert Table 1)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2021, LRIs were responsible for 0.04 million neonatal deaths and 0.10 million under-five deaths in India. Table 1 illustrates the trends in neonatal mortality rate (NMR) and under-five mortality rate (U5MR) from 1990 to 2021. The data reveals a significant decline in both indicators over this period. NMR decreased by 65.99%, from 6989.96 (95% UI: 5719.32 to 8445.18) in 1990 to 2377.36 (95% UI: 1777.43 to 3104.95) per 100,000 live births in 2021. Similarly, U5MR declined by 73.74%, a decline from 358.52 (95% UI: 289.87 to 430.25) to 94.15 (95% UI: 72.02 to 120.6) per 100,000 live births during the same period. Among the states, Telangana, Maharashtra, and Tripura recorded the highest declines in NMR, with annual percentage changes (APC) of 82.72%, 81.84%, and 80.98%, respectively. In contrast, Sikkim exhibited a slower decline, with an APC of 55.38%, as NMR declined from 8382.78 (95% UI: 6665.27 to 10,412.01) in 1990 to 3238.59 (95% UI: 2057.45 to 4987.03) in 2021. Regarding U5MR, Kerala (85.96%), Tamil Nadu (87.3%), and Maharashtra (86.21%) experienced the most significant decrease, while Sikkim experienced the slowest decline at 54.38%. Within the IGP states (Figure S2), Rajasthan reported the highest NMR in 2021 at 4560.87 (95% UI: 3251.81 to 6475.76), followed by Uttar Pradesh at 3599.66 (95% UI: 2517.11 to 4805.51) and Madhya Pradesh at 3374.92 (95% UI: 2140.84 to 4879.41). Similarly, the highest U5MR in 2021 was observed in Rajasthan (173.94), Uttar Pradesh (158.02), and Madhya Pradesh (148.66). Kerala recorded the highest ratio of NMR to U5MR in 2021, indicating a substantial reduction in post-neonatal child mortality, while Meghalaya had the lowest ratio, suggesting that neonatal mortality was a more prominent contributor to under-five mortality in the state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Trends in neonatal and under-five mortality in India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Insert Figure 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Insert Table 2)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e presented the joinpoint regression analysis of NMR and U5MR per 100,000 live births due to LRIs in India from 1990 to 2021. Each trend segment is represented with a distinct colour, and the APC is depicted in \u003cstrong\u003eFigure 1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e The analysis revealed a substantial overall decline in both NMR and U5MR per 100,000 live births throughout the study period. NMR decreased significantly from 6,989.96 in 1990 to 2,377.36 in 2021, with key inflection points identified in 2002, 2013, and 2019. The APC for NMR consistently declined, with the most notable decrease of 11.59% between 2019 and 2021 (p\u0026lt;0.05). Similarly, U5MR reduced from 358.52 to 94.12 deaths per 100,000 live births over the same period. The analysis identified three key joint points for U5MR, with a consistent downward trend, and the highest decline observed from 2019 to 2021, with an APC of -16.21% (p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Distribution of pollution parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Insert Figure 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 (Section A) illustrates PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003econcentrations across India. Regions with PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003elevels below the NAAQS threshold of 40 µg/m³ are considered to have relatively better air quality. The map highlights that the highest concentrations of PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003ewere recorded in the IGP states, including Bihar, Uttar Pradesh, Haryana, and Punjab, where the levels exceeded the NAAQS threshold. Furthermore, Figure 2 (Section B) presents the distribution of PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003elevels across India, using Getis-Ord Gi* statistics to identify significant hotspots and cold spots. States such as Rajasthan, Haryana, Uttar Pradesh, and Bihar are identified as major hotspots with a 99% confidence level, while the national capital, Delhi, is also recognized as a significant hotspot of PM2.5 pollution at the same confidence level. In contrast, the northern union territory of Ladakh is detected as a cold spot with 95% confidence. The remaining states did not show statistically significant deviations in PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003elevels.\u003c/p\u003e\n\u003cp\u003eFigure 2 (Section C) reveals that over 45% of households in Uttar Pradesh, Bihar, and Mizoram lack separate kitchen facilities. In comparison, Gujarat, Madhya Pradesh, Telangana, Jharkhand, West Bengal, Odisha, and Arunachal Pradesh showed a lower percentage, with 30% to 45% of households lacking separate kitchen facilities. Furthermore, Figure 2 (Section D) highlights the variation in unclean cooking fuel usage across India. The northern, central, and eastern states showed a significantly higher prevalence of unclean fuel used for household cooking. Specifically, states such as Rajasthan, Madhya Pradesh, Chhattisgarh, Bihar, Jharkhand, Odisha, West Bengal, Assam, Meghalaya, Nagaland, and Tripura report over 60% of households relying on unclean cooking fuels. In contrast, states like Goa, Telangana, and Andhra Pradesh have much lower usage, with less than 20% of households using unclean cooking fuel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePatterns of neonatal and under-five mortality in India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Insert Figure 3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 illustrates the patterns of NMR and U5MR per 100,000 live births, highlighting regional disparities due to LRIs in India. The map shows that the highest NMR is predominantly in the northern and northeastern states in Figure 3 (Section A). Notably, Rajasthan, Uttar Pradesh, Madhya Pradesh, Bihar, Jharkhand, and Sikkim report significant NMR per 100,000 live births. In contrast, states like Maharashtra and Telangana exhibit the lowest NMR, below 1,002 per 100,000 live births in 2021. Furthermore, Figure 3 (Section B) reveals significant regional disparities in under-five mortality rates (U5MR) across India. The geo-spatial map indicates that the highest U5MR, ranging between 102 and 174 per 100,000 live births, is concentrated in the northern and northeastern states. States such as Rajasthan, Haryana, Uttar Pradesh, Madhya Pradesh, and Bihar have experienced considerable U5MR attributable to LRIs. However, western and southern states including Maharashtra, Goa, Kerala, and Tamil Nadu reported the lowest U5MR, ranging from 8.08 to 29.02 per 100,000 live births due to the infections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHeat map depicting neonatal and under-five mortality rate in India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Insert Figure 4)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4 presents a heat map of the critical risk factors associated with NMR and U5MR due to LRIs in India. The figure highlighted five key risk factors contributing to LRI-related mortality. A colour gradient was used, ranging from green to red, where green represented lower mortality rates and red indicated higher mortality rates due to LRIs. Each cell in the heat map ranked Indian states based on the influence of specific risk factors on mortality outcomes. It was found that Rajasthan, Uttar Pradesh, Madhya Pradesh, Bihar, and Jharkhand had notably high NMR linked to these factors in Figure 4 (Section A). Rajasthan was the most vulnerable, with the highest neonatal mortality influenced by household air pollution and extreme temperatures (both high and low). It also ranked second for mortality due to exposure to ambient particulate matter. In contrast, Kerala had the lowest NMR from all identified risk factors. A similar pattern was observed for U5MR related to LRIs. While Rajasthan had the highest U5MR per 100,000 live births, household air pollution and extreme temperature fluctuations (both high and low) were the main contributing factors in Figure 4 (Section B). Conversely, Kerala had the lowest U5MR, showing minimal impact from these risk factors.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study reveals a strong probable association between PM\u003csub\u003e2.5\u003c/sub\u003e concentrations on neonatal and under-five mortality due to LRIs in India. The study findings revealed that significant declines in NMR and U5MR per 100,000 live births were observed between 1990 and 2021 in India, with reductions of nearly 66% and 74%, respectively. Furthermore, a significant decline in mortality was observed for both sexes from 2019 to 2021. The study also found that Rajasthan, Haryana, Uttar Pradesh, Bihar, and Delhi are major PM\u003csub\u003e2.5\u003c/sub\u003e pollution hotspots region. Household-level factors such as using unclean cooking fuels and the lack of separate kitchen facilities significantly elevate indoor particulate matter pollution, leading to increased health risks among children. The states such as Rajasthan, Uttar Pradesh, Madhya Pradesh, and Bihar recorded high NMR and U5MR related to LRIs. Key contributors to these mortality rates include household air pollution, extreme temperature fluctuations, and ambient particulate matter exposure, particularly in northern and eastern states.\u003c/p\u003e \u003cp\u003eIndia has experienced a significant decline in neonatal and under-five mortality, particularly from lower respiratory illnesses in the last three decades[31]. This improvement can be primarily attributed to enhanced healthcare access, expanded vaccine coverage, and reduced childhood wasting[31, 32]. Additionally, the introduction of critical vaccines like Haemophilus influenzae type b (Hib) and pneumococcal conjugate vaccines (PCV) has helped decrease severe respiratory infections among children[33, 34]. However, the significant decline in LRIs among neonatal and under-five children in India between 2019 and 2021 can be attributed to several factors. These include reducing atypical and bacterial pneumonia cases, the impact of lockdown restrictions, and decreased exposure to respiratory pathogens[35, 36]. However, regional heterogeneity is observed across states in India. States like Rajasthan, Haryana, Uttar Pradesh, and Bihar have reported elevated neonatal and under-five mortality from LRIs, likely linked to exposure to fine particulate matter[25, 37] and household-level determinants[38].\u003c/p\u003e \u003cp\u003eThe study finds that elevated PM\u003csub\u003e2.5\u003c/sub\u003e levels in Rajasthan are mainly caused by secondary sulphate, resuspended dust, emissions from household cooking fuels, and vehicular emissions, with concentrations peaking during the winter season [39, 40]. Similarly, in the IGP, industrial emissions from factories dependent on fossil fuel combustion are major contributors to PM\u003csub\u003e2.5\u003c/sub\u003e pollution [41, 42]. Despite the tireless efforts by the government, the region has a limited adaptation of the Pradhan Mantri Ujjwala Yojana (PMUY) due to a lack of awareness and socio-economic factors[43]. The limited adoption of cleaner cooking fuels in the IGP region can be attributed to the widespread availability and affordability of traditional fuels, such as firewood, which remain abundant, easily accessible, and low-cost energy sources[44, 45]. The concentrations are further intensified by the region's high population density, rapid urbanization, and heavy traffic congestion [46, 47]. Furthermore, a study by Shupler et al. (2024) emphasizes that the combination of unclean cooking fuels and inadequate kitchen facilities is a key driver of elevated household air pollution levels [48]. These pollutants significantly increase indoor PM\u003csub\u003e2.5\u003c/sub\u003e levels, which can penetrate deep into the respiratory tract and cross the alveolar barrier into the bloodstream, causing systemic effects [49, 50]. Children are especially vulnerable due to their higher respiratory rates and developing respiratory systems, making them more susceptible to PM\u003csub\u003e2.5\u003c/sub\u003e-related health impacts [2]. Prolonged exposure to PM\u003csub\u003e2.5\u003c/sub\u003e can precipitate severe respiratory conditions, including asthma[51], bronchitis[52], and pneumonia[53], and may also diminish lung function and compromise the immune system[54]. The findings of the present study align with the past studies.\u003c/p\u003e \u003cp\u003eThe findings of the study revealed that states like Rajasthan, Haryana, Uttar Pradesh, Bihar, and Jharkhand had significantly high concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e, which are associated with elevated NMR and UMR per 100,000 live births. Similarly, George et al. (2024), revealed that PM\u003csub\u003e2.5\u003c/sub\u003e and unclean fuel exposure significantly increase child mortality due to LRIs in India [20]. Adhikary et al. (2024) further reported that elevated PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, along with household-level pollution, contributed to the high mortality rates among children in these states[2]. There are several factors, such as ambient air pollution[2, 55], household air pollution[55], and temperatures (both high and low)[56], play a crucial role in elevating PM\u003csub\u003e2.5\u003c/sub\u003e levels, which are significant contributors to the high mortality among neonatal and under-five children in these states[57].\u003c/p\u003e \u003cp\u003eThe objective of this study is to examine the relationship between PM\u003csub\u003e2.5\u003c/sub\u003e pollution on child mortality from LRIs in India. Additionally, it investigates child mortality linked to LRI risks associated with various contributing factors. Our study has several strengths, including the use of advanced model-based satellite data for PM\u003csub\u003e2.5\u003c/sub\u003e concentrations across the country. Furthermore, the study extracted data from a large-scale, nationally representative survey with a high household response rate. The integration of data from the GBD study provides a comprehensive analysis of child mortality and its association with PM\u003csub\u003e2.5\u003c/sub\u003e exposures in India. Nonetheless, this study has certain limitations. Firstly, the use of model-based state-level PM\u003csub\u003e2.5\u003c/sub\u003e satellite data may result in misclassification and introduce bias regarding the actual concentrations of fine particulate matter[2]. Secondly, the lack of cohort data limits our ability to establish a strong causal link between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and child mortality. It also confines our ability to accurately monitor the actual years of child mortality, as the study primarily relies on cross-sectional data to explore the relationship. Despite these limitations, our study robustly demonstrated a causal association between PM\u003csub\u003e2.5\u003c/sub\u003e on child mortality, strongly supported by findings from several studies [20, 58, 59]. To reduce PM\u003csub\u003e2.5\u003c/sub\u003e pollutants and enhance the quality of life, the study emphasizes the importance of promoting clean fuel usage in the IGP region through the implementation of various flagship schemes, particularly in vulnerable areas of the country. Additionally, it emphasizes prioritizing the NAAQS over the WHO air quality guidelines to significantly reduce anthropogenic particulate matter pollution and prevent premature mortality among children.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study revealed a significant association between PM\u003csub\u003e2.5\u003c/sub\u003e pollution and mortality from LRIs among neonatal and under-five children in India. Our findings also indicate that states within the IGP region are critical zones for PM\u003csub\u003e2.5\u003c/sub\u003e, where high child mortality is more likely to link with elevated PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. Additionally, household-level factors such as the use of unclean cooking fuels are crucial in elevating indoor air pollutants, significantly impacting children's health in the IGP region. Therefore, it is imperative to implement strict air quality guidelines, adopt targeted policies, and raise public awareness to prevent long-term health complications among neonatal and under-five.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eLRIs\u003c/strong\u003e - Lower Respiratory Infections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e - Neonatal Mortality Rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eU5MR\u003c/strong\u003e - Under-Five Mortality Rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSDG\u003c/strong\u003e - Sustainable Development Goals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAAQS\u003c/strong\u003e - National Ambient Air Quality Standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIGP\u003c/strong\u003e - Indo-Gangetic Plain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGBD\u003c/strong\u003e - Global Burden of Disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNFHS:\u003c/strong\u003e National Family Health Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHAP\u003c/strong\u003e - Household Air Pollution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDHS:\u003c/strong\u003e Demographic and Health Survey\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge\u003cstrong\u003e\u0026nbsp;the Global Burden of Disease Study 2021 for providing the data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available data from the GBD 2021 database, and no individual-level data were analysed. Therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GBD data is publicly available for research purposes and restricted from commercial use. It can be accessed through the open-access repository of the Institute for Health Metrics and Evaluation (IHME) (https://vizhub.healthdata.org/gbd-results/). The PM\u003csub\u003e2.5\u003c/sub\u003e dataset is publicly available on the following website: https://sites.wustl.edu/acag/datasets/surface-pm2-5/. The socio-economic datasets generated or analysed in this study are available in the DHS Program repository: https://dhsprogram.com/methodology/survey/survey-display-541.cfm. The maps in the article were created using ArcGIS desktop version 10.4.1 and the software is available at: https://www.esri.com/en-us/arcgis/products/index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePandey A, Brauer M, Cropper ML, Balakrishnan K, Mathur P, Dey S, et al. Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019. The Lancet Planetary Health. 2021;5(1):e25-e38.\u003c/li\u003e\n \u003cli\u003eAdhikary M, Mal P, Saikia N. 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Environmental Science \u0026amp; Technology. 2021;55(22):15287-300.\u003c/li\u003e\n \u003cli\u003eIIPS I. National Family Health Survey (NFHS‐5), 2019‐21 [Dataset] 2021 [Available from: https://dhsprogram.com/methodology/survey/survey-display-541.cfm.\u003c/li\u003e\n \u003cli\u003eIHME. Global Burden of Disease Study 2021 (GBD 2021), Results 2022 2021 [Available from: https://vizhub.healthdata.org/gbd-results/.\u003c/li\u003e\n \u003cli\u003ePerin J, Mulick A, Yeung D, Villavicencio F, Lopez G, Strong KL, et al. Global, regional, and national causes of under-5 mortality in 2000\u0026ndash;19: an updated systematic analysis with implications for the Sustainable Development Goals. The Lancet Child \u0026amp; Adolescent Health. 2022;6(2):106-15.\u003c/li\u003e\n \u003cli\u003eTroeger CE, Khalil IA, Blacker BF, Biehl MH, Albertson SB, Zimsen SR, et al. Quantifying risks and interventions that have affected the burden of lower respiratory infections among children younger than 5 years: an analysis for the Global Burden of Disease Study 2017. The Lancet Infectious Diseases. 2020;20(1):60-79.\u003c/li\u003e\n \u003cli\u003eDunne EM, Nunes MC, Slack MPE, Theilacker C, Gessner BD. Effects of pneumococcal conjugate vaccines on reducing the risk of respiratory disease associated with coronavirus infection. Pneumonia. 2023;15(1).\u003c/li\u003e\n \u003cli\u003eBirindwa AM, Manegabe JT, Mindja A, Nord\u0026eacute;n R, Andersson R, Skovbjerg S. Decreased number of hospitalized children with severe acute lower respiratory infection after introduction of the pneumococcal conjugate vaccine in the Eastern Democratic Republic of the Congo. Pan African Medical Journal. 2020;37.\u003c/li\u003e\n \u003cli\u003eVarghese JS, Muhammad T. Prevalence, potential determinants, and treatment-seeking behavior of acute respiratory infection among children under age five in India: Findings from the National Family Health Survey, 2019-21. BMC Pulmonary Medicine. 2023;23(1).\u003c/li\u003e\n \u003cli\u003eLamrani Hanchi A, Guennouni M, Ben Houmich T, Echchakery M, Draiss G, Rada N, et al. Changes in the Epidemiology of Respiratory Pathogens in Children during the COVID-19 Pandemic. Pathogens. 2022;11(12).\u003c/li\u003e\n \u003cli\u003eAnwar A, Ayub M, Khan N, Flahault A. Nexus between Air Pollution and Neonatal Deaths: A Case of Asian Countries. International Journal of Environmental Research and Public Health. 2019;16(21).\u003c/li\u003e\n \u003cli\u003eYounger A, Alkon A, Harknett K, Jean Louis R, Thompson LM. Adverse birth outcomes associated with household air pollution from unclean cooking fuels in low- and middle-income countries: A systematic review. Environmental Research. 2022;204.\u003c/li\u003e\n \u003cli\u003eRoy S, Habib G, Raman RS. Identification of source contributions to fine particulate matter at Indian desert-urban mixed region. Atmospheric Environment. 2024;320.\u003c/li\u003e\n \u003cli\u003eRumana HS, Sharma RC, Beniwal V, Sharma AK. A retrospective approach to assess human health risks associated with growing air pollution in urbanized area of Thar Desert, western Rajasthan, India. Journal of Environmental Health Science and Engineering. 2014;12(1).\u003c/li\u003e\n \u003cli\u003eGupta L, Bansal M, Nandi P, Habib G, Sunder Raman R. Source apportionment and potential source regions of size-resolved particulate matter at a heavily polluted industrial city in the Indo-Gangetic Plain. Atmospheric Environment. 2023;298.\u003c/li\u003e\n \u003cli\u003eOjha N, Sharma A, Kumar M, Girach I, Ansari TU, Sharma SK, et al. On the widespread enhancement in fine particulate matter across the Indo-Gangetic Plain towards winter. Scientific Reports. 2020;10(1).\u003c/li\u003e\n \u003cli\u003eRanjan R, Singh S. Household Cooking Fuel Patterns in Rural India: Pre- and Post-Pradhan Mantri Ujjwala Yojana. Indian Journal of Human Development. 2020;14(3):518-26.\u003c/li\u003e\n \u003cli\u003eDas D, Ahmad S, Kirshner J. Opportunities and Challenges Associated with the Uptake of Residential Clean Fuel Usage. Current Environmental Health Reports. 2024;11(2):204-9.\u003c/li\u003e\n \u003cli\u003eGarba I, Bellingham R. The Impact of Lack of Clean Cooking Fuels on Sustainable Development in Developing Countries. ASME 2018 12th International Conference on Energy Sustainability2018.\u003c/li\u003e\n \u003cli\u003eDevi NL, Kumar A, Yadav IC. PM10 and PM2.5 in Indo-Gangetic Plain (IGP) of India: Chemical characterization, source analysis, and transport pathways. Urban Climate. 2020;33.\u003c/li\u003e\n \u003cli\u003eNazneen, Patra AK, Kolluru SSR, Dubey R, Kumar S. Determinants of traffic related atmospheric particulate matter concentrations and their associated health risk at a highway toll plaza in India. Atmospheric Pollution Research. 2023;14(6).\u003c/li\u003e\n \u003cli\u003eShupler M, Tawiah T, Nix E, Baame M, Lorenzetti F, Betang E, et al. Household concentrations and female and child exposures to air pollution in peri-urban sub-Saharan Africa: measurements from the CLEAN-Air(Africa) study. The Lancet Planetary Health. 2024;8(2):e95-e107.\u003c/li\u003e\n \u003cli\u003eJones ER, Laurent JGC, Young AS, MacNaughton P, Coull BA, Spengler JD, Allen JG. The effects of ventilation and filtration on indoor PM2. 5 in office buildings in four countries. Building and environment. 2021;200:107975.\u003c/li\u003e\n \u003cli\u003eHu H, Ye J, Liu C, Yan L, Yang F, Qian H. Emission and oxidative potential of PM2. 5 generated by nine indoor sources. Building and Environment. 2023;230:110021.\u003c/li\u003e\n \u003cli\u003eMcCarron A, Semple S, Braban CF, Gillespie C, Swanson V, Price HD. Personal exposure to fine particulate matter (PM2.5) and self-reported asthma-related health. Social Science \u0026amp; Medicine. 2023;337.\u003c/li\u003e\n \u003cli\u003eLim S, Said B, Zurba L, Mosler G, Addo-Yobo E, Adeyeye OO, et al. Characterising sources of PM2\u0026middot; 5 exposure for school children with asthma: a personal exposure study across six cities in sub-Saharan Africa. The Lancet Child \u0026amp; Adolescent Health. 2024;8(1):17-27.\u003c/li\u003e\n \u003cli\u003eWang X, Xu Z, Su H, Ho HC, Song Y, Zheng H, et al. Ambient particulate matter (PM1, PM2.5, PM10) and childhood pneumonia: The smaller particle, the greater short-term impact? Science of The Total Environment. 2021;772.\u003c/li\u003e\n \u003cli\u003eKuang Z, Wang K, Ma Z, Zhan Y, Liu R, Peng M, et al. Long-term air pollution exposure accelerates ageing-associated degradation of lung function. Atmospheric Pollution Research. 2023;14(10).\u003c/li\u003e\n \u003cli\u003eGhosh R, Causey K, Burkart K, Wozniak S, Cohen A, Brauer M. Correction: Ambient and household PM2.5 pollution and adverse perinatal outcomes: A meta-regression and analysis of attributable global burden for 204 countries and territories. PLOS Medicine. 2021;18(11).\u003c/li\u003e\n \u003cli\u003eFaurie C. Increased temperatures and child health outcomes: a systematic review. European Journal of Public Health. 2023;33(Supplement_2).\u003c/li\u003e\n \u003cli\u003eOwili PO, Lin T-H, Muga MA, Lien W-H. Impacts of discriminated PM2.5 on global under-five and maternal mortality. Scientific Reports. 2020;10(1).\u003c/li\u003e\n \u003cli\u003edeSouza PN, Dey S, Mwenda KM, Kim R, Subramanian SV, Kinney PL. Robust relationship between ambient air pollution and infant mortality in India. Science of The Total Environment. 2022;815.\u003c/li\u003e\n \u003cli\u003eBalasubramani K, Prasad KA, Kodali NK, Abdul Rasheed NK, Chellappan S, Sarma DK, et al. Spatial epidemiology of acute respiratory infections in children under 5 years and associated risk factors in India: District-level analysis of health, household, and environmental datasets. Frontiers in Public Health. 2022;10.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Neonatal and under-five mortality per 100,000 live births across Indian states from 1990 to 2021\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 262px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeonatal mortality rate per 100,000 live births\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnder-five mortality rate per 100,000 live births\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRatio of NMR to U5MR, 2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990 (95%UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021 (95%UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPC (95%UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990 (95%UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021 (95%UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPC (95%UI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6989.96 (5719.32 to 8445.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2377.36 (1777.43 to 3104.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-65.99 (-75.28 to -52.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e358.52 (289.87 to 430.25)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e94.15 (72.02 to 120.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-73.74 (-80.69 to -64.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"30\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eAndhra Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4882.75 (3371.24 to 6658.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e987.8 (654.11 to 1444.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-79.77 (-87.34 to -64.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e280.02 (207.06 to 361.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e43.6 (31.49 to 58.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-84.43 (-89.75 to -76.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eArunachal Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4109.97 (3132.66 to 5274.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1120.44 (692.13 to 1694.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-72.74 (-84.08 to -53.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e322.49 (243.04 to 417.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e50.5 (30.8 to 75.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-84.34 (-90.83 to -74.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eAssam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e6036.65 (4480.78 to 8030.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2285.23 (1559.79 to 3151.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-62.14 (-77.01 to -39.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e340.08 (263.6 to 435.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e83.91 (57.03 to 117.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-75.33 (-84.55 to -61.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e27.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eBihar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e8819.32 (6418.81 to 11814.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2679.17 (1808.06 to 3891.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-69.62 (-80.97 to -51.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e444.13 (325.74 to 579.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e93.71 (68.76 to 125.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-78.9 (-85.85 to -67.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e28.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eChhattisgarh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e10311.35 (7810.83 to 13182.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3124.96 (2128.41 to 4411.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-69.69 (-80 to -53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e468.17 (380.3 to 580.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e102.14 (62.97 to 143.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-78.18 (-86.17 to -68.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e30.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3011.61 (2156.67 to 4109.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e638.82 (392.64 to 963.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-78.79 (-87.9 to -63.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e201.08 (142.53 to 256.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e22.49 (14.15 to 33.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-88.82 (-93.39 to -81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e28.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3142.83 (2288.24 to 4101.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e699.79 (452.58 to 998.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-77.73 (-86.04 to -63.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e118.23 (60.29 to 154.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e21.59 (14.47 to 30.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-81.74 (-89.21 to -64.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e32.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGujarat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5288.76 (3877.71 to 6896.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1740.27 (1154.76 to 2582.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-67.09 (-80.44 to -46.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e283.75 (216.01 to 351.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e69.81 (49.94 to 95.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-75.4 (-84.26 to -62.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e24.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eHaryana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4632.32 (3505.37 to 6124.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1357.23 (941.44 to 1882.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-70.7 (-81.03 to -55.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e269.69 (210.31 to 345.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e78.87 (56.05 to 107.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-70.75 (-81.25 to -56.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e17.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eHimachal Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4042.48 (2794.85 to 5258.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1316.24 (851.76 to 1969.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-67.44 (-80.59 to -43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e200.93 (147.47 to 259.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e57.76 (40.44 to 80.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-71.25 (-81.01 to -56.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eJammu and Kashmir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e6543.43 (4960.51 to 8109.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1801.14 (1273.72 to 2482.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-72.47 (-80.94 to -59.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e279.21 (217.62 to 339.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e64.04 (46.41 to 83.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-77.06 (-83.65 to -67.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e28.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eJharkhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e8404.58 (6180.78 to 10980.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2118.38 (1420.23 to 3182.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-74.79 (-83.61 to -58.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e387.54 (289.61 to 494.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e70.92 (46.94 to 103.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-81.7 (-87.8 to -72.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e29.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eKarnataka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2920.91 (2101.78 to 3944.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e820.82 (554.01 to 1133.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-71.9 (-82.96 to -55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e183.88 (133.84 to 238.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e41.68 (28.91 to 57.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-77.33 (-85.49 to -62.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e19.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eKerala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1654.13 (1248.93 to 2100.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e364.72 (250.78 to 516.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-77.95 (-85.95 to -67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e74.38 (53.69 to 98.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.44 (7.22 to 14.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-85.96 (-91.11 to -77.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e34.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eMadhya Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e10815.44 (7491.36 to 14749.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3374.92 (2140.84 to 4879.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-68.8 (-82.1 to -46.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e607.1 (464.17 to 762.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e148.66 (103.09 to 200.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-75.51 (-83.59 to -63.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eMaharashtra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4768.9 (3619.11 to 6165.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e866 (595.53 to 1212.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-81.84 (-88.55 to -73.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e210.52 (163.59 to 260.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e29.02 (20.71 to 39.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-86.21 (-90.67 to -79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e29.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eManipur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3690.6 (2814.75 to 4632.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1002.5 (620.93 to 1553.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-72.84 (-83.62 to -55.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e202.86 (153.75 to 257.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e47.69 (31.79 to 73.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-76.49 (-85.53 to -61.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e21.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eMeghalaya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e6018.97 (4657.26 to 7534.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1281.94 (861.53 to 1906.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-78.7 (-86.57 to -66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e323.84 (245.46 to 408.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e82.31 (55.03 to 118.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-74.58 (-83.53 to -59.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eMizoram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5762.24 (4520.54 to 7389.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1961.51 (1311.1 to 2840.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-65.96 (-78.38 to -47.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e214.2 (167.62 to 269.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e75.45 (44.58 to 115.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-64.78 (-78.26 to -45.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNagaland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5849.62 (4306.6 to 7757.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1493.01 (986.97 to 2231.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-74.48 (-84.19 to -57.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e204.8 (155.78 to 266.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e65.66 (44.65 to 91.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-67.94 (-79.26 to -50.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eOdisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e9576.61 (7217.95 to 12262.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1795.74 (1195 to 2487.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-81.25 (-88.17 to -70.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e434.67 (334.69 to 539.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e74.27 (50.69 to 103.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-82.91 (-88.62 to -74.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e24.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003ePunjab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3476.6 (2598.53 to 4681.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e874.44 (592.63 to 1283.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-62.91 (-78.02 to -37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e183.29 (140.52 to 230.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e39.82 (27.46 to 55.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-78.28 (-85.26 to -65.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e21.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eRajasthan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e10222.54 (7700.44 to 12819.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e4560.87 (3251.81 to 6475.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-74.85 (-84.22 to -56.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e534.28 (412.74 to 659.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e173.94 (127.47 to 226.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-67.44 (-77.36 to -53.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e26.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSikkim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e8382.78 (6665.27 to 10412.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3238.59 (2057.45 to 4987.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-55.38 (-70.85 to -35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e279.46 (221.6 to 342.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e127.48 (83.45 to 191.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-54.38 (-71.61 to -23.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eTamil Nadu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3416.69 (2356.88 to 4551.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e590.53 (403.35 to 842.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-61.37 (-77.22 to -36.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e208.56 (154.72 to 264.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26.49 (18.51 to 37.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-87.3 (-91.62 to -81.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eTelangana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e4638.73 (3326.98 to 6235.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e882.19 (609.02 to 1271.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-82.72 (-89 to -71.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e276.22 (208.76 to 364.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e39.21 (28.59 to 51.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-85.8 (-90.82 to -78.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eTripura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e9758.73 (7216.6 to 12650.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1892.65 (1228.74 to 2754.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-80.98 (-87.78 to -69.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e372.96 (271.1 to 481.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e66.48 (43.58 to 97.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-82.17 (-88.11 to -72.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e28.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eUnion Territories other than Delhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2867.78 (2143.63 to 3739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1063.58 (649.06 to 1614.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-80.61 (-87.69 to -69.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e112.1 (82.69 to 148.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e40.42 (24.92 to 61.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-63.94 (-78.41 to -38.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e26.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eUttar Pradesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e9119.35 (6658.67 to 12209.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3599.66 (2517.11 to 4805.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-60.53 (-74.09 to -39.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e507.71 (368.2 to 657.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e158.02 (116.1 to 204.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-68.88 (-78.23 to -53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e22.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eUttarakhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5686.27 (4175.71 to 7418.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1630.95 (1068.39 to 2306.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-71.32 (-82.43 to -55.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e250.33 (194.66 to 308.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e58.34 (39.85 to 79.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-76.7 (-84.57 to -66.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e27.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eWest Bengal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e6703.69 (4976.1 to 8562.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1694.89 (1190.72 to 2350.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-74.72 (-83.06 to -61.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e267.1 (199.73 to 347.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e53.7 (39.08 to 71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-79.89 (-86.44 to -69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e31.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"4\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Trends in neonatal and under-five mortality rates per 100,000 live births due to lower respiratory illness in India using joinpoint regression analysis (1990\u0026ndash;2021)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003eSegment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 271px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeonatal Mortality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 268px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnder-five Mortality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eAPC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eAPC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1990\u0026ndash;2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;-2.64 (-3.89 to -2.36)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1990-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003e\u0026nbsp;-2.97 (-3.12 to -2.79)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e2002\u0026ndash;2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;-1.82 (-2.15 to -0.39)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e2013-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003e\u0026nbsp;-5.64 (-6.63 to -4.48)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e2013\u0026ndash;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;-5.44 (-6.06 to -4.48)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e2019-2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003e\u0026nbsp;-16.21 (-19.96 to -12.08)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e2019\u0026ndash;2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;-11.56 (-14.29 to -8.80)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eFull Range (AAPC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1990-2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;-3.50 (-3.69 to -3.39)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1990-2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003e\u0026nbsp;-4.40 (-4.66 to -4.27)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: *APC: Annual Percentage change;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e**AAPC: Annual Percentage Change\u003c/p\u003e\n\u003cp\u003eSignificant at: *p\u0026lt;0.10; **p\u0026lt;0.05; ***p\u0026lt;0.01\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval \u0026nbsp; \u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Mizoram University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Child mortality, Fine particulate matter, Health and well-being, India, Lower respiratory infections","lastPublishedDoi":"10.21203/rs.3.rs-6015754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6015754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLower respiratory infections (LRIs) caused by PM\u003csub\u003e2.5\u003c/sub\u003e pollution are a major factor in neonatal and under-five mortality across India. Therefore, this study explores the linkage between PM\u003csub\u003e2.5 \u003c/sub\u003epollution on neonatal and under-five mortality from LRIs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized ambient PM\u003csub\u003e2.5 \u003c/sub\u003egeographic mean estimates from Washington University and a household air pollution dataset from the fifth round of the National Family Health Survey (NFHS-5). Furthermore, child mortality data were extracted from the Global Burden of Disease 2021 to assess the impact of PM\u003csub\u003e2.5 \u003c/sub\u003eon child mortality attributable from LRIs in India. The study employed 'Getis-Ord-Gi*' statistics in ArcMap 10.4 to identify PM\u003csub\u003e2.5\u003c/sub\u003e hotspots and cold spots. Temporal trends for neonatal and under-five mortality were analyzed using joinpoint regression analysis, and risk factors of LRIs were visualized through a heat map using MS Excel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 2021, the neonatal mortality rate (NMR) per 100,000 live births declined significantly by 66%, from 6,989.96 in 1990 to 2,377.36 in 2021. Similarly, the under-five mortality rate (U5MR) per 100,000 live births declined by 74%, from 358.52 to 94.15 per 100,000 live births. Additionally, from 2019 to 2021, a notable decline in mortality was observed for both sexes (NMR: -11.56%; U5MR: -16.21%). However, states such as Rajasthan, Haryana, Uttar Pradesh, and Bihar had notably experienced elevated PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, which were likely contributing factors to the higher burden of neonatal and under-five mortality. Additionally, HAP was a major contributor to PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in the Indo-Gangetic Plain region (IGP), largely due to the limited usage of clean fuels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study revealed that elevated PM\u003csub\u003e2.5\u003c/sub\u003e concentrations are likely linked to contributing factors for higher child mortality, particularly in the IGP region. To address this issue, the study suggests increasing public awareness and implementing targeted policies to reduce neonatal and under-five mortality across India.\u003c/p\u003e","manuscriptTitle":"Epidemiological burden and trends of neonatal and under-five mortality from lower respiratory infections associated with PM2.5 pollutions in India: A systematic analysis of the Global Burden of Disease Study (1990-2021)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 10:56:49","doi":"10.21203/rs.3.rs-6015754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"477e747e-99a6-48ca-9e5c-d68cfe207ca9","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44233102,"name":"Health Policy"}],"tags":[],"updatedAt":"2025-02-14T10:56:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-14 10:56:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6015754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6015754","identity":"rs-6015754","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}